microbiota and weight gain in human
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by diet or other lifestyle factors. Moreover it has been difficult to characterize the composition of the human gut microbiota due to large variations between individuals.
The human gut microbiota has been also associated with a number of disease states that include allergy, inflammatory bowel disease, cancer and diabetes [7]. Allergy, for example, has been associated with perturbations in the
microbiota is likely to be more sophisticated than the simple phylum-level Bacteroidetes :Firmicutes ratio that was initially identified [13], and it is likely to involve a microbiota–diet interaction [14]. Phages have also been proposed to play a possible role in driving the biodiversity of the gut flora by their influence on their bacterial hosts [15] and, recently, a novel pathway that involves dietary lipid phosphatidylcholine and choline
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metabolism, an obligate role for the intestinal microbial community, and regulation of sur-face expression levels of macrophage scavenger receptors that were known to participate in the atherosclerotic process was proposed [16]. More subtle alterations in the levels of other bacteria in the gut may also impact human health. In the last few years, new technologies have been devel-oped that have allowed researchers to attempt more systematic studies on intestinal bacterial flora and have given more realistic information about its composition (by way of detecting non-cultivable species). As a result, an increasing number of studies have related imbalances in the composition of the gut microbiota to obesity and its associated diseases. The approaches used to characterize the human gut flora vary widely, and this might explain, in part, why specific alterations in the microbiota that are associated with excess body fat or weight loss, can also vary between studies. This review summarizes the latest research on the association between the microbial ecology and host weight.
Human gut microbiota
The gut microbiota harbors large bacterial popu-lations in the intestine and colon, approximately 1011–12 microorganisms per gram of content, and are comprised of mainly anaerobes (95% of the total organisms). The initial overview of the com-position of the gut microbiota was culture based, and the predominant cultivable species that were identified included Bacteroides sp., Eubacterium sp., Bifidobacterium sp., Peptostreptoccocus sp., Fusobacterium sp., Ruminococcus sp., Clostridium sp. and Lactobacillus spp. [17]. The first, large-scale, 16S rDNA sequencing ana l ysis of the gut microbiota by Eckburg et al. [18] revealed a high inter-individual variability at the species taxo-nomic level that was not recovered at the phylum level, as only nine phyla out of 70 were repre-sented [1]. The overall and individual microbiota structures were dominated by the Bacteroidetes and Firmicutes phyla [18]. Finally, three gut micro-biota studies [19] assigned 98% of 16S rRNA sequences to only four bacterial phyla: Firmicutes (64%), Bacteroidetes (23%), Proteobacteria (8%) and Actinobacteria (3%). Verrucomicrobia , Fusobacteria and the TM7 p h ylum together accounted for the remaining 2%.
The earliest large-scale, 16S rRNA or metage-nomic studies identified Methanobrevibacter smithii as the dominant, methanogenic archaeon species in the human gut microbiota [18]. M. smithii in three healthy individuals com-prised up to 11.5% of the gut microorganisms
[18], and in a study of 650 individuals, the prev-
alence of M. smithii was 95.5%, whereas the prevalence of Methanosphaera stadtmanae was 29.4% in the human gut [20]. Moreover, molecu-lar analyses provided various degrees of evidence for the presence of groups of archaea, including Methanosarcina , Thermoplasma , Crenarchaeota and halophilic archaea in the human gastro-intestinal tract, but isolates have not been obtained [21].
Age & gut flora modification
During the first days to months of life, the micro-biota of the infant gut and the temporal pattern in which it evolves is remarkably variable from individual to individual [22]. At birth, humans are essentially free of bacteria and over time, in a process of colonization that begins shortly after delivery and continues through to adulthood, the body becomes a host to complex microbial communities. The initial infant gut microbiota is usually dominated by Bifidobacteria , and through a series of successions and replace-ments, it migrates to a more complex, adult pat-tern [22]. Vael et al. found that the population of Bacteroides fragilis in the microbiota increased in infants from the age of 3 weeks until the age of 1 year, whereas the populations of Staphylococcus , Lactobacillus , Bifidobacterium , Clostridium and total anaerobes decreased starting at the age of 3 weeks and remained stable until 52 weeks [23].Traditionally, it has been thought that between 1 and 2 years of age, the human gut microbiota start to resemble that of an adult [22]. Young children between 1 and 7 years of age presented higher numbers of enterobacteria than adults [24]. Moreover, a large-scale study by Enck et al. found significant shifts in relative genus abundances during the first 2 years of life and no noticeable changes in children between 2 and 18 years of age, including stable levels of Bifidobacterium and Lactobacillus [25]. In a recent study, the comparison of intestinal microbiota composition between adolescents and adults revealed a statistically significantly higher abun-dance of genera Bifidobacterium and Clostridium among adolescent samples [26].
The adult intestinal microbiota has been shown to be relatively stable over time [27] and is sufficiently similar between individuals. This observation allowed for identification of a core microbiome that was comprised of 66 dominant, operational, taxonomic units that corresponded to 38% of the sequence reads from 17 individu-als [28]. Turroni et al. found that Bifidobacterium pseudolongum and Bifidobacterium bifidum
, are
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exclusively dominant in the adult bifidobacte-rial population, whereas Bifidobacterium longum , Bifidobacterium breve , Bifidobacterium pseudoca-tenulatum and Bifidobacterium adolescentis , were found to be widely distributed, irrespective of host age [29].
In the elderly, both Bacteroides numbers and species diversity is declined [30,31]. The analyses of fecal samples collected from subjects from four European study groups indicated higher proportions of enterobacteria in all elderly vol-unteers [32]. Zwielehner et al., found that the proportion of Bacteroidetes in the fecal micro-biota of 17 institutionalized, elderly subjects was significantly higher than in younger adults, but these patients had lower proportions of Bifidobacterium and Clostridium cluster IV [33]. Analysis of the core microbiota in the elderly showed a clear shift to a more Clostridium cluster IV-dominated community [34].
Several host factors have been correlated with methanogenic archaea carriage, and it has been proposed that the acquisition of methanogenic archaea occurs by environmental contamination. Additionally, it has been hypothesized that once methanogenic archaea find favorable physico-chemical conditions and available substrates in the gut, stable colonization is established [21]. archaea were not detected in children who were younger than 27 months, but it has been shown that carriage increases with age, up to 60% in 5-year-old children. Moreover, it is possible that an adult diet may create an intestinal microbiota that is favorable for the implantation of metha-nogenic archaea [35]. A possible direct, mother-to-child route of transmission has also been pro-posed because archaea have been detected in the vaginal flora of pregnant women [21].
Gut flora variations among different populations
It is not yet completely understood how the different environments and wide range of diets that modern humans around the world experi-ence has affected the microbial ecology of the human gut. Certain lifestyles of a person may have an impact on the composition of his/her gut microbiota (F igure 1), but these impacts are currently poorly understood. Qin et al., in the largest study to date, found that only one-third of the bacterial gene clusters that were conserved across individuals of all 124 European (Nordic and Mediterranean) origins could be associated with a broad functional assignment [6]. Nearly 40% of the genes from each individual were shared with at least half of the individuals of
the cohort. Of these, 99.1% of the genes had bacterial origin, and the remainder was mostly archaeal, with only 0.1% of eukaryotic or viral origins [6]. Therefore, it seems that important variations in the gut flora between close coun-tries do not exist. As a result, Dicksved et al. did not observe differences between fecal sam-ples collected from children from Germany, Switzerland and Sweden by the use of termi-nal restriction fragment length polymorphism [36]. Lay et al., when testing the composition of the fecal microbiota assessed by FISH com-bined with flow cytometry, also did not find a significant correlation between the microbial compositions, with regard to age, geographical origin, or gender, among subjects from France, Denmark, Germany, the Netherlands and the UK [37]. However, 16S rDNA pyrosequencing ana l ysis revealed that geographical origin has an important impact on the intestinal micro-biota. As a result, differences in the gut micro-biota have been observed between people living in northern and southern European countries. For instance, Fallani et al. observed that human infants from northern European countries were associated with higher Bifidobacteria in their gut microbiota, whereas infants with higher Bacteroides and lactobacilli were characteristic of southern countries [38]. Mueller et al. found that the proportion of Bifidobacteria was two- to three-fold higher in Italians than in the French, Germans or Swedes [32]. A bigger dif-ference has been observed between European and Africans, and De Filippo et al. found that children from a rural African village presented more Actinobacteria and Bacteroidetes but less Firmicutes and Proteobacteria in their gut flora than European children [39]. Moreover, African children presented significantly more short-chain fatty acids in their gut flora than European children [39]. Li et al. found that there were distinct microbiota profiles at the species level between a Chinese family and American volunteers. Moreover, they identified a higher proportion of Bacteroidetes thetaiotaomicron in males than in females [40]. Finally, Arumugam et al., by combining 22 sequenced, fecal metage-nomes of individuals from four countries, iden-tified three enterotype clusters that were not nation- or continent-specific [41]. Enterotype 1 was enriched in Bacteroides and seemed to derive energy primarily from carbohydrates and pro-teins through fermentation. Enterotype 2 was enriched in Prevotella and Desulfovibrio , which can act in synergy to degrade mucin glycopro-
teins that are present in the mucosal layer of the
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gut. Enterotype 3 was the most frequent and was enriched in Ruminococcus and Akkermansia, which degrade mucins [41]. Moreover, entero-types 1 and 2 were capable of biosynthesis of different vitamins. The authors proposed that these three enterotypes used different routes to generate energy from fermentable substrates that were available in the colon, reminiscent of a potential specialization in ecological niches or guilds [41].
Effect of the alimentation on human gut flora
Dietary habits are considered to be one of the main factors that contribute to the diversity of the human gut microbiota [42], and the pattern of variation in copy number of the human sali-vary amylase gene is consistent with a history of diet-related selection pressures, demonstrating the importance of starchy foods in human evolu-tion [43]. Prevotella , Xylanibacter and Treponema were present in the gut flora of children from a rural African village but not from Europe, and the authors of this study hypothesized that the presence of these three genera could be a consequence of high fiber intake, maximiz-ing metabolic energy extraction from ingested plant polysaccharides [39]. These bacteria could ferment both xylan and cellulose through car-bohydrate-active enzymes, such as xylanase,
carboxymethylcellulase and endoglucanase [39]. Moreover, Bacteroides and Faecalibacterium spe-cies and particularly Faecalibacterium prausnit-zii , which were found in both children popula-tions, could generally indicate the importance of maintaining a microflora with potential anti-inflammatory capability [39,44]. Liszt et al. found that a vegetarian diet affected the intestinal microbiota, especially by decreasing the amount and changing the diversity of Clostridium clus-ter IV [45]. Similar results found by Hayashi et al., who based their studies on RFLP ana l ysis, revealed that the major composition of the veg-etarian gut microbiota consisted of Clostridium rRNA subcluster XIVa and Clostridium rRNA cluster XVIII [46]. Recently, Walker et al. tested overweight men with a control diet, diet high in resistant starch or nonstarch polysaccharides and a reduced carbohydrate weight loss diet, over 10 weeks and they found no significant effect of diet upon the proportions of Bacteroidetes , Firmicutes , Actinobacteria or Proteobacteria within the fecal microbiota [47]. However, two individual phylotypes, Eubacterium rectale and Ruminococcus bromii , showed increased propor-tions on the resistant starch diet while Collinsella aerofaciens showed decreased proportions on the weight loss diet [47]. Finally, Wu et al. analyzed the fecal samples from 98 individuals and found that fecal communities clustered into enterotypes
Figure 1. Impact factors for the composition of the human gut microbiota.
Vegetarian diet 1. Increase Bacteroidetes 2. Decrease Clostridia
Human microbiota
Mostly First days of life
Adults
Elderly Stable:
Firmicutes
(64%)Bacteroidetes (23%)Proteobacteria (8%)Actinobacteria (3%)
1. Increase Bacteroidetes
2. Decrease 1. Increase Actinobacteria and Bacteroidetes
2. Decrease Firmicutes and Proteobacteria
Increase Bifidobacteria
Southern vs northern
Europeans
Different species level Origin
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distinguished primarily by levels of Bacteroides and Prevotella [48]. They also found that long-term diet, particularly protein and animal fat versus carbohydrate diet were strongly associ-ated with enterotype partitioning. Moreover, in a controlled-feeding study authors found that the microbiome composition changed detect-ably within 24 h of initiating a high-fat/low-fiber or low-fat/high-fiber diet, but that enterotype identity remained stable [48].
Bacteria species & obesity The Bacteroidetes phylum
Armougom et al. found a significant reduction of Bacteroidetes proportions in obese, compared with lean and anorexic, individuals [49] and reported lower Bacteroidetes concentrations in obese subjects (T able 1) [50]. Moreover, the ana l ysis of 16S rDNA sequences from 154 individuals indicated that the microbiota of obese subjects was associated with a decrease in the diversity level and was composed of significantly fewer Bacteroidetes [13]. On the other hand, Schwiertz et al. quantified bacterial communities in over-weight, obese and lean individuals and found a significant increase in the proportions of Bacteroidetes in obese and overweight groups [51]. Likewise, before pregnancy, overweight women have a higher number of Bacteroidetes than women of normal weight, and excessive weight gain during pregnancy is associated with an increase in Bacteroidetes numbers [52]. Assuming that Type 2 diabetes and reduced glucose toler-ance is linked to obesity, Larsen and colleagues also found higher levels of Bacteroidetes in dia-betic patients than in control patients [10]. Using 16S rDNA pyrosequencing, Zhang et al. studied the composition of the gut microbiota in mor-bidly obese, normal-weight and post-gastric-bypass subjects [53]. Their results indicated that the obese microbiota is significantly enriched in Prevotellaceae, a subgroup of Bacteroidetes [53]. Zuo et al., using culture methods for organisms found in the feces of obese and normal weight participants, found that obese people had fewer cultivable Bacteroides than control individuals [54]. Moreover, they found that obese individuals with a Pro/Ala genotype of the nuclear hormone receptor peroxisome proliferator-activated recep-tor g 2, which modulates cellular differentiation and lipid accumulation during adipogenesis, had lower levels of Bacteroides than obese partici-pants with a Pro/Pro genotype [54]. Interestingly, the monitoring of the proportions of two major bacterial communities in obese participants dur-
ing a weight loss program resulted in linking an
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increase in levels of Bacteroidetes to weight loss, independent of energy intake [5]. The impact of an obesity treatment program, including a calorie-restricted diet and increase of physi-cal activity on gut microbiota composition in overweight and obese adolescents was reported [55,56]. The FISH method indicated that a sig-nificant increase in the ratio of Bacteroides and Prevotella correlated to weight loss in the ado-lescent group that exhibited the highest weight loss [55]. Using the same population, the results obtained by FISH [55] were verified by a quanti-tative PCR (qPCR) method, which detected a notable increase in Bacteroides fragilis after the weight loss program [56]. Lastly, Vael et al. found that high intestinal Bacteroides fragilis concen-trations and low Staphylococcus concentrations in infants between the ages of 3 weeks and 1 year were associated with a higher BMI in preschool children [23].
Others studies have not found any correla-tion between the proportions of Bacteroidetes and obesity or type of diet. Both qPCR and FISH methods have been applied to subsets of lean and obese subjects, and both have failed to associate a reduced level of Bacteroidetes to obesity [57]. In an attempt to study whether the composition of early gut microbiota can affect weight development throughout early child-hood, Kalliom?ki et al. monitored weight, height and bacterial community abundances in children of 6 months, 12 months and 7 years of age. Children who became overweight or obese at 7 years did not present any significant reduc-tion in the proportion of Bacteroides-Prevotella , compared with those maintaining a normal weight [58]. The relationships between weight loss and Bacteroidetes abundance were examined in adults, but no difference between obese and nonobese subjects was observed [59].
Figure 2. Meta-ana l ysis of the obesity-associated gut microbiota alterations at the phylum level (Bacteroidetes and
Firmicutes ) comparing the absolute (abs) or relative (percentage of total sequences) number of sequences (generated by quantitative PCR or cloning/sequencing or pyrosequencing) or cells (flow cytometry-FISH). Meta-analysis was performed with the comprehensive meta-analysis software version 2 [93,94]. Each line represents a comparison between an obese group (right) and a control group (left). The first reported alteration [5] was a decrease in the relative proportion of Bacteroidetes (percentage decrease) represented by a deviation of the square (standardized difference in the means) to the left. The size of the square represents the relative weight of each comparison (random model). The length of the horizontal line represents the 95% CI and the diamond represents the summarized effect. The presence of a square to the right and left of the midline means studies with conflicting results corresponding to a substantial heterogeneity (I 2 >50%). Here, the only reproducible and significant alteration at the phylum level is the decrease in the
absolute number of sequences of Firmicutes in obese subjects. Relative count of Bacteroidetes (n = 4; SDM = -0.51; 95% CI = -1.7–0.67; p = 0.40 [I 2 = 81%]); absolute count of Bacteroidetes (n = 4; SDM = -0.07; 95% CI = -0.78–0.65; p = 0.86 [I 2 = 85]); relative count of Firmicutes (n = 3; SDM = 0.88; 95% CI = -0.21–1.97; p = 0.11 [I 2 = 79%]); absolute count of Firmicutes (n = 3; SDM = -0.43; 95% CI = -0.72 to -0.15; p = 0.003 [I 2 = 0%]).
FCM: Flow cytometry; Ow: Overweight; qPCR: Quantitative PCR; SDM: Standardized difference in the means.
Group by phyla
Study (year)
Subgroup within study
Sample size
SDM and 95% CI
Ow/obese Control
Ley et al . (2006)16S clonal sequencing 122Turnbaugh et al . (2009)V2 pyrosequencing , African ancestry 628Turnbaugh et al . (2009)V2 pyrosequencing , European ancestry 4226Zhang et al . (2009)Pyrosequencing
33Bacteroidetes relative count (% of total sequences)
Collado et al . (2008)FCM-FISH 1836Armougom et al . (2009)qPCR 2020Schwiertz et al . (2010)qPCR 3330Million et al . (2011)qPCR
5339Bacteroidetes absolute count (log cells or copies of DNA)
Ley et al. (2006)16S clonal sequencing 122Turnbaugh et al . (2009)V2 pyrosequencing, African ancestry 628Turnbaugh et al . (2009)V2 pyrosequencing, European ancestry
4226Firmicutes relative count (% of total sequences)
Armougom et al . (2009)qPCR
2020Schwiertz et al . (2010)
3330Million et al . (2011)qPCR
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Firmicutes absolute count (log copies DNA)
-2.00
-1.000.00
1.00
2.00
Lean status
Ow /obese
qPCR
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Meta-analysis of the obesity-associated gut microbiota alteration at the phylum level (Bacteroidetes ) comparing the absolute (abs) or relative (percentage of total sequences) number of sequences (generated by qPCR or cloning/sequencing or pyrosequencing) or cells (flow cytometry [FCM]-FISH) was performed for the seven studies [5,13,49–53]. These studies revealed no difference in the Bacteroidetes concentrations between obese people and people of normal weight (F igure 2).
The Firmicutes phylum
Ley et al. reported that the reduced level of Bacteroidetes found in obese humans was counter-balanced by a proportional increase in Firmicutes [5]. The greater Firmicutes proportion tended to decrease when patients were submitted to a weight-loss program [5]. These results were in agreement with other works, which found that significantly reduced levels of Clostridium hys-toliticum , Eubacterium rectale and Clostridium coccoides correlated to weight loss in an obese, adolescent population [55,56]. Moreover, obese,
Indian children presented significantly higher levels of Faecalibacterium prauznitzii but no difference between the levels of Bacteroides and that of Prevotella , Bifidobacterium species, the Lactobacillus acidophilus group or Eubacterium rectal , compared with lean children [60]. Duncan et al. identified a significant, diet-dependent reduction in levels of Roseburia-E. rectale , a group of butyrate-producing Firmicutes , for obese patients that were on a weight-loss diet [59]. Zuo et al. found a lower amount of C. perfringens and a higher proportion of Enterococci in obese sub-jects when compared with normal-weight indi-viduals [54]. Finally, Schwiertz et al. found that overweight and obese volunteers exhibited lower cell numbers of the Ruminococcus flavefaciens subgroup [51].
Meta-analysis of the obesity associated gut microbiota alteration at the phylum level (Firmicutes ) comparing the absolute (abs) or relative (percentage of total sequences) number of sequences (generated by qPCR or cloning/sequencing or pyrosequencing) or cells (FCM-FISH) was performed for the five studies
Figure 3. Meta-ana l ysis of the obesity-associated gut microbiota alterations at the genus level for Bifidobacteria and Lactobacilli comparing the absolute number of sequences generated by genus-specific quantitative PCR. For Bifidobacteria , a consistent difference was found by our meta-ana l ysis between 159 obese subjects and 189 controls from six published studies showing that the digestive microbiota of the obese group was significantly depleted in Bifidobacteria . Low heterogeneity (I 2 = 17%) shows that this result is very robust. Additional tests have shown that there was no small studies bias (Egger’s regression intercept test, p = 0.92; no change after Duval and Tweedie’s trim and fill). For Lactobacilli , no consistent and significant summary effect was found comparing 127 obese subjects and 110 controls from three studies. Bifidobacterium sp. (n = 6;
SDM = -0.45; 95% CI = -0.69 to -0.20; p < 0.001 [I 2 = 17%]); Lactobacillus spp. (n = 3; SDM = 0.29; 95% CI = -0.31–0.90; p = 0.34 [I 2 = 80%]).
Ow: Overweight; SDM: Standardized difference in the means.
Group by genus
Study (year)Sample size SDM and 95% CI
Obese Control
Bifidobacterium (log copies DNA/ml)
Lactobacillus (log copies DNA/ml)
Zuo et al. (2011)
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52
Zuo et al. (2011)5252Collado et al. (2008)1836Kalliom?ki et al. (2008)2524Schwiertz et al. (2009)3330Balamurugan et al. (2010)1513Santacruz et al. (2010)1634Armougom et al. (2009)2020Million et al. (2011)
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Ow/obese
-2.00 2.00
1.00-1.99
0.00
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[5,13,49–51]. The only reproducible and significant
alteration at the phylum level is the decrease in the absolute number of sequences of Firmicutes in obese (n = 3; standardized difference in the means [SDM] = -0.43; 95% CI = -0.72 to -0.15; p = 0.003 [I 2 = 0%]) (F igure 2).
Recent studies suggest a role for Lactobacillus spp. in weight changes, and the quantification of Lactobacillus species in lean, anorexic and obese subjects revealed significantly higher Lactobacillus concentrations in nearly half of the obese population [49]. Obese Type 2 dia-betic patients displayed significantly higher levels of Bacilli and Lactobacillus spp. in their gut microbiota [10]. However, an increase in Lactobacillus number in an obese, adolescent group after a weight-loss program was also reported [56]. Thuny et al. reported significant weight gain in patients with infected endo-carditis after treatment with high doses of vancomycin and proposed that Lactobacillus spp. that were resistant to vancomycin were responsible for this weight gain [61]. Similarly, Million et al. found that L. reuteri was asso-ciated with obesity [50]. Meta-ana l ysis of the obesity associated gut microbiota alteration at the genus level for lactobacilli comparing the absolute number of sequences generated by genus-specific qPCR revealed a nonsignificant summary effect in Lactobacillus spp. levels in obese subjects (F igure 3).
The Actinobacteria phylum
Recent gut microbiota studies that have been associated with obesity have focused on shifts in Firmicutes and Bacteroidetes populations. However, the Actinobacteria phylum, which is comprised of the Bifidobacterium genus as well as other genera, has also been linked to weight gain. Indeed, in an investigation of gut micro-bial communities of 18 lean or obese twins and their mothers, the obese subjects showed higher levels of Actinobacteria [13]. Interestingly, most of the obesity related genes were found to be from Actinobacteria (75%), and many of the obesity associated genes that were identified were involved in carbohydrate, lipid and amino acid processing [13]. In addition, the sequencing ana l ysis by Zhang and colleagues revealed that the Coriobacteriaceae family of Actinobacteria was enriched in the obese microbiota [53].
The fecal concentration of the Bifidobacterium genus was reported to be significantly lower in obese subjects when compared with lean sub-jects [51,52,58,62]. Moreover, Santacruz et al. found significantly lower Bifidobacteria counts in obese subjects after they had been subjected to a dietary program [56]. Furthermore, Zuo et al . found a nonsignificant decrease in the concentration of bifidobacteria between obese and normal weight humans [54]. Meta-analysis of the obesity-asso-ciated gut microbiota alteration at the genus level for bifidobacteria comparing the absolute
Figure 4. Meta-ana l ysis of the obesity-associated gut microbiota alterations for archaea representatives comparing the absolute number of archaeal sequences generated by
quantitative PCR. One study, focused on the Methanobacteriales order level, comparing only three obese subjects and three controls, found an increase of this bacterial group in the obese group [53] (square deviated to the right) instead of the three other studies. Our meta-ana l ysis showed, by observing the funnel plot, that this study was an outlier that was subsequently excluded. The comparison of 106 obese subjects and 89 controls including ana l ysis at the Methanobrevibacter genus level by Schwiertz et al. [51] and at the Methanobrevibacter smithii species level [49,50] is justified because it shows a consistent and reproducible effect with a significant reduction of Methanobrevibacter sp. in obese subjects (Egger’s regression intercept test, p value = 0.39; and Duval’s and Tweedie’s trim and fill did not change these results). Methanobrevibacter sp. (n = 3; SDM = -0.51; 95% CI: -0.79 to -0.22; p = 0.001 [I 2 = 0%]).Ow: Overweight; SDM: Standardized difference in the means.
Study (year)
Subgroup within study Sample size Ow/obese Control
SDM and 95% CI
Armougorn et al . (2009)Schwiertz et al . (2010)Million et al . (2011)
M. smithii specific qPCR Methanobrevibacter sp. qPCR M. smithii specific qPCR
20
33
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-2.00-1.000.00
1.00
2.00Lean status
Ow/obese
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Archaea & obesity
Using the data of Armougom et al ., but cal-culating means of log 10 copies DNA/ml of M. smithii , we found, contrary to Armougom et al ., that there was a decrease in the M. smithii load in the obese group, compared with the normal group [49]. Correspondingly, Zhang et al. found more M. smithii in obese individu-als than in lean controls [53], and Schwiertz et al. identified lower levels of M. smithii in obese subjects compared with lean subjects [51]. However, Million et al. recently found higher concentrations of M. smithii in nonobese sub-jects [50]. Overall, methanogenic archaea could indirectly promote caloric intake by the colon and further fat accumulation-related obesity
in individuals who were on a high-fiber diet [21]. During the fermentation process, the accumulation of excess H 2 reduces the yield of ATP, which leads to a gradual decrease in the fermentation efficiency [21]. The importance of methanogenic Archaea to humans lies in their ability to improve fermentation efficiency by removing H 2 from the gut [21]. It has been speculated that the coexistence of Prevotellaceae with methanogenic Archaea species in the obese gut allows for greater efficiency of dietary poly-saccharide fermentation and therefore increases their conversion into short-chain fatty acids, resulting in their excessive storage [53].
Meta-analysis of the obesity-associated gut microbiota alteration at the genus level for Methanobrevibacter spp., main representative of Archaea known in the digestive microbiota, comparing the absolute number of sequences generated by qPCR revealed that obese sub-jects presented less Methanobrevibacter than nonobese subjects (F igure 4). However, the rea-sons linking methanogens to weight gain still remain unclear. To date, Methanobrevibacter is the main representative of archaea in the gut microbiota but archaea could not be extrapo-lated from Methanobrevibacter assessment. This is extremely important since domain-level and genus-level could lead to very different results.
Ability to process polysaccharides
The gut microbiome is also involved in the complex carbohydrate metabolism of food owing to its ability to process indigestible
Figure 5. Outline of carbohydrate fermentation by gut microbiota.
Propionate
Butyrate H 2 excess CO 2
SO 4
Acetate
Bacteroides as major propionate producers
Bacteroides and Firmicutes as major acetate producers Firmicutes as major butyrate producers
Lactobacillus , Bifidobacterium and Streptococcus as major lactate producers Desulfovibrio as major sulfato-reducers
Methanobrevibacter smithii archaeon species as major methane producers
–
+
2-
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components of diets, such as plant polysaccha-rides [6,13,63]. The human gut microbiome plays an essential role in the catabolism of dietary fibers, the part of plant material in the human diet that is not metabolized by the upper digestive tract, because the human genome does not encode for an adequate carbohydrate active enzyme (CAZymes) (F igure 5). Dietary fibers are the components of vegetables, cere-als, leguminous seeds, and fruits that are not digested in the stomach or in the small intes-tine. Instead, they are fermented in the colon by the gut microbiome and/or excreted in the feces. Additionally, dietary fibers have been identified as strong, positive dietary factors in the prevention of obesity [64]. The human gut bacteria produce a huge panel of CAZymes, with widely different substrate specificities, to degrade these compounds into metabolizable monosaccharides and disaccharides. The array of CAZymes in gut microbes is highly diverse, exemplified by Bacteroides thetaiotaomicron , which contains 261 glycoside hydrolases and polysaccharide lyases, as well as 208 homologs of susC and susD genes, which code for two outer membrane proteins that are involved in starch utilization [65,66]. The CAZymes repre-sent, on average, 2.6% of the sequenced genes in each microbiome [13]. As the human genome encodes, at best, 20–25 digestive enzymes from CAZyme families (i.e., GH1 [lactase], GH13 [a -amylase] and GH31 [maltase, isomaltase and sucrase]), the ability to digest dietary plant carbohydrates resides entirely in gut microbi-omes [67]. The CAZymes represented in dif-ferent human populations that consume dif-ferent diets may be influenced by their varied cultural traditions. Hehemann et al. found that porphyranase and agarase genes are specifically
encountered in Japanese gut bacteria and are
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probably absent in the microbiome of western individuals [68]. The authors proposed that con-sumption of sushi that contains algae from the genus Porphyramay , which is associated with the marine bacteria Zobellia galactanivorans and Bacteroides plebeius , has been the route through which these CAZymes were acquired in human gut bacteria [68,69].
Recently, Benjdia et al. hypothesized that sulfatases are critical, evolved fitness factors [70]. To be active, sulfatases must undergo a critical post-translational modification that is catalyzed in anaerobic bacteria by the radical AdoMet enzyme, anaerobic sulfatase-maturat-ing enzyme (anSME). They found that human gut Bacteroidetes possessed an anSME gene, and several genes that encoded sulfatases were pres-ent within many species, including B. fragilis , Bacteroides dorei or Parabacteroides distasonis [70]. On the other hand, Firmicutes did not pos-sess genes encoding predicted sulfatases, and it was proposed that this demonstrated that sulfatases were an important and evolution-ary conserved feature among Bacteroidetes that inhabited the human digestive tract [70,71].
Gut flora of twins
Turnbaugh et al. compared the fecal microbial communities of young, adult female monozy-gotic and dizygotic twin pairs, who were either lean or obese, along with those of their moth-ers, to assess the gut microbiota relationship to host weight. Comparisons between all partici-pants showed that obesity was associated with reduced bacterial diversity and a reduced rep-resentation of the Bacteroidetes [13]. In a more recent study, they found that the majority of species-level phylotypes were shared between deeply sampled monozygotic twins, despite large variations in the abundance of each phy-lotype [72]. From the gene clusters present in their microbiome bins, only 17% were shared between the two co-twins. Bins exhibited differences in their degree of sequence varia-tion, gene content, including the repertoire of carbohydrate active enzymes present within, and between twins (e.g., predicted cellulases, dockerins) and transcriptional activities [72].
Gnotobiotic mice for the ana l ysis of human gut microbes
Germ-free mice provide a complementary approach for characterizing the properties of the human gut microbiome. Backhed et al. found that young, conventionally reared mice have a 40% higher body fat content and 47%
higher gonadal fat content than germ-free mice, even though they consumed less food than their germ-free counterparts [73]. When the microbi-ota of normal mice were transplanted into gno-tobiotic mice, there was a 60% increase in body fat within 2 weeks without any increase in food consumption or obvious differences in energy expenditure [73]. Moreover, in a separate study using genetically modified (fasting-induced adipocyte factor [Fiaf]) knockout mice, the same authors showed that gut microbes sup-press intestinal Fiaf. Fiaf suppression resulted in increased lipoprotein lipase activity in adi-pocytes and promoted storage of calories as fat. These findings suggested that the gut micro-biota could affect both sides of the energy bal-ance equation, influencing energy harvest from dietary substances (Fiaf) and affecting genes that regulate how energy is expended and stored [74]. Turnbaugh et al. were the first to determine that differences in the microbial community could be a factor for obesity [75]. They found that transfer of the gut microbiota from obese (ob/ob) mice to germ-free, wild-type recipients led to an increase in fat mass in the recipients. This led to speculation that the gut microbiota promoted obesity by increasing the capacity of the host to extract energy (calories) from ingested food [75]. Controlled diet manipula-tion in gnotobiotic mice, which were colonized with a complete human gut (fecal) microbiota,
Figure 6. Population of bacteria found to increase in obese and lean individuals.
Coriobacteriaceae Lactobacillus Enterococcus
Faecalibacterium prausnitzii Prevotella Clostridium Eubacterium Roseburia Methanobrevibacter T reponema Xylanibacter Bacteroides Bifidobacterium
Staphylococcus
Escherichia coli
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revealed that the composition of their human gut microbial communities changed dramati-cally within a single day after the animals were switched from a plant polysaccharide-rich chow to a high-fat, high-sugar ‘‘western’’ diet [14]. Goodman et al. developed an approach called insertion-sequencing (INSeq), which is based on a mutagenic transposon that cap-tures adjacent chromosomal DNA to define its genomic location [76]. In this approach, complex populations of tens of thousands of transposon mutants are simultaneously introduced into wild-type or genetically manipulated, germ-free mice in the presence or absence of other microbes. Using this assay, they discovered that B. thetaiotaomicron employed the products of five adjacent genes (BT1957–49) in response to variations in vitamin B12 levels [76]. Moreover, mice colonized with complete or cultured fecal communities from two human donors displayed significantly greater fat pad to body weight ratios than germ-free controls [77]. Notably, 18 species-level phylotypes were significantly affected when these gnotobiotic mice received a western diet for 2 weeks. Specifically, the rela-tive proportion of representatives of one class of Firmicutes (the Erysipilotrichi ) was increased, and the relative proportion of the Bacteroidia class was decreased [77]. Hildebrandt et al. found that both wild-type and RELM b knockout mice were lean on a standard chow diet, but upon switching to a high-fat diet, the wild-type mice became obese, whereas RELM b knockout mice remained comparatively lean [78]. After the switch to the high-fat diet, the proportions of Proteobacteria, Firmicutes and Actinobacteria increased, whereas the levels of Bacteriodetes decreased [78]. When adult, germ-free, male mice were colonized with Marvinbryantella for-matexigens and B. thetaiotaomicron , it was found that B. hydrogenotrophica targeted aliphatic and aromatic amino acids and increased the effi-ciency of fermentation by consuming reducing equivalents, thereby maintaining a high NAD +/NADH ratio and boosting acetate production [79]. By contrast, M. formatexigens consumed oli-gosaccharides, did not impact the redox state of the gut and boosted the yield of succinate [79]. Normalized RNA-Seq counts, generated from the cecal contents and fecal samples of the mice revealed that prophages in M. for-matexigens were completely activated and that two gene pairs were constitutively expressed in all fecal and cecal samples [80]. The authors proposed that a prophage might be liberated from its host cell when that cell is present in
a fecal community [80]. Colonization of germ-free mice that consumed a plant polysaccharide-rich or a simple sugar diet with wild-type or anSME-deficient strains revealed that active sulfatase production by B. thetaiotaomicron was essential for competitive colonization of the gut, especially when the organism was forced to adaptively forage on host mucosal glycans because complex dietary polysaccharides were not available [70]. The authors proposed that anSME activity and the subsequent activation of sulfatases represented an important pathway that allowed this Bacteroidetes species to adapt to life in the gut [70]. Fleissner et al. showed that changes in energy expenditure rather than “energy harvest” were responsible for changes in fat deposition and weight gain in mice as they found no difference in body weight gain between germ-free and conventional mice fed a semi-synthetic low-fat diet [81]. By contrast, germ-free mice gained more body weight and body fat than conventional mice on a high-fat diet. Moreover they found that the pro-portion of Firmicutes increased in both mice high-fat and on a western diet. This increase was mainly due to the proliferation of the Erysipelotrichaceae [81]. Murphy et al. treated ob/o
b mice with a low-fat diet and wild-type mice with either a low-fat diet or a high-fat diet and found that the proportions of Firmicutes , Bacteroidetes and Actinobacteria did not corre-late with energy harvesting markers [82]. Higher concentrations of taurine-conjugated bile acids were identified in the livers and intestines of germ-free mice [83] and in those colonized by human baby microbiota [84] compared with con-ventional animals. Historically, bile acids have been primarily viewed as detergent molecules important for the absorption of dietary fats and lipidsoluble vitamins in the small intestine and the m a intenance of
c h olesterol homeostasis in the liver [83].
Conclusion
Obese and lean subjects presented increased levels of different bacterial populations (T able 2 & F igure 6). In addition, a caloric diet restriction impacted the composition of the gut microbiota in obese/overweight individuals and weight loss [5,55,56]. Interestingly, the initial microbiota of overweight adolescents, before any treatment, drove the efficiency of weight loss [56], and differences in the gut composition at infancy could lead to weight gain [23,58]. Studies using gnotobiotic mice have shown that the gut
microbiota was critical for normal digestion of
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nutrients [74]. It was proposed that the meta-bolic activities of the gut microbiota facilitated the extraction of calories from ingested dietary substances, helped to store these calories in host adipose tissue for later use and provided energy and nutrients for microbial growth and prolifer-ation [85]. A more recent hypothesis is based on data from vegetarian human populations who presented bacteria that were commonly found in plants, like B. thetaiotaomicron , which pro-duced CAZymes and metabolized monosaccha-rides and disaccharides [6,13,62]. Moreover, it was predicted that other unknown factors in the microbiota and, recently, the manipulation of gut microbial with probiotics, prebiotics, anti-biotics or other interventions, were factors for weight gain and obesity [1,86,87], which should be investigated more [88,89]. These results sug-gest that manipulating the composition of the gut microbiota may prevent weight gain or facilitate weight loss in humans.
Future perspective
During the last few years, an increasing num-ber of studies have related imbalances in the composition of the gut microbiota to obesity. Many studies have reported shifts in the relative abundances of bacterial communities in the gut microbiota of obese relative to normal-weight individuals, and each study has attempted to link obesity with a species- or genus-specific composition profile of the gut microbiota. However, it is possible that the design and/or interpretation of the results has been affected by a conflict of interest of each team. It has recently been shown that published papers in nutrition and obesity research in which the authors were funded by industry were more likely than other papers to contain results or interpretations that favored the industry or company that was producing the product or service that was being studied [90]. Moreover, the heterogeneous methods that were utilized in individual microbiota studies to estimate bacterial proportions prevented rational com-parisons of results [12]. Notably, 16S rRNA sequencing-based methods are biased by the heterogeneity of the copy number of the 16S rRNA gene that is present in an individual bac-terial genome [91] and can lead to an overesti-mation of bacterial proportions. However, it is noteworthy that the current 16S rDNA pyrose-quencing [53], as well as clonal, Sanger sequenc-ing, studies [5] of gut microbiota within obese populations were not able to detect bacterial concentrations that were below 107 organisms
per gram of feces [49]. Indeed, the characteriza-tion of the 1011 bacterial copies per gram of feces that was used in these studies remains superfi-cial. The use of FISH and qPCR methods were dependent on both sensitivity and specificity of the targeted bacterial group. Additionally, the Bac303 probe, which was used in most of the FISH- and qPCR-based studies [55,57–59], underestimated the Bacteroidetes proportions because the probe targeted only the Bacteroides-Prevotella groups, and it was inadequately sen-sitive to the Prevotella group [92]. Ley et al. sug-gested that it will be interesting to study and compare the effects of these molecular methods using the same sample stool [12]. An integration of mechanistically based investigations and microbial ecology studies using high-through-put sequencing will provide insights into how to best reshape host–microbial interactions to promote weight loss.
Food is a source of bacteria and viruses, and changes in patterns of food consumption results in differences in human gut flora among different groups of people. A question being investigated is whether it is important to iden-tify the source of the gut microorganisms as the most are ingested with food, drinks, and in the course of physical contact and interhuman relationships. Data from agriculture, laboratory animals and humans show that manipulating gut microbiota results in weight modifications and, recently, it was proposed that is neces-sary to further investigate the effects of rou-tinely adding high amounts of bacteria to food [1,86,87]. In the last few years, the number of published descriptions of the organisms and genes that comprise and manipulate the gut microbiota is increasing dramatically, but these studies have so far been limited to fairly small populations. Moreover, little effort has been made to standardize the microbiota ana l ysis methodology and different sample collection, storage and ana l ysis methods have only been superficially investigated in human studies. This makes it almost impossible to directly compare findings from different groups, lim-iting our ability to generalize findings. Further well-designed studies should be conducted into how gut microbial communities normally operate, how they shape host physiology, and how they may be altered by probiotic, prebi-otic, antibiotic or other interventions. For that reason, massive parallel sequencing technolo-gies and the necessary bioinformatics tools to handle the resulting large datasets should be adapted for human microbiota ana l
ysis.
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Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the sub-
ject matter or materials discussed in the manuscript.
This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
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