Structural changes in the gut microbiome of constipated patients
Lixin Zhu, Wensheng Liu, Razan Alkhouri, Robert D. Baker, Jonathan E. Bard, Eamonn M. Quigley and Susan S. Baker Physiol. Genomics 46:679-686, 2014. First published 29 July 2014; doi:10.1152/physiolgenomics.00082.2014 You might find this additional info useful... This article cites 49 articles, 18 of which can be accessed free at: /content/46/18/679.full.html#ref-list-1 Updated information and services including high resolution figures, can be found at: /content/46/18/679.full.html Additional material and information about Physiological Genomics can be found at: http://www.the-aps.org/publications/pg
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Physiol Genomics 46: 679–686, 2014. First published July 29, 2014; doi:10.1152/physiolgenomics.00082.2014.
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Structural changes in the gut microbiome of constipated patients Lixin Zhu,1 Wensheng Liu,1 Razan Alkhouri,1 Robert D. Baker,1 Jonathan E. Bard,2 Eamonn M. Quigley,3 and Susan S. Baker1 1
Digestive Diseases and Nutrition Center, Department of Pediatrics, the State University of New York at Buffalo, Buffalo, New York; 2Next-Generation Sequencing and Expression Analysis Core, Department of Biochemistry, the State University of New York at Buffalo, Buffalo, New York; and 3Division of Gastroenterology and Hepatology, Houston Methodist Hospital, Houston, Texas; and Alimentary Pharmabiotic Centre, University College, Cork, Ireland Submitted 3 July 2014; accepted in final form 28 July 2014
constipation; microbiota; Prevotella; butyrate CONSTIPATION IS A COMMON condition, with a worldwide prevalence which ranges from 2.5% to 79% in adults and 0.7% to 29.6% in children (31). The prevalence of constipation is higher in obese children than lean controls or the general pediatric population (18, 35). Since bacteria account for ⬃50% of the stool volume (45), it is conceivable that prolonged fecal stasis in the colons of constipated patients has an impact on the
Address for reprint requests and other correspondence: L. Zhu, Digestive Diseases and Nutrition Center, Dept. of Pediatrics, State Univ. of New York at Buffalo, 3435 Main St., 422BRB, Buffalo, NY 14214 (e-mail: lixinzhu @buffalo.edu).
microbial ecology of the colon. On the other hand, whether mediated by bacterial metabolites (such as short chain fatty acids) or bacterial cell components (such as lipopolysaccharide) or through interactions between bacterial cells and the host immune system, both commensal and pathogenic bacteria can influence a variety of gut functions and, thereby, could have a role in the pathology of constipation. Two distinct lines of evidence support the association between constipation and the gut microbiota. First, patient intake of some probiotic species leads to relief from constipation (34, 41). Second, studies of the microbiota revealed significant differences between healthy subjects and constipated patients. Zoppi et al. (52) found that clostridia and bifidobacteria were significantly increased in the gut microbiota of constipated children and that the clostridium species isolated from constipated children were different from those isolated from healthy children. On the other hand, Khalif et al. (25) reported that the gut microbiome of the constipated adults also differed from that of the healthy adults, the major difference being that the constipated microbiome exhibited decreased abundance in Bifidobacteria. As they were based on the use of bacterial cultures, the studies performed by Zoppi et al. and Khalif et al. provide a limited view of the microbiota as they would not have included many bacterial species. Culture-independent, quantitative PCR using 16S rRNA gene-specific primers has provided insightful knowledge in the gut microbiome composition in patients with constipation-predominant irritable bowel syndrome (26). Recently, high-throughput sequencing techniques have made it possible to examine the DNA sequence of every taxon in gut microbiome, which is a more direct approach than PCR and provides higher resolution of the microbiome composition. The aim of the present study, therefore, was to use the culture-independent 16S rRNA gene pyrosequencing method to examine the gut microbiomes of constipated patients. Since the majority of available constipated patients are obese, and obesity is known to have a large impact on gut microbial composition (28, 43), we decided to enroll only obese patients, with or without constipation, so that the gut microbial features associated with constipation may not be obscured by those associated with obesity. Our study revealed significant differences in gut microbiome between constipated and nonconstipated patients at all taxonomic levels. As diet exerts a significant impact on the gut microbiome and has rarely been controlled for in prior studies, our study employed a number of validated
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Zhu L, Liu W, Alkhouri R, Baker RD, Bard JE, Quigley EM, Baker SS. Structural changes in the gut microbiome of constipated patients. Physiol Genomics 46: 679 – 686, 2014. First published July 29, 2014; doi:10.1152/physiolgenomics.00082.2014.—Previous studies using culture-based methods suggested an association between constipation and altered abundance of certain taxa of the colonic microbiome. We aim to examine the global changes in gut microbial composition of constipated patients. A cross-sectional pilot study using 16S rRNA gene pyrosequencing was performed to compare stool microbial composition of eight constipated patients and 14 nonconstipated controls. Only obese children were enrolled so that the microbiome features associated with constipation would not be obscured by those associated with obesity. The sequencing reads were processed by QIIME for quantitative analysis of the microbial composition at genus and above levels. Dietary intake for all the individuals was assessed by dietary recalls and a food frequency questionnaire. The ecological diversities of fecal microbiome of the constipated patients differed from those of the controls. Significantly decreased abundance in Prevotella and increased representation in several genera of Firmicutes were observed in constipated patients compared with controls. The conventional probiotic genera Lactobacillus and Bifidobacteria were not decreased in the microbiomes of the constipated patients. These alterations in the fecal microbiome of constipated patients suggested that a novel probiotic treatment including certain Prevotella strains may be more effective than conventional probiotic products incorporating Lactobacillus or Bifidobacterium species. While it is possible that the observed changes in the microbiome in constipated subjects are a consequence of a low-fiber diet, these changes also predict a different pattern of bacterial fermentation end-products, such as increased butyrate production, which may contribute to pathogenesis of constipation.
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Table 1. Characteristics of the study groups Characteristics
Control
Constipated
Sex Age, yr BMI BMI z-score AST ALT Ethnicity (black/white/other)
F7/M7 13.2 ⫾ 0.7 34.8 ⫾ 1.6 2.39 ⫾ 0.07 27.4 ⫾ 4.4 27.7 ⫾ 3.8 2/10/2
F4/M4 11.8 ⫾ 1.5 30.0 ⫾ 2.3 2.25 ⫾ 0.09 30.8 ⫾ 4.5 26.9 ⫾ 6.1 1/5/2
Values are means ⫾ SE. F, female; M, male; BMI, body mass index; AST, aspartate transaminase; ALT, alanine transaminase. A z-score of 1.6449 is equivalent to 95 percentile.
methods to assess the relative dietary intakes of the two study groups. MATERIALS AND METHODS
Fig. 1. Ecological diversities of the gut microbiome in the constipated patients and control subjects. Alpha diversities (phylogenetic diversity and species richness) were evaluated based on the rarefied operational taxonomic unit (OUT) tables. The sampling sizes were 10, 1,268, 2,526, 3,784, and 5,042 OTUs. A: phylogenetic diversity metric based on PD_whole_tree method. No significant difference was observed. B: species richness metric based on Chao1 method. Significant differences were observed at the sampling size of 2,526 OTUs (P ⬍ 0.05, Student t-test). C: beta-diversities of the gut microbiomes were evaluated by UniFrac-based principle co-ordinates analysis. The majority of the samples clustered by health status at the PC1 vs. PC2 plot, indicating that constipation was a major effect factor for the phylogenetic composition of these samples. Exceptions from both study groups were observed, reflecting the effect from other genetic and environmental factors on these microbiomes.
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Human subjects. This cross-sectional pilot study was approved by the Children and Youth Institutional Review Board of the State University of New York at Buffalo. The diagnosis of constipation was based on the clinical practice guideline developed by the North American Society for Pediatric Gastroenterology, Hepatology and Nutrition (13). According to this guideline, constipation is defined as a delay or difficulty in defecation, present for 2 or more weeks, and sufficient to cause significant distress to the patient. Patients recruited in this study were all obese [body mass index (BMI) higher than the
95th percentile, z-score ⬎ 1.6449]. Patients with any abnormal condition other than constipation [such as biopsy proven steatohepatitis, elevated alanine transaminase (ALT), elevated aspartate transaminase (AST), other serological evidence of liver inflammation, or other liver diseases] were excluded. Recruited into the control group were nonconstipated obese adolescents. For all recruited patients, no antibiotics, probiotics, prebiotics, proton pump inhibitors, or histamine receptor antagonist should have been taken in the 3 mo prior to sample collection. A single stool sample was collected from each patient between July 26, 2010 and June 1, 2011. All stool samples were analyzed within 12 mo. The characteristics of human subjects are summarized in Table 1. Dietary assessment. Prior to sample collection, dietary intake for all the individuals was assessed by a 3-day diet history supplemented by a 24 h dietary recall (46) and the Centers for Disease Control and Prevention 1 wk Food Frequency Questionnaire (6, 9, 22) as an additional assessment tool for fiber intake. Diet history and dietary recall data were analyzed with DINE Healthy version 7.0.1. Genomic DNA extraction. Stool samples were stored at ⫺80°C before genomic DNA was isolated with the DNeasy Blood and Tissue Kit (Qiagen, Valencia, CA) and mechanical lysis. Fecal samples were incubated in ATL buffer with proteinase K, mechanically lysed with a FastPrep FP120 (MP Biomedical, Solon, OH), treated with RNaseA, and lysates purified on Qiagen columns. One tube containing 10 l of sterile water was extracted in parallel to detect possible contamination. 454 16S rRNA gene V4 –V5 pyrosequencing. Amplicon libraries for pyrosequencing were constructed using a forward primer containing the 454 primer B and the 515F 16S sequences and a reverse primer
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containing the 454 primer A and the 806R 16S sequences. Each 50 l PCR reaction mix contained 50 ng template DNA, 1 U Platinum Taq High Fidelity polymerase and buffers (Invitrogen, Carlsbad, CA), 400 nM primer, 2 mM MgSO4, 0.2 mM dNTP mix and was amplified by 30 cycles of touch-down PCR (37). Duplicate PCR amplicons for each sample were pooled and purified with Agencourt AMPure XP (BeckmanCoulter Genomics, Danvers, MA). Barcoded amplicons were combined in equimolar ratios for pyrosequencing on a 454-FLX-Titanium Genome Sequencer (Roche 454 Life Sciences, Branford, CT). All raw 454 sequencing reads and the associated meta-data are archived at MG-rast (http://metagenomics.anl.gov/linkin.cgi?project⫽1195). Quantitative analysis of the microbiome composition. Pyrosequence reads were analyzed by phylogenetic and operational taxonomic unit (OTU) methods in the Quantitative Insights into Microbial Ecology (QIIME) software version 1.4.0 (8). QIIME was first used to de-multiplex the barcoded reads and perform chimera filtering. Filtered sequence reads were grouped into OTUs at a sequence similarity level of 97%, which approximates species-level phylotypes. Taxonomy of the OTUs was assigned and sequences were aligned with RDP classifier (12) and Pynast (7). Phylogenetic trees were built with FastTree2, which infers approximately-maximum-likelihood trees from alignments of nucleotide sequences (38). Phylogenetic distance
between taxa was determined with UniFrac (29). To evaluate the alpha diversities of each microbiota community, we calculated two metrics: phylogenetic distance metric [PD_whole_tree(17)] to estimate the phylogenetic diversities within each community and the Chao1 metric (10) to estimate the species abundance within each community. Beta diversities were evaluated with the UniFrac-based principle coordinates analysis (8) performed with a rarefaction depth of 5,666 based on the minimum OTU count of all samples. Other statistical analysis. For normally distributed data, Student t-tests were performed to evaluate the differences in dietary intake, taxonomic abundance, and alpha diversities between constipated patients and controls. Mann-Whitney U-tests were performed when data were not normally distributed. A P value ⬍ 0.05 was considered statistically significant. RESULTS
Ecological diversities of the microbiomes in constipated obese children and control obese children. The fecal microbiomes of eight constipated obese children and 14 control obese children were analyzed by 16S rRNA gene pyrosequencing. The constipated patient group and the control group were Downloaded from on November 16, 2014
Fig. 2. Phylum distribution of gut microbiomes of the constipated patients and the control subjects. A: average phylum distribution of the gut microbiomes of the control subjects. B: average phylum distribution of the gut microbiomes of the constipated patients. C: phylum distribution of the individual microbiome.
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Table 2. Abundant taxa in the gut microbiome of the constipated patients and controls Taxa
Control, %
Constipation, %
Foldchange
P Value
Firmicutes* Clostridiales Family XI. IncertaeSedis† Anaerococcus‡ Finegoldia Peptoniphilus Unknown Lachnospiraceae Blautia Clostridium Coprococcus Roseburia Ruminococcus§ Ruminococcaceae Faecalibacterium Oscillospira Ruminococcus Unknown Veillonellaceae Dialister Megasphaera Unknown
34.38
57.98
1.69
0.126
14.79 1.55 1.69 2.34 8.90 7.53 1.64 0.93 0.64 1.03 0.54 6.27 2.73 1.22 0.44 0.75 3.48 1.57 0.59 0.13
11.48 2.23 2.81 4.07 2.19 21.03 5.66 1.73 2.45 2.20 2.46 16.76 7.66 1.48 2.66 1.93 4.86 1.49 2.06 1.32
0.78 1.44 1.67 1.74 0.25 2.79 3.46 1.87 3.82 2.13 4.57 2.68 2.80 1.22 6.09 2.59 1.40 0.95 3.48 9.97
0.840 0.603 0.973 0.935 0.535 0.042 0.023 0.187 0.029 0.165 0.020 0.024 0.165 0.238 0.061 0.070 0.550 0.569 0.370 0.825
Bacteroidetes Bacteroidaceae Bacteroides Porphyromonadaceae Parabacteroides Porphyromonas Prevotellaceae Prevotella
59.18 22.69 22.69 3.56 1.13 2.34 31.68 31.51
33.69 21.29 21.29 8.55 2.92 5.39 2.78 2.40
0.57 0.94 0.94 2.40 2.58 2.30 0.09 0.08
0.041 0.943 0.943 0.913 0.480 0.651 0.010 0.010
Proteobacteria Alcaligenaceae Campylobacteraceae Campylobacter
2.39 1.14 0.92 0.92
4.06 0.70 2.91 2.91
1.70 0.62 3.15 3.16
0.388 0.232 0.764 0.764
Actinobacteria Bifidobacteriaceae Bifidobacterium
1.94 1.07 0.98
3.56 2.47 2.43
1.83 2.32 2.48
0.145 0.169 0.124
Fusobacteria Fusobacteriaceae Cetobacterium
1.44 1.44 1.26
0.06 0.06 0.00
0.04 0.04 0.00
0.433 0.433 1.000
Control column shows average abundance in gut microflora of nonconstipated control subjects, n ⫽ 14. Constipation column shows average abundance in gut microflora of constipated patients, n ⫽ 8. Fold-change column shows Constipation/Control. P value from 2-tailed Student t-test or Mann-Whitney U test. *Phyla with average abundance ⬎1% in any of the study groups are listed. †Families with average abundance ⬎1% in any of the study groups are listed. “Unknown” refers to theoretical family or genus based on 16S rRNA sequence similarity clustering. ‡Genera with average abundance ⬎1% in any of the study groups are listed. §Some Ruminococcus species belong to family Ruminococcaceae, while the others belong to family Lachnospiraceae.
constipated subjects. Almost all Prevotellaceae sequences identified in this study belong to the genus Prevotella. Therefore, it is no surprise that Prevotella was significantly decreased in the constipated subjects(Table 2, Fig. 5A). Although many Prevotella OTUs were detected in this study, ⬎50% of the Prevotella sequences belonged to four of the OTUs: OTU#315224, OTU#20595, OTU#484, and OTU#11384. BLAST search against the 16S rRNA gene database found that the best matches for these were Prevotella copri, Prevotella
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similar in female/male ratio, BMI, BMI z-score, ethnicity, and serum ALT and AST (Table 1). A total of 287,470 sequencing reads were obtained from a total of 22 samples. The species diversity within each microbiome sample (␣ diversity) was evaluated by a phylogenetic-distance metric PD_whole_tree (17) (Fig. 1A) and a species-richness metric Chao1 (10) (Fig. 1B). No statistical difference was detected with the PD_whole_tree metric between the constipated and the control group. However, a significant difference between these two groups was detected with the Chao1 metric at the sampling size of 2,826 OTUs. To access the similarities and distances of the ecological complexities among all microbiome samples ( diversity), we performed a phylogenetic distance-based principle co-ordinates analysis, UniFrac, with all 22 microbiome samples from the constipated patients and controls (29). The majority of the samples clustered by health status (Fig. 1C), but not by age, ethnicity, or sex (data not shown), indicating a connection between constipation and the phylogenetic diversity of the gut microbiome. Exceptions from both study groups were observed, reflecting the influence of other genetic and environmental factors on the gut microbiome. Diet can exert a major impact on the gut microbiome and constipation. Dietary assessments were conducted to compare dietary intake at the time of stool sample collection between the constipated and the control groups. Although the percent energy intake from protein, fat, and carbohydrate was similar between two groups, fiber intake was marginally higher (fiber score for constipated 2.8 ⫾ 0.37, for control 4.2 ⫾ 0.37; P ⫽ 0.052) in the nonconstipated controls. Microbiomes at the phylum level. A total of 11 bacterial phyla were detected in the gut microbiomes of controls (Fig. 2A) and constipated patients (Fig. 2B), including four frequently detected phyla Firmicutes, Bacteroidetes, Proteobacteria, and Actinobacteria, and seven minor phyla Fusobacteria, Tenericutes, Lentisphaerae, Spirochaetes, Euryarchaeota, Verrucomicrobia, and Synergistetes. Compared with the gut microbiomes of healthy children, we did not detect phyla Acidobacteria, OP3, or TM7 in the gut microbiome of the obese children. The four frequently detected phyla were present in all 22 samples, while each of the seven minor phyla was detected in no more than 10 samples. Although exhibiting a broad distribution (Fig. 2C), the abundances of Bacteroidetes were significantly decreased in the gut of the constipated patients compared with the control groups. Our data showed a trend of increased abundance of Firmicutes in the gut of the constipated patients, but no statistical significance was achieved with the small sample size in this pilot study (Table 2, Fig. 3A). No differences were identified between the groups with respect to Actinobacteria, Proteobacteria (Fig. 3B), or other minor phyla. Microbiomes at the family and genus levels. An examination of the microbiotas at the family level revealed significant differences between the constipated and the control groups in three families, including Prevotellaceae, Lachnospiraceae, and Ruminococcaceae. Prevotellaceae is a family in phylum Bacteroidetes, and the others belong in Firmicutes. Prevotellaceae was the only family significantly decreased in the constipated patients (Table 2, Fig. 4). Compared with the average abundance of 31.68% for Prevotellaceae in the control group, their abundance was greatly diminished to 2.78% in
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Fig. 3. Comparison of the phylum abundance in the gut microbiome between the control subjects and the constipated patients. Plotted are the most abundant phyla in the gut: Bacteroidetes and Firmicutes (A) and Actinobacteria and Proteobacteria (B). *P ⬍ 0.05.
Fig. 4. Gut microbial families exhibiting significant difference in abundance between the control subjects and the constipated patients. *P ⬍ 0.05.
fold that of the control subjects; though not statistically significant (Table 2). Similarly, two other common probiotic genera Lactobacillus (constipation/control ⫽ 7.88, P ⫽ 0.102) and Streptococcus (constipation/control ⫽ 3.06, P ⫽ 0.169) exhibited trends of increased abundance in the gut of the constipated patients. The differentially represented phyla, families, and genera between constipated and control microbiomes are summarized in Table 3. DISCUSSION
Here we report the first study using 16S rRNA gene pyrosequencing to examine the quantitative composition of the gut microbiome in constipated obese children and nonconstipated obese controls. Ecological diversities of the gut microbiomes were different between the constipated patients and the control subjects, demonstrating a strong association of constipation with the gut microbiome. Significant differences were identified at all taxonomic levels, indicating that constipation is associated with an altered microbiome in the gut. The microbiomes of the constipated patients exhibited decreased Bacteroidetes, which was mostly explained by the decreased abundance in the genus Prevotella (family of Prevotellaceae). On the other hand, the increased representation of several families and genera in Firmicutes was observed in the gut of the constipated patients. Our observation of an altered microbiome in constipated patients suggests a role for probiotic treatment of constipation. However, randomized controlled trials (RCTs) of probiotics in constipation showed that dietary supplementation with Lactobacillus casei strain Shirota had either a modest effect or no effect on constipation in adult patients (27, 30). Similarly, an RCT using L. rhamnosus GG in constipated children showed no advantage over placebo (3). The ineffectiveness of these probiotic species may be explained by our observation that Bifidobacterium, Lactobacillus, and Streptococcus were not decreased in the constipated patients. Rather, they all exhibited a trend of increased abundance in the gut of the constipated patients. Importantly, the microbiome composition of the constipated patients as defined in our study suggested that Prevotella species may be a better probiotic treatment to restore the ecology of the gut microbiota in constipated patients. As a
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disiens, Prevotella disiens, and Prevotella corporis, respectively (data not shown). Families Lachnospiraceae and Ruminococcaceae belong to Firmicutes and were increased in the constipated patients (Table 2, Fig. 4). Families Lachnospiraceae and Ruminococcaceae were abundantly represented in both study groups. The increased abundance of these two families in the constipated patients accounted for much of the increased abundance of Firmicutes in these same subjects. The increased abundance of Lachnospiraceae in the constipated subjects was mostly explained by an increased abundance of genera Blautia, Coprococcus, and Ruminococcus (Table 2, Fig. 5B). The increased abundance of Ruminococcaceae in the constipated subjects was mainly explained by the increased abundance of genera Ruminococcus, Faecalibacterium, and one undefined genus (Table 2, Fig. 5B). However, no statistical significance was achieved for these genera. Two minor genera in Ruminococcaceae, Anaerotruncus and Clostridium, were significantly increased in the constipated subjects (Table 2, Fig. 5C). Note that some Ruminococcus species belong to the family Ruminococcaceae, while others belong to family Lachnospiraceae. This reflects the ongoing reclassification of bacteria prompted by molecular evidence such as the 16S rRNA gene sequences. It is noteworthy that the abundance of Bifidobacterium, a common probiotic genus, in the constipated patients was 2.48-
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major component of the gut microbiome from healthy subjects, probiotic use of Prevotella likely will be tolerated and may benefit Prevotella-depleted patients. Interestingly, P. bryantii 25A, used as a probiotic in dairy cows, improved milk production (11). In humans, probiotic use of P. histicola to treat autoimmune diseases is a subject of current research (32). Prevotella spps. are well known members of the rumen community, accounting for up to 70% of the rumen bacterial population (47). The capability of Prevotella to metabolize polysaccharide is explained by genomic sequencing data. For example, the P. bryantii genome encodes 203 carbohydrate active enzymes including 107 glycoside hydrolases, 53 glycosyl transferases, 14 polysaccharide lyases, and 19 carbohydrate esterases (40). Recently, Prevotella came to be recognized as an abundant member of the microbiomes of the gut (14), oral cavity (1, 51), esophagus (36), stomach (49), nasopharynx (2, 21), and vagina (23) of healthy people. In the gut, the abunTable 3. Taxa differentially represented in the gut microbiomes of constipated patients and controls Taxa
Control, %
Constipation, %
Fold-change
P Value
Firmicutes Lachnospiraceae* Blautia Coprococcus Ruminococcus† Ruminococcaceae Anaerotruncus Clostridium
7.53 1.64 0.64 0.54 6.27 0.01 0.06
21.03 5.66 2.45 2.46 16.76 0.03 0.22
2.79 3.46 3.82 4.57 2.68 5.59 3.76
0.042 0.023 0.029 0.020 0.024 0.029 0.030
59.18 31.68 31.51
33.69 2.78 2.40
0.57 0.09 0.08
0.041 0.010 0.010
Bacteroidetes Prevotellaceae Prevotella
*Families Lachnospiraceae and Ruminococcaceae belong to order Clostridiales. †Some Ruminococcus species belong to family Ruminococcaceae, while the others belong to family Lachnospiraceae.
dance of Prevotella may decrease as a consequence of insufficient intake of plant-based foods. Thus a comparison of the gut microbiome between African children (predominantly vegetarian) and European children (eating a typical Western diet high in animal protein, sugar, starch, and fat and low in fiber) revealed higher abundance of Prevotella and Xylanibacter in the microbiomes of African children (14). Note that Xylanibacter and Prevotella are close relatives, and recently one Xylanibacter species has been reclassified as a Prevotella species (42). Similarly, plant-based foods also increase the abundance of Prevotella in the gut microbiomes of adults (48). Our dietary analysis results suggest that constipated patients tend to consume less fiber than nonconstipated controls, which is consistent with previous reports (16, 33). We, therefore hypothesize that, by decreasing the abundance of Prevotella, a low-fiber diet is a major cause of dysbiosis in the gut of constipated patients. Decreased abundance in Prevotella may affect host physiology through bacterial metabolites. In this study, the most frequently detected Prevotella species were P. disiens, P. copri, and P. corporis. P. disiens produces succinic, acetic, lactic, and iso-valeric acid, but very little butyric acid and no propionic acid (50). The fermentation end-products of P. copri are not well characterized. However, the most closely related species, P. veroralis (20), may share a similar pattern in fermentation products, with the latter producing acetic, isovaleric, lactic, and succinic acids, little iso-butyric acid, no propionic, and no butyric acid (50). P. corporis, previously known as Bacteroides melaninogenicus subspecies intermedius and Bacteroides corporis (24), does not produce butyric or propionic acid (19). P. ruminicola, another common gastrointestinal bacterium, consumes butyrate and produces propionic, acetic, formic, and succinic acids (15). A shared characteristic among these Prevotella species is that they do not produce
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Fig. 5. Gut microbial genera exhibiting significant difference in abundance between the control subjects and the constipated patients. A: Prevotella is the only genus exhibiting significant reduction in abundance in the gut microbiome of the constipated patients, compared with the controls. B: major gut microbial genera exhibiting significantly increased abundance in the gut microbiomes of the constipated patients, compared with the controls. C: minor gut microbial genera exhibiting significantly increased abundance in the gut microbiomes of the constipated patients compared with the controls. *P ⬍ 0.05.
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ACKNOWLEDGMENTS
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10. 11.
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The authors thank Dr. Steven R. Gill (University of Rochester) for pyrosequencing of the fecal microbiome samples. GRANTS
15.
This work was supported by a grant from the Peter and Tommy Fund, Inc., Buffalo, NY (to S. S. Baker) and a departmental start-up fund (to L. Zhu). The funders had no role in study design, data collection, analysis or interpretation.
16.
17. DISCLOSURES No conflicts of interest, financial or otherwise, are declared by the author(s).
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AUTHOR CONTRIBUTIONS Author contributions: L.Z. and S.S.B. conception and design of research; L.Z., W.L., R.A., and S.S.B. performed experiments; L.Z., W.L., R.A., R.D.B., J.E.B., E.M.Q., and S.S.B. analyzed data; L.Z. and S.S.B. interpreted results of experiments; L.Z. prepared figures; L.Z. drafted manuscript; L.Z., W.L., R.A., R.D.B., J.E.B., E.M.Q., and S.S.B. approved final version of manuscript; W.L., R.A., R.D.B., J.E.B., E.M.Q., and S.S.B. edited and revised manuscript. REFERENCES
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butyric acid. On the other hand, butyrate-producing Coprococcus (39) is increased in the gut of the constipated patients (Table 3). Other significant butyrate-producing genera, Roseburia and Faecalibacterium (39), also tended to be increased in constipated patients (Table 2). Therefore, our data would predict increased butyrate production in the gut of constipated patients. Increased butyrate production may contribute to the pathogenesis of constipation via several mechanisms. First, high concentration of butyrate may inhibit mucin secretion by intestinal goblet cells (4). Second, butyrate may reduce stool volume by stimulating water and electrolyte absorption in colon (5). Third, butyrate may inhibit smooth muscle contraction in colon and cause slow colonic transit (44). Further studies with larger sample sizes are needed to confirm the observations made in this pilot study. Further studies are also needed to provide direct evidence for increased butyrate production and to examine the possible role of butyrate in the pathogenesis of constipation. In summary, our data defined an altered fecal microbiome in constipated patients. We found significantly decreased representation of Prevotella and increased representation of several genera of Firmicutes in the gut microbiome of the constipated patients. The altered microbiome suggests that a novel design of probiotic treatment including possible Prevotella strains may be more effective than conventional probiotic Lactobacillus or Bifidobacterium species. The dysbiosis is possibly a consequence of low-fiber diet. The drastic structural changes in the gut microbiome predict a different pattern of bacterial fermentation end-products (e.g., increased butyrate production), which may have a role in the pathogenesis of constipation.
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