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Analysis of the Duodenal Microbiome in Autistic Individuals: Association With Carbohydrate Digestion

Kushak, Rafail I.*; Winter, Harland S.*; Buie, Timothy M.*; Cox, Stephen B.; Phillips, Caleb D.†,‡; Ward, Naomi L.§,||

Journal of Pediatric Gastroenterology and Nutrition: May 2017 - Volume 64 - Issue 5 - p e110–e116
doi: 10.1097/MPG.0000000000001458
Original Article: Gastroenterology

Objectives: There is evidence that symptoms of maldigestion or malabsorption in autistic individuals are related to changes in the indigenous microbiota. Analysis of colonic bacteria has revealed microbial dysbiosis in children with autism; however, characteristics of the duodenal microbiome are not well described. In the present study the microbiome of the duodenal mucosa of subjects with autism was evaluated for dysbiosis, bacteria overgrowth, and microbiota associated with carbohydrate digestion. The relationship between the duodenal microbiome and disaccharidase activity was analyzed in biopsies from 21 autistic subjects and 19 children without autism.

Methods: Microbiota composition was determined by 16S ribosomal RNA gene sequencing, and disaccharidase activity via biochemical assays.

Results: Although subjects with autism had a higher frequency of constipation (P < 0.005), there was no difference in disaccharidase activity between groups. In addition, no differences in microbiome diversity (species richness and evenness) were observed. Bacteria belonging to the genus Burkholderia were more abundant in subjects with autism, whereas members of the genus Neisseria were less abundant. At the species level, a relative decrease in abundance of 2 Bacteroides species and Escherichia coli was found in autistic individuals. There was a positive correlation between the abundance of Clostridium species, and disaccharidase activity, in autistic individuals.

Conclusions: There are a variety of changes at the genus and species level in duodenal microbiota in children with autism that could be influenced by carbohydrate malabsorption. These observations could be affected by variations in individual diets, but also may represent a more pervasive dysbiosis that results in metabolites that affect the behavior of autistic children.

Supplemental Digital Content is available in the text

*Department of Pediatrics, Massachusetts General Hospital, Boston, MA

Research and Testing Laboratory

Department of Biological Sciences, Texas Tech University, Lubbock

§Department of Molecular Biology

||Department of Botany, University of Wyoming, Laramie.

Address correspondence and reprint requests to Rafail I. Kushak, PhD, Dr. Sc, Division of Pediatric Gastroenterology, Massachusetts General Hospital, 175 Cambridge St, CRPZ 5-560, Boston, MA 02114 (e-mail:

Received 27 April, 2016

Accepted 26 October, 2016

Supplemental digital content is available for this article. Direct URL citations appear in the printed text, and links to the digital files are provided in the HTML text of this article on the journal's Web site (

The work was supported by the Department of Defense (Grant number W81XWH-10-1-0477).

Drs Kushak and Winter contributed equally to this article.

H.S.W. has potential sources of conflict of interests in the past year: Pediatric IBD Foundation (scientific advisor, grant support); Janssen Pharmaceutical (consultant, grant support); Prometheus (consultant); Salix (consultant); AstraZeneca (consultant, grant support); Shire (consultant, grant support); UCB (consultant, grant support); Avaxia (consultant); Paraxel (consultant); Nutrica (grant support); Nestle (grant support); Abbvie (grant support); Autism (Research Institute, Grant support).

What Is Known

  • Gastrointestinal disorders in autistic children may trigger behavioral problems.
  • Microbial dysbiosis in the colon is frequent in children with autism.
  • Low disaccharidase activities, particularly lactase, are known in children with autism.
  • What Is New
  • Changes in the relative abundance of several genera and species in the duodenum between children with and without autism were observed.
  • No statistically significant differences in microbiome diversity in the duodenum were found between autistic and nonautistic children with gastrointestinal symptoms.
  • A positive correlation between the abundance of Clostridium species and disaccharidase activity was found in autistic children that may affect lactose absorption.

Children with autism spectrum disorders (ASDs) frequently experience significant gastrointestinal (GI) problems, including abdominal pain, constipation, diarrhea, and bloating that may trigger behavioral problems (1). Disruption of indigenous microbiota resulting in the overgrowth of potentially pathogenic microorganisms, such as Clostridia, potential producers of neurotoxins (2), may be related to symptoms of maldigestion or malabsorption in patients with autism. (3). Using culture methods, Finegold et al (4) reported finding 9 Clostridium species in the stool of autistic children that were not found in fecal samples from healthy subjects. Quantitative analysis of these species was performed by real-time polymerase chain reaction (5). In another study using 16S Ribosomal RNA (16S rRNA)-based oligonucleotide probes, Clostridium histolyticum was found in greater abundance in children with autism in comparison with a healthy comparison group (6). These studies suggest a possible link between levels of Clostridia and GI function in patients with ASD.

A significant microbial dysbiosis characterized by decreased numbers of Firmicutes and Actinobacteria and increased Bacteroidetes and Proteobacteria has been described in the stool of autistic children compared with nonautistic children. In addition, Desulfovibrio species and Bacteroides vulgatus were present in significantly higher numbers in stools of severely autistic children (7). The stool of children with autism also contained lower abundances of Bifidobacterium species and the mucolytic bacterium Akkermansia muciniphila (8). Kang et al (9) reported a less diverse microbiome in the stool of autistic individuals than in unaffected people with lower levels of Prevotella, Coprococcus, and unclassified Veillonellaceae. They noted that a less diverse microbiome in the children with ASD was associated with the presence of autistic symptoms rather than the severity of GI symptoms. There was no correlation in this study between diet and both changes in overall microbiome diversity or abundance of individual genera. Other investigators reported that children with autism have much lower levels of Bifidobacterium, slightly lower levels of Enterococcus, and much higher levels of Lactobacillus in comparison with unaffected children (10).

These findings were obtained from colonic bacteria, which normally include large numbers of predominantly Gram-negative microorganisms. In contrast, the small intestinal microbiota is populated predominantly by Gram-positive microorganisms derived from the oropharynx (11,12), and their composition in individuals with autism has been understudied. Recent metagenomic analysis in the ileum of autistic children, however, demonstrated compositional dysbiosis, specifically, with decreases in Bacteroidetes, increases in the ratio of Firmicutes to Bacteroidetes, and increases in Betaproteobacteria. Interestingly, expression levels of intestinal disaccharidases and sugar transporters in the ileum of children with ASD in this study are associated with the abundance of affected bacterial phylotypes. Specifically, microbial dysbiosis was associated with deficiencies in mRNA for lactase, sucrase-isomaltase, maltase-glucoamylase, and sugar transporters (13). Changes in microbial composition in children with autism have also been associated with an increase in short-chain fatty acids level representing the end products of polysaccharide fermentation suggesting that utilization of fermentation products may be altered in children with ASD (14). In another study, Williams et al (15) found that Sutterella was a major component of the mucosal microbiota in more than half of children with autism and GI dysfunction, but was absent in neurotypical children with GI dysfunction.

In contrast to the fecal and ileal microbiome, the composition of the duodenal microbiome in individuals with autism is not well characterized. The duodenal flora in autistic individuals was analyzed in the duodenal fluid but not in the mucosa (4). This study, conducted on a small number of subjects (7 autistic and 4 controls), demonstrated significant number of non–spore-forming anaerobes and microaerophilic bacteria in duodenal specimens of children with autism and total absence of such bacteria in control children.

Because carbohydrate malabsorption may increase substrate availability for bacterial fermentation, specific disaccharidase activity in the duodenum was measured to determine the relation between duodenal microbiota and disaccharidase activity.*

We hypothesize that there is a difference in duodenal mucosal microbiome between the children with autism and age-matched controls. Also dysbiosis may alter the function of the disaccharidases on the duodenal enterocytes of patients with autism differently than in controls.*

*Preliminary data from the present study were presented at Digestive Disease Week meeting, Chicago 2014 (16).

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Patients and Sampling

The study population consisted of 21 autistic subjects and 19 unaffected subjects. Children affected with autism were identified by history and Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition evaluation. All autistic and nonautistic individuals had undergone an upper GI endoscopy under general anesthesia at the Pediatric GI and Nutrition Unit for evaluation of suspected GI disorders. Duodenal biopsies from the second part of the duodenum were taken during the procedure for routine pathological examination and snap frozen for analysis of disaccharidase activity. Residual tissue was preserved at –80°C and subsequently used for duodenal microbiome analysis with the approval of the institutional review board. This is a retrospective study and the biopsies for microbiome analysis were taken from the tissue biorepository approved by the institutional review board.

DNA was isolated from the intestinal biopsies using Mo Bio DNA Isolation kits (Mo Bio Laboratories, Inc, Carlsbad, CA), and 16S rRNA gene pyrosequencing performed as described below. Analysis of lactase, sucrase, maltase, and palatinase activities in biopsies was performed using the Dahlqvist (17) method and normalized to protein level. Protein content was measured by the Bradford (18) method. Enzyme activity was expressed in micromole of hydrolyzed substrate/min/g protein (U/g protein; specific disaccharidase activity). Enzyme deficiency was defined as lactase <15 U/g protein; sucrase <25 U/g protein; maltase <100 U/g protein; and palatinase <5 U/g protein (19,20).

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16S Ribosomal RNA Gene Pyrosequencing

Tag-encoded FLX amplicon pyrosequencing was performed as described (21,22) using Gray28F 5′TTTGATCNTGGCTCAG and Gray519r 5′GTNTTACNGCGGCKGCTG, with primers numbered in relation to the primary sequence of Escherichia coli 16S rRNA (23). Tag-encoded FLX amplicon pyrosequencing extending from 28F used a Roche 454 FLX instrument with Titanium reagents, and Titanium procedures performed at the Research and Testing Laboratory (Lubbock, TX).

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Processing of Pyrosequencing Data

Sequence data were processed using Research and Testing Laboratory's in-house pipeline, described at:

Briefly, sequences were grouped using their barcodes and any sequence that contained a low-quality barcode or that failed to be at least half the expected amplicon length (or 250 bp, whichever was shortest) was removed from the data pool. All sequencing reads then were denoized using an algorithm based on USEARCH (24) and then checked for chimeras using UCHIME (25). Finally, sequence data were separated into operational taxonomic units (OTUs, clustering was conducted at 97% similarity) and annotated using USEARCH (24). GreenGenes v. 12.10 (26) was used as the reference database for taxonomic assignment.

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Diversity Estimates

Alpha Diversity (Examination of Microbiota Diversity Within Samples)

Two measures of alpha diversity were calculated from the final OTU table. The first, Chao1, provided an estimate of true species richness by incorporating information about the number of species observed as singletons and doubletons (27). The second metric, Shannon (28), provided an estimate of ecological diversity by considering both species evenness and richness.

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Beta Diversity (Comparison of Diversity Between Samples)

To quantify microbiota compositional distances among samples the UniFrac metric was employed (29). The UniFrac metric incorporates branch length and evolutionary relationship information from the bacterial phylogenetic tree when estimating distances among samples. The unweighted UniFrac metric is calculated from a bacterial presence/absence matrix, whereas, the weighted UniFrac metric is calculated from a bacterial matrix of relative abundances. Using UniFrac distance matrices relations among samples were visualized using principle coordinates analysis.

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Statistical Analyses

Data were expressed as mean ± standard error of the mean. Statistical analysis was performed using SPSS (version 21 for Mac OS; SPSS Inc, Chicago, IL) and R Software (30). Statistical testing for multivariate differences among groups was performed by permutational analysis of variance (ANOVA) using the UniFrac distance matrices (31). Bacterial lineages classified at the generic and OTU levels were screened for statistically significant differences in abundances among groups. For bacterial genera and species arcsine-transformed relative abundances were analyzed using ANOVA. P values from individual ANOVAs were adjusted to maintain a false discovery rate of 5%. OTU count data were analyzed using a generalized linear model with a negative binomial distribution (32). All statistical analyses were conducted in R (30) using the vegan (31), ladbsv (33), and DESeq (32) packages. Comparison of specific disaccharidase activity between groups was performed by 2-sided t test. Frequency of GI symptoms was examined by chi-square test. The results were considered as statistically significant at P < 0.05.

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Characteristics of Patients and Enzyme Activities

The age of subjects in the autistic group was 14.43 ± 1.07 years compared to 16.05 ± 1.25 years in the control group. The autistic group consisted of 19 boys and 2 girls, whereas the control group contained 10 boys and 9 girls. All individuals in both groups had GI symptoms including constipation, abdominal pain, diarrhea, or gastroesophageal reflux disease (Supplemental Digital Content 1, Table 1, The most common symptoms in individuals with autism were constipation (67%) and gastroesophageal reflux disease (43%) in comparison to controls (16% for both symptoms). Chi-square test demonstrated statistically significant difference (P < 0.005) for frequency of constipation between groups (14/21 in autism vs 3/19, in controls). Other GI disorders such as food allergy (7 patients), diarrhea (3 patients), and irritable bowel syndrome (2 patients) were found in group with autism but not in control group. Control group, however, included 3 patients with ulcerative colitis not found in autistic group. Frequency of other symptoms was similar in both groups.

Patients in control group were mostly on a normal diet (11/19; 58%), although some of them received gluten-free, lactose-free, fat-free, soy-free, or other restricted diets. In autistic group only 6 out of 21 patients (29%) were on a normal diet. Six individuals received gluten-free, casein-free diet and other patients received some foods restricted diets.

Analysis of disaccharidase activities did not show any statistically supported difference between the 2 groups (Supplemental Digital Content 2, Table 2, Number of patients with hypolactasia in the autistic group with GI symptoms (15/21; 71%) and control group with GI symptoms (13/19; 68%) was similar. Nonautistic subjects had higher frequency of sucrase (6/19; 32% vs 4/21; 19%) and maltase (5/19; 26% vs 3/21; 19%) deficiencies than control subjects. Decreased palatinase activity was found only in 1 patient with autism.

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Comparison of Microbial Diversity Between Samples

No statistically supported differences were observed in microbiome diversity between autistic and control subjects for either species richness alone (number of OTUs) or evenness (Shannon diversity index) (Supplemental Digital Content 3, Fig. 1,, although a trend for a relatively higher number of OTUs in control subjects approached statistical significance (P = 0.0686) (Supplemental Digital Content 4, Table 3A, There was support for differences in both diversity indices due to the effects of age (P = 0.0018 for number of OTUs; P = 0.0112 for Shannon values) and sex (P = 0.0215 for number of OTUs; P = 0.0221 for Shannon values) (Supplemental Digital Content 4, Table 3B, Multivariate analysis of phylogenetic distances of microbial communities (UniFrac distances) among groups using ADONIS showed no statistically supported differences between autistic and control subjects (P > 0.05), as illustrated through principle coordinates analysis of weighted and unweighted UniFrac distances (Fig. 1A).



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Taxonomic Composition of Microbial Communities

Dominant phyla in the duodenal samples from both autistic and control subjects were Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria. The lack of compositional differences between autistic and nonautistic groups by analysis of UniFrac distances (Fig. 1A) was supported by hierarchical clustering of subject microbiomes according to relative abundance of dominant genera. As seen in Figure 1B, autistic and nonautistic subjects are interspersed in virtually all clusters. There were, however (after controlling for false discovery rate with a Benjamini-Hochberg correction), significantly different relative abundances between subject groups at the genus (Fig. 2A) and species (Fig. 2B) levels. Bacteria belonging to the genus Burkholderia were significantly more abundant in subjects with autism (P = 0.03), whereas members of the genus Neisseria were less abundant in autistic subjects than in controls (P = 0.01) (Fig. 2A). Microbiota analysis at the species level demonstrated a statistically supported (borderline in one case) lower abundance in autistic individuals of 2 Bacteroides species (P = 0.005, P = 0.04) and E coli (P = 0.05) in comparison with the control group (Fig. 2B).



A further analysis at the OTU level showed statistically supported differences (P < 0.05) between autistic and nonautistic subjects in the relative abundance of OTUs belonging to several genera (Fig. 2C). OTUs assigned to the genera Oscillospira, Actinomyces, Neisseria, Peptostreptococcus, and Ralstonia were over-represented in autistic subjects relative to controls, whereas members of Devosia, Prevotella, Bacteroides, Streptococcus, and a group that could not be classified at the genus level (unknown) were depleted in autistic subjects in comparison to nonautistic subjects.

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Correlations Between Intestinal Enzyme Activities and Microbiota Composition

In samples from autistic subjects, the relative abundance of 3 genera (Bacteroides, Faecalibacterium, Clostridium) showed at least 1 statistically supported correlation with disaccharidase activity (Fig. 3A). All 3 genera showed a strong positive correlation with lactase activity, whereas only Clostridium had a strong positive correlation with maltase, palatinase, and sucrase activity; the other 2 genera displayed only weak correlations for these enzymes. None of the 3 were observed to be correlated with disaccharidase activity in control samples, which instead showed strong positive correlations between lactase activity and the relative abundance of the genera Porphyromonas, Barnesiella, Gemella, and Leptotrichia (Fig. 3B). These 4 genera, in general, showed only weak positive correlation with the activity of sucrase, palatinase, and maltase. Correlation between activity of intestinal disaccharidases and abundance of microbiota was also found at the species level and again was more expressed in autistic individuals (Fig. 3C) than in nonautistic subjects (Fig. 3D). In autistic children, lactase activity highly correlated with abundance of Bacteroidetes spp and all 4 enzymes correlated with abundance of Clostridium spp.



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The mucosal microbiome of duodenum has been affected in individuals with GI disorders including celiac disease, irritable bowel syndrome, and Helicobacter pylori (34–36); however, data in children with autism are limited. The aim of the present study was to analyze the microbiome in duodenal samples from autistic subjects and a group without autism all of whom had GI symptoms. Individuals with autism were more likely statistically to have constipation. Similar observations in individuals with autism have been previously reported (1,37,38). Interestingly, neurotypical children with chronic constipation has smaller concentration of Lactobacillus per milligram of stool (39). In individuals with ASD no associations was, however, detected between bacterial subgroups and functional GI disorders including constipation (40).

In contrast to previous reports of a less diverse microbiome in the stool of autistic subjects (9), no statistical differences between subjects with and without ASD in the microbial diversity or in multivariate analysis of UniFrac distances of the duodenal microbiome was observed. Patients from both groups were intermixed in hierarchical clustering analysis of genus-level abundance. We, however, did observe statistically relevant group-based differences in the taxonomic composition of the microbiome. As reported previously in colonic samples (7,13), there were differences in the relative abundance of members of the phylum Bacteroidetes, with B vulgatus and unidentified Bacteroides species that were less abundant in autistic subjects than in controls. In the duodenum, relative abundance of the genera Burkholderia and Neisseria and the species E coli also differed between the groups, as did OTUs assigned to the genera Oscillospira, Actinomyces, Neisseria, Peptostreptococcus, and Ralstonia, and members of Devosia, Prevotella, Bacteroides, Streptococcus, and a group that could not be classified at the genus level (unknown). An OTU assigned to the genus Prevotella was relatively depleted in the duodenum of autistic subjects compared with controls, supporting a previous observation on reduction of the genus Prevotella in fecal samples (9). There appear to be no comparable reports on associations between duodenal microbiota in patients with autism and members of the other genera listed above. Our data confirmed earlier observations by Son et al (40) who could not find significant difference in diversity or overall microbial composition between ASD children with their neurotypical siblings, however, identified several low-abundance taxa at the genus level that were associated with ASD.

We detected no difference in frequency of lactase deficiency between autistic subjects with GI symptoms and controls with GI symptoms and slightly higher frequency in sucrase and maltase deficiency in the last group. Lactase, sucrase, maltase, and palatinase activities in both groups of patients were similar. Enzyme values in the present study were not different from values found in our previous study (41). The bacterial genera correlated with these enzyme activities in autistic subjects (Bacteroides, Faecalibacterium, and Clostridium) differed from the corresponding genera in control subjects (Porphyromonas, Barnesiella, Gemella, and Leptotrichia). Those genera showing only weakly positive correlation with maltase, palatinase, and sucrase activity in autistic subjects (Bacteroides and Faecalibacterium) could be relevant to putative connections between microbial dysbiosis and malabsorption. The positive correlation between disaccharidase activity and Clostridium species is interesting to compare with previous observations that lactose and human milk neutral oligosaccharides are associated with proliferation of Clostridia in the lower intestine (42).

Low disaccharidase activities, particularly lactase, have been reported in children with ASD (43). These observations are supported by Williams et al (13) who found that the microbial dysbiosis in the ileum of children with ASD was associated with host deficiencies in mRNA expression levels of lactase, sucrase-isomaltase, maltase-glucoamylase, and sugar transporters (SGLT1 and GLUT2). They hypothesized that deficiencies in disaccharidases and hexose transporters change the environment in the ileum and proximal colon, resulting in additional growth substrates for bacteria, which in turn promotes significant and specific compositional changes in the microbiota of autistic children.

The information on the effect of gut microbiota on intestinal disaccharidases is interesting to compare with data obtained from conventional and germ-free model animals (rats and mice). Germ-free animals after weaning demonstrate significantly higher enzyme activity (disaccharidases, alkaline phosphatase) than conventional animals. After conventionalization by introducing cecal content from conventional animals, disaccharidase activities in the germ-free animals, however, start decreasing and reach the level of conventional animals (44,45). Thus, intestinal microbiota may not contribute to the production of disaccharidases, but appear to affect their activity.

The present study is not free from some limitations including the heterogeneity in age and sex in study population and presence of various GI problems in children with and without autism. Another limiting factor is absence of control group without autism and GI symptoms. The role of diet and particularly dietary restrictions in children with autism remains a confusing factor in data analysis, because changes in human microbiome occur within 24 hours of a change in diet (46).

In conclusion, we report statistically significant differences in duodenal microbiota at the genus and species level between children with ASD and controls. We also observed differences in the taxa associated with disaccharidase activity. These observations could be explained by variation in individual diets, but also may represent a more pervasive dysbiosis throughout the GI tract that could affect behavior in children with autism.

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The authors would like to thank Martin Schlaff (H.S.W.) for philanthropic support and the Autism Research Institute for supporting the biorepository.

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1. Buie T, Campbell DB, Fuchs GJ, et al. Evaluation, diagnosis, and treatment of gastrointestinal disorders in individuals with ASDs: a consensus report. Pediatrics 2010; 125:1–18.
2. Hatheway CL. Toxigenic clostridia. Clin Microbiol Rev 1990; 3:66–98.
3. Bolte ER. Autism and Clostridium tetani. Med Hypotheses 1998; 51:133–144.
4. Finegold SM, Molitoris D, Song Y, et al. Gastrointestinal microflora studies in late-onset autism. Clin Infect Dis 2002; 35 (suppl 1):S6–S16.
5. Song Y, Liu C, Finegold SY. Real-time PCR quantification of clostridia in feces of autistic children. Appl Environ Micobiol 2004; 70:6459–6465.
6. Parracho HM, Bingham MO, Gibson GR, et al. Differences between the gut microflora of children with autistic spectrum disorders and that of healthy children. J Med Microbiol 2005; 54:987–991.
7. Finegold SM, Dowd SE, Gontcharova V, et al. Pyrosequencing study of fecal microflora of autistic and control children. Anaerobe 2010; 16:444–453.
8. Wang L, Angley MT, Gerber JP, et al. A review of candidate urinary biomarkers for autism spectrum disorder. Biomarkers 2011; 16:537–552.
9. Kang DW, Park JG, Ilhan ZE, et al. Reduced incidence of Prevotella and other fermenters in intestinal microflora of autistic children. PLoS One 2013; 3:e68322.
10. Adams JB, Johansen LJ, Powell LD, et al. Gastrointestinal flora and gastrointestinal status in children with autism—comparisons to typical children and correlation with autism severity. BMC Gastroenterol 2011; 11:22.
11. Simon GL, Gorbach SL. The human intestinal microflora. Dig Dis Sci 1986; 31:147–162.
12. Riordan SM, McIver CJ, Wakefield D, et al. Small intestinal mucosal immunity and morphometry in luminal overgrowth of indigenous gut flora. Am J Gastroenterol 2001; 96:494–500.
13. Williams BL, Hornig M, Buie T, et al. Impaired carbohydrate digestion and transport and mucosal dysbiosis in the intestines of children with autism and gastrointestinal disturbances. PLoS One 2011; 6:e24585.
14. Wang L, Christophersen CT, Sorich MJ. Elevated fecal short chain fatty acid and ammonia concentrations in children with autism spectrum disorder. Dig Dis Sci 2012; 57:2096–2102.
15. Williams BL, Hornig M, Parekh T, et al. Application of novel PCR-based methods for detection, quantitation, and phylogenetic characterization of Sutterella species in intestinal biopsy samples from children with autism and gastrointestinal disturbances. MBio 2012; 3:e00261–e00311.
16. Kushak R, Buie T, Cox SB, et al. Analysis of the duodenal microbiome in children with autism. Gastroenterology 2014; 146: (5 suppl 1):S347.
17. Dahlqvist A. Assay of intestinal disaccharidases. Anal Biochem 1968; 22:99–107.
18. Bradford MM. A refined and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 1976; 72:248–254.
19. Belmont JW, Reid B, Taylor W, et al. Congenital sucrase-isomaltase deficiency presenting with failure to thrive, hypercalcemia, and nephrocalcinosis. BMC Pediatr 2002; 2:4.
20. Pfeffecorn MD, Fitzgerald JF, Croffie JM, et al. Lactase deficiency: not more common in pediatric patients with inflammatory bowel disease than in patients with chronic abdominal pain. J Pediatr Gastroenterol Nutr 2002; 35:339–343.
21. Callaway TR, Dowd SE, Edrington T, et al. Evaluation of bacterial diversity in the rumen and feces of cattle fed different levels of dried distillers grains plus solubles using bacterial tag-encoded FLX amplicon pyrosequencing. J Anim Sci 2010; 88:3977–3983.
22. Handl S, Dowd SE, Garcia-Mazcorro JF, et al. Massive parallel 16S rRNA gene pyrosequencing reveals highly diverse fecal bacterial and fungal communities in healthy dogs and cats. FEMS Microbiol Ecol 2011; 76:301–310.
23. Brosius J, Palmer ML, Kennedy PJ, et al. Complete nucleotide sequence of a 16S ribosomal RNA gene from Escherichia coli. Proc Natl Acad Sci USA 1978; 75:4801–5485.
24. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010; 26:2460–2461.
25. Edgar RC, Haas BJ, Clemente JC, et al. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 2011; 27:2194–2200.
26. McDonald D, Price MN, Goodrich J, et al. An improved green genes taxonomy with explicit ranks for ecological and evolutionary analyses of bacteria and archaea. ISME J 2012; 6:610–618.
27. Chao A. Estimating the population size for capture-recapture data with unequal catchability. Biometrics 1987; 43:783–791.
28. Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948; 27: 379-423 and 623-656.
29. Lozupone C, Knight R. UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol 2005; 71:8228–8235.
30. R. Development Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
31. Oksanen J, Blanchet FG, Kindt GB, et al. Vegan: community ecology package. R package version 2.4-1. 2016. Accessed November 7, 2016.
32. Anders S, Huber W. Differential expression analysis for sequence count data. Genome Biol 2010; 11:R106.
33. Roberts DW. Labdsv: ordination and multivariate analysis for ecology. R package version 1.8-0. 2016. Accessed November 7, 2016.
34. Giamarellos-Bourboulis E, Tang J, Pyleris E. Molecular assessment of differences in the duodenal microbiome in subjects with irritable bowel syndrome. Scand J Gastroenterol 2015; 50:1076–1087.
35. Nistal E, Caminero A, Herrán AR, et al. Study of duodenal bacterial communities by 16s rRNA gene analysis in adults with active celiac disease versus non celiac disease controls. J Appl Microbiol 2016; 120:1691–1700.
36. Schulz C, Schütte K, Malfertheiner P. Helicobacter pylori and other gastric microbiota in gastroduodenal pathologies. Dig Dis 2016; 34:210–216.
37. Ibrahim SH, Voigt RG, Katusic SK, et al. Incidence of gastrointestinal symptoms in children with autism: a population-based study. Pediatrics 2009; 124:680–686.
38. Gondalia SV, Palombo EA, Knowles SR, et al. Molecular characterisation of gastrointestinal microbiota of children with autism (with and without gastrointestinal dysfunction) and their neurotypical siblings. Autism Res 2012; 5:419–427.
39. De Moraes JG, Motta ME, Beltrão MF, et al. Fecal microbiota and diet of children with chronic constipation. Int J Pediatr 2016; 2016:6787269.
40. Son JS, Zheng LJ, Rowehl LM, et al. Comparison of fecal microbiota in children with autism spectrum disorders and neurotypical siblings in the simons simplex collection. PLoS One 2015; 10:e0137725.
41. Kushak R, Lauwers G, Winter H, et al. Intestinal disaccharidases activity in patients with autism: effect of age, gender, and intestinal inflammation. Autism 2011; 15:285–294.
42. Mielcarek C, Romond PC, Romond MB, et al. Modulation of bacterial translocation in mice mediated through lactose and human milk oligosaccharides. Anaerobe 2011; 17:361–366.
43. Horvath K, Papadimitriou JC, Rabsztyn A, et al. Gastrointestinal abnormalities in children with autistic disorder. J Pediatr 1999; 135:559–563.
44. Reddy BS, Wostmann BS. Intestinal disaccharidase activities in the growing germfree and conventional rats. Arch Biochem Biophys 1966; 113:609–616.
45. Whitt DD, Savage DC. Kinetics of changes induced by indigenous microbiota in the activity levels of alkaline phosphatase and disaccharidases in small intestinal enterocytes in mice. Infect Immun 1980; 29:144–151.
46. David LA, Maurice CF, Carmody RN, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 2014; 505:559–563.

autism; duodenum; intestinal disaccharidases; microbiota

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