Small intestinal microbial fiber metabolism dysfunction in celiac disease

Domain: biology
Published: 2026-03-31
Editorial outcome: major_revision

Abstract
Celiac disease (CeD) is an immune-mediated condition driven by dietary gluten resulting in small intestinal mucosal inflammation and injury, along with myriads of symptoms. The only treatment is a lifelong gluten-free diet (GFD) and although most patients improve, the restriction can lead to nutrient deficiencies including fiber. Duodenal microbiota is known to be altered in CeD, but whether and how the microbial fiber metabolism may be affected is unknown. Here we show that CeD patients had impaired microbial fiber metabolism in the small intestine which associated with depletion of the fiber degrading taxa, Prevotella spp, independent of treatment with the GFD. Colonization of germ-free mice with Prevotella spp increased small intestinal short chain fatty acids (SCFA). In gluten-sensitized mice expressing the celiac risk gene, HLA-DQ8, an inulin-supplemented diet facilitated microbial saccharolytic function and SCFA production to accelerate mucosal healing in the small intestine during the GFD. The results support clinical investigations of dietary fiber supplementation and microbial fiber degradation to enhance responses to the GFD in CeD.

Introduction
Celiac disease (CeD) is an immune-mediated disorder driven by prolamin proteins termed “gluten” in wheat, rye, and barley1. The disease affects the proximal small intestine, leading to intraepithelial lymphocyte infiltration, villus blunting, and crypt hyperplasia. CeD has autoimmune traits, including HLA-DQ2 or -DQ8 association, anti-tissue transglutaminase 2 (TG2) autoantibodies, and immune-mediated intestinal epithelial cell destruction1,2. While about 30% of the global population is genetically predisposed, only 2–3% of those at risk develop the condition3,4. This, tied to the rapid growth in incidence in the past decades, suggests that there are environment determinants in addition to gluten5,6.

Longitudinal studies in genetically at-risk populations suggest that gastrointestinal infections at early life, especially when combined with high gluten intake, are associated with a higher risk of developing related CeD6–8. Cross-sectional studies have demonstrated differences in microbiota composition and function in the small intestine of active CeD9–11. Indeed, diet is a major driver of microbiota establishment and function, and recent studies have suggested that higher consumption of fiber-rich foods such as vegetables is associated with lower odds of developing celiac autoimmunity5,12. A recent reanalysis of the large longitudinal cohort study, The Environmental Determinants of Diabetes in the Young (TEDDY), suggests an association between increased fiber intake in early life (<3 years) and reduced risk of CeD13. In the colon, short-chain fatty acids (SCFAs; such as acetate, propionate, and butyrate) are produced mainly by microbial fiber metabolism and have been linked to gastrointestinal health through improved motility, anti-inflammatory activity, and microbiome modulation. These effects are primarily mediated through G-protein coupled receptors, GPR41 and GPR43, which are widely expressed on immune and epithelial cells14,15. However, the role of microbial fiber metabolism and SCFA production in the small intestine in the context of CeD remains understudied.

The only current treatment for CeD is the lifelong elimination of gluten. While a gluten-free diet (GFD) is effective in most patients, mucosal healing is slow and often impeded by rapid reactivation of memory cells, likely related to accidental gluten exposure. Although the GFD is usually low in fiber16, especially when composed of processed gluten-free products, little is known about the fiber metabolic capacity of CeD microbiota along the gastrointestinal tract. Indeed, increased fiber intake is often recommended to CeD patients on GFD to manage constipation, without mechanistic insight into microbial saccharolytic capacity or its impact on inflammation.

Our study reveals that patients with active and treated CeD had impaired microbial fiber metabolism in the small intestine independently of differences in fiber intake. Fiber-degrading taxa depleted in the CeD duodenum include Prevotellaceae family members17. Colonization of germ-free NOD/DQ8 mice with Prevotella spp increased SCFA in the small intestine. In gluten-sensitized NOD/DQ8 mice, the soluble fiber inulin accelerated mucosal healing by supporting saccharolytic activity of the small intestinal microbiota. Our results support intervention with dietary fibers, such as inulin, in CeD patients alongside the GFD, to improve mucosal healing, so long as patients harbor the necessary microbial function.

Results
Patient demographics
A total of 16 CeD patients diagnosed within the previous 3 months (newly diagnosed CeD), 11 GFD-treated-CeD patients (T-CeD; on at least >2 years of GFD) and 26 healthy controls were recruited for the study. Details on participant demographics, clinical characteristics and samples collected are summarized in Table 1.

Microbial saccharolytic function in the small intestine was lower in CeD than in controls, independent of GFD
Microbiota composition of duodenal aspirates was assessed using 16S rRNA gene sequencing. Duodenal α-diversity was lower in CeD than in controls or T-CeD (Fig. 1a and Supplementary Fig. 1a), and β-diversity indicated profiles clustered distinctly between the three groups (Fig. 1b). In the newly diagnosed CeD patients, Rothia sp, Granulicatella sp, Leptotrichia wadei, F0058, and Ruminococcus torques group sp relative abundance was higher, while Oribacterium asaccharolyticum, Bergeyella, Alloprevotella, Treponema, Granulicatella elegans, Porphyromonas endodontalis, Prevotella sp, Prevotella pallens, and Actinobacillus sp were lower than in healthy controls. The T-CeD group had higher Rothia sp, Ruminococcus torques group sp, Faecalibacterium prausnitzii, unclassified Veillonellaceae, Rothia mucilagenosa, Ruminococcus bromii, Gemella parahemolysans, Agathobacter sp, unclassified Eubacterium group, unclassified P5D1.392, Blautia massiliensis, Subdoligranulum sp, Eubacterium hallii group sp, Dialister, Fusobacterium nucleatum, Dorea longicatena, Dorea sp, Blautia sp, Haemophilus sp, Anaerostipes hadrus, Lachnoclostridium sp, Ruminococcus sp, Romboustia sp, and Fusicatenibacter saccharivorans and lower relative abundance of Eubacterium nodatum group sp, Gemella sanguinis, Porphyromonas endodontalis, Prevotella pallens, Mogibacterium sp, Johnsonella sp, and Staphylococcus sp compared with healthy controls (Supplementary Fig. 1b).

After sequencing, functional predictions using PICRUSt218 revealed no differences in total number of glycosidase hydrolase genes (EC 3.2.1) between CeD, T-CeD, and controls (Fig. 1c). However, specific microbial genes encoding starch-degrading α-amylase (EC 3.2.1.1; K07405) and fructan β-fructosidase (EC 3.2.1.80; K03332), an inulin-like fiber degrading enzyme19, were lower in CeD and T-CeD versus controls (Fig. 1d). The predicted contributions to these genes from the top genera are visualized in Fig. 1e, indicating that the top 4 predicted contributors belonged to Prevotellaceae family, which were less abundant in CeD and T-CeD versus controls (Supplementary Fig. 2a, b). To confirm the PICRUSt2-predicted results, we performed qRT-PCRs for the highest-abundance bacterium identified at the species level (Prevotella 7 melaninogenica) for genes K07405 and K03332. We found that K07405 had a similar RNA expression profile as the predicted gene content, with reduced expression in CeD compared with controls. No statistical differences were observed for K03332 (Supplementary Fig. 2c). Overall, microbial alterations associated with CeD were characterized by lower predicted microbial saccharolytic function, independently of treatment with the GFD.

Diet does not fully explain lower microbial saccharolytic function or gastrointestinal symptoms in CeD
Diet was investigated to determine whether altered microbial predicted function was associated with fiber consumption. In most participants, fiber intake was below the minimum recommendation set by Health Canada (25 g/day)20 based on a food frequency questionnaire (FFQ21), with 86% of T-CeD being fiber-deficient (Fig. 2a). Within the total fiber consumed, there were no differences in the proportion of soluble or insoluble fibers between groups (Fig. 2b). To compare dietary patterns at time of sample collection, plant DNA present in fecal samples was analyzed using FoodSeq22, which indicated lower observed plant richness (total plant species) in T-CeD group compared with controls (Fig. 2c), although Shannon diversity was similar (Fig. 2d). The post-hoc analysis of overall dietary plant composition revealed a significant difference between T-CeD and controls (Fig. 2e), characterized by patterns such as opposing loadings of Oryza sativa (rice) and Poaceae (rye, wheat family), consistent with the elimination of gluten-containing foods. Overall, food sequencing patterns demonstrated GFDs were highly individualized (Supplementary Fig. 3). Patient-reported gastrointestinal symptom scores (GSRS; total score and individual sub-scores) did not correlate with fiber intake, suggesting no obvious relationship with symptoms. Thus, while T-CeD participants had a shift in plant DNA signatures, overall fiber intake was not significantly different from CeD or controls.

To assess microbial metabolism of fiber in our cohorts, fecal SCFA were measured as a marker of fiber fermentation using gas chromatography mass spectrometry (GC/MS). CeD patients had lower total SCFA compared with controls (Fig. 2f). Specifically, acetic acid levels were lower in CeD compared to controls and T-CeD, while butyric acid levels were lower in both CeD and T-CeD groups as compared to controls. Overall, T-CeD had the lowest fiber intake of the 3 groups, yet these patients had modestly higher SCFA levels than CeD. The results suggest T-CeD partially recovers microbial saccharolytic capacity, despite the sub-optimal amount of fiber ingested by these patients, perhaps due to improvements in digestion and motility.

Inulin accelerates recovery of gluten-immunopathology in NOD/DQ8 mice on GFD
Micewith transgenic expression of the HLA-DQ8 gene on a non-obese diabetic background (NOD/DQ8) were sensitized with digested gluten by oral gavage23 to determine the timing of mucosal recovery from gluten-immunopathology after 2, 6, 10, and 24 weeks of GFD (Supplementary Fig. 4a). CD3+ IELs, villus-to-crypt (V/C) ratios and composite VCIEL scores24 normalized by week 10 of GFD in the gluten-sensitized mice (Supplementary Fig. 4b, c, d). However, small intestinal antibodies against gliadin or TG2 remained elevated in some mice (Supplementary Fig. 5a, b).

Once it was determined that sensitized NOD/DQ8 mice normalized histological parameters at week 10 of GFD, the effect of fiber supplementation was investigated. Another cohort of sensitized NOD/DQ8 mice (Fig. 3a) was given GFD with no added fiber (NAF), or supplemented with prebiotic substrates, either inulin (52 g/kg) or HylonVII (136 g/kg) (described in Table 2), for 6 weeks and clinically monitored. Compared with non-sensitized controls (naïve), sensitized mice had higher CD3+ IELs (Fig. 3b), and lower V/C ratios (Fig. 3c) and lower composite VCIEL scores (Fig. 3d). Supplementation with inulin during GFD improved all histological parameters versus the no-added fiber group. HylonVII did not normalize IELs, but mice had higher V/C ratios versus the no-added fiber group. Neither of the fiber diets normalized antibodies in intestinal content at 6 weeks of follow-up. However, all mice became seronegative after 12 weeks of inulin or HylonVII (Supplementary Fig. 5c, d).

Inulin increases small intestinal SCFA without major changes in microbial profiles
Inulin supplementation during GFD, but not HylonVII, was associated with higher SCFA, primarily acetic acid, in the small intestine of SPF NOD/DQ8 mice, compared with no added fiber (Fig. 4a). There was no difference in microbial α-diversity between no added fiber, inulin, and HylonVII fed mice after 6 weeks (Supplementary Fig. 6a). However, HylonVII fed mice clustered separately from the no added fiber and inulin groups as evaluated by microbial β-diversity (Fig. 4b). Specifically, HylonVII had higher Helicobacter, Alistepes, Aneroplamasma, Intestinimonas, Candidatus saccharimonas, Streptococcus, Gemella and Escherichia/Shigella and lower abundance of Roseburia, Turicibacter, Parasutterella, Desulfovibrio, Dorea, Lachnoclostridium, Muribaculum, Enterorhabdus, A2, Parabacteroides, Aerotruncus, and Marvinbyantia than no added fiber controls. Inulin, which did not affect β-diversity compared with the no-added fiber diet, had higher Faecalibaculum, Gemella, Staphylococcus, Coriobacteriaceae UCG.002, and lower abundance of Intestinimonas, Alistepes, Oscillibacter, Parabacteroides, Aerotruncus, and Marvinbryantia versus the no-added fiber control GFD (Supplementary Fig. 6b). When all groups were considered, higher Escherichia/Shigella (Fig. 4c) and lower Turicibacter and Muribaculum relative abundance were associated with reduced small intestinal SCFAs (Fig. 4d).

Inulin and Prevotella spp boost SCFA production in the small intestine
To further investigate small intestinal microbial fiber metabolism in vivo, we first compared SCFA production in germ-free (GF) versus SPF NOD/DQ8 mice in response to inulin (Fig. 5a). Inulin increased total small intestinal SCFA in SPF versus GF mice (Fig. 5b), confirming that fiber metabolism required microbiota presence. This was driven by increases in acetic acid, with no differences in propionic or butyric acids. As expected, the magnitude of SCFA increase was 10 times higher in feces than in the small intestine, also evidencing differences in the main SCFA produced between the two sites (Supplementary Fig. 7). Next, to investigate host responses to local SCFA availability, expression of SCFA receptors, GPR41 and GPR43, was assessed along the intestinal tract. Overall, in SPF mice, inulin increased the mean Gpr41 and Gpr43 expression at most small intestinal sites, including the duodenum (Table 3). No differences in SCFA receptors were observed in GF mice after an inulin-supplemented diet.

Finally, based on our observation that fiber-degrading bacteria from the Prevotellaceae family were depleted in the duodenum of CeD patients, GF NOD/DQ8 mice were colonized with a cocktail of 10 Prevotellaceae members (Fig. 5c and Table 4). Prevotella-colonized mice, but not GF mice, supplemented with inulin had higher small intestinal SCFA (Fig. 5d). The results demonstrate that small intestinal bacteria, such as Prevotella spp, can produce SCFA from dietary fiber in the small intestine when the appropriate substrate is present.

Methods
Human participants
Patients attending the McMaster University Celiac Clinic were recruited after signing informed consent. New cases of CeD (consuming gluten) were diagnosed based on positive serological markers, the presence of either HLA-DQ2 or HLA-DQ8, and evaluation of duodenal biopsies (n=16). Treated CeD (T-CeD) included patients with a verified diagnosis, who were on GFD >2 years and attending the clinic as part of their follow-up (n=11). Healthy controls included patients attending the McMaster University Digestive Diseases Clinic with a history of gastroesophageal reflux disease without other symptoms, and/or having confirmed normal endoscopy and biopsy, negative celiac serology, and negative Helicobacter pylori. They also did not fulfill the criteria for disorders of gut-brain interaction (DGBI) based on Rome IV criteria58. All participants were asked to complete a validated food frequency questionnaire (Victoria Cancer Council Dietary Questionnaire for Epidemiological Studies v2) to assess their habitual diet, and a validated gastrointestinal symptoms ratings score (GSRS)59,60 to assess symptoms. No participant had prescribed proton pump inhibitors, antibiotics, probiotics, or fiber supplementation at the time of sample collection. Sex, but not gender information, was collected and included in the analysis. The study was registered at clinicaltrials.gov (ID: NCT06532110) and approved by the Hamilton Integrated Research Ethics Board (HiREB #15311). For details, see Supplementary Data 1.

Human sample collection
Fecal samples were collected and stored in anaerobic packages. After transport to the hospital, the samples were aliquoted in an anaerobic chamber (90% CO2, 5% H2, 5% N2)61. Duodenal aspirates were collected during endoscopy, mixed with 10% glycerol and aliquoted in aerobic conditions62. All samples were frozen at −80 °C until analysis.

Mouse model
Breeding pairs of non-obese diabetic AB°DQ8 (NOD/DQ8) mice63 were originally received from Dr. Joseph Murray (Mayo Clinic, USA)43. Female and male specific-pathogen-free (SPF) mice were bred and housed in the McMaster Central Animal Facility (CAF) and were 8–10 weeks old and sex-matched for all experimental groups. SPF mice were housed in clean rooms, with ad libitum water and an irradiated grain-based diet formulated without gluten (GFD, TD.05620, Envigo; Table 2). The rooms had a 12-h dark/light cycle, and at room temperature and ambient humidity was maintained between 40–60%. Germ-free (GF) NOD/DQ8 mice were generated by two-stage embryo transfer and housed within the McMaster Axenic Gnotobiotic Unit (AGU) in flexible film isolators with free access to autoclaved food and water44. Diets were autoclaved to ensure sterility. Mice were sacrificed under anaesthesia (Isofluorane) using cervical dislocation before necropsy and tissue collection. All experiments were conducted with approval from the McMaster University Animal Care Committee and McMaster Animal Research Ethics Board (AREB) under the Animal Utilization Protocol (AUP #210930).

Gluten sensitization and dietary interventions
To minimize litter and cage effects, littermates were distributed across treatment groups using random assignment to sex-balanced cages before sensitization with gluten using an established protocol where mice were gavaged with 25 μg cholera toxin and 1 mg of pepsin/trypsin-digested gliadin once a week for 3 weeks23. Mice were then switched from a gluten-free diet (TD.05620) to a calorically balanced gluten-containing wheat-based diet (TD.200056) for 3 weeks, and finally returned to the GFD for recovery over 2, 6, 10, or 24 weeks. Age-matched naïve controls (non-sensitized on GFD with no gluten exposure) were used to determine normal and baseline values and compare against experimental groups. Mice receiving fiber-supplemented diets were transitioned over the first week with a 50:50 mixture of no added fiber GFD and either 52.8 g/kg inulin (IN, TD.2100884) diet or 136 g/kg HylonVII (RS, TD.210885) to allow the mice to acclimatize. The total fiber content in the supplemented diets was increased to approximately 15% from a grain-based chow formula with ~7.5% dietary fiber. Diet composition is listed in Table 2 and was calculated based on allometric species nutritional requirements and not direct mass-to-mass conversions64. Mice were monitored for signs of distress, changes in food consumption, weight gain, and fecal output through the duration of the diet intervention study.

Evaluation of gluten-immunopathology
Cross sections from the proximal small intestine were fixed in 10% formalin and embedded in paraffin for blinded evaluation using brightfield microscopy (Olympus) using open-source QuPath software65. The CD3+ intraepithelial lymphocytes (IELs) were quantified by immunohistochemical staining23,66. The primary antibody, polyclonal rabbit anti human CD3 (A045229-2; Agilent), was diluted 1:1000 (1 μL in 1 mL of 1% BSA in PBS) and incubated overnight. CD3+ IELs were visualized with 3,3’-diamonobenzideine (DAB+ substrate chromogen system, K346811-2; Agilent) and counterstained with Mayer’s hematoxylin (S330930-2; Agilent) for 60 s. CD3+ IELs were quantified in 5 randomly chosen villi tips and reported as the number of IELs per 100 enterocytes. The same paraffin-embedded sections were stained with hematoxylin and eosin for villus-height and crypt-depth measurements for the villus-to-crypt ratio (V/C). Ten villi and adjacent crypt pairs were randomly selected and measured. An equally weighted composite VCIEL scoring system was used to combine histological measures and create a standardized baseline around the naïve control group24. At necropsy, the small intestine was washed out and contents collected in 0.05 M EDTA in PBS with soy trypsin inhibitor (0.1 mg/mL; T9128-1G; Sigma) and phenylmethanesulfonyl fluoride (0.8 mM; P7626; Sigma-Aldrich) to prevent protein degradation. Direct ELISA was performed to quantify anti-TG2 or anti-gliadin IgA antibodies in mouse luminal contents23,66. 96-well plates were coated with either transglutaminase in PBS (0.1 μg per well; T5398-2UN; Sigma-Aldrich) or gliadin dissolved in 70% ethanol and diluted in PBS (5 μg per well; G3375; Sigma-Aldrich). Non-specific binding was blocked with 2% BSA in PBS-Tween before a 2-h incubation of 50 μL small intestinal wash solution in triplicate. Detection with 1 h incubation with HRP-conjugated secondary anti-mouse IgA antibody (diluted 1:5000; 1040-05; Southern Biotech) followed by TMB substrate (Agilent) reaction for 20 min before measuring absorbance at 450 nm using a Spectramax M3 plate reader (Molecular Devices). OD values were normalized to the total amount of protein in the sample using a protein quantification assay (Bio-Rad) according to the manufacturer’s instructions.

Prevotellaceae cocktail colonization. Ten isolated strains from the Prevotellaceae family were obtained from the collection of Dr. Michael Surette’s Lab (McMaster University49,50), based on their reduced abundance in CeD (Table 4). Isolates’ identities were confirmed with MALDI-TOF and cultured anaerobically in BHI-3 for 48 h with agitation. NOD/DQ8 mice were gavaged with PBS (negative control) or approximately 1 million cells of each strain combined, given on days 1 and 3 of the inulin diet.

Metabolite determinations with gas chromatography mass spectrometry. A targeted selected ion monitoring (SIM) gas chromatography mass-spectrometry (GC-MS) protocol was used to quantify SCFA in the small intestinal washes and feces collected at endpoint. Samples were extracted with 10% isobutanol before the addition of 125 µL of 20 mM NaOH and 400 µL of chloroform. After mixing, 400 µL of the upper aqueous phase was transferred to a new tube before the addition of 80 µL isobutanol, 100 µL pyridine, and 70 µL H2O. Finally, 50 µL of isobutylchloroformate and 150 µL of hexane were added, mixed, and the upper aqueous phase was transferred to pre-slit autosampler vials. GC-MS measurements were carried out on an Agilent 5975C inert mass selective detector (MSD) equipped with an Agilent 7890A GC and an Agilent 7693 autosampler (Agilent, Santa Clara, CA, USA), in the Centre for Microbial Chemical Biology (CMCB) at McMaster University. Separation of the analytes was achieved on an Agilent (Santa Clara, CA, USA) DB-1MS UI (30 m x 0.25 mm x 0.25 µm) column. The injector, ion source, quadrupole and the GC-MS interface temperatures were 260, 230, 150, and 280 °C, respectively. The flow rate of helium carrier gas was kept at 1 mL/min. 1 µL of derivatized sample was injected in duplicate with a 4.5 min solvent delay time and split ratio of 10:1. The initial column temperature was 40 °C and held for 5 min, ramped to 165 °C at the rate of 10 °C/min, and then increased to 310 °C at the rate of 100 °C/min and kept at this temperature for 3 min. The total run time was 22 min. The ionization was carried out in the electron impact (EI) mode at 70 eV. The MS data were acquired in the SIM mode using GC-MS MassHunter and MS quantitative analysis. The identification of compounds was confirmed by the injection of pure standards and comparison of the retention time and target ions.

RNA extraction and quantitative reverse-transcription PCR (RT-qPCR). Mouse tissue samples from the duodenum, jejunum, ileum, and proximal colon were collected in RNALater (Invitrogen) before RNA extraction using the RNEasy Mini kit (Qiagen) according to the manufacturer’s instructions. Extracted RNA was used for cDNA synthesis using iScript Reverse Transcriptase (Bio-Rad) with SsoFast EvaGreen Supermix (Bio-Rad) according to the manufacturer’s instructions. The qPCR cycling consisted of initial denaturation at 95 °C for 3 min, followed by 40 cycles of denaturation at 95 °C for 10 s and annealing/extension at 65 °C for 30 s. GAPDH was used as an endogenous control for normalization of gene expression, and data were reported as the fold change in expression in response to inulin compared to GFD alone. Validated primer sequences for mouse GPR41 (142345193c1) and GPR43 (22122727a1) were obtained from Primer Bank50. Bacterial RNA was extracted from human duodenal aspirate samples using a Stool Total RNA purification kit (Norgen Biotek) and cDNA was synthesized using SuperScriptTM IV Reverse Transcriptase (Invitrogen) with random hexamer primers (Thermo Scientific) according to manufacturer instructions. RNA quality was measured using the differential ratio amplicons (RAMP) method of the bacterial glutamine synthetase A transcript (glnA)67 as previously reported for feces61,68. Samples with negative RAMP were removed from analysis. Primers for α-amylase (K07405) and fructan β-fructosidase (K03332) were designed using Primer3 using the following sequences for K07405 (5’−3’: AGGGAATGTTGACAGAGGGC and AAGCGGAAGTTGTCACTTGC) and K03332 (5’−3’ CAGCGTATCTCTGGGCAGAA and AGATGATGGCCCCTTCTCCA). The qRT-PCR cycling consisted of initial denaturation at 95 °C for 3 min, followed by 45 cycles of denaturation at 95 °C for 10 s, annealing at 62 °C for 30 s, and extension at 72 °C for 30 s, and normalized to the expression of glnA. Relative quantitation was performed using standard curves from serial dilutions of the PCR product of P. melaninogenica K07405 and K0333269.

Microbiota sequencing and predicted microbial function
After genomic DNA extraction, an established protocol consisting of a 2-stage nested polymerase chain reaction (PCR) to amplify the variable regions 3–4 of the 16S rRNA gene from low biomass samples was used to sequence the mouse duodenum and human aspirates using the MiSeq Illumina platform9. Sequences were then processed in R, version 4.4.2, using the package Divisive Amplicon Denoising Algorithm 2 (DADA270) and the SILVA reference database, version 138.171. FastTree 272 was used to calculate a phylogenetic tree of sequences, and data were explored using the phyloseq package73. Functional predictions based on 16S rRNA sequences were made using PICRUSt218, annotated using the Kyoto Encyclopedia of Genes and Genomes database74. Beta-Diversity was calculated using the Aitchison distance75, and differences between groups were calculated using PERMANOVA. Taxonomic and functional microbial differences were evaluated using a generalized linear mixed model with a negative binomial distribution76 with adjustment for age and sex as confounding variables for patient data. Statistical differences between groups were identified using estimated marginal means using the emmeans package77. 16S rRNA gene sequences were deposited in Sequence Read Archive (PRJNA1256891).

FoodSeq (Sequencing, bioinformatic pipeline, and diversity analysis)
We performed DNA extraction on randomized fecal samples using the DNeasy PowerSoil Pro kit (Qiagen) following the manufacturer’s instructions. We prepared libraries using a two-step PCR process78 using trnL g and h primers79–81. Primary PCR amplification was conducted as previously described81. Secondary PCR amplification contained 5 µL each of 2.5 µM forward and reverse indexing primers, 25 µL of 2X KAPA HiFi HotStart Ready Mix, 0.5 µL of 100X SYBR Green I, 9.5 µL nuclease-free water, and 5 µL of primary PCR product diluted 1:100 in nuclease-free water. Amplicon libraries were then cleaned and pooled as previously described81. Paired-end sequencing was carried out on an Illumina MiniSeq using a 300-cycle Mid kit (Illumina). We demultiplexed, trimmed, filtered, and denoised sequence reads using a QIIME282 pipeline v. 2023.5.1 with DADA270 plugin. Sequence reads were aligned to a custom trnL reference database81 (available at https://github.com/LAD-LAB/mbpipeline/blob/main/reference/trnLGH.fasta), and taxonomic assignments were made using assignSpecies() in the DADA2 R package v. 1.30.0. We calculated observed richness and Shannon diversity using estimate_richness() in the phyloseq73 R package v. 1.46.0 for each sample after removing taxa unassigned to any dietary species in our reference database. Principal component analysis was performed using prcomp(), centered and not scaled, on CLR-transformed relative abundance data. A distance matrix was then generated using Euclidean distance calculations. Prior to PERMANOVA, homogeneity of dispersion was assessed using betadisper(). PERMANOVA was performed using adonis2() in the vegan R package v. 2.6.6.183 with 999 permutations, adjusting for age, sex, ethnicity/race, with post hoc pairwise comparisons using pairwiseAdonis() v. 0.4.184, adjusting p-values using the Bonferroni method. Age was significant in the PERMANOVA model (df = 1, F=1.41, R2=0.028, p=0.039) but showed significant dispersion (p<0.05), indicating heterogeneous variance in dietary composition across ages, and was therefore interpreted with caution due to the assumption violation. The trnL gene sequences were deposited in the Sequence Read Archive (PRJNA1256896)

Statistical analysis
Data are presented for each individual sample (human or mouse). Graphpad Prism Version 10.4.2 was used for most statistical analyses and graph creation. R version 4.4.2 was used for the analysis of gene sequences. In Prism, all data were first tested for statistical outliers for exclusion (ROUT, Q=1%). For analysis in R, outliers were excluded if they exceeded 3 times the interquartile range. Data normality was then checked using the Kolmogorov-Smirnov test, such that non-parametric tests were used for non-normally distributed data. For comparisons between two groups, the Student’s t test or the nonparametric Mann–Whitney was used. For three or more groups, one-way analysis of variance (ANOVA) was used with Tukey’s post hoc test, or the nonparametric Kruskal-Wallis followed by Dunn’s test. A minimum threshold of p<0.05 was used to determine statistically significant effects.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Summary & General Comments\nIn the present study, Wulczynski et al. set up to investigate a critical gap in knowledge of celiac disease, namely targeting\nthe chronic fiber insufficiencies commonly observed in patients. They studied the microbial fiber metabolism in the small\nintestine in adult patients with active and treated celiac disease and compared the results with healthy age-matched\ncontrols. This manuscript presents an investigation into the relationship between dietary fiber intake, gut microbiota\ncomposition, and gastrointestinal symptoms, complemented by an animal model to explore potential mechanisms. They\nused 16S rRNA gene from low biomass samples to sequence the human aspirates and state that they found impaired\nmicrobial fiber metabolism in the small intestine which associates with depletion of the carbolytic taxa Prevotella spp. To\nfurther investigate this mechanistically, they next sensitized mice with transgenic expression HLA-DQ8 gene in non-obese\ndiabetic background (NOD/DQ8) with gluten to determine timing of mucosal recovery after 2, 6, 10, and 24 weeks of gluten-\nfree diet. After 10 weeks of gluten-free diet (GFD) when sensitized NOD/DQ8 mice normalized histological parameters, the\neffect of fiber supplementation with either inulin or HylonVII supplemented for 6 weeks.\nThey found that supplementation with inulin during GFD improved histology versus the no added fiber group, whereas\nHylonVII did not normalize IELs, but had improved histology versus the no added fiber group. All mice became seronegative\nafter 12 weeks of inulin or HylonVII. More importantly, colonization with Prevotella spp increased small intestinal short chain\nfatty acids (SCFA) with inulin supplementation during GFD, compared with no added fiber. Based on these results, the\nauthors conclude that the small intestinal microbiota in adult patients with active or treated celiac disease patients contains\nless fiber-degrading bacteria and that certain fibers could be used as adjuvant therapy for the gluten-free diet to promote\nmucosal healing.\nFiber insufficiency and microbiome dysbiosis are topics of importance in celiac disease, especially if studied prospectively,\nalthough this study centers around individuals already with the disease. However, while the introduction raises important\npoints, the study design and evidence presented do not adequately address the key gaps and limitations initially outlined.\nWhile a mechanistic model is used, there are challenges in translatability to a clinical context in humans. The central\nmessage is a bit unclear and hard to follow and can be improved to strengthen the paper.\nHere are some specific comments for the authors to consider when revising their manuscript.\nMajor Concerns\n• The exclusive use of 16S rRNA sequencing and PICRUSt is a critical limitation, especially given the small sample size of\nboth the human and mouse arms of this paper. PICRUSt inherently does not allow robust functional inference. At minimum,\nshotgun metagenomics should have been used, if not also metatranscriptomics, especially given the small sample size in\nboth the human and mouse arms of this study. This would greatly strengthen the paper. Drawing strong functional\nconclusions based on imputed gene content, given the tone and scope of this paper (centered around microbial metabolic\npotential as it relates to fiber), is not sufficient.\n• It is notable that some of the reported findings are only marginally significant (e.g., Prevotella, p=0.07). We appreciate that\nthese samples are challenging to obtain, but with a low n and high inter-individual variability in adults, the authors should be\ncautious of overinterpretation. Again, metagenomics would help overcome these limitations and make the findings more\nrobust. The rationale for selecting 10 Prevotellaceae strains is unclear, especially given that not all were significantly\nassociated with human data.\n• The authors report no correlation between fiber intake and gastrointestinal scores in patients with celiac disease (page 6),\nyet they proceed as though fiber depletion plays a key role in symptoms. This contradiction is not acknowledged or\nreconciled in the discussion.\n• Regarding the attempt to address potential mechanisms, there are some challenges in the present design. There is no\nwell-validated mouse model for celiac disease. This should be noted as a limitation. The use of histology in germ-free mice\nis not a sufficient substitute and should be discussed more critically.\n• The diet intervention in the mice is undermined by the extreme dosages, which simply cannot scale to human study. The\ndoses of inulin (52.8 g/kg) and Hylon VII (136 g/kg) used in mice are extraordinarily high and would be impractical and\nunsafe in humans, likely resulting in significant GI distress. The manuscript should estimate human-equivalent doses and\ndiscuss feasibility and tolerability (e.g., https://pmc.ncbi.nlm.nih.gov/articles/PMC4804402/. Using the formula HED = animal\ndose x[Km_mouse/Km_human] (or 3/37), the doses used by the authors equate to 299 g/day of inulin and 772 g/day of\nresistant starch for a 70-kg human. These levels far exceed tolerable human intakes (typically 5–15 g/day for inulin and 20–\n40 g/day for resistant starch), and would likely cause significant GI distress, making them unfeasible for human consumption\nand especially problematic for celiac disease patients. To put this in context, 772 g of resistant starch is equivalent to ~1.7 lb\nof purified starch or >22 lb of cooked green bananas/potatoes. Similarly, 299 g of inulin equals ~1.5 lb of raw chicory root.\nThis exercise underscores a fundamental issue in the design and by extension, translational relevance. While the diets may\nbe commercially available by Envigo (Inotiv), they are not translatable. No dosage response experiments were conducted.\nThe HED calculations call into question the feasibility, safety, interpretability, and real-world relevance of these experiments.\n• Bridging the human data and the mouse model more explicitly might enhance the study’s overall flow. How do the authors\ninterpret the lack of microbial changes after inulin treatment in mice? Does this undercut one of the core premises? Are\nmicrobial dysbiotic shifts seen after supplementation with Prevotella in addition to inulin?\nMinor Suggestions\n• The introduction would benefit from additional references studying celiac disease in adults, as microbiome stability and\nresilience differ significantly by age group, i.e., the bacterial differences observed in very young children with (or preceding)\nceliac disease may not be the same as those experienced by adults with new-onset or treated celiac disease.\n• The effects of diet + bacterial supplementation on microbial dynamics are warranted, which was not presented.\n• Supplementary Figure 2b is missing.\n• The authors colonized mice with specific strains of bacteria from the human study. It is important to address in the paper\nthat Prevotella is not universally beneficial. For example, P. copri has been linked to both pro- and anti-inflammatory roles,\nincluding associations with RA and JIA, other autoimmune diseases that are also often comorbid with celiac. The\nmanuscript does not discuss this nuance, nor the delivery challenges associated with strict anaerobes.\n• The sample size could be problematic in this study. A power analysis within the methods or beginning of results or\naddressing this further with a statement on generalizability is therefore warranted.\n• Discuss whether any microbial changes were observed after combined supplementation with inulin and Prevotella—this is\nalluded to but not clearly reported.\nOther Remarks\n• The figures are clear and very well done, for instance Figure 5. They are easy to follow. The panels demonstrating study\ndesign (like Figure 3a) are especially helpful to the reader. Colors for Hylon VII and inulin are consistent across figures,\nwhich facilitates understanding and flow of the results. The histological findings in this and other figures for gluten\nimmunopathology using brightfield microscopy are a strength of the paper.\nConclusion\nAlthough the study presents interesting findings with therapeutic potential and potential novel insights in the etiopathology of\nceliac disease, it also has some methodological limitations and ambiguous results. Although it is noteworthy that soluble\nfiber inulin accelerated mucosal healing by supporting carbolytic activity of the small intestinal microbiota in the mouse, a\nlimitation lies in studying patients either at diagnosis with active celiac disease or in clinical remission after prolonged\ngluten-free diet, as both states complicate interpretation of causality in microbial alterations. It remains unresolved whether\nimpaired microbial fiber metabolism is a contributing factor to disease pathogenesis or a consequence of mucosal injury,\nraising the fundamental question of causality.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "Dear Authors,\nIt was with great interest that I’ve read your manuscript, dedicated to a disease that increasingly more people worldwide are\nfacing, as well as its highly social and financially impairing treatment. Understanding and demonstrating the pitfalls of a\nlifelong gluten-free diet is noteworthy in order to propel research towards other treatment options, apart from the highly\nconcerning fact that for some patients, current treatment does not suffice. However, I’ve missed the groundbreaking novelty\nof your work. It appears not to deliver information that it is not already widely known and recognized as important to all\npeople, regardless their illnesses: the importance of fiber intake for health, which is, indeed, the main focus and conclusion\nof your work. In fact, there are already recent published work that demonstrate the lack of fiber-foraging bacterial species in\nthe gut of celiac disease patients, and there are several papers addressing the importance of fiber intake to our health,\nregardless our comorbidities. The food wheel is a worldwide acknowledged graphic that greatly highlights the importance of\nfiber intake and, as already mentioned, several published work has already demonstrated the importance of fiber intake for\nall people, including celiac patients. Thus, although technically sounding, your work does not appear to contribute with\nsignificant insights to the disease pathophysiology or to specifically scope for novel therapies. Maybe your goal was to\nemphasize the importance of fiber intake to the patients from a specific region of the world, as it is also well known that\ngeography greatly determines the diet. If so, I believe it is best that you publish your work in a journal targeting that\ngeographical region. Also, you lack to discuss your gut microbiota results with those from previously published work, apart\nfrom the fact that you analyzed a very small sample of celiac patients. Regarding the mice work, as technically sounding as it\nmay be, results again showed what is already known by the scientific community, that Prevotella species are fiber-foraging\nbacterial species and that fiber intake helps reduce inflammation, with Prevotella contributing to that, again lacking a\nprominent novelty in your work, as you did not demonstrate any specific and distinct association to celiac disease and its\npathophysiology, i.e., if you had conducted the same experiments with a different mice model the results would have been\nthe same as those obtained with the HLA-DQ8 models. Moreover, as already acknowledged by the scientific community, the\nuse of axenic animal models places the obtained results farther from reality than other models, as it is unclear how much the\nlack of natural microbiota in the animal influences the experiments conducted in them, particularly regarding the immune\nsystem, highly impaired in these animals. In this experiment you demonstrated the obvious association of Prevotella and\nSCFAs production. In HLA DQ8 mice you showed the positive effect of inulin supplementation, which is already known, but\ndid not tested Prevotella contribution. Considering all this, it would have been more pertinent to conduct a clinical trial\nfocused on the diet of celiac patients, controlling their fiber intake and/or supplementing them with Prevotella probiotics, or\ncommercially available ones, and evaluate serum and gut biomarkers of inflammation and you would have had a study\nsystem closer to reality with similar conclusions being obtained. In fact, you suggest that as future work, indicative that you\nhadn’t had the opportunity to conduct a clinical trial. However, as said, available literature already indicates the conclusions\nyou obtained with your work, thus failing in the required groundbreaking novelty.\nConsidering all this, I believe the work does not suffice to be considered for Nature Communications. Perhaps you may want\nto consider a journal focused on celiac disease or nutrition.", "reviewer_4": "The manuscript from Wulczynski et. al. aims to define the metabolic capacity of the gut microbiota in the context of Celiac\ndisease (CeD). Using human fecal samples and duodenal aspirate samples, the authors show that microbiota composition,\nfunction, and metabolic profile differ between patients with CeD (including Treated CeD) and healthy individuals. The\nauthors also show that colonization with Prevotella species as well as dietary supplementation with inulin increases SCFA\nproduction in the small intestine enhances mucosal healing in a mouse model of CeD.\nThis work addresses an important and understudied topic—the potential benefit of dietary fiber to improve CeD outcomes.\nThe authors also show that Prevotella species may be an important genus for SCFA production during CeD. Unfortunately,\nthe manuscript has some major limitations that limit enthusiasm for its publication—please see below for details.\nIntroduction\n• First paragraph: using both “prevalence and incidence” is redundant and needs to be revised\n• The authors used the term “carbolytic” in the introduction and throughout the manuscript to describe fiber-degrading\nbacteria—the more appropriate terminology for describing fiber-degrading bacteria is saccharolytic\nFigure 1 Results\n• The authors need to revise this section for readability by combining the text describing the demographic data and\nmicrobiota characterization for CeD patients into a more cohesive narrative that starts off with a clear scientific rationale for\nwhy the study was conducted. Specifically, the authors need to emphasize why fecal samples were collected—to identify\nfiber-degrading taxa potentially lacking in CeD patients.\n• When describing samples/data from humans, please consider using the term “healthy individuals” instead of “controls”.\n• Include a brief description of the population demographics in the Results section. It would also be helpful to compare these\ncharacteristics to larger CeD cohorts reported int eh literature either in the introduction or the discussion.\n• Clarify the meaning of T-CeD—does “T” denote “treated,” and is the treatment a gluten-free diet?\n• There is inconsistency in the methods reported: this section begins by stating 16S sequencing was used, but later\nreferences metagenomic data (e.g., Fig. 1e). Please clarify whether both 16S rRNA gene sequencing and shotgun\nmetagenomics were performed (or if it was one or the other).\n• Replace “carbolytic” with a more appropriate term\n• Faith’s Phylogenetic Diversity (PD) is the sole metric presented for alpha-diversity. It is important to explain why this metric\nwas chosen over others and to clarify whether additional metrics were analyzed (e.g., Shannon, Simpson). Faith’s PD\naccounts for phylogenetic breadth but not abundance, which may limit interpretability of the results.\n• Did the authors see changes in the abundance of Prevotella when assessing compositional changes in patient\nmicrobiomes?\n• Confirm the spelling of “Prevtella nanciensis” in the Figure 1 legend.\nFigure 2 Results\n• A citation is needed when referencing daily fiber intake recommendations.\n• The comparison between CeD and controls regarding fiber intake needs improvement in flow. The authors make the\nconclusion that there is no difference but it’s not clear until the end of that paragraph what question the authors are asking;\nit’s also not clear which treatments are being compared to arrive at that conclusion\n• The sentence “Although a similar trend was observed for total fiber, there were no statistical differences between the\ncalculated soluble and insoluble fiber fractions between groups (Fig. 2b)” is awkward. Please revise for clarity and flow.\n• Why was Shannon diversity used as the sole alpha-diversity metric in this section? How does this choice relate to earlier\nuse of Faith’s PD? The authors should consider using multiple complementary metrics throughout the paper.\n• “No statistical difference” should be changed to “no significant difference.”\n• Again, the authors need to not use the term “carbolytic”. Also, the authors need to recognize that fiber degradation is not the\nsole pathway for SCFA production— gut proteolytic bacteria can ferment protein to produce SCFA.\n• The sentence “The results suggest T-CeD partially recovers microbial carbolytic capacity, despite the sub-optimal amount\nof fiber ingested by these patients, which may be related to histological amelioration” is vague. Amelioration of what—villous\natrophy, inflammation? Please clarify the intended meaning.\n• The terminology “fecal plant DNA” is confusing. Please make it more clear that you are detecting plant DNA from the diet in\nthe human fecal samples.\nFigure 3 Results\n• The choice of Hylon VII as a source of dietary fiber is a major limitation. In particular:\no Hylon VII—although 70% RS2—is only 12% fiber (at least according to the technical sheet that I found on Ingredion’s\nwebsite), which significantly limits the ability to ask if dietary fiber can improve CeD outcomes. This choice of “fiber” also has\nsignificant implications for how the authors are interpreting their results. This results section and the corresponding\ndiscussion need to be re-written to reflect what the authors are actually feeding to mice.\no The authors also need to provide justification for the selected dosages of Hylon VII and inulin—for instance, the HylonVII\ndose is nearly 3X the inulin dose—why? Calculating the human dose equivalent would also be helpful.\no Provide vendor information for the inulin and HylonVII.\n• The sentence “Mice with transgenic expression HLA-DQ8 gene in non-obese diabetic background…” is missing a word;\nlikely “of the.”\n• The schematic timelines in Figures 3A and S3A should indicate the 3-week immunization period.\n• The transition into the Figure 3 results is abrupt. The authors need to reorder the paragraph to begin with the question\nabout fiber intervention and then integrate the time-course data as justification for evaluating the 6-week timepoint for\nhealing.\n• There is a discrepancy between the text and figure: the fiber intervention is described as lasting 10 weeks in the text, but\nthe figure indicates 6 weeks. Please address.\n• Current microbiome analyses appear to be limited only to endpoint analyses, which is disappointing. More information\nabout the microbiomes at baseline before fiber interventions and prior to disease induction would provide a more thorough\nanalysis.\nFigure 4 Results\n• Were changes in the abundance of Prevotellaceae observed in the mouse study? These results are not shown and would\nbe relevant given the experiments to come in Figure 5.\n• Is the “SCFA” reported in Fig 4A the total SCFA? Or just the sum of the three reported SCFAs (acetic, propionic, butyric)?\n• Why was Aitchison distance used here? Bray-Curtis is commonly seen in microbiome assessments.\nFigure 5 Results\n• This section opens with SCFA data from inulin-fed GF and SPF mice, but this is not novel nor is it the main point of this\nresults section. This choice for order also interrupts the flow of the paper. The authors should reorganize so that they lead\nwith the important research question and then present these data later as confirmation that SCFA production after inulin\nconsumption is microbiome-dependent.\n• The justification, design , and interpretation of the Prevotella experiments in Figure 5 represent a major limitation of this\nwork:\no The rationale for introducing Prevotella species into GF mice is not clearly explained. The authors need to expand on why\nthis experiment was conducted and how it relates to the overall hypothesis.\no The use of a cocktail of 10 Prevotellaceae strains should also be justified. How were these strains selected? Did the\nauthors confirmation that they all colonized the mice? Table 3 includes taxonomic info, but no such detail appears in the\nmain text or methods.\no The authors write that strain selection was “based on their reduced abundance in CeD” but it is not clear what this means.\nDid the authors select Prevotella strains for the mouse studies that were decreased in CeD patients? Or did the selected\nstrains possess the fiber-metabolizing genes that are depleted in CeD patients. If this is a key assumption, it should be\nexplicitly stated and supported.\no Why not evaluate disease parameters and mucosal healing in the Prevotella-associated mice? This is a missed\nopportunity.\n• Fig5B: need to define AA, PA, BA in the figure legend\n• Throughout the study, it’s not always clear in the results sections which mice were used for which studies, i.e., GF or SPF.\nPlease specify this throughout.\nDiscussion\n• The statement “Our results are consistent with previous descriptions that duodenal microbiota profiles differ between active\nCeD, T-CeD and healthy subjects” needs a citation(s) to support it.\n• The statement “It is possible that inulin undergoes more rapid partial digestion in the small intestine compared with\nHylonVII, a type-2 resistant starch made of linear α-1,4 and branching α-1,6 linkages” is not correct. Neither inulin nor RS2\nare digested in the small intestine, save for some RS2 fermentation in the terminal ileum. Please see excellent work in swine\nmodels on this topic:\no Tan, F.P.Y., Beltranena, E. & Zijlstra, R.T. Resistant starch: Implications of dietary inclusion on gut health and growth in\npigs: a review. J Animal Sci Biotechnol 12, 124 (2021). https://doi.org/10.1186/s40104-021-00644-5\no Haenen D et al. A diet high in resistant starch modulates microbiota composition, SCFA concentrations, and gene\nexpression in pig intestine. J Nutr 143, 3 (2013). https://doi.org/10.3945/jn.112.169672\n• The explanation for why inulin led to more mucosal healing than RS2 is underdeveloped and needs work. As was\nmentioned above, there is a lot of digestible starch in the HylonVII that the authors chose to feed the mice as a source of\n“fiber,” and there is a lot of literature describing microbiome changes that occur when feeding diets rich in digestible\ncarbohydrates.\n• For the statement “We did not perform direct measurement of small intestinal SCFA from human aspirates because it is\ntechnically challenging,” please explain why this was challenging? Not enough material?\nMethods\n• Need to define anaerobic conditions for human sample collection (what ratio of hydrogen, nitrogen, and/or CO2 was\nused?)\n• Need to describe how the bacteria were prepared for administration to the mice. What media? Anaerobically? How was\ncell count quantified?\n• Was bioavailability/composition of the diets affected by autoclaving? Non-chow diets are most commonly irradiated to\navoid affecting non-temperature stable compounds that could be found in the diet.\n• How was the small intestine washed out for the necropsy?\n• Were the GF mice used in this study confirmed to be germ-free at the end of the study? This information should be\nprovided.", "reviewer_5": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts."}, "author_response": "We would like to thank the editors and reviewers for their thorough and thoughtful comments.\nConsidering the suggestions and criticisms raised, we hereby submit an extensively revised\nmanuscript.\nDetailed responses to each of the reviewers’ comments and suggestions are provided below. We\nare grateful for the opportunity to strengthen the manuscript, which we hope will now be\nacceptable for publication in Nature Communications.\nPoint-by-point answer to Reviewers’ comments:\nREVIEWERS 1&2\n“The exclusive use of 16S rRNA sequencing and PICRUSt is a critical limitation, especially\ngiven the small sample size of both the human and mouse arms of this paper. PICRUSt\ninherently does not allow robust functional inference. At minimum, shotgun metagenomics\nshould have been used, if not also metatranscriptomics, especially given the small sample\nsize in both the human and mouse arms of this study. This would greatly strengthen the\npaper. Drawing strong functional conclusions based on imputed gene content, given the\ntone and scope of this paper (centered around microbial metabolic potential as it relates to\nfiber), is not sufficient.”\nResponse:\nWe agree that PICRUSt2 is mainly focused on prediction of bacterial core genomes and has\nlower resolution for functional inference compared with shotgun metagenomics. However, 16S\nrRNA gene sequencing and functional predictions are still relevant (Jovel et al., 2016), especially\nfor low biomass samples. On the other hand, metagenomics or metatranscriptomics analysis of\nthe duodenum predominantly captures host-derived DNA and transcripts. Duodenal aspirates\nhave low bacterial biomass and a high host-to-microbe DNA ratio, making metagenomic\napproaches difficult, less reliable, and more costly (Caminero et al., 2025; Jervis-Bardy et al.,\n2015; Matchado et al., 2024). Removing the host DNA from low biomass samples remains a\nchallenge for small intestinal microbiota research. New techniques are being proposed but, we\nwill need to wait to have those validated specifically for proximal small intestinal biopsies from\nthe duodenum and jejunum, as well as luminal aspirates. Thus, we employed 16S rRNA gene\nsequencing, as our study targeted a low-biomass site and utilized a method that was previously\nvalidated for this condition in humans (Constante et al., 2022). We however acknowledge these\nlimitations and raise the methodology constraints of shotgun metagenomics and\nmetatranscriptomics in low yield ecosystems in the discussion (Pg 14, lines- 297-300). We agree\nthat while PICRUSt is most valuable as a data mining tool (Jovel et al., 2016), ideally predictions\nshould be confirmed using other methods. Our group has previously used PICRUSt to guide the\nanalysis of bacterial stool transcripts to confirm how the presence of the gene and the mRNA\nexpression (similar to what one may obtain in a metatranscriptomics analysis) affect microbial\nfunction and result in disease activity (Cholet et al., 2019; Santiago et al., 2023). Thus, to address\nthe reviewers’ point we completed a similar experiment to validate the expression of glycoside\nhydrolases in the aspirates using targeted quantitative reverse-transcriptase PCR. The\nquantification of bacterial RNA demonstrates not only the relevance of the PICRUSt predictions\nbut also, as suggested by the reviewer, it goes one step further by quantifying RNA expression\ninstead of DNA levels. We thank the reviewer for this comment as we believe it made the results\nstronger by including this transcript-level data. This result is included in Supplementary Figure\n2c, and we also briefly discuss it in limitations paragraph (Discussion, page 14).\nPage 5 (line 102 – 106):\n“To confirm the PICRUSt2-predicted results, we performed qRT-PCRs for the highest abundance\nbacterium identified at the species level (Prevotella 7 melaninogenica) for genes K07405 and\nK03332. We found that K07405 had a similar RNA expression profile as the predicted gene content\nwith reduced expression in CeD compared with controls. No statistical differences were observed\nfor K03332 (Supplementary Figure 2c)”.\nSupplementary figure 2c:\n“c Duodenal microbial mRNA transcript expression of α-amylase (K07405) and fructan-β-\nfructosidase (K03332) normalized to glnA transcript levels in healthy controls (blue, n = 12-14),\nCeD (red, n = 4) and T-CeD (green, n = 6). Samples with low RNA integrity were excluded from\nanalysis. Multiple differences were compared using one-way ANOVA followed by Tukey’s test,\nand sex was represented by females (squares) and males (circles).”\nPage 14 (line 297 –300):\n“For microbiome analysis, next-generation sequencing such as shotgun metagenomics may\nprovide more robust functional analysis than 16S rRNA gene sequencing, especially if they include\njejunum or ileum, but could not be completed in our low-biomass duodenal aspirates due to high\nlevels of host contamination (Caminero et al., 2025; Jovel et al., 2016). We used a previously\nvalidated 16S rRNA gene sequencing protocol (Constante et al., 2022, and performed qRT-PCR\nfor targeted measurement of gene expression.”\n“The rationale for selecting 10 Prevotellaceae strains is unclear, especially given that not all\nwere significantly associated with human data.”\nResponse:\nWe have now revised the discussion to expand on the rationale for the selection of\nPrevotellaceae strains. The 10 species were selected from our large biobank of human derived\nbacteria (Lau et al., 2016; Whelan et al., 2020). We centered on species belonging to\nPrevotellaceae, which is a family of bacteria that are at high abundance in the oral cavity and\ncolonize the small intestine (Könönen & Gursoy, 2022; Tett et al., 2021). They are known to be a\nfiber-degrading taxa with capacity for SCFA production (Chen et al., 2017; Prasoodanan P. K. et\nal., 2021) due to the many glycoside hydrolase enzymes they carry in addition to the two\nidentified in our study (α-amylase and fructan- β-fructosidase). Our observation that the relative\nabundance of Prevotella spp is decreased in CeD compared with healthy controls is supported by\nour previous paper (Constante et al. 2022), and other independent cohorts (Nistal et al., 2012;\nRoque et al., 2024). Thus, given that our mouse SPF colony contained a low relative abundance\nof Prevotellaceae, we designed a mechanistic experiment in gnotobiotic mice using well\ncharacterized Prevotellaceae strains from Surette’s lab collection at McMaster to functionally\nvalidate their role in fiber metabolism and SCFA production in the small intestine. This is now\nbetter explained in the discussion of the revised manuscript on page 12.\nPage 12 (lines 266-273)\n“The relative abundance of Prevotella spp was low in the mouse small intestine and was not\nsignificantly affected by either fiber diet. Thus, we designed a mechanistic experiment by\ncolonizing germ-free mice with a collection of Prevotella spp from our biobank of human derived\nbacteria (Lau et al., 2016; Whelan et al., 2020). These species were all confirmed to carry the\ngenes of interest for α-amylase (K07405) and fructan-β-fructosidase (K03332), and in colonized\nmice, they increased small intestinal SCFA upon inulin supplementation.”\n“The authors report no correlation between fiber intake and gastrointestinal scores in\npatients with celiac disease (page 6), yet they proceed as though fiber depletion plays a key\nrole in symptoms. This contradiction is not acknowledged or reconciled in the discussion.”\nResponse:\nThank you for this important comment and the opportunity to clarify it. Our study was not\ndesigned to investigate symptoms in celiac disease in response to fiber challenge or\nsupplementation or due to differences in fiber intake. This would require a different design, such\nas a clinical study with dietary interventions, and longitudinal tracking of symptom responses\nand dietary evaluations. The results of our study could help better design such an intervention or\nlongitudinal study by providing valuable mechanistic insight and microbial (structure and\nfunction) targets. In our cohort, the estimated fiber intake did not correlate with gastrointestinal\nsymptoms rating scores patients were reporting at a cross-sectional level. There was no fiber\nchallenge; patients were on their usual diet and only T-CeD were on a GFD. Many participants\nwere below the recommended fiber intake levels, and this was more evident in the GFD group.\nWe do not claim that fiber plays a role in symptoms in this study, although there is literature that\nsuggests some IBD patients can be intolerant to some fibers (Armstrong et al., 2023), and these\nstudies are cited as general background to highlight beneficial and detrimental effects of fiber as\nshown by others. We were intrigued though by the finding that dietary fiber intake did not fully\nexplain the lower saccharolytic activity observed in CeD patients. This suggested a primary\nmicrobial functional deficiency, rather than lack of dietary substrate, motivating the mechanistic\nstudies. We have explained this better in the manuscript on page 10.\nPage 10 (lines 203-208)\n“There was no relationship between estimated fiber consumption and symptoms in our cohort,\nalthough many participants were already consuming lower than recommended fiber. Our study\nwas not designed to investigate fiber intolerance in humans with CeD, as has been done previously\nin inflammatory bowel disease (Armstrong et al., 2023; Kuang & Binion, 2022), but to investigate\nthe impact of diet on microbial saccharolytic function in the small intestine.”\n“Regarding the attempt to address potential mechanisms, there are some challenges in the\npresent design. There is no well-validated mouse model for celiac disease. This should be\nnoted as a limitation. The use of histology in germ-free mice is not a sufficient substitute\nand should be discussed more critically.”\nResponse:\nNo animal model will ever reproduce human disease completely and there is no human disease\nfor which there is a single animal model. For instance, in IBD, T cell transfer, DSS, TNBS, IL-\nis no reason why there should be only one model for CeD, rather a large toolbox of in vitro and\nin vivo models should complement clinical interventions and samples, including organoids.\nWe would like to share a quote from a commentary from the Scientific Advisory Board for AGA\nCenter for Gut Microbiome Research and Education that specifically addresses the role of animal\nmodels in GI disease:\n“Preclinical models of disease … aim to reproduce specific features of the disease being\nstudied, leading to new insights that require validation in humans. The three key\naspects traditionally used to evaluate preclinical models relate to (1, face validity) the\nfeatures of the disease being modelled, (2, construct validity) whether the features of the\nmodel arise for biologically relevant reasons, and (3, predictive validity) how well the\nmodel predicts expression of those pathways or responses to treatment in humans...”\n(Britton et al., 2024).\nWe agree that there is no perfect model for any human disease (including celiac disease) and,\nthat models are used based on the 3 criteria above and depending on the question under study.\nFor celiac disease, most current animal models are based on MHC class II transgenic mice\nexpressing HLA-DQ2 or HLA-DQ8, which can, or not, include transgenic expression of IL-15 to\nstudy particularly that pathway, but no single mouse model recapitulates all features of human\ndisease. With respect to the NOD-DQ8 model, it expresses the human DQ8 gene in the absence\nof endogenous mouse MHC-II and it has extensively been characterized for the study of gluten-\nspecific immune responses at the Mayo Clinic (Black et al., 2002; Marietta et al., 2004; Verdu et\nal., 2008). The model was further validated by our group and others for the past 15-20 years to\nstudy the influence of microbial colonization and composition on gluten-induced\nimmunopathology (Bodkhe et al., 2021; Caminero et al., 2016, 2019; Galipeau et al., 2015;\nLamas et al., 2020), demonstrating face and to certain extent, construct validity. More recently,\nthe group of Detlef Schuppan (U of Mainz) has used this model to study mechanistic pathways,\nand reproduce effectiveness of the TG2 inhibitor ZED1227, showing promising predictive\nvalidity (unpublished, presented at UEGW 2025). Thus, in our opinion the model is useful to\ncomplement our clinical observations and to study microbial pathogenesis as the model shows\nmicrobial colonization affects and modulates gluten immunopathology.\nWe believe the strength of our paper is the use of a variety of experimental tools including\ngnotobiotic mouse models for direct interrogation of specific microbial function\n(Prevotellaceae), functional assays, molecular biology and biochemistry and validations in\nclinical cohorts, truly encompassing what translational research should represent. This is now\ndiscussed in limitations on Page 14.\nPage 14 (lines 302-308)\n“ … the NOD/DQ8 mouse employed here requires sensitization and the use of a microbial\nadjuvant and is well suited for the study of environmental triggers or modulators of disease\nseverity (Caminero, McCarville, Galipeau, et al., 2019) and fulfills validity criteria of animal\nmodels (Britton et al., 2024) such as ability to produce mechanistic insight, for clinical trial\nvalidation. This translational study provides unique insight into the relevance of fiber\nsupplementation in improving gluten-specific immunity through microbial metabolism, a finding\nthat warrants further evaluation in clinical trials using human-equivalent doses.”\n“The diet intervention in the mice is undermined by the extreme dosages, which simply\ncannot scale to human study. The doses of inulin (52.8 g/kg) and Hylon VII (136 g/kg) used\nin mice are extraordinarily high and would be impractical and unsafe in humans, likely\nresulting in significant GI distress. The manuscript should estimate human-equivalent\ndoses and discuss feasibility and tolerability\n(e.g., https://pmc.ncbi.nlm.nih.gov/articles/PMC4804402/). This exercise underscores a\nfundamental issue in the design and by extension, translational relevance. While the diets\nmay be commercially available by Envigo (Inotiv), they are not translatable. No dosage\nresponse experiments were conducted. The HED calculations call into question the\nfeasibility, safety, interpretability, and real-world relevance of these experiments.”\nResponse:\nOur study was not intended to determine human doses of fiber supplementation in future studies.\nThe value of our study is the mechanistic demonstration that some fibers can influence the\nfunction of the small intestinal microbiome differentially. This concept will be important to\nbetter design future challenge studies in humans, for instance for the choice of biogeographic\nlocation of fiber-microbiome interactions and taxa of interest as outcomes. Our study did not\nevaluate effective doses in humans, which we clarify now in the revised Discussion.\nWe would like to further explain how our calculations for the mouse feed were done. As\nreviewers suggest, doses based on mass, are well suited for calculations of “human equivalent\ndoses” of pharmacological intervention or drugs. However, these conversions are less\nappropriate for studies like ours incorporating nutritional substrates in mouse feeds. The\nformulation of the animal diets used in our study is based on allometric scaling which was\ncalculated to consider how the metabolic and biological processes scale with the size of the\nanimal (Rucker, 2022). When using direct mass conversion, the assumption is that the human\nwould require 2500-2800 times more nutrients than the mouse. However, when using metabolic\ncalculations based on resting energy expenditure, humans only require 200-400 times more\nenergy than mice. Therefore, we calculate that the equivalent dose for humans could range from\n40-80g inulin daily, which is slightly higher than the currently recommended doses by Health\nCanada for females (25g/d) and males (38g/d). The transition to the fiber-supplemented diets\nthough was performed progressively not all at once; allowing the mice in our study to acclimate\nto a reduced dose of fiber over the first week, ensuring they were well adapted to the higher\ndoses. Moreover, as the fiber was incorporated in the feed (not by a challenge), ingestion was\nphysiological.\nAn unknown factor to consider is the relative contribution of the microbiota to metabolism;\nand how disease-associated alterations in microbiota can further impact metabolism in vivo\n(Caminero et al., 2016; Caminero, McCarville, Zevallos, et al., 2019). This is now explained in\nmethods:\nPage 13 (lines 284-289)\n“Our dietary intervention was designed to investigate the effect of fiber on the small intestinal\nmicrobiome and how that related to intestinal healing in genetically susceptible sensitized mice,\nnot to determine the relevant dose of fiber sufficient to increase microbial activity, promote\nhealing, or impact symptoms in human CeD patients. Our study should encourage future clinical\ninvestigation with prospective trials to further elucidate the relationship between fiber and\ngastrointestinal symptoms in CeD.”\nPage 14 (lines 306-308)\n“This translational study provides unique insight into the relevance of fiber supplementation in\nimproving gluten-specific immunity through microbial metabolism, a finding that warrants\nfurther evaluation in clinical trials using human-equivalent doses.”\nPage 18 (lines 363-366)\n“The total fiber content in the supplemented diets was increased to approximately 15% from a\ngrain-based chow formula with approximately 7.5% dietary fiber. Diet composition is listed in\nTable 2 and was calculated based on allometric species nutritional requirements and not direct\nmass-to-mass conversions (Rucker, 2022).”\nIt is relevant to point out for context, that the dose of supplemented fiber (+5% inulin or\nHylonVII = 15% total fiber from all sources) is lower in our study than what has used to\ndemonstrate health benefit in other inflammatory mouse models (Corrêa et al., 2023; Le Leu et\nal., 2009; Niu et al., 2024), including delaying spontaneous diabetes onset in the NOD mouse\n(Zou et al., 2021).\nFinally, during our study, the mice were monitored for potential signs of distress, food\nconsumption, weight gain, fecal output, and at sacrifice, for presence of gas in the bowels. None\nof these were observed in any of the groups. We now indicate this in the Results section\nmanuscript on page 7, and in the Methods on Page 17.\nPage 7 (lines 141-144)\n“Another cohort of sensitized NOD/DQ8 mice (Fig 3a) were given GFD with no added fiber (NAF)\nor supplemented with either inulin (52g/kg) or HylonVII (136g/kg) supplemented diets (described\nin Table 2) for 6 weeks and monitored for signs of distress.”\nPage 18 (lines 366-368)\n“Mice were monitored for signs of distress, changes in food consumption, weight gain, and fecal\noutput through the duration of the diet intervention study.”\n“Bridging the human data and the mouse model more explicitly might enhance the study’s\noverall flow. How do the authors interpret the lack of microbial changes after inulin\ntreatment in mice? Does this undercut one of the core premises?”\nResponse:\nThank you for this comment. We now better explain these findings. Changes in the relative\nabundance of different taxa (see new supplementary Fig 6b, included below as well for the\nreviewer) were observed following inulin supplementation, however, overall clustering\naccording to β-diversity was not affected when inulin and Hylon VII-fed mice were compared.\nWe do not see this as “undercutting any premise”, as composition and diversity reflect different\naspects of ecology. For example, diversity metrics summarize overall richness and evenness, so\nspecific taxa can change without affecting total diversity. This occurs when shifts in some\nmicrobes are balanced by opposing changes in others. Due to the inherent inter-variability of the\nmicrobiome, we agree that it is more appropriate to describe the observed changes or shifts after\nan intervention.\nSupplementary figure 6b displays the microbial taxa that were significantly affected by the diet\nintervention at endpoint. Changes in the relative abundance of several taxa were observed\nbetween the different groups, suggesting distinct effects of fiber interventions on the small\nintestinal microbiome. In the revised version we summarize these changes in the text as follows:\nPage 7 (lines 154-167)\n“There was no difference in microbial α-diversity between no added fiber, inulin, and HylonVII\nfed mice after 6 weeks (Supplementary Fig. 6a). Duodenal microbial β-diversity was\nsignificantly affected by HylonVII, which clustered separately from the no added fiber and inulin\ngroups (Fig. 4b). Specifically, HylonVII had higher Helicobacter, Alistepes, Aneroplamasma,\nIntestinimonas, Candidatus saccharimonas, Streptococcus, Gemella and Escherichia/Shigella\nand lower abundance of Roseburia, Turicibacter, Parasutterella, Desulfovibrio, Dorea,\nLachnoclostridium, Muribaculum, Enterorhabdus, A2, Parabacteroides, Aerotruncus and\nMarvinbyantia. Inulin, which did not affect β-diversity compared with no added fiber diet, had\nhigher Faecalibaculum, Gemella, Staphylococcus, Coriobacteriaceae UCG.002 and lower\nabundance of Intestinimonas, Alistepes, Oscillibacter, Parabacteroides, Aerotruncus and\nMarvinbryantia versus no added fiber GFD (Supplementary Fig. 6b).”\nSupplementary figure 6d:\n“b Heatmap summary of differentially abundant bacterial taxa grouped by diet. Each row\nrepresents the z-score for one bacterium and O indicates statistical outlier defined as a value\nmore than three times the interquartile range.\n“Are microbial dysbiotic shifts seen after supplementation with Prevotella in addition to\ninulin?”\nResponse:\nWe prefer not to refer to “dysbiosis”, as this is a term that is not easy to define, in the same way,\nit is difficult to define “eubiosis” (Britton et al., 2024). Instead, we describe the significant shifts\nafter interventions. In experiments using SPF mice, the relative abundance of Prevotella in the\nmouse duodenum was less than 1% and did not significantly expand when fed inulin compared\nwith the no added fiber GFD. We have clarified this in the discussion on page 12.\nPrevotellaceae were used exclusively to colonize germ-free mice that were kept gnotobiotically\nto test a specific role of this taxon in the functional response to fiber (the rationale for using\nPrevotellaceae is explained above in an earlier response). Prevotella histicola has been\npreviously demonstrated as beneficial in the NOD-DQ8 model (Bodkhe et al., 2021).\nSupplementation of SPF mice with 108 CFU Prevotella spp, would not be expected to cause\n“overgrowth”, as this is usually the probiotic dose administered to mice (for humans daily doses\ncan reach up to 5x1010) which can range between 10x8 to 10x11 CFUs (Bodkhe et al., 2021;\nCaminero, McCarville, Zevallos, et al., 2019; McCarville et al., 2017; Natividad et al., 2012).\nMoreover, the newly administered taxon would never colonize permanently an SPF mouse and\nwould disappear within 48 hours after stopping administration. This has well been demonstrated\nin murine and human probiotic supplementation studies (Laursen et al., 2017; Zmora et al.,\n2018). Furthermore, fiber consumption, Prevotella relative abundance, and SCFA are all\npositively associated with decreased risk of developing food allergy (Goldberg et al., 2020;\nVuillermin et al., 2020), supporting the notion of members of this taxon have potential use in\nmicrobial therapeutics, that will need to be confirmed in human studies (Itano et al., 2023).\nPage 12 (lines 267-273)\n“The relative abundance of Prevotella spp was low in the mouse small intestine and was not\nsignificantly affected by either fiber diet. Thus, we designed a mechanistic experiment by\ncolonizing germ-free mice with a collection of Prevotella spp. from our biobank of human-derived\nbacteria (Lau et al., 2016; Whelan et al., 2020). These species were all confirmed to carry the\ngenes of interest for α-amylase (K07405) and fructan-β-fructosidase (K03332), and in colonized\nmice, they increased small intestinal SCFA upon inulin supplementation.”\nREVIEWERS 4&5\nFigure 1 Results: “Faith’s Phylogenetic Diversity (PD) is the sole metric presented for\nalpha-diversity. It is important to explain why this metric was chosen over others and to\nclarify whether additional metrics were analyzed (e.g., Shannon, Simpson). Faith’s PD\naccounts for phylogenetic breadth but not abundance, which may limit interpretability of the results.”\nResponse: Thank you for this comment. We show Faith’s PD figure to account for changes in\nphylogenetic diversity which reflects changes in function due to evolutionary proximity, more\nthan changes in species richness or evenness alone. We agree with the reviewer that this limits\ninterpretation on evenness analysis or simple richness. We therefore added in the revised\nmanuscript Chao1 and Shannon diversity measures for the microbiota in Supplementary Fig 1,\nwhich have similar results. We also complemented those with the microbial Beta-diversity and\nrelative abundance plots in Supplementary Fig 1 and 2.\nFigure 2 Results: “Why was Shannon diversity used as the sole alpha-diversity metric in\nthis section? How does this choice relate to earlier use of Faith’s PD? The authors should\nconsider using multiple complementary metrics throughout the paper.”\nResponse: The analysis for FoodSeq plant DNA was completed independently from the\nmicrobiota analysis. Shannon diversity was chosen because it accounts for both species richness\nand evenness, without relying on phylogenetic relationships. This approach was intentionally\nchosen as the trnL gene region that was amplified is shorter (length ranging from 50-150bp) than\nthe bacterial 16S V3V4 regions, providing less phylogenetic resolution between closely related\ntaxa. Furthermore, the evolutionary distances in plants do not necessarily translate to nutritional\ndifferences relevant in the human diet. Please note that the primary goal of FoodSeq Plant DNA\nis to estimate dietary plant intake through fecal DNA profiling, rather than to assess ecological\ndynamics, which is what the metrics used in microbiome studies capture.\nPage 11 (Lines 222-225)\n“Notably, the FoodSeq analysis was used for the first time as a complementary metric to estimate\ndifferences in dietary plant intake between unrestricted diets and gluten-free diets through fecal\nDNA profiling, not to assess ecological dynamics which is common to microbial DNA profiling.”\nFigure 3 Results: “The choice of Hylon VII as a source of dietary fiber is a major\nlimitation. In particular:\n• Hylon VII—although 70% RS2—is only 12% fiber (at least according to the\ntechnical sheet that I found on Ingredion’s website), which significantly limits the\nability to ask if dietary fiber can improve CeD outcomes. This choice of “fiber” also\nhas significant implications for how the authors are interpreting their results. This\nresults section and the corresponding discussion need to be re-written to reflect what\nthe authors are actually feeding to mice.\n• The authors also need to provide justification for the selected dosages of Hylon VII\nand inulin—for instance, the HylonVII dose is nearly 3X the inulin dose—why?\nCalculating the human dose equivalent would also be helpful.”\nResponse:\nThank you for allowing us to clarify this. Our rodent diets are grain-based chow with additional\nprebiotic supplements. This means that the mice get a variety of different natural fibers,\ncompared to a purified diet where the intervention is all or nothing. Our choice for including\nHylonVII (Ingredion 03451B00) was based on its potential as prebiotic candidate to investigate\nalongside the GFD. HylonVII and other type 2 resistant starches are considered prebiotic fibers\nby North American and European governing agencies for their beneficial properties (EFSA Panel\non Dietetic Products and Allergies (NDA), 2010; Sobh et al., 2022). HylonVII contains 70%\namylose and is estimated to contain 48% bioavailable RS2 prebiotic substrate (Le Leu et al.,\n2009). It is difficult to directly compare the amount of different prebiotic substrates for an\nintervention in an animal model, so the diets were formulated to contain similar amounts of\nbioavailable carbohydrates and caloric density, which is why there is such a large difference in\nmass of ingredients (Table 2). The total fiber/prebiotic substrate from all sources in both diets\nwas formulated to be 15%. The vendor Envigo (Inotiv) supplied the inulin (TIC Prestested Inulin\nLV-110 Powder (Ingredion 38910903) and HylonVII for the diets in consultation with us and\ntheir specialized technicians. Specifically, we revised the Hylon VII diet with expert Envigo\ntechnologists and the values for NDF for the natural ingredients are described in a sheet that can\nbe appended to the manuscript if the editors consider it necessary. Fiber content was calculated\nusing the formulas below:\no Inulin diet fiber: dietNDF + dietFiber - Inulin fiber calc + Inulin = 158.5 g/kg (15.8%)\no H7 diet fiber: dietNDF + dietFiber - H7 fiber calc + (0.48*H7) = 155 g/kg (15.5%)\nCalculations for human equivalent doses based on mass are less appropriate for nutritional\nstudies like ours than pharmacological interventions. The formulation of the animal diets used in\nour study is based on allometric scaling which was calculated to consider how the metabolic and\nbiological processes scale with the size of the animal (Rucker, 2022). Another factor to consider\nis the relative contribution of the microbiota to metabolism; and how diseased-associated\nalterations in microbiota can further impact metabolism in vivo (Caminero et al., 2016;\nCaminero, McCarville, Zevallos, et al., 2019). This is now explained better in methods on page\n18.\nPage 18 (lines 363-368)\n“The total fiber content in the supplemented diets was increased to approximately 15% from a\ngrain-based chow formula with approximately 7.5% dietary fiber. Diet composition is listed in\nTable 2 and was calculated based on allometric species nutritional requirements and not direct\nmass-to-mass conversions (Rucker, 2022). Mice were monitored for signs of distress, changes in\nfood consumption, weight gain, and fecal output through the duration of the diet intervention study.\nTable 2: Composition of mouse diets.\nTD.05620 TD.200056 TD.210884 TD.210885\nDiets and Nutrient Information\nGFD Wheat diet Inulin HylonVII\nProtein 20.9 20.9 21.9 20.1\n% kcal from Carbohydrate 65.9 65.9 64.3 67.8\nFat 13.2 13.2 13.8 12.1\nKcal/g 3.2 3.2 3 3.1\nDietary Fiber 5-10% 5-7% 15.80% 15.50%\nIngredients (g / kg)\nCorn 500 N/A 500 364\nSoybean Meal 270 265 270 270\nSoybean Hull 40 40 40 40\nDL-Methionine, FG (99%) 1.5 1.5 1.5 1.5\nMaltodextrin 115.73 115.73 62.93 115.73\nLard 25 25 25 25\nMineral Mix, U.S. Rodent (99115) 2.5 2.5 2.5 2.5\nCalcium Carbonate, FG (38%) 13 13 13 13\nDicalcium Phosphate, FG,\n12 12 12 12\n(18.5% P, 21% C)\nMagnesium Oxide, FD (58%) 2.2 2.2 2.2 2.2\nSodium Chloride, iodized 10 10 10 10\nVitamin Mix, U.S. Rodent (99114) 2.5 2.5 2.5 2.5\nVitamin Mix, U.S. Supplement\n2.5 2.5 2.5 2.5\n(99116)\nCholine Chloride, FG (60%) 2.07 2.07 2.07 2.07\nLocust Bean Gum w/ gelatin 1 1 1 1\nWheat N/A 375 N/A N/A\nCorn Starch N/A 116 N/A N/A\nCellulose N/A 1.8 N/A N/A\nCorn Oil N/A 12.2 N/A N/A\nInulin * N/A N/A 52.8 N/A\nHigh Amylose Corn Starch** N/A N/A N/A 136\nCustom diets were based on our mouse gluten-free diet (GFD) formulated by Envigo (Inotiv) to\nsubstitute macromolecule sources (e.g. corn vs wheat) or specific nutrients (e.g. inulin or\nHylonVII).\n* TIC Pretested Inulin LV-110 Powder (Ingredion 38910903)\n** Hylon® VII (Ingredion 03451B00)\nFigure 3 Results: “Current microbiome analyses appear to be limited only to endpoint\nanalyses, which is disappointing. More information about the microbiomes at baseline\nbefore fiber interventions and prior to disease induction would provide a more thorough\nanalysis.”\nResponse:\nOur design was limited to endpoint analysis because these tissue samples could only be collected\nat necropsy and cannot be completed longitudinally (paired), unlike feces. The primary outcome\nwas to investigate in parallel (cross-sectional), with appropriate controls, the changes in the\nsmall intestinal microbiome that accompanied improvements in immunopathology over time\nwith different prebiotic substrates compared to the no added fiber GFD. The longitudinal\nsampling of the small intestine in mice would require sacrifice at different timepoints and would\nbe comparing different mice as there is not available technique to obtain mucosal biopsies in vivo\nfrom the mouse duodenum. The NAF GFD group is well suited as a parallel control comparison\nagainst the inulin or HylonVII fed mice in the context of recovery from gluten exposure on a\ngluten-free diet. All the mice were housed in the same room of a well-controlled SPF facility that\nis regularly monitored for the absence of exogenous contaminations pathogens. Moreover, at the\nstart of the study, and anticipating this, we distributed mice from same litters to all treatment\ngroups, minimizing biases at baseline due to cage effects. This is better explained under methods\nin the revised manuscript on page 17:\nPage 17 (lines 353-354)\n“To minimize litter and cage effects, littermates were distributed across treatment groups using\nrandom assignment to sex-balanced cages before sensitization with gluten using a previously\nestablished protocol (Galipeau et al., 2011).”."}, {"round": 2, "reviewer_comments": {"reviewer_1": "We are very pleased with the revisions, and the manuscript has improved a lot in its clarity and message. Overall, their\nresponses to our comments are thorough and thoughtful. In particular, the newly added qRT-PCR analyses (of two genes\nfrom the Prevotella strain) using RNA substantially strengthen the manuscript by providing transcript-level validation of the\nfunctional predictions, and we commend the authors for incorporating this experiment in response to feedback.\nStill, we have just a few, minor comments that need to be addressed below to clarify the message to readers.\nMinor comments:\n1.Please include sample size consideration in limitations of the discussion.\n2.Line 75 page 4: consider adding “independent of GFD” to subheading.\n3.Line 107 page 5: consider adding “nor gastrointestinal symptoms” to subheading, as this point is notable.\n4.Lines 233-235 page 11 are **critical in the premise of the study** and should be further highlighted (abstract). Now with all\nthe major revisions and new experiments, the title and abstract could align better if they are revisited in the lens of the new\nfindings.", "reviewer_2": "I co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is part of the Nature\nCommunications initiative to facilitate training in peer review and to provide appropriate recognition for Early Career\nResearchers who co-review manuscripts.", "reviewer_3": "Dear authors,\nYou have greatly improved your manuscript accordingly to the reviewers comments."}, "author_response": "We would like to thank the editors and reviewers for their thorough and thoughtful comments.\nConsidering the suggestions and criticisms raised, we hereby submit an extensively revised\nmanuscript.\nDetailed responses to each of the reviewers’ comments and suggestions are provided below. We\nare grateful for the opportunity to strengthen the manuscript, which we hope will now be\nacceptable for publication in Nature Communications.\nPoint by Point responses to Reviewer 1’s comments:\n“Please include sample size consideration in limitations of the discussion.”\nResponse: Thank you for this suggestion. We have now clarified in the Discussion (limitations\nparagraph) that this was an exploratory analysis of celiac samples obtained from our biobank and\nthat the sample size was limited in part by the invasive nature of small intestinal aspirate\ncollection. While these aspirates are highly relevant to celiac disease, they are substantially more\ninvasive than fecal sampling; consequently, some patients declined additional procedures. We\nhave explicitly acknowledged this as a limitation in the revised manuscript.\nPage 14 (lines 292): “Our cohort was small and exploratory.”\n(line 295) “There was limited availability of relevant small intestinal samples for microbial\nmetabolic analysis.”\nSuggested changes to results section subheadings:\nLine 75 page 4: consider adding “independent of GFD” to subheading.\nLine 107 page 5: consider adding “nor gastrointestinal symptoms” to subheading, as this point is\nnotable.\nResponse: Thank you. We have revised the titles as suggested:\nPage 4 (lines 75-76): “Microbial saccharolytic function in the small intestine was lower in CeD\nthan controls independent of GFD”\nPage 5 (lines 108-109): “Diet does not fully explain lower microbial saccharolytic function or\ngastrointestinal symptoms in CeD.”\n“Lines 233-235 page 11 are **critical in the premise of the study** and should be further\nhighlighted (abstract). Now with all the major revisions and new experiments, the title and\nabstract could align better if they are revisited in the lens of the new findings.”\nResponse: The abstract has been amended. The title, however, has been changed following\nNature Com editorial recommendations."}]

Decision letter