Diet is a major factor that shapes the gut microbiome1, but the consequences of diet-induced changes in the microbiome for host pathophysiology remain poorly understood. We conducted a randomized human intervention study using a very-low-calorie diet (NCT01105143). Although metabolic health was improved, severe calorie restriction led to a decrease in bacterial abundance and restructuring of the gut microbiome. Transplantation of post-diet microbiota to mice decreased their body weight and adiposity relative to mice that received pre-diet microbiota. Weight loss was associated with impaired nutrient absorption and enrichment in Clostridioides difficile, which was consistent with a decrease in bile acids and was sufficient to replicate metabolic phenotypes in mice in a toxin-dependent manner. These results emphasize the importance of diet–microbiome interactions in modulating host energy balance and the need to understand the role of diet in the interplay between pathogenic and beneficial symbionts.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
16S rRNA gene and metagenomic sequencing reads have been deposited in the SRA under BioProject PRJNA412411. The genome of C. difficile JBZPo1 has been deposited under BioProject PRJNA503906. The following public databases were used: The Genome Taxonomy Database (https://gtdb.ecogenomic.org/), DADA2 Taxonomic Training Sets (http://benjjneb.github.io/dada2/assign.html), CAZy (http://www.cazy.org/), and KEGG (https://www.genome.jp/kegg/). Source data are provided with this paper.
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
Johansson, K., Neovius, M. & Hemmingsson, E. Effects of anti-obesity drugs, diet, and exercise on weight-loss maintenance after a very-low-calorie diet or low-calorie diet: a systematic review and meta-analysis of randomized controlled trials. Am. J. Clin. Nutr. 99, 14–23 (2014).
Louis, S., Tappu, R. M., Damms-Machado, A., Huson, D. H. & Bischoff, S. C. Characterization of the gut microbial community of obese patients following a weight-loss intervention using whole metagenome shotgun sequencing. PLoS ONE 11, e0149564 (2016).
Heinsen, F.-A. et al. Beneficial effects of a dietary weight loss intervention on human gut microbiome diversity and metabolism are not sustained during weight maintenance. Obes. Facts 9, 379–391 (2016).
Spranger, L. et al. Thrifty energy phenotype predicts weight regain — results of a randomized controlled trial. Preprint at https://www.medrxiv.org/content/10.1101/2021.03.25.21254300v1 (2021).
Kohl, K. D., Amaya, J., Passement, C. A., Dearing, M. D. & McCue, M. D. Unique and shared responses of the gut microbiota to prolonged fasting: a comparative study across five classes of vertebrate hosts. FEMS Microbiol. Ecol. 90, 883–894 (2014).
Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).
Harris, J. K. et al. Specific microbiome changes in a mouse model of parenteral nutrition associated liver injury and intestinal inflammation. PLoS ONE 9, e110396 (2014).
van Passel, M. W. et al. The genome of Akkermansia muciniphila, a dedicated intestinal mucin degrader, and its use in exploring intestinal metagenomes. PLoS ONE 6, e16876 (2011).
Morrison, D. J. & Preston, T. Formation of short chain fatty acids by the gut microbiota and their impact on human metabolism. Gut Microbes 7, 189–200 (2016).
Uchiyama, T., Irie, M., Mori, H., Kurokawa, K. & Yamada, T. FuncTree: functional analysis and visualization for large-scale omics data. PLoS ONE 10, e0126967 (2015).
Lombard, V., Golaconda Ramulu, H., Drula, E., Coutinho, P. M. & Henrissat, B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 42, D490–D495 (2014).
Langille, M. G. et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31, 814–821 (2013).
Bäckhed, F. et al. The gut microbiota as an environmental factor that regulates fat storage. Proc. Natl Acad. Sci. USA 101, 15718–15723 (2004).
Cani, P. D. et al. Microbial regulation of organismal energy homeostasis. Nat. Metab. 1, 34–46 (2019).
Hunt, J. J. & Ballard, J. D. Variations in virulence and molecular biology among emerging strains of Clostridium difficile. Microbiol. Mol. Biol. Rev. 77, 567–581 (2013).
Bauer, M. P. et al. Clostridium difficile infection in Europe: a hospital-based survey. Lancet 377, 63–73 (2011).
Kuehne, S. A. et al. The role of toxin A and toxin B in Clostridium difficile infection. Nature 467, 711–713 (2010).
Wüst, J., Sullivan, N. M., Hardegger, U. & Wilkins, T. D. Investigation of an outbreak of antibiotic-associated colitis by various typing methods. J. Clin. Microbiol. 16, 1096–1101 (1982).
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).
Sorg, J. A. & Sonenshein, A. L. Bile salts and glycine as cogerminants for Clostridium difficile spores. J. Bacteriol. 190, 2505–2512 (2008).
Festi, D. et al. Gallbladder motility and gallstone formation in obese patients following very low calorie diets. Use it (fat) to lose it (well). Int. J. Obes. Relat. Metab. Disord. 22, 592–600 (1998).
Carmody, R. N. et al. Cooking shapes the structure and function of the gut microbiome. Nat. Microbiol. 4, 2052–2063 (2019).
Fang, F. C., Polage, C. R. & Wilcox, M. H. Point-counterpoint: what is the optimal approach for detection of Clostridium difficile infection? J. Clin. Microbiol. 55, 670–680 (2017).
Furuya-Kanamori, L. et al. Asymptomatic Clostridium difficile colonization: epidemiology and clinical implications. BMC Infect. Dis. 15, 516 (2015).
Zacharioudakis, I. M., Zervou, F. N., Pliakos, E. E., Ziakas, P. D. & Mylonakis, E. Colonization with toxinogenic C. difficile upon hospital admission, and risk of infection: a systematic review and meta-analysis. Am. J. Gastroenterol. 110, 381–390, quiz 391 (2015).
Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
Fernandes, A. D., Macklaim, J. M., Linn, T. G., Reid, G. & Gloor, G. B. ANOVA-like differential expression (ALDEx) analysis for mixed population RNA-seq. PLoS ONE 8, e67019 (2013).
Fernandes, A. D. et al. Unifying the analysis of high-throughput sequencing datasets: characterizing RNA-seq, 16S rRNA gene sequencing and selective growth experiments by compositional data analysis. Microbiome 2, 15 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Kanehisa, M., Goto, S., Sato, Y., Furumichi, M. & Tanabe, M. KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109–D114 (2012).
Zhao, Y., Tang, H. & Ye, Y. RAPSearch2: a fast and memory-efficient protein similarity search tool for next-generation sequencing data. Bioinformatics 28, 125–126 (2012).
Nayfach, S. & Pollard, K. S. Average genome size estimation improves comparative metagenomics and sheds light on the functional ecology of the human microbiome. Genome Biol. 16, 51 (2015).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
Wu, D. et al. ROAST: rotation gene set tests for complex microarray experiments. Bioinformatics 26, 2176–2182 (2010).
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
Menzel, P., Ng, K. L. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2016).
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2019).
Edwards, U., Rogall, T., Blöcker, H., Emde, M. & Böttger, E. C. Isolation and direct complete nucleotide determination of entire genes. Characterization of a gene coding for 16S ribosomal RNA. Nucleic Acids Res. 17, 7843–7853 (1989).
Sarafian, M. H. et al. Bile acid profiling and quantification in biofluids using ultra-performance liquid chromatography tandem mass spectrometry. Anal. Chem. 87, 9662–9670 (2015).
Cai, J. et al. Orthogonal comparison of GC-MS and 1H NMR spectroscopy for short chain fatty acid quantitation. Anal. Chem. 89, 7900–7906 (2017).
Zheng, X. et al. A targeted metabolomic protocol for short-chain fatty acids and branched-chain amino acids. Metabolomics 9, 818–827 (2013).
Erben, U. et al. A guide to histomorphological evaluation of intestinal inflammation in mouse models. Int. J. Clin. Exp. Pathol. 7, 4557–4576 (2014).
Chen, E. Z. & Li, H. A two-part mixed-effects model for analyzing longitudinal microbiome compositional data. Bioinformatics 32, 2611–2617 (2016).
Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009).
Fouladi, F. et al. Sequence variant analysis reveals poor correlations in microbial taxonomic abundance between humans and mice after gnotobiotic transfer. ISME J. 14, 1809–1820 (2020).
Persson, S., Torpdahl, M. & Olsen, K. E. New multiplex PCR method for the detection of Clostridium difficile toxin A (tcdA) and toxin B (tcdB) and the binary toxin (cdtA/cdtB) genes applied to a Danish strain collection. Clin. Microbiol. Infect. 14, 1057–1064 (2008).
Kubota, H. et al. Longitudinal investigation of carriage rates, counts, and genotypes of toxigenic Clostridium difficile in early infancy. Appl. Environ. Microbiol. 82, 5806–5814 (2016).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Alneberg, J. et al. Binning metagenomic contigs by coverage and composition. Nat. Methods 11, 1144–1146 (2014).
Uritskiy, G. V., DiRuggiero, J. & Taylor, J. MetaWRAP—a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome 6, 158 (2018).
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
Pritchard, L., Glover, R. H., Humphris, S., Elphinstone, J. G. & Toth, I. K. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal. Methods 8, 12–24 (2015).
Segata, N., Börnigen, D., Morgan, X. C. & Huttenhower, C. PhyloPhlAn is a new method for improved phylogenetic and taxonomic placement of microbes. Nat. Commun. 4, 2304 (2013).
Wattam, A. R. et al. Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. Nucleic Acids Res. 45, D535–D542 (2017).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).
Brouns, F. et al. Glycaemic index methodology. Nutr. Res. Rev. 18, 145–171 (2005).
We thank the UCSF Gnotobiotic Core Facility, and the Lynch lab (UCSF) for technical support; S. Brachs from the Spranger Lab for technical and scientific support with the animal experiments; and P. B. Smith in the Metabolomics Facility of Penn State. The Turnbaugh lab was supported by the National Institutes of Health (R01HL122593; R21CA227232; P30DK098722; 1R01AR074500; 1R01DK114034) and the Sugar, Stress, Environment, and Weight (SSEW) Center. P.J.T. was a Chan Zuckerberg Biohub investigator and a Nadia’s Gift Foundation Innovator supported, in part, by the Damon Runyon Cancer Research Foundation (DRR-42-16), the UCSF Program for Breakthrough Biomedical Research (partially funded by the Sandler Foundation), and the Searle Scholars Program. J.E.B. was the recipient of a Natural Sciences and Engineering Research Council of Canada Postdoctoral Fellowship and received support from the National Institute of Allergy and Infectious Diseases (K99AI147165). P.S. was supported by the Canadian Institutes of Health Research Fellowship program. Q.Y.A. was the recipient of a graduate fellowship from A*STAR (Agency for Science, Technology and Research), Singapore. Funding was also provided by the Berlin Institute of Health (J.S., R.J.v.S., K.M.) and the German Research Foundation (DFG) to J.S. (CRC/TR 296 Locotact, CRG192 and CRG218) as well as the DZHK (German Centre for Cardiovascular Research) partner site Berlin (R.J.v.S., J.S.), DZD (German Centre for Diabetes Research) partner site Berlin (J.S.), and the BMBF (German Ministry of Education and Research) (K.M., J.S.). R.J.v.S. was a participant in the BIH Charité Clinician Scientist Program funded by the Charité-Universitätsmedizin Berlin and the Berlin Institute of Health. Further funding was provided by the Einstein Foundation Berlin via the Einstein Center for Regenerative Therapies (R.J.v.S.) and the Institute for Diabetes Research and Metabolic Diseases (IDM) of the Helmholtz Center Munich, Germany (R.J.v.S.). Funding support was also provided via the Gladstone Institutes and NSF grant DMS-1850638 (K.S.P.).
P.J.T. is on the scientific advisory boards for Kaleido, Pendulum, Seres, and SNIPRbiome; there is no direct overlap between the current study and these consulting duties. All other authors declare no competing interests.
Peer review information Nature thanks Daniel Tancredi and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
a, CONSORT diagram describing enrolment, allocation, and data analysis. b, Per time point group sizes and timing of data collection. Participants in the diet group underwent 2 months of a very-low calorie liquid diet followed by an additional month of a conventional low-calorie diet. During the fourth month, they were instructed to maintain a stable weight. During all diet periods individuals were counselled by clinical nutritionists. Time points with data collection are shown for both diet and control participants. c, e, Daily consumption of macronutrients measured by mass (c) and percentage of daily energy intake (e) in diet group (nbaseline = 34, nVLCD = 34, nCONVD = 30, nMAINT = 32 participants). d, Total daily caloric intake during diet phases (nintervention as in c, e, control group nbaseline = 30, nCONVD = 22, nMAINT = 24 participants). f, Decreases in relative body fat are observed in the intervention group (nbaseline = 40, nCONVD = 36, nMAINT = 33 participants) relative to the control group (nbaseline = 40, nCONVD = 24, nMAINT = 26 participants). g, Diet leads to improvement in glucose tolerance as measured by OGTT within and between diet (nbaseline = 40, nCONVD = 36, nMAINT = 35 participants) and control (nbaseline = 40, nCONVD = 25, nMAINT = 26 participants) groups. LMM with Tukey’s two-sided all-pair comparison. In boxplots: centre line, median; box, first and third quartiles; whiskers, 1.5× interquartile range (IQR) with outliers individually plotted.
Extended Data Fig. 2 Reproducible and reversible shifts are observed in the gut microbiota as a result of caloric restriction in both 16S rRNA amplicon and shotgun metagenomic sequencing data.
a, Concentration of faecal DNA reveals decreased microbiota abundances in response to VLCD contrasting within diet group (nbaseline = 21, nVLCD = 16, nCONVD = 17, nMAINT = 7 participants) and between diet and control groups (nbaseline = 19, nCONVD = 5, nMAINT = 7 participants). Microbiota determined by 16S rRNA gene sequencing revert to a state more closely resembling baseline samples after VLCD (n = 19–33 per group per time point; Supplementary Table 3) by both Bray–Curtis dissimilarity (b) and Jensen Shannon divergence (c; also P < 0.001, R2 = 0.031 by ADONIS with Participant ID as stratum). d, Reproducible shifts in community membership are observed across individuals on the second principal coordinate in response to diet followed by reversion during conventional diet and maintenance (Unweighted UniFrac). Each series of connected points represents a single individual in the study with the number representing the time point of the study in months. e, Genera whose abundances were altered by VLCD demonstrate rapid reversion during the conventional diet (30 most important genera by GINI coefficient displayed, Random Forest, *FDR Q < 0.1 ALDEx2). f, Shannon diversity is significantly decreased after conventional diet (P = 0.047, n = 29, mean difference = 0.149 (0.002–0.296 95% CI), paired Welch’s two-sided t-test). g, Microbiota revert to a state more closely resembling baseline samples after VLCD, as measured through species-level metagenomic assignments (P = 0.012, n = 15–29 per group per time point; Supplementary Table 3, two-sided Wilcoxon signed rank test). h, Metagenomic species whose abundances are altered by VLCD demonstrate rapid reversion during the conventional diet and maintenance phases (30 most important genera by GINI coefficient displayed). i, Volcano plots of differentially abundant KOs by contrast in metagenome data. VLCD demonstrates the largest effect size with apparent reversion when contrasted against CONVD. Effects of CONVD compared to baseline and cross-group comparisons yield minimal significant results. Statistical analysis was conducted using Limma. j, The BCAAs leucine and isoleucine are significantly reduced during VLCD (nintervention = 18, ncontrol = 10 participants per time point, Wilcoxon signed-rank test). k, The butanoyl-CoA:acetoacetate CoA-transferase α-subunit, an enzyme that catalyses the final step in the production of butyrate, is increased in relative abundance during VLCD (FDR Q = 0.032, Limma; Supplementary Table 4). l, Normalizing relative abundance of butanoyl-CoA:acetoacetate CoA-transferase with qPCR quantification of 16S rRNA gene copies to infer the absolute number of genome equivalents (GE) in the sample demonstrates a decrease in the absolute abundance of the enzyme family (P = 0.017, LMM with Tukey two-sided all-pair comparison). Sample size in number of participants for k, l: nbaseline = 29, nVLCD = 24, nCONVD = 29, nMAINT = 28. In boxplots: centre line, median; box, first and third quartiles; whiskers, 1.5× interquartile range (IQR) with outliers individually plotted.
Extended Data Fig. 3 Diet-dependent changes in the microbiome are maintained following transplantation to GF mice.
a, Distribution of weight loss in diet intervention participants identified the five individuals that lost the most weight for transplantation of stool samples to GF C57BL/6J mice. b, Experiment design and microbiota sampling times. c, Differential 16S rRNA ASV abundances in human donors and recipient mice demonstrate 58 candidate effectors of the weight-loss phenotype (zero-inflated beta regression model with random effects (ZIBR), FDR Q < 0.1, Supplementary Table 6). Note: taxonomy assigned using SILVA 123 with Peptoclostridium difficile synonymous for C. difficile. d, Functional differences between pre- and post-diet recipient communities by enrichment of KEGG functional pathways based on inferred gene content from amplicon sequencing (PICRUSt, Supplementary Table 7). Comparison of groups predicts altered amino acid, carbohydrate, and SCFA metabolic function. Central nodes represent KEGG pathways significantly enriched by their constituent significant differentially abundant KOs, shown with fold-change (colour) and FDR value (size) indicated (FDR Q < 0.1, LMM). e, Detection of C. difficile and TcdA/TcdB by endpoint PCR, ELISA, and selective and differential culture demonstrates active toxin production in post-diet mice at time of death.
a, Replication of experiment with transplantation of stool samples from five participants who lost most weight during conventional diet demonstrates significant differences in body weight between mice that received pre-diet and post-diet samples (Fig. 2b); there are no significant differences in body fat (b) or food consumption (c; npre-diet = 7, npost-diet = 8 mice, LMM). Each point in c represents the measurement for a single mouse on a single day. d, Transplantation of pooled faecal samples from five participants who lost most weight during VLCD reveals significantly more weight loss in post-diet sample recipient mice (LMM). e, f, Post-diet recipient mice also show a trend towards reduced body fat (e) and improved OGTT (f; P = 0.18 and P = 0.43 respectively, two-sided Mann–Whitney U test). In d–f, npre-diet = 5, npost-diet = 6 mice. g, Food intake was not significantly different between pre- and post-diet recipient mice over time or between groups (P = 0.70, LMM npost-diet = 6, npre-diet = 5 mice measured over 16 time points as in d). h, i, Transplantation of pooled faecal samples from the median four weight losers revealed a small but significant effect on weight gain in recipient mice (h; LMM, npre-diet = 13, npost-diet = 12 mice per time point) with an associated reduction in body fat as measured by epididymal fat pad weight (i; P = 0.036, one-tailed Welch’s t-test). j, This cohort showed no significant differences in OGTT results (AUC, P = 0.650, two-sided Mann–Whitney U test). Data shown as mean ± s.e.m. where relevant. TcdA/B ELISA demonstrates a lack of stable C. difficile colonization in k (CONVD) and l (VLCD) replication experiments (ELISA reactions shown for individual animals at days 4 and 20 after colonization, respectively). LMM with participant as random effect and Tukey two-sided all-pair comparison unless otherwise noted. In boxplots: centre line, median; box, first and third quartiles; whiskers, 1.5× interquartile range (IQR) with outliers individually plotted.
a, JBZPo1 was assembled with 255-fold coverage (Illumina MiSeq 250) into 120 contigs (inner grey track; N50 = 87,201 bp) with an average GC content of 28.6% (green–purple centre track showing GC content), and 3,777 coding sequences (outer red and blue tracks displaying positive and negative strands, respectively). b, The C. difficile pathogenicity locus (PaLoc) encodes both toxin A (tcdA) and B (tcdB). c, The binary toxin (cdt) locus (CdtLoc) does not encode intact binary toxin. d, Phylogenetic tree of 717 C. difficile genomes and associated virulence factor carriage places JBZPo1 flanked by Ribotype 014-20 strains and separate from the hypervirulent epidemic NAP1/B1/027 strains (for example, R20291).
a, Experimental design relating to JBZPo1 transplantation experiment (Fig. 3b). b, Establishment of colonization with C. difficile JBZPo1 did not lead to dehydration as determined by hydration ratio (hydration ratio = [total body water − free water]/lean mass; P = 0.59, two-sided Mann–Whitney U test). Centre line, median; box, first and third quartiles; whiskers, 1.5× interquartile range (IQR) with outliers individually plotted. c, Body composition analysis revealed a significant difference between humanized vehicle control and C. difficile colonized mice at the end of the experiment (P < 0.001, LMM with Tukey’s two-sided all-pair comparison, n = 6 mice per group), which suggests that C. difficile caused increased adiposity. d, Quantification of JBZPo1 in recipient mice (P = 0.003–0.006, n = 6 mice per group per time point except n = 5 mice at 8 days in humanized control owing to missing sample; two-sided Mann–Whitney U test). Data shown as mean ± s.e.m. e, ELISA of caecal contents from recipient mice confirmed production of TcdA/TcdB only in JBZPo1 recipient mice at end of experiment. f, Blinded pathological analysis revealed minor neutrophil infiltration with reactive changes (two-sided Mann–Whitney U test, n = 6 mice per group).
Extended Data Fig. 7 Diet-induced changes in microbiota influence C. difficile-associated weight loss and Toxin B expression.
a, Experimental design. b, Weight loss over time demonstrates a significant effect of diet of donor (n = 7–8 mice per group per day as in a; P = 5.9 × 10−5, estimate = −4.6% (−6.6 to −2.7 95% CI) for VLCD, P = 1.5 × 10−7, estimate = −6.6% (−8.5 to −4.6 95% CI) for CONVD). c, Mice colonized with stool samples from donors on CONVD showed an increase in TcdB expression over baseline 2 days after colonization (P = 0.003, n = 7–8 mice per group as in a, Kruskal–Wallis with two-sided Dunn’s test). d, C. difficile carriage did not differ significantly among groups (suggesting no modulation of virulence), with the exception of 8 days after colonization in CONVD-recipient mice (P = 0.017 CONVD versus baseline; n = 7–8 mice per group as in a; two-sided Mann–Whitney U test). Data shown as mean ± s.e.m. in c, d. e, Concentrations of key bile acids in P50 in response to diet (n = 1 participant per time point). LMM with participant as random effect and Tukey two-sided all-pair comparison unless otherwise noted.
a, Experimental design relating to C. difficile 630 toxin-deficient mutant transplantation experiment (Fig. 3g). b, Colonization with TcdA/B+ strains does not lead to dehydration in GF animals and increases hydration in humanized animals (P = 0.06 and P = 0.0016 respectively, Kruskal–Wallis with two-sided Dunn’s test). c, Caecal C. difficile colonization level is not significantly different between strains (P = 0.37 and P = 0.11 for GF and humanized mice, respectively, Kruskal–Wallis with two-sided Dunn’s test), but is altered in mono-colonization versus humanized mice. d, ELISA of caecal contents confirms production of toxin only in C. difficile 630 ∆erm mutants. e, Dense neutrophil and lymphocyte infiltration along with moderate epithelial hyperplasia and goblet cell loss due to TcdA+ TcdB+ C. difficile irrespective of colonization background (Kruskal–Wallis test with two-sided Dunn’s post hoc test). In b–e, n = 5–6 mice per group as in a.
Extended Data Fig. 9 Metabolomic profiling of the five participants who lost the most weight supports a working model for the effect of caloric restriction on colonization resistance.
a, b, BCAAs (a) and SCFAs (b) were decreased during VLCD and CONVD relative to baseline in the five participants who lost the most weight (n = 5 individuals per time point; mean ± s.e.m.). c, Significant differences in bile acid levels between baseline and VCLD phases in these individuals implicate altered bile acid profiles in permissibility to C. difficile (n = 5 individuals per time point). a–c, LMM with Tukey’s two-sided all-pair comparison. d, Working model for the complex interactions between caloric restriction, the gut microbiome, and C. difficile. We propose that caloric restriction decreases host production of primary bile acids, including cholic acid, while also lowering total gut microbial colonization and altering the gut microbial community structure. Together, these effects lead to decreased production of the C. difficile-inhibitory deoxycholic acid, which allows expansion of C. difficile, which, in turn, disrupts host energy balance. Notably, our data also support the existence of C. difficile-independent mechanisms for weight loss owing to the restructuring of the gut microbiome following caloric restriction. e, Representative culture plate showing presumptive C. difficile colonies with characteristic yellow appearance and filamentous edges.
About this article
Cite this article
von Schwartzenberg, R.J., Bisanz, J.E., Lyalina, S. et al. Caloric restriction disrupts the microbiota and colonization resistance. Nature 595, 272–277 (2021). https://doi.org/10.1038/s41586-021-03663-4