Abstract
The gut microbiome changes with age and has been proposed to mediate the benefit of lifespan-extending interventions such as dietary restriction. However, the causes and consequences of microbiome ageing and the potential of such interventions remain unclear. Here we analysed 2,997 metagenomes collected longitudinally from 913 deeply phenotyped, genetically diverse mice to investigate interactions between the microbiome, ageing, dietary restriction (caloric restriction and fasting), host genetics and a range of health parameters. Among the numerous age-associated microbiome changes that we find in this cohort, increased microbiome uniqueness is the most consistent parameter across a second longitudinal mouse experiment that we performed on inbred mice and a compendium of 4,101 human metagenomes. Furthermore, cohousing experiments show that age-associated microbiome changes may be caused by an accumulation of stochastic environmental exposures (neutral theory) rather than by the influence of an ageing host (selection theory). Unexpectedly, the majority of taxonomic and functional microbiome features show small but significant heritability, and the amount of variation explained by host genetics is similar to ageing and dietary restriction. We also find that more intense dietary interventions lead to larger microbiome changes and that dietary restriction does not rejuvenate the microbiome. Lastly, we find that the microbiome is associated with multiple health parameters, including body composition, immune components and frailty, but not lifespan. Overall, this study sheds light on the factors influencing microbiome ageing and aspects of host physiology modulated by the microbiome.
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Data availability
We generated the following fastq files, which are available in Sequence Read Archive: DRiDO metagenomic sequencing, PRJNA1054518; longitudinal B6 metagenomic sequencing, PRJNA1073968; 16S sequencing of the cohousing experiment, PRJNA1072097; and 16S sequencing of cohousing in the germ-free mice experiment, PRJNA1128683. Processed data are available via GitHub at https://github.com/levlitichev/DRiDO_microbiome. Host phenotypes collected as part of the DRiDO study are available via Figshare at https://doi.org/10.6084/m9.figshare.24600255.v1. The genetic kinship matrix and genotype probabilities are available via Figshare at https://doi.org/10.6084/m9.figshare.13190735. Other databases used in this study include the mm10 genome (http://igenomes.illumina.com.s3-website-us-east-1.amazonaws.com/Mus_musculus/UCSC/mm10/Mus_musculus_UCSC_mm10.tar.gz), MetaPhlan (http://cmprod1.cibio.unitn.it/biobakery4/metaphlan_databases/) and HUMAnN databases (http://huttenhower.sph.harvard.edu/humann_data/chocophlan/full_chocophlan.v201901_v31.tar.gz, http://huttenhower.sph.harvard.edu/humann_data/uniprot/uniref_annotated/uniref90_annotated_v201901b_full.tar.gz), MGBC Kraken2 database (https://github.com/BenBeresfordJones/MGBC), curatedMetagenomicData (https://doi.org/10.18129/B9.bioc.curatedMetagenomicData) and SILVA 138 (https://www.arb-silva.de/documentation/release-138/).
Code availability
All figures in this paper may be reproduced using code (and processed data) available via GitHub at https://github.com/levlitichev/DRiDO_microbiome. This GitHub repository also contains an example analysis tutorial.
References
Green, C. L., Lamming, D. W. & Fontana, L. Molecular mechanisms of dietary restriction promoting health and longevity. Nat. Rev. Mol. Cell Biol. 23, 56–73 (2022).
Fontana, L. & Partridge, L. Promoting health and longevity through diet: from model organisms to humans. Cell 161, 106–118 (2015).
Liao, C.-Y., Rikke, B. A., Johnson, T. E., Diaz, V. & Nelson, J. F. Genetic variation in the murine lifespan response to dietary restriction: from life extension to life shortening. Aging Cell 9, 92–95 (2010).
Mitchell, S. J. et al. Effects of sex, strain, and energy intake on hallmarks of aging in mice. Cell Metab. 23, 1093–1112 (2016).
Pak, H. H. et al. Fasting drives the metabolic, molecular and geroprotective effects of a calorie-restricted diet in mice. Nat. Metab. 3, 1327–1341 (2021).
Mitchell, S. J. et al. Daily fasting improves health and survival in male mice independent of diet composition and calories. Cell Metab. 29, 221–228.e3 (2019).
Acosta-Rodríguez, V. et al. Circadian alignment of early onset caloric restriction promotes longevity in male C57BL/6J mice. Science 376, 1192–1202 (2022).
Di Francesco, A. et al. Dietary restriction impacts health and lifespan of genetically diverse mice. Nature 634, 684–692 (2024).
Bárcena, C. et al. Healthspan and lifespan extension by fecal microbiota transplantation into progeroid mice. Nat. Med. 25, 1234–1242 (2019).
Smith, P. et al. Regulation of life span by the gut microbiota in the short-lived African turquoise killifish. eLife 6, e27014 (2017).
Kim, K. H. et al. Gut microbiota of the young ameliorates physical fitness of the aged in mice. Microbiome 10, 238 (2022).
Fabbiano, S. et al. Functional gut microbiota remodeling contributes to the caloric restriction-induced metabolic improvements. Cell Metab. 28, 907–921.e7 (2018).
Jie, Z. et al. The baseline gut microbiota directs dieting-induced weight loss trajectories. Gastroenterology 160, 2029–2042.e16 (2021).
Diener, C. et al. Baseline gut metagenomic functional gene signature associated with variable weight loss responses following a healthy lifestyle intervention in humans. mSystems 6, e00964-21 (2021).
Nielsen, R. L. et al. Data integration for prediction of weight loss in randomized controlled dietary trials. Sci. Rep. 10, 20103 (2020).
Langille, M. G. et al. Microbial shifts in the aging mouse gut. Microbiome 2, 50 (2014).
Low, A., Soh, M., Miyake, S. & Seedorf, H. Host age prediction from fecal microbiota composition in male C57BL/6J mice. Microbiol. Spectr. 10, e00735-22 (2022).
Zhang, C. et al. Structural modulation of gut microbiota in life-long calorie-restricted mice. Nat. Commun. 4, 2163 (2013).
Odamaki, T. et al. Age-related changes in gut microbiota composition from newborn to centenarian: a cross-sectional study. BMC Microbiol. 16, 90 (2016).
Biagi, E. et al. Through ageing, and beyond: gut microbiota and inflammatory status in seniors and centenarians. PLoS ONE 5, e10667 (2010).
Johansen, J. et al. Centenarians have a diverse gut virome with the potential to modulate metabolism and promote healthy lifespan. Nat. Microbiol. 8, 1064–1078 (2023).
Zhang, X. et al. Sex- and age-related trajectories of the adult human gut microbiota shared across populations of different ethnicities. Nat. Aging 1, 87–100 (2021).
Badal, V. D. et al. The gut microbiome, aging, and longevity: a systematic review. Nutrients 12, 3759 (2020).
Martino, C. et al. Microbiota succession throughout life from the cradle to the grave. Nat. Rev. Microbiol. 20, 707–720 (2022).
Rothschild, D. et al. An atlas of robust microbiome associations with phenotypic traits based on large-scale cohorts from two continents. PLoS ONE 17, e0265756 (2022).
Ghosh, T. S., Shanahan, F. & O’Toole, P. W. Toward an improved definition of a healthy microbiome for healthy aging. Nat. Aging 2, 1054–1069 (2022).
Wilmanski, T. et al. Gut microbiome pattern reflects healthy ageing and predicts survival in humans. Nat. Metab. 3, 274–286 (2021).
Rothschild, D. et al. Environment dominates over host genetics in shaping human gut microbiota. Nature 555, 210–215 (2018).
Carmody, R. N. et al. Diet dominates host genotype in shaping the murine gut microbiota. Cell Host Microbe 17, 72–84 (2015).
Kurilshikov, A. et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat. Genet. 53, 156–165 (2021).
Goodrich, J. K. et al. Genetic determinants of the gut microbiome in UK twins. Cell Host Microbe 19, 731–743 (2016).
Gacesa, R. et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature 604, 732–739 (2022).
Turpin, W. et al. Association of host genome with intestinal microbial composition in a large healthy cohort. Nat. Genet. 48, 1413–1417 (2016).
Bonder, M. J. et al. The effect of host genetics on the gut microbiome. Nat. Genet. 48, 1407–1412 (2016).
Grieneisen, L. et al. Gut microbiome heritability is nearly universal but environmentally contingent. Science 373, 181–186 (2021).
Thaiss, C. A. et al. Persistent microbiome alterations modulate the rate of post-dieting weight regain. Nature 540, 544–551 (2016).
Sonnenburg, J. L. & Bäckhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).
Turnbaugh, P. J. et al. An obesity-associated gut microbiome with increased capacity for energy harvest. Nature 444, 1027–1031 (2006).
Lobo, A. K. et al. Identification of sample mix-ups and mixtures in microbiome data in diversity outbred mice. G3 11, jkab308 (2021).
Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).
Beresford-Jones, B. S. et al. The Mouse Gastrointestinal Bacteria Catalogue enables translation between the mouse and human gut microbiotas via functional mapping. Cell Host Microbe 30, 124–138.e8 (2022).
Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10, e65088 (2021).
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases. Nucleic Acids Res. 42, D459–D471 (2014).
Huang, S. et al. Human skin, oral, and gut microbiomes predict chronological age. mSystems 5, e00630-19 (2020).
Galkin, F. et al. Human gut microbiome aging clock based on taxonomic profiling and deep learning. iScience 23, 101199 (2020).
Chen, Y. et al. Human gut microbiome aging clocks based on taxonomic and functional signatures through multi-view learning. Gut Microbes 14, 2025016 (2022).
Pasolli, E. et al. Accessible, curated metagenomic data through ExperimentHub. Nat. Methods 14, 1023–1024 (2017).
Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (MPB-32) (Princeton Univ. Press, 2001).
Sherrill-Mix, S. et al. Allometry and ecology of the bilaterian gut microbiome. mBio https://doi.org/10.1128/mbio.00319-18 (2018).
Sieber, M. et al. Neutrality in the metaorganism. PLoS Biol. 17, e3000298 (2019).
Schlamp, F. et al. High-resolution QTL mapping with diversity outbred mice identifies genetic variants that impact gut microbiome composition. Preprint at bioRxiv https://doi.org/10.1101/722744 (2021).
Kok, D. E. G. et al. Lifelong calorie restriction affects indicators of colonic health in aging C57Bl/6J mice. J. Nutr. Biochem. 56, 152–164 (2018).
Pan, F. et al. Predominant gut Lactobacillus murinus strain mediates anti-inflammaging effects in calorie-restricted mice. Microbiome 6, 54 (2018).
Fraumene, C. et al. Caloric restriction promotes rapid expansion and long-lasting increase of Lactobacillus in the rat fecal microbiota. Gut Microbes 9, 104–114 (2017).
Kurup, K. et al. Calorie restriction prevents age-related changes in the intestinal microbiota. Aging 13, 6298–6329 (2021).
Zeng, T. et al. Short-term dietary restriction in old mice rejuvenates the aging-induced structural imbalance of gut microbiota. Biogerontology 20, 837–848 (2019).
Depommier, C. et al. Pasteurized Akkermansia muciniphila increases whole-body energy expenditure and fecal energy excretion in diet-induced obese mice. Gut Microbes 11, 1231–1245 (2020).
Meyer, D. H. & Schumacher, B. Aging clocks based on accumulating stochastic variation. Nat. Aging 4, 871–885 (2024).
Svenson, K. L. et al. High-resolution genetic mapping using the mouse diversity outbred population. Genetics 190, 437–447 (2012).
Perea, C. et al. Caloric restriction in group-housed mice: littermate and sex influence on behavioral and hormonal data. Front. Vet. Sci. 8, 639187 (2021).
Clarke, E. L. et al. Sunbeam: an extensible pipeline for analyzing metagenomic sequencing experiments. Microbiome 7, 46 (2019).
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26, 589–595 (2010).
Blanco-Míguez, A. et al. Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. Nat. Biotechnol. 41, 1633–1644 (2023).
Wagner, G. P., Kin, K. & Lynch, V. J. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci. 131, 281–285 (2012).
Oksanen, J. et al. vegan: Community Ecology Package. (2022).
Wilson, A. J. et al. An ecologist’s guide to the animal model. J. Anim. Ecol. 79, 13–26 (2010).
Wright, K. M. et al. Age and diet shape the genetic architecture of body weight in diversity outbred mice. eLife 11, e64329 (2022).
Almasy, L. & Blangero, J. Variance component methods for analysis of complex phenotypes. Cold Spring. Harb. Protoc. 2010, pdb.top77 (2010).
Gilmour, A. R. et al. ASReml User Guide Release 4.1 Structural Specification (VSN, 2015).
Mallick, H. et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 17, e1009442 (2021).
Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 1–26 (2017).
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
Hamady, M. & Knight, R. Microbial community profiling for human microbiome projects: tools, techniques, and challenges. Genome Res. 19, 1141–1152 (2009).
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).
Robeson, I. I. et al. RESCRIPt: reproducible sequence taxonomy reference database management. PLoS Comput. Biol. 17, e1009581 (2021).
Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90 (2018).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Ziyatdinov, A. et al. lme4qtl: linear mixed models with flexible covariance structure for genetic studies of related individuals. BMC Bioinformatics 19, 68 (2018).
Camarinha-Silva, A. et al. Host genome influence on gut microbial composition and microbial prediction of complex traits in pigs. Genetics 206, 1637–1644 (2017).
Wallace, R. J. et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci. Adv. 5, eaav8391 (2019).
Li, F. et al. Host genetics influence the rumen microbiota and heritable rumen microbial features associate with feed efficiency in cattle. Microbiome 7, 92 (2019).
Zhang, G. et al. Intermittent fasting and caloric restriction interact with genetics to shape physiological health in mice. Genetics 220, iyab157 (2022).
Broman, K. W. et al. R/qtl2: software for mapping quantitative trait loci with high-dimensional data and multiparent populations. Genetics 211, 495–502 (2019).
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. mediation: R package for causal mediation analysis. J. Stat. Softw. 59, 1–38 (2014).
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).
Acknowledgements
We thank the members of the Thaiss and Levy laboratories for valuable discussions and feedback. For their assistance with sequencing, we thank M. Roy and T. Vijay at the genomics core at Calico Life Sciences LLC, J. Schug at the Penn Genomics and Sequencing Core Facility (RRID:SCR_024999) and A. M. Misic at Illumina, Inc. For their assistance with animal husbandry, we thank University Laboratory Animal Resources (ULAR) at the University of Pennsylvania, D. Kobuley and M. Albright for dedicated germ-free husbandry, and the animal husbandry team at The Jackson Laboratory. This work was in part supported by Calico Life Sciences LLC. L.L. was supported by the Blavatnik Family Fellowship in Biomedical Research and T32HG000046. E.R.D. was supported by NIH R35GM146980. G.A.C. was supported by NIH P30AG038070. M. Li was supported by NIH R01GM125301, R01EY030192, R01EY031209, R01HL113147 and R01HL150359. C.A.T. is a Pew Biomedical Scholar and a Burroughs Wellcome Fund Investigator in the Pathogenesis of Infectious Diseases, and is supported by NIH DP2-AG-067492, R01-DK-129691, R01-NS-134976 and DP1-DK-140021, the Kenneth Rainin Foundation, a McKnight Brain Research Foundation Innovator Award, the Human Frontier Science Program and the Penn Institute on Aging.
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L.L.: investigation, formal analysis, data curation, writing—original draft and writing—review and editing. M. Considine: investigation. J.G.: investigation. V.S.: investigation. T.O.C.: investigation. H.C.D.: investigation. K.M.W.: software and formal analysis. K.R.A.: software and formal analysis. L.D.: validation. M.J.L.: validation. M.T.: investigation. M.R.G.-F.: investigation. A.C.W.: validation. P.L.: validation. J.K.: validation. G.T.U.: validation. R.J.R.: investigation. S.M.: investigation. C.M.: formal analysis. F.D.B.: supervision. A.R.: software. F.H.: formal analysis. Z.C.: formal analysis. G.V.P.: formal analysis. M.M.: formal analysis. A.G.D.: data curation. L.R.: investigation and data curation. C.T.: software. K.B.: supervision. M. Chakraborty: software. A.S.B.: supervision. H.L.: methodology and supervision. I.B.: methodology. E.R.D.: methodology. K.W.B.: methodology. M. Levy: supervision. R.L.C.: conceptualization and funding acquisition. D.B.: conceptualization and funding acquisition. A.F.: conceptualization, supervision and project administration. A.D.F.: supervision and project administration. G.A.C.: formal analysis, data curation and supervision. M. Li: methodology and supervision. C.A.T.: supervision, funding acquisition, writing—original draft and writing—review and editing.
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K.M.W., A.R., F.H., Z.C., G.V.P., M.M., R.L.C., D.B., A.F. and A.D.F. are current or former employees of Calico Life Sciences LLC. The other authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Positive and negative controls.
a, Number of read pairs per sample (prior to aggregation), grouped by sample type (n = 3577 samples prior to aggregation by stool ID). Boxes extend from the 25th to 75th percentiles, whiskers extend to 1.5 times the interquartile range, and the center line is the median. b, PCoA of all samples prior to aggregation. Two positive controls (BZIZNTZA and JVOMNOOB, highlighted in red) clustered separately from the other positive controls. PCoA1 and PCoA2 explain 30% and 10% of overall variance, respectively. c, Species-level relative abundances (MetaPhlAn4) for positive controls. Two positive controls (BZIZNTZA and JVOMNOOB, highlighted in red) did not display the expected community composition. d, PCoA of non-control samples prior to aggregation. e, Same ordination as d, with lines connecting samples originating from the same DNA. f, Same ordination as d, with lines connecting samples in which a library was sequenced multiple times.
Extended Data Fig. 2 Identifying sample mix-ups.
a, Sample mix-ups were identified by comparing host reads from each microbiome sample against all host genotypes (we term this pipeline “mbmix”). For more details, see Supplementary Note 1. b, Example of concordance between a microbiome sample and the expected host genotype. The x-axis is each host genotype, the y-axis is the proportion of single nucleotide polymorphisms (SNPs) that were discordant between the microbiome sample and the host genome. c, Example of discordance. Microbiome sample DO_20_1188_021w was supposed to originate from mouse DO-20-1188, but it appears to have come from DO-2D-4188. d, Best proportion discordant SNPs versus proportion of discordant SNPs against the expected genotype. The fate of each sample is indicated by its color: kept (green), discarded (red), or renamed (blue). e, Plate view of mbmix categorization. Each sub-panel is a “final plate”, a 96-well plate of libraries prior to pooling. White regions either didn’t contain a sample, contained a sample that obtained no reads (for example, left half of final plate 31), contained a sample whose mouse did not have a genotype, or contained a control sample. f, Proportion discordant SNPs for stool samples from mice DO-AL-0097 and DO-AL-0105. Samples from 44 weeks were concordant with the expected mouse genotype. All other samples from mouse DO-AL-0097 appeared to come from mouse DO-AL-0105, except DO_AL_0097_148w, which was inconclusive. The two other samples from mouse DO-AL-0105 appeared to come from DO-AL-0097. For discordant results, the mouse with the lowest proportion of discordant SNPs is colored red. g, Body weight for mice DO-AL-0097 and DO-AL-0105. The vertical dashed line at 56 weeks represents the likely time that these mice were swapped in their cages.
Extended Data Fig. 3 Quality-control and details related to taxonomic and functional classification.
a, Histogram of all pairwise sample distances (Bray-Curtis on relative abundances). Distances involving any of 13 outlier samples are shown in red. b, Percentage of reads that could be classified for non-control samples using either Kraken2+MGBC or MetaPhlAn4. Mean percent classified indicated in black text. c, Difference between Kraken2+MGBC and MetaPhlAn4 genus-level relative abundances for the 41 genera present in both taxonomic databases. Each horizontal line shows the mean ± standard deviation across all 2997 non-control samples. d, Examples of community-wide and specialized pathways. The largest correlations for the specialized pathway (PWY-8004) were with Lactobacillus and Limosilactobacillus. e, PCA plot based on microbial pathways (n = 2997 metagenomes). PC1 and PC2 explain 21% and 8% of overall variance, respectively. For the boxplots, boxes extend from the 25th to 75th percentiles, whiskers extend to 1.5 times the interquartile range, and the center line is the median. For more details, see Fig. 1 legend.
Extended Data Fig. 4 Effect of sampling timepoint.
a, Timeline of stool collection. X-axis shows the day of stool collection, with the first day of the overall experiment as day 1. Y-axis indicates the age of a mouse when a stool sample was collected. The color of each circle corresponds to one of 12 DO breeding cohorts that were sequentially entered into the study. The size of each circle corresponds the number of samples collected for each cohort-age combination. Gray rectangles correspond to three cross-sectional data slices used in later analyses. b, Number of genera associated (conditional Wald test, Benjamini-Hochberg adjusted p-value < 0.01) with age using linear mixed models that included sampling timepoint as a fixed effect (Model 2), random effect (Model 1), or not at all (Model 3). c, Correlations between age coefficients calculated using cross-sectional (columns) and longitudinal (rows) models. Blue line represents the line of best fit and 95% confidence interval (linear regression). Spearman correlation (ρ) indicated above each scatterplot. Black dashed line at y = x represents perfect agreement between two models.
Extended Data Fig. 5 Additional details related to age-associated microbiome changes.
a, Uniqueness increases with age even when the number of mice per cage is kept fixed. For various n, cages with at least n mice at that age were considered. If the number of mice was greater than n, then n mice were randomly chosen. Uniqueness was then recomputed on this subset of samples. b, ɑ-diversity (as measured by Shannon and Simpson indexes) appears to increase with host age (n = 2988 metagenomes with age ≤ 40 months), but this trend is not significant (Model 1, conditional Wald test, Benjamini-Hochberg adjusted p-value > 0.01). c, Fraction of features associated (Model 1, conditional Wald test, Benjamini-Hochberg adjusted p-value < 0.01) with age when using genus-level or species-level data. d, Uniqueness increases with age when using species-level relative abundances (n = 2988 metagenomes). e, Comparison of age coefficients calculated using Kraken2 or MetaPhlAn taxonomic results. Diagonal dashed line at y = x represents perfect agreement. Spearman correlation (ρ) and p-value are indicated above the plot. f, Effect of age on microbial pathways. Age coefficients and standard errors were calculated with Model 1. p-values were calculated with a conditional Wald test and adjusted with the Benjamini-Hochberg procedure. g, Glycolysis IV (PWY − 1042) decreases with age (n = 2988 metagenomes). h, Correlations between PWY − 1042 and all genera. PWY − 1042 is a community-wide pathway because it has no correlations above 0.5. i, L-lysine biosynthesis II (DAPLYSINESYN-PWY) increases with age (n = 2988 metagenomes). j, DAPLYSINESYN-PWY is a specialized pathway because it is highly correlated with Bifidobacterium. k, Functional uniqueness increases with age (n = 2988 metagenomes). l-n, Age prediction based on 10-fold cross validation. Green line represents the line of best fit and 95% confidence intervals (linear regression). Black dashed line at y = x represents perfect accuracy. MAE = mean absolute error. l, Prediction considering all mice, rather than just AL mice. m, Prediction using species-level relative abundances in AL mice. n, Prediction using pathway log2(TPM) abundances in AL mice. o, Top 10 most important pathways for age prediction (just AL mice, n = 573 metagenomes). Each dot is one of 10 cross-validation folds. X-axis shows the percent increase in mean squared error (MSE) when that particular pathway is excluded from a tree within the random forest regressor. In a, b, d, g, i, and k, data are presented as mean ± SEM.
Extended Data Fig. 6 Additional details related to universal age-associated microbiome changes.
a, Percentage of pathways associated with age (Models 5-7, Benjamini-Hochberg adjusted p-value < 0.1) within each dataset. b-d, Associations with age within human studies. Coefficients, standard errors, and p-values were calculated with Model 8, and p-values were adjusted with the Benjamini-Hochberg procedure. Adjusted p-values < 0.1 are shown in green. The number of individuals per study indicated in b is the same as in c and d. b, Uniqueness tends to increase with age in most human studies, including the largest (LifeLinesDeep_2016). c, Blautia appears to increase with age in some studies and decrease with age in others, and when regressing against age separately per study, no studies have an adjusted p-value < 0.1. d, ɑ-diversity versus age, separately per human study. p-values were adjusted separately per metric. e, Comparison of age-associated functional changes across datasets. Each pairwise comparison shows all features that passed prevalence filtration in both datasets. Line of best fit and 95% confidence interval shown in gray. Spearman correlation and corresponding p-value shown above each plot. Features associated with age and with the same sign in the pairwise comparison are shown in green. f, Flavin biosynthesis I (RIBOSYN2-PWY) decreases with age in all three datasets. Each panel includes the line of best fit and 95% confidence interval (linear regression). g, Histograms of pathway-genus correlations. For the specialized pathway (PWY-7234), the largest genus correlation is to Ligilactobacillus. h, Schematic of cohousing experiment in germ-free mice. Young germ-free mice (gray) received fecal microbiome transplants (FMTs) from young donors (Y FMT) or old donors (O FMT). Mice that received Y FMT are shown in blue, mice that received O FMT are shown in red. YGF = Y FMT recipients housed with other Y FMT recipients, OGF = O FMT recipients housed with other O FMT recipients, CYGF = Y FMT recipients cohoused with O FMT recipients, COGF = O FMT recipients cohoused with Y FMT recipients. i, PCoA (based on genus-level Bray-Curtis distances) of samples at baseline, after two weeks of cohousing, and after one month of cohousing. Ordination based on all samples shown in this plot. + denotes group centroid. In b, c, and d, data are presented as mean ± SEM.
Extended Data Fig. 7 Additional details related to microbiome heritability.
a, Heritability of pathways. Heritability was calculated with Model 1. p-values were calculated using a likelihood ratio test and adjusted with the Benjamini-Hochberg procedure. Yellow vertical dashed line shows mean heritability for heritable features. b, Histograms of pathway-genus correlations. For the specialized pathway (LACTOSECAT-PWY), the largest genus correlation is to Lactobacillus. c, Fraction of heritable (Model 1, likelihood ratio test, Benjamini-Hochberg adjusted p-value < 0.01) features when using genus-level or species-level data. d, Comparison of heritability calculated using Kraken2 or MetaPhlAn taxonomic results. Diagonal dashed line at y = x represents perfect agreement. Spearman correlation (ρ) and p-value are indicated above the plot. e, Heritability computed with lme4qtl or ASReml using the same model and data. f, Comparison of heritability estimates from a different DO mouse study (Schlamp et al. 2021). Plot shows the eight genera for which heritability was assessed in both datasets. Of these eight, the most heritable taxon in both studies was Lactobacillus (highlighted in yellow). g, Cross-sectional (Model 9) versus longitudinal (Model 1) versus downsampled longitudinal (Model 9, downsampled to 110 mice) heritability. Heritable genera (Benjamini-Hochberg adjusted p-value < 0.01) are shown in blue. The longitudinal results are the primary heritability results presented throughout the manuscript. h, Proportion of variance explained (PVE) by all experimental variables for n = 273 pathways (Model 10, p-values calculated with likelihood ratio test, adjusted with Benjamini-Hochberg). Horizontal lines show the mean PVE. i, Allele effects across ages for the top six age-specific QTL (permutation test, adjusted p-value < 0.01). QTL mapping was performed using n = 569, 513, 646, 522, and 368 samples respectively at 5, 10, 16, 22, and 28 months. Data are presented as mean ± SEM. The title above each sub-panel indicates the genus, chromosome, and genotyping marker for the QTL result. Color of each line represents the allele effect for each of eight founders comprising the Diversity Outbred genetic pool.
Extended Data Fig. 8 Additional details related to the effects of dietary restriction.
a, Effect of dietary restriction (DR) on 273 pathways. DR coefficients and standard errors were calculated with a linear mixed model (Model 1). p-values were calculated using a conditional Wald test and adjusted with the Benjamini-Hochberg procedure. b, The L-lysine biosynthesis II pathway (PWY-2941) is increased by DR (n = 2988 metagenomes with age ≤ 40 months). c, PWY-2941 is a specialized pathway, most highly correlated with Ligilactobacillus. d, The urea cycle pathway (PWY-4984) is decreased by DR (n = 2988 metagenomes). e, PWY-4984 is a community-wide pathway with no correlations with genera above 0.5. f, Absolute magnitude of DR coefficients for 273 pathways. Gray lines connect the same pathway in different dietary groups. Horizontal bars show the mean. Statistical significance evaluated by a paired t-test. g, Comparison of DR coefficients for pathways. Pearson correlation and p-value is indicated above each scatterplot. Lines of best fit and 95% confidence intervals (linear regression) are shown in purple. h, Mean CR versus mean fasting coefficients for pathways. Vertical lines highlight the difference in mean CR coefficients (red) versus mean fasting coefficients (blue). Pathways with opposite signs are opaque, while pathways with the same sign are transparent. Dashed horizontal line at 0. i, Receiver operating characteristic (ROC) curves for the prediction of binary DR using pathways, separately at each age. Each gray line is the ROC curve for one of 5 cross-validation folds. The purple line is the mean ROC curve. The diagonal dashed line at y = x represents no predictive accuracy. AUC = area under the curve. j-k. Top 10 most important genera (j) and pathways (k) for prediction of binary DR status. Each dot is one of 20 cross-validation folds (4 post-randomization ages x 5 folds per age). X-axis shows the mean decrease in accuracy, that is between trees in the random forest that do include the feature of interest and trees that do not. l, Predicting dietary group using pathways before (gray) and after (purple) initiation of DR. Each dot represents prediction accuracy in one of 10 cross-validation folds. Horizontal dashed line at 20% represents expected accuracy by chance. Statistical significance evaluated by a one-sided t-test (testing whether the mean accuracy is greater than 20%). m, Prediction accuracy stratified by dietary group using pathways. Only predictions after the start of dietary restriction were considered. n, Fraction of features associated (Model 1, conditional Wald test, Benjamini-Hochberg adjusted p-value < 0.01) with DR when using genus-level or species-level data. o, Comparison of DR coefficients calculated using Kraken2 or MetaPhlAn taxonomic results. Diagonal dashed line at y = x represents perfect agreement. Spearman correlation (ρ) and p-value are indicated above the plot. For the boxplots in b and d, boxes extend from the 25th to 75th percentiles, whiskers extend to 1.5 times the interquartile range, and the center line is the median. In b, d, f, g, and l, p-value symbols are defined as follows: ns: p ≥ 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001, ****: p < 0.0001.
Extended Data Fig. 9 Dietary restriction does not rejuvenate the microbiome.
a, Age prediction with a random forest regressor trained on pathway data from n = 573 AL samples. Vertical dashed line at six months represents start of dietary restriction, diagonal dashed line represents perfect prediction. Statistical significance evaluated by a t-test between AL (gray) and DR (purple) predictions at each age. b, Age prediction of a regressor trained on n = 623 40% CR samples and evaluated on all other samples (n = 2374), using genera (top) or pathways (bottom). Horizontal dashed line shows the actual age of samples collected at that timepoint. Boxes extend from the 25th to 75th percentiles, whiskers extend to 1.5 times the interquartile range, and the center line is the median. Statistical significance evaluated with a t-test against the AL group. c, PCoA of AL and 40% CR samples from middle-aged (10 months) and old (28 months) samples. Ordination based on just these samples. Group centroids are depicted by the four large points, along with 95% data ellipses. Arrows connect group centroids to depict the effect of age (gray) and the effect of caloric restriction (red). PCoA1 and PCoA2 explain 39% and 8% of overall variance, respectively. In a and b, p-value symbols are defined as follows: ns: p ≥ 0.05, *: p < 0.05, **: p < 0.01, ***: p < 0.001, ****: p < 0.0001.
Extended Data Fig. 10 Additional details related to microbiome-phenotype associations.
Histogram of p-values for associations between phenotypes and genera (left) or pathways (right). Associations were performed using a linear mixed model (Model 11), and p-values were calculated using a likelihood ratio test in which the null model omitted the microbiome term. Associations with a Benjamini-Hochberg adjusted p-value < 0.01 are shown in blue.
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Litichevskiy, L., Considine, M., Gill, J. et al. Gut metagenomes reveal interactions between dietary restriction, ageing and the microbiome in genetically diverse mice. Nat Microbiol 10, 1240–1257 (2025). https://doi.org/10.1038/s41564-025-01963-3
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DOI: https://doi.org/10.1038/s41564-025-01963-3
