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Metagenomic and metabolomic remodeling in nonagenarians and centenarians and its association with genetic and socioeconomic factors

An Author Correction to this article was published on 27 June 2022

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Abstract

A better understanding of the biological and environmental variables that contribute to exceptional longevity has the potential to inform the treatment of geriatric diseases and help achieve healthy aging. Here, we compared the gut microbiome and blood metabolome of extremely long-lived individuals (94–105 years old) to that of their children (50–79 years old) in 116 Han Chinese families. We found extensive metagenomic and metabolomic remodeling in advanced age and observed a generational divergence in the correlations with socioeconomic factors. An analysis of quantitative trait loci revealed that genetic associations with metagenomic and metabolomic features were largely generation-specific, but we also found 131 plasma metabolic quantitative trait loci associations that were cross-generational with the genetic variants concentrated in six loci. These included associations between FADS1/2 and arachidonate, PTPA and succinylcarnitine and FLVCR1 and choline. Our characterization of the extensive metagenomic and metabolomic remodeling that occurs in people reaching extreme ages may offer new targets for aging-related interventions.

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Fig. 1: Analysis of gut microbiota composition and structure.
Fig. 2: Analysis of gut microbial functions.
Fig. 3: Analysis of fecal metabolites.
Fig. 4: Analysis of plasma metabolites.
Fig. 5: Analysis of QTL of metagenomic and metabolomic phenotypes.
Fig. 6: Associations between socioeconomic, lifestyle and medical factors and metagenomic or metabolomic phenotypes.
Fig. 7: Significance of shared genotype–phenotype connections in two generations of longevous families.

Data availability

Data supporting the findings of this study are available in Supplementary Tables. The metagenomic sequencing and exon sequencing data have been deposited in the National Center for Biotechnology Information Sequence Read Archive database under accession numbers PRJNA613947 and PRJNA790003. The stool and plasma metabolomic raw data of the primary and validation Qidong cohorts are in Supplementary Tables 1012.

Code availability

All other analyses were performed using custom codes written in bash (v.4.2) or R (v.4.0.2). These codes are available in GitHub at https://github.com/RYWCY/Longevous.git.

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Acknowledgements

This work was supported by funding from the National Natural Science Foundation of China grant nos. 81730102 (H.L.Q.), 81230057 (H.L.Q.) and 31970111 (N.Q.). We thank B. Zhou, P. He, Z. Cai, T. Liu, Y. Li and Y. Luan at Realbio Genomics Institute for sequencing and data analysis, Q. Yang at Shanghai Tenth Hospital for sample collections and J. Zhang, Y. Fang, J. Zhou and M. Liang at Realbio Genomics Institute for graphic assistance. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

H.L.Q. conceptualized and led the project. H.L.Q., Q. X. and N.Q. designed the study. C.Y.W. led data analyses. Q.X., C.Y.W. and Q.Z. wrote the manuscript. M.V.C. and N.S. critiqued the original manuscript and provided constructive suggestions. S.D.E. contributed to study design. Q.Z., J.Q.L., J.Z., F.Y. and J.F. organized a collection of stool and blood samples, questionnaire survey and documentation of metadata. L.S.H., L.L.D., X.H.Z., Y.H.Z., Y.F.Z., L.L., Y.Q.L., Y.S.C., Y.S., J.W., Z.C. and C.S.F. assisted sample collection, questionnaire survey, documentation of clinical measures and compilation of data. R.Y.G., F.Y. and L.S.H. organized metabolomic analyses. X.X., M.M.B. and X.C. performed data compilation and analyses. All authors read the manuscript and offered valuable suggestions.

Corresponding authors

Correspondence to Nan Qin or Huanlong Qin.

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The authors declare no competing interests.

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Nature Aging thanks Dario Riccardo Valenzano and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Differential gut microbiota species between the advanced age group from Qidong longevous cohort and unrelated CMD healthy controls age-matched with the offspring.

The unrelated healthy controls were 589 healthy controls at ages of 50–75 years (matched with the offspring group) from 26 metagenomic projects deposited in CuratedMetagenomicData (CMD). The boxes represent the interquartile range (IQR), from the first and third quartiles and the inside line represents the median. The whiskers denote the lowest and highest values within 1.5 IQR from the first and third quartiles. The circles represent outliers beyond the whiskers.

Source data

Extended Data Fig. 2 Prevalence distributions of all species of Longevous Gut Microbiota Signature (LGMS) in several advanced age groups and non-advanced age groups.

The advanced age groups included that from Qidong, China and Sardinia, Italy, whereas the non-advanced age groups included the offspring group of Qidong, China, non-advanced age group of Sardinia, Italy (< 80 YO), and 589 CMD healthy controls at ages of 50–75 years.

Source data

Extended Data Fig. 3 Analysis of gut microbiome functions.

a, Comparison of types and RPKM (reads per kilobase per million reads) values of antibiotic resistance genes. b, Differential antibiotic resistance genes between the advanced age (N=116) and offspring (N=232) groups (Wilcoxon rank-sum test, FDR < 0.05). Data are presented as mean values ± SD, indicated by error bars). c and d, The 10 most abundant antibiotic resistance targets (c) and types (d) across advanced age (orange) and offspring (blue) samples. e and f, Highly differential KOs between the two generations (Wilcoxon rank-sum test, FDR < 5 × 10-5) that have (e) or have no (f) Metacyc pathway annotations. Top genus-level contributors to differential KOs in 6 major Metacyc pathways are shown. g, Age-stable KOs between the two generations (Wilcoxon rank-sum test, FDR > 0.1 and ICC < 0). The boxes (a, c and d) represent the interquartile range (IQR), from the first and third quartiles, and the inside line represents the median. The whiskers denote the lowest and highest values within 1.5 IQR from the first and third quartiles. The circles represent outliers beyond the whiskers.

Source data

Extended Data Fig. 4 The most differential KOs between the advanced age (N=116) and offspring (N=232) groups (FDR < 5 × 10-5 and fold change > 2) and their taxonomic contributors.

Fold change is indicated at the top of each scatter plot. The boxes represent the interquartile range (IQR), from the first and third quartiles, and the inside line represents the median. The whiskers denote the lowest and highest values within 1.5 IQR from the first and third quartiles. The circles represent outliers beyond the whiskers.

Source data

Extended Data Fig. 5 Sex-adjusted analysis of androgenic steroids.

FDR and fold change values of each metabolite are marked at its left. The boxes represent the interquartile range (IQR), from the first and third quartiles, and the inside line represents the median. The whiskers denote the lowest and highest values within 1.5 IQR from the first and third quartiles. The circles represent outliers beyond the whiskers.

Source data

Extended Data Fig. 6 Analyses of plasma metabolome.

a, Spearman association analysis between MetaCyc pathways of gut microbiome and plasma metabolites. Rows show MetaCyc pathways and columns show plasma metabolites. Asterisks denote spearman correlation test results (*FDR < 5 × 10-4, **FDR < 1 × 10-4). b, Associations between plasma levels of dehydroepiandrosterone sulfate (DHEA-S; its decline in elderly people is linked to Alzheimer’s disease) or biochemically related epiandrosterone sulfate and microbiome pathways. c, Analysis of two inflammatory cytokines in the Qidong validation cohort (33 pairs of nonagenarians (N=33) and their offspring (N=33)). The boxes represent the interquartile range (IQR), from the first and third quartiles, and the inside line represents the median. The whiskers denote the lowest and highest values within 1.5 IQR from the first and third quartiles. The circles represent outliers beyond the whiskers.

Source data

Extended Data Fig. 7 Analysis of quantitative trait loci (QTL) of plasma metabolites.

Genomic regions accommodating a cluster of associations with a circulating metabolite that are shared by the two generations (a) or found only in the offspring (b). The associations between 3-aminoisobutarate and 5p13.2 were largely offspring-specific, although there were 3 pairs of cross-generational QTLs. FDR value and fold change of the relevant metabolite are marked at the upper left of each box plot (top row or single row, primary Qidong cohort; bottom row; validation Qidong cohort).

Source data

Extended Data Fig. 8 Associations between socioeconomic, lifestyle or medical factors and metagenomic or metabolomic phenotypes.

a, Heatmaps of MaAsLin-based associations between socioeconomic, lifestyle or medical factors and gut microbes, gut Metacyc pathways, plasma metabolites or stool metabolites. Significant associations were marked by + (FDR < 0.1) and * (FDR < 0.05). b, Examples of associations between a metagenomic/metabolic features and a demographic/socioeconomic factors.

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Xu, Q., Wu, C., Zhu, Q. et al. Metagenomic and metabolomic remodeling in nonagenarians and centenarians and its association with genetic and socioeconomic factors. Nat Aging 2, 438–452 (2022). https://doi.org/10.1038/s43587-022-00193-0

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