GUTSY Atlas

Welcome to the website that is a companion resource to the article, 'An online atlas of human plasma metabolite signatures of gut microbiome composition' by Dekkers et al.

Abstract:

  • We used deep shotgun metagenomics and UHPLC-MS/MS in 8,583 participants of the SCAPIS study to characterize the gut microbiome and fasting plasma metabolome, respectively. Partial Spearman’s rank correlations were calculated between 1,528 metagenomic species and 1,321 metabolites and adjusted for sex, age, place of birth, study site, microbial DNA extraction plate, metabolomics delivery batch and multiple testing. Enrichment analyses were performed using GSEA method for gut microbiome modules and for metabolite subclasses.

The website contains three tabs:

  • The Tables tab contains summary statistics for individual metagenomic species or metabolites. Table numbering is the same as the article. The selected tables can be viewed and downloaded.
  • The Figure tab contains a heatmap of correlation coefficients of individual metagenomic species and metabolites, which can be subset by taxonomic genus, gut metabolic module, metabolite subclass and top findings. The selected figure can be visualized and downloaded.
  • The Downloads tab contains tables of complete summary statistics for all metagenomic species and metabolites. Table numbering is the same as the article.

For questions, bug reports, or requests, please contact Koen Dekkers (koen.dekkers@medsci.uu.se)

Please cite the following publication if you use this resource: K. F. Dekkers, S. Sayols-Baixeras, G. Baldanzi, C. Nowak, U. Hammar, D. Nguyen, G. Varotsis, L. Brunkwall, N. Nielsen, A. C. Eklund, J. B. Holm, H. B. Nielsen, F. Ottosson, Y. Ling, S. Ahmad, L. Lind, J. Sundström, G. Engström, J. G. Smith, J. Ärnlöv, M. Orho-Melander and T. Fall. An online atlas of human plasma metabolite signatures of gut microbiome composition. Nature Communications volume 13, Article number: 5370 (2022). https://doi.org/10.1038/s41467-022-33050-0

We acknowledge the support of the following organizations:

  • We acknowledge the financial support from the European Research Council [ERC-2018-STG801965 (TF); ERC-CoG-2014-649021 (MO-M); ERC-STG-2015-679242 (JGS)], the Swedish Research Council [VR 2019-01471 (TF); 2018-02784 (MO-M); 2018-02837 (MO-M); 2021-03291 (MO-M); EXODIAB 2009-1039 (MO-M); 2019-01015 (JÄ); 2020-00243 (JÄ); 2019-01236 (GE); 2021-02273 (JGS)], the Swedish Heart-Lung Foundation [Hjärt-Lungfonden, 20190505 (TF); 20200711 (MO-M); 20180343 (JÄ); 20200173 (GE); 20190526 (JGS)], Göran Gustafsson foundation [2016 (TF)], Axel and Signe Lagerman’s foundation (TF), the A.L.F. governmental grant [2018-0148 (MO-M)], the Novo Nordic Foundation [NNF20OC0063886 (MO-M)], the Swedish Diabetes foundation [DIA 2018-375 (MO-M)], the Swedish Foundation for Strategic Research [LUDC-IRC 15-0067 (MO-M)], and Formas [2020-00989 (SA)].
  • The main funding body of the Swedish CArdioPulmonary bioImage Study (SCAPIS) is the Swedish Heart-Lung Foundation. The study is also funded by the Knut and Alice Wallenberg Foundation; the Swedish Research Council, and VINNOVA (Sweden’s Innovation agency); the University of Gothenburg and Sahlgrenska University Hospital; Karolinska Institutet and Stockholm county council; Linköping University and University Hospital; Lund University and Skåne University Hospital; Umeå University and University Hospital; and Uppsala University and University Hospital.
  • The computations and data handling were made possible by resources from project sens2019512 provided by the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. We would like to acknowledge SciLifeLab Data Centre for hosting, compute and storage services, offered as part of the national Data-driven life science (DDLS) program, funded by Knut and Alice Wallenberg foundation.

This page contains summary statistics for an individual metagenomic species or metabolite. A metagenomic species or metabolite is first selected from the drop down menu. Metabolites follow the last metagenomic species Weissella confusa MGS:1557 in the drop down menu. Various tables are generated. The search function will become activated once a metagenomic species or metabolite has been selected.

Depending on whether you choose a metabolite or a metagenomic species, the table for annotations for one or the other appears.













This page generates a heatmap based on the metagenomic species and metabolite associations, which are adjusted for age, sex, place of birth, study site, microbial DNA extraction plate and metabolomics delivery batch. You can select a metagenomic species, metabolite, taxonomic genus, gut metabolic module, metabolite subclass or top findings in the search boxes. Hover over the figure to look at individual data points. The figure shows at most 30 metagenomic species or metabolites. It can take a few seconds for the plot to be generated.





This page contains download links to the full results generated for the article

Please cite the following publication if you use this resource: K. F. Dekkers, S. Sayols-Baixeras, G. Baldanzi, C. Nowak, U. Hammar, D. Nguyen, G. Varotsis, L. Brunkwall, N. Nielsen, A. C. Eklund, J. B. Holm, H. B. Nielsen, F. Ottosson, Y. Ling, S. Ahmad, L. Lind, J. Sundström, G. Engström, J. G. Smith, J. Ärnlöv, M. Orho-Melander and T. Fall. An online atlas of human plasma metabolite signatures of gut microbiome composition. Nature Communications volume 13, Article number: 5370 (2022). https://doi.org/10.1038/s41467-022-33050-0



Supplementary Table 1. Taxonomic annotation of 1,528 metagenomic species

Each metagenomic species was uniquely identified by Clinical Microbiomics. For taxonomic annotation, catalog genes were compared to those in the NCBI RefSeq database (downloaded on 2 May 2021). Species-level taxonomy was assigned to metagenomic species with ≥75% of genes with ≥95% sequence similarity to a single species. For the genus, family, order, class, and phylum annotations, different thresholds were used (≥60, 50, 40, 30, and 25% of genes; with ≥85, 75, 65, 55, and 50% sequence similarity, respectively). Each species name is followed by an internal species identifier (MGS). Column "Detected" is the percentage of samples that contain a measurable level of the respective species.

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Supplementary Table 2. Annotation of 1,321 plasma metabolites

Each metabolite was uniquely profiled by Metabolon. Column "Detected" is the percentage of samples that contain a measurable level of the respective metabolite. Column "Study site" are the study sites in which the metabolite was measured above the detection threshold. U1, U2, Uppsala batch 1 and 2, M, Malmö. Columns Platform indicates which method was used to report the values associated with the detected metabolite. The Metabolon untargeted metabolomics platform consists of four different methods (i.e. Pos Late, Pos Early, Polar, and Neg). Column "Metabolite class" is the metabolite grouping intended for sorting metabolites by broad metabolite classes. Column "Metabolite subclass" is the metabolite grouping intended for sorting metabolites by more specific metabolite classes. Column "CAS" is a unique numerical identifier assigned by the Chemical Abstracts Service (CAS) to every chemical substance described in the open scientific literature. Column "HMDB" is an alphanumeric compound identifier and link to compound information maintained by the Human Metabolome Database (HMDB). Column "KEGG" is an alphanumeric compound identifier and link to compound information in the Kyoto Encyclopedia of Genes and Genomes (KEGG). Specific considerations: * and ** denotes metabolites annotated without an internal standard.

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Supplementary Table 3. Baseline characteristics of the study participants from all six study sites, total study sample from SCAPIS-Uppsala and SCAPIS-Malmö, and the present study sample

Information on the characteristics from all six study sites are derived from the paper by Bonander et al. Hypertension, cholesterol and diabetes medication are self-reported.

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Supplementary Table 4. Association between Shannon diversity index and plasma metabolites

Partial Spearman’s rank correlations were calculated for 1,321 metabolites, adjusting for age, sex, place of birth, study site, microbial DNA extraction plate and metabolomics delivery batch. Results are shown for associations with a q-value < 0.05 based on the Benjamini-Hochberg method at 5% FDR (q-value < 0.05). Specific considerations: * and ** denotes metabolites annotated without an internal standard.

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Supplementary Table 5. Variance explained of plasma metabolite levels by variation in the gut microbiota

Nested 10-fold cross-validated ridge regression models were applied to estimate the explained variance in 1,321 metabolites by gut microbiota species. Shown are the 1,179 metabolites for which the variance was partly explained by variation in the gut microbiota. Column "r2" is the variance explained calculated on the test set. Column "MSE (train)" is the test mean squared error. Column "MSE (train)" is the training mean squared error. Each species name is followed by an internal species identifier (MGS). Specific considerations: * and ** denotes metabolites annotated without an internal standard.

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Supplementary Table 6. Associations between gut microbial species and plasma metabolites

Partial Spearman’s rank correlations were calculated for 1,528 species and 1,321 metabolites, adjusting for age, sex, place of birth, study site, microbial DNA extraction plate and metabolomics delivery batch. Shown are the significant associations after adjusting for multiple testing using the Benjamini-Hochberg method at 5% FDR (q-value < 0.05). Columns with "+Shannon" are additionally adjusted for Shannon diversity index. Each species name is followed by an internal species identifier (MGS). Specific considerations: * and ** denotes metabolites annotated without an internal standard.

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Supplementary Table 7. Enrichment for metabolite subclasses in alpha diversity associations with metabolites

Enrichment analysis was performed using GSEA on ranked p-values of the partial Spearman’s rank correlations between Shannon diversity index and metabolites for positive associations and negative associations separately as a one-sided test. Shown are the significant associations after adjusting for multiple testing using the Benjamini-Hochberg method at 5% FDR (q-value < 0.05). Column "NES" is the GSEA enrichment score normalized to mean enrichment of random samples of the same size. Column "Size" is the number of metabolites in the metabolite subclass. Each species name is followed by an internal species identifier (MGS).

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Supplementary Table 8. Enrichment for metabolite subclasses in single metagenomic species associations with metabolites

Enrichment analysis was performed using GSEA on ranked p-values of the partial Spearman’s rank correlations between metagenomic species and metabolites for positive associations and negative associations separately as a one-sided test. Shown are the significant associations after adjusting for multiple testing using the Benjamini-Hochberg method at 5% FDR (q-value < 0.05). Columns with "+Shannon" are based on Spearman’s rank correlations additionally adjusted for Shannon diversity index. Column "NES" is the GSEA enrichment score normalized to mean enrichment of random samples of the same size. Column "Size" is the number of metabolites in the metabolite subclass. Each species name is followed by an internal species identifier (MGS).

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Supplementary Table 9. Enrichment for GMM modules in single metagenomic species associations with metabolites

Enrichment analysis was performed using GSEA on ranked p-values of the partial Spearman’s rank correlations between metagenomic species and metabolites for positive associations and negative associations separately as a one-sided test. Shown are the significant associations after adjusting for multiple testing using the Benjamini-Hochberg method at 5% FDR (q-value < 0.05). Columns with "+Shannon" are based on Spearman’s rank correlations additionally adjusted for Shannon diversity index. Column "NES" is the GSEA enrichment score normalized to mean enrichment of random samples of the same size. Column "Size" is the number of analyzed species within the GMM module. Specific considerations: * and ** denotes metabolites annotated without an internal standard.

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Supplementary Table 10. Summary of results for uremic toxins, omeprazole, metformin and coffee metabolites

Column "Detected" is the percentage of samples that contain a measurable level of the respective metabolite. Columns "Alpha diversity ρ" and "Alpha diversity p-value" is based on Supplementary Table 4. Columns "Variance explained" is based on Supplementary Table 5. Column "Species" is the number of species associated with the respective metabolite (based on Supplementary Table 6). Column "Modules" is the number of enriched modules for the respective metabolite-species associations (based on Supplementary Table 8).

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Supplementary Table 11. Comparison of plasma uremic toxin levels and kidney function

Spearman’s rank correlations were calculated for plasma levels of uremic toxins and estimated glomerular filtration rate (eGFR). Columns "Low eGFR", "Medium eGFR" and "High eGFR" are the mean metabolite levels per tertile.

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Supplementary Table 12. Comparison of plasma coffee metabolite levels and coffee intake

Spearman’s rank correlations were calculated for plasma levels of uremic toxins and ranked categories of self-reported coffee intake. Columns "<1 times/d", "1-2 times/d", "3-4 times/d" and ">4 times/d" show the mean metabolite levels per category of self-reported coffee intake.

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