Archaea methanogens are associated with cognitive performance through the shaping of gut microbiota, butyrate and histidine metabolism

Abstract

The relationship between bacteria, cognitive function and obesity is well established, yet the role of archaeal species remains underexplored. We used shotgun metagenomics and neuropsychological tests to identify microbial species associated with cognition in a discovery cohort (IRONMET, n = 125). Interestingly, methanogen archaeas exhibited the strongest positive associations with cognition, particularly Methanobrevibacter smithii (M. smithii). Stratifying individuals by median-centered log ratios (CLR) of M. smithii (low and high M. smithii groups: LMs and HMs) revealed that HMs exhibited better cognition and distinct gut bacterial profiles (PERMANOVA p = 0.001), characterized by increased levels of Verrucomicrobia, Synergistetes and Lentisphaerae species and reduced levels of Bacteroidetes and Proteobacteria. Several of these species were linked to the cognitive test scores. These findings were replicated in a large-scale validation cohort (Aging Imageomics, n = 942). Functional analyses revealed an enrichment of energy, butyrate, and bile acid metabolism in HMs in both cohorts. Global plasma metabolomics by CIL LC-MS in IRONMET identified an enrichment of methylhistidine, phenylacetate, alpha-linolenic and linoleic acid, and secondary bile acid metabolism associated with increased levels of 3-methylhistidine, phenylacetylgluamine, adrenic acid, and isolithocholic acid in the HMs group. Phenylacetate and linoleic acid metabolism also emerged in the Aging Imageomics cohort performing untargeted HPLC-ESI-MS/MS metabolic profiling, while a targeted bile acid profiling identified again isolithocholic acid as one of the most significant bile acid increased in the HMs. 3-Methylhistidine levels were also associated with intense physical activity in a second validation cohort (IRONMET-CGM, n = 116). Finally, FMT from HMs donors improved cognitive flexibility, reduced weight, and altered SCFAs, histidine-, linoleic acid- and phenylalanine-related metabolites in the dorsal striatum of recipient mice. M. smithii seems to interact with the bacterial ecosystem affecting butyrate, histidine, phenylalanine, and linoleic acid metabolism with a positive impact on cognition, constituting a promising therapeutic target to enhance cognitive performance, especially in subjects with obesity.

Keywords: Microbiota; archaea; cognition; cognitive flexibility; executive function.

Figure 1.

Gut microbial profile and cognitive capabilities associated with M. smithii groups. Volcano plot of differential microbial abundance associated with SCWT-CW test (a) and DSTF test (b) in the IRONMET cohort. Volcano plot of differential microbial abundance associated with SCWT-CW test (c) and DSTF test (d) in the Aging Imageomics cohort. Significant species were identified using the ANCOM-BC from shotgun metagenomics data adjusted for age, sex, BMI, and years of education. The log2 fold change of the association with a unit change in the ANCOM-BC-transformed variable values and the log10 p values adjusted for multiple comparisons using a sequential goodness of fit were plotted for each taxon. Significantly different taxa are colored according to phylum. Significance was set at padj<0.1. Violin plots of the SCWT-CW (e) and DSTF (f) tests scores in the IRONMET cohort grouped according to the median of the CLR abundance of M. smithii (LMs below median, HMs above median). Significance was assessed using a Wilcoxon test. Red dots represent the mean. #p<0.1 *p<0.05, **p<0.01; ***p<0.001. Violin plots of the SCWT-CW (g) and DSTF (h) tests scores in the Aging Imageomics cohort grouped according to the M. smithii Median. Significance was assessed using a Wilcoxon test. Red dots represent the mean. #p<0.1 *p<0.05, **p<0.01; ***p<0.001. (i)PCA score plot based on clr-transformed shotgun sequencing metagenomic microbial taxonomy data of IRONMET cohort, coloured according to the median of the CLR abundance of M. smithii (LMs below median, HMs above median). Differences in the microbiome composition were assessed by PERMANOVA using 999 permutations and Euclidean distances. (j) Volcano plot of differential microbial abundance associated with LMs–HMs groups adjusted for age, sex, BMI and years of education in IRONMET cohort. (k) PCA score plot based on clr-transformed shotgun sequencing metagenomic microbial taxonomy data of IRONMET cohort, coloured according to the LMs–HMs groups. Differences in the microbiome composition were assessed by PERMANOVA using 999 permutations and Euclidean distances. (l) Volcano plot of differential microbial abundance associated with LMs–HMs groups in Aging Imageomics cohort. Significant species were identified using the ANCOM-BC from shotgun metagenomics data adjusted for age, sex, BMI, and years of education. The log2 fold change of the association with a unit change in the ANCOM-BC-transformed variable values and the log10 p values adjusted for multiple comparisons using a sequential goodness of fit were plotted for each taxon. Significantly different taxa are coloured according to phylum. Significance was set at padj<0.1.

Figure 2.

Functional and metabolic profile associated with M. smithii groups. Dotplot of KEGG enriched pathways (q-value <0.1) from significantly differentially expressed microbial microbial molecular functions associated with the HMs group in IRONMET cohort (a) and in Aging Imageomics cohort (b). Significant KEGG orthologues were identified by ANCOM-BC after adjusting for age, sex, BMI and years of education. Dots are coloured according to the q-value. Common pathways are highlighted. (c) Boxplots of the normalized variable importance measure for the metabolites associated with the LMs–HMs groups in IRONMET cohort. Significant metabolites (confirmed) were identified using a machine learning variable selection strategy based on applying multiple random forests as implemented in the Boruta algorithm with 100000 trees, a confidence level cut-off of 0.005 for the Bonferroni adjusted p-values, and a number of features randomly sampled at each split given by the rounded down number of features/3, and controlling for age, sex and BMI. The spot over the boxplot are colored according to the LMs–HMs groups. (d) SHAP summary of the metabolites associated with the LMs–HMs groups in IRONMET cohort. Significant metabolites identified by the machine learning approach are highlighted in bold. Each dot represent an individual sample. The X-axis represents the SHAP value, i.e., the impact of a specific metabolite on the prediction of the affinity to LMs or HMs group for a given individual. Colours represent the values of the metabolites levels, ranging from blue (low concentrations) to red (high concentrations). (e) Volcano plot of metabolites associated with LMs–HMs groups in the IRONMET cohort identified with Limma pipeline performing robust linear regression models adjusting for age, sex and BMI. Dotplot of SMPDB-based Over-representation analysis using different set of metabolites associated with the LMs–HMs group identified with Boruta (f), SHAP (g), and Limma (h) in IRONMET cohort. (i) Pearson’s correlation between the 3MH levels and the log transformed mean of intense physical activity residuals adjusted for age, sex, fat free mass and adherence rate in the in IRONMET-CGM cohort. (j) Volcano plot of SMPDB-based Over-representation analysis using the metabolites associated with LMs–HMs groups, identified with Limma pipeline in Aging Imageomics cohort. Dots are coloured according to the p-value. (k) Volcano plot of metabolites associated with LMs–HMs groups in the Aging Imageomics cohort identified with Limma pipeline performing robust linear regression models adjusting for age, sex and BMI. (l) Volcano plot of bile acid metabolites associated with LMs–HMs groups in the Aging Imageomics cohort identified with Limma pipeline performing robust linear regression models adjusting for age, sex and BMI.

Figure 3.

FMT effect on inhibitory control and weight balance in mice. (a) Experimental design for the faecal microbiota transplantation (FMT) study. The microbiota from LMs group, n = 11 and HMs group, n = 11 human donors were delivered to recipient mice pre-treated with antibiotics for 14 d. n = 10 control mice were treated with saline. Violin plots of the reversal learning (RL) tests (b) and weight measurement (c) performed at day 18. Significance was assessed using a Wilcoxon test. Red dots represent the mean. #p<0.1 *p<0.05, **p<0.01; ***p<0.001. (d) Volcano plot of metabolites associated with LMs–HMs groups in the dorsal striatum of mice identified with Limma R pipeline performing robust linear regression models adjusting for age, sex, BMI and years of education of donors. (e) Dotplot of SMPDB-based Over-representation analysis from significantly differentially expressed metabolites associated with LMs–HMs groups of mice. Dots are coloured according to the p-value. Significant pathways are highlighted. (f) Overenriched pathway network displaying the significant metabolites associated with LMs–HMs groups involved in the fatty acid biosynthesis, phenylacetate metabolism and alpha linoleic and linoleic acid metabolism, colored according to the fold change.