Obesity-associated deficits in inhibitory control are phenocopied to mice through gut microbiota changes in one-carbon and aromatic amino acids metabolic pathways

María Arnoriaga-Rodríguez, Jordi Mayneris-Perxachs, Oren Contreras-Rodríguez, Aurelijus Burokas, Juan-Antonio Ortega-Sanchez, Gerard Blasco, Claudia Coll, Carles Biarnés, Anna Castells-Nobau, Josep Puig, Josep Garre-Olmo, Rafel Ramos, Salvador Pedraza, Ramon Brugada, Joan C Vilanova, Joaquín Serena, Jordi Barretina, Jordi Gich, Vicente Pérez-Brocal, Andrés Moya, Xavier Fernández-Real, Lluis Ramio-Torrentà, Reinald Pamplona, Joaquim Sol, Mariona Jové, Wifredo Ricart, Manuel Portero-Otin, Rafael Maldonado, Jose Manuel Fernández-Real, María Arnoriaga-Rodríguez, Jordi Mayneris-Perxachs, Oren Contreras-Rodríguez, Aurelijus Burokas, Juan-Antonio Ortega-Sanchez, Gerard Blasco, Claudia Coll, Carles Biarnés, Anna Castells-Nobau, Josep Puig, Josep Garre-Olmo, Rafel Ramos, Salvador Pedraza, Ramon Brugada, Joan C Vilanova, Joaquín Serena, Jordi Barretina, Jordi Gich, Vicente Pérez-Brocal, Andrés Moya, Xavier Fernández-Real, Lluis Ramio-Torrentà, Reinald Pamplona, Joaquim Sol, Mariona Jové, Wifredo Ricart, Manuel Portero-Otin, Rafael Maldonado, Jose Manuel Fernández-Real

Abstract

Background: Inhibitory control (IC) is critical to keep long-term goals in everyday life. Bidirectional relationships between IC deficits and obesity are behind unhealthy eating and physical exercise habits.

Methods: We studied gut microbiome composition and functionality, and plasma and faecal metabolomics in association with cognitive tests evaluating inhibitory control (Stroop test) and brain structure in a discovery (n=156), both cross-sectionally and longitudinally, and in an independent replication cohort (n=970). Faecal microbiota transplantation (FMT) in mice evaluated the impact on reversal learning and medial prefrontal cortex (mPFC) transcriptomics.

Results: An interplay among IC, brain structure (in humans) and mPFC transcriptomics (in mice), plasma/faecal metabolomics and the gut metagenome was found. Obesity-dependent alterations in one-carbon metabolism, tryptophan and histidine pathways were associated with IC in the two independent cohorts. Bacterial functions linked to one-carbon metabolism (thyX,dut, exodeoxyribonuclease V), and the anterior cingulate cortex volume were associated with IC, cross-sectionally and longitudinally. FMT from individuals with obesity led to alterations in mice reversal learning. In an independent FMT experiment, human donor's bacterial functions related to IC deficits were associated with mPFC expression of one-carbon metabolism-related genes of recipient's mice.

Conclusion: These results highlight the importance of targeting obesity-related impulsive behaviour through the induction of gut microbiota shifts.

Keywords: intestinal microbiology; obesity.

Conflict of interest statement

Competing interests: None declared.

© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Figures

Figure 1
Figure 1
A microbiota taxonomic and functional signature is associated with inhibitory control. (A) Volcano plot of differential bacterial abundance (pFDR <0.05) associated with the Stroop Color Word Test (SCWT) as calculated by DESeq2 from shotgun metagenomic sequencing in the IRONMET cohort (n=114), adjusting for age, sex and education years. Fold change (FC) associated with a unit change in the SCWT and Benjamini-Hochberg adjusted p values (pFDR) are plotted for each taxon. Significantly different taxa are coloured according to phylum. In the same graph, the violin plots for the SCWT scores in patients with and without obesity are also shown. Differences between groups were analysed by a Wilcoxon tests. (B) Volcano plot of differential bacterial abundance (pFDR <0.1) associated with Iowa Gambling Test (IGT) as calculated by DESeq2 from shotgun metagenomic sequencing in an independent cohort (n=24), adjusting for age, sex and education years. (C) Manhattan-like plot of significantly expressed KEGG metagenome functions associated with the SCWT (pFDR <0.020) identified from DESeq2 analysis in the IRONMET cohort (n=114) adjusted for age, sex and educations. The −log10(pFDR) values are multiplied by the FC sign to take into account the direction of the association. Bars are coloured according to the pFDR. (D) Manhattan-like plot of significantly expressed KEGG metagenome functions associated with the IGT (pFDR <0.05) identified from DESeq2 analysis in an independent cohort (n=24) adjusted for age, sex and educations.
Figure 2
Figure 2
The microbiota taxonomic and functional signature linked to inhibitory control is modulated by obesity. (A, B) Volcano plot of differential expressed (pFDR <0.1) bacterial abundance and (C, D) bacterial functions associated with the Stroop Color and Word Test (SCWT) as calculated by DESeq2 from shotgun metagenomic sequencing in the patients without and with obesity from the IRONMET cohort, respectively, controlling for age, sex and education years. Fold change associated with a unit change in the SCWT and Benjamini-Hochberg adjusted p values (pFDR) are plotted for each taxon. Significantly different taxa are coloured according to phylum.
Figure 3
Figure 3
Plasma and faecal metabolomics linked to inhibitory control in the Ironmet and Imageomics cohorts. Bar plots of normalised variable importance measure (VIM) for the metabolites associated with the Stroop Color and Word Test (SCWT) in (A, B) plasma and (C–E) faecal samples identified by HPLC-ESI-MS/MS in positive mode (n=130), negative mode (n=130) and NMR (n=156), respectively, in the Ironmet cohort. Bar plots of VIM for the metabolites associated with the SCWT in plasma samples of the Imageomics cohort (n=970) identified by HPCL-ESI-MS/MS in (F) positive and (G) negative mode. In all cases, metabolites were identified using a multiple random forest-based machine learning variable selection strategy using the Boruta algorithm with 5000 trees and 500 iterations. All metabolites were identified based on exact mass, retention time and MS/MS spectrum, except those with (*) that were only identified based on exact mass and retention time. Unidentified metabolites are shown as exact mass at retention time. 2-PPA, 2-phenylpropanoic acid; 4-HPLA, 4-hydroxyphenyllactic acid; 5-HIAA, 5-hydroxyindoleacetic acid; DMSO2, dimethyl sulfone; FA, fatty acid; IPAM, indolepropionamide; MA, methylamine; TMA, trimethylamine.
Figure 4
Figure 4
Plasma and faecal metabolomics linked to inhibitory control in the Ironmet and Imageomics cohorts according to obesity status. Bar plots of normalised variable importance measure (VIM) for the metabolites associated with the Stroop Color and Word Test (SCWT) in (A–D) plasma and (E–J) faecal samples identified by HPLC-ESI-MS/MS in positive mode (n=130), negative mode (n=130) and NMR (n=156), respectively, in the Ironmet cohort in patients with and without obesity. Bar plots of VIM for the metabolites associated with the SCWT in plasma samples of the Imageomics cohort (n=970) identified by HPCL-ESI-MS/MS in (K) positive and (L, M) negative mode in patients with and without obesity. The above colour bar indicates the sign of the association among the metabolites and the SCWT, with red indicating negative correlation and green positive correlation. In all cases, metabolites were identified using a multiple random forest-based machine learning variable selection strategy using the Boruta algorithm with 5000 trees and 500 iterations. All metabolites were identified based on exact mass, retention time and MS/MS spectrum, except those with (*) that were only identified based on exact mass and retention time. Unidentified metabolites are shown as exact mass at retention time. 4-HPLA, 4-hydroxyphenyllactic acid; BA1, Bile acid1: 4,4-dimethyl-5-α-cholesta-8,14-dien-3β-ol; EPA, eicosapentaenoic acid; FA, fatty acid; IPAM, indolepropionamide; MA, methylamine; TMA, trimethylamine.
Figure 5
Figure 5
Main metabolic pathways involved in the associations among metagenomics, metabolomics and the Stroop Color and Word Test (SCWT). (A) Overview of the main catabolic pathways of tryptophan and tyrosine. Tryptophan and tyrosine are the precursors for the synthesis of the neurotransmitters serotonin and dopamine, respectively. The gut microbiota can also metabolise tryptophan and tyrosine to indoles and hydroxyphenolic acids, respectively. Dietary tryptophan is mostly metabolised via the Kynurenine pathway, which is activated by inflammation. (B) Overview of the folate-mediated one-carbon metabolism. The folate cycle (green) is required for the synthesis of DNA (pink and blue) as well as methylation reaction (DNA, proteins and lipids) through the methionine cycle (orange). Histidine (purple), choline and betaine are two sources of 1C units feeding into the one-carbon metabolism. Bacterial pathways have been shaded in red. Metabolites involved in the one-carbon metabolism and significantly associated with the SCWT are highlighted in bold in a yellow box. Bacterial functions participating in the one-carbon metabolism and significantly associated with the SCWT are highlighted in bold in a red box. AICAR, 5-aminoimidazole-4-carboxamide 1-β-D-ribofuranoside; bgtB, arginine/lysine/histidine/glutamine transport system substrate-binding and permease protein; bluB, 5,6-dimethylbenzimidazole synthase; btuB, vitamin B12 transporter; DHF, dihydrofolate; DMA, dimethylamine; DMSO2, dimethylsulfone; dut, dUTP pyrophosphatase; FAICAR, 5-formamidoimidazole-4-carboxamide ribotide; FGAR, 5’-phosphoribosyl-N-formylglycineamide; FIGlu, N-Formimino-glutamate; folK, 2-amino-4-hydroxy-6-hydroxymethyldihydropteridine diphosphokinase; GAR, 5’-phosphoribosylglycineamide; hisB, imidazoleglycerol-phosphate dehydratase; IGP, imidazole glycerol-phosphate; IMP, inosine 5’-monophosphate; MA, methylamine; nudB, dihydroneopterin triphosphate diphosphatase; pdxA, 4-hydroxythreonine-4-phosphate dehydrogenase; proV, glycine betaine/proline transport system ATP-binding protein; proW, glycine betaine/proline transport system permease protein; proX, glycine betaine/proline transport system substrate-binding protein; purT, phosphoribosylglycinamide formyltransferase 2; ribBA, 3,4-dihydroxy 2-butanone 4-phosphate synthase/GTP cyclohydrolase II; ribE, riboflavin synthase; SAH, S-adenosylhomocysteine; SAM, S-adenosylmethionine; THF, tetrahydrofolate; thyX, thymidylate synthase; TMA, trimethylamine.
Figure 6
Figure 6
The gut microbiota is associated with the brain area involved in inhibitory control. (A, B) The anterior cingulate cortex (ACC) volume was positively associated with the Stroop Color and Word Test (SCWT) in the Ironmet cohort (n=95) after controlling for age, sex, education years and total intracranial volume. (C) Volcano plots of differential bacterial abundance and (D) KEGG metagenome functions associated with the ACC volume as calculated by DESeq2 controlling for previous covariates. Fold change (FC) associated with a unit change in the corresponding volumes and Benjamini-Hochberg adjusted p values (pFDR) are plotted for each taxon. (E) Manhattan-like plot of significantly expressed KEGG metagenome functions associated with the ACC volume highlighting those bacterial functions also associated with the SCWT in blue. The −log10(pFDR) values are multiplied by the FC sign to take into account the direction of the association. Bars are coloured according to the pFDR.
Figure 7
Figure 7
Faecal microbiota transplantation (FMT) mice studies. (A) Experimental design for the first FMT study. The microbiota from human donors without obesity (body mass index (BMI) 2, n=11) and with obesity (BMI ≥30 kg/m2, n=11) was delivered to recipient mice pretreated with antibiotics for 14 days. Reversal learning tests (RLT) were performed after 18 days. Violin plot for the recipient’s mice RLT scores at 18 days based on human donor obesity status. (B) Volcano plot of differential human donor bacterial abundance associated with the recipient’s mice RLT at day 18, identified from DESeq2 analysis controlling for donor’s age, sex and education years. Fold change (FC) associated with a unit change in the corresponding memory test and Benjamini-Hochberg adjusted p values (pFDR) are plotted for each taxon. Significantly different taxa are coloured according to phylum. (C) Manhattan-like plot showing only the significantly expressed KEGG metagenome functions associated with the recipient’s mice RLT (pFDR <0.1) that were also associated with the Stroop Color and Word Test (SCWT) in humans. The −log10(pFDR) values are multiplied by the FC sign to take into account the direction of the association. Bars are coloured according to the pFDR. (D) Experimental design for the second FMT study. The microbiota from human donors with low (n=11) and high (n=11) SCWT scores was delivered to recipient mice pretreated with antibiotics for 14 days. RNA sequencing of the medial prefrontal cortex (mPFC) was performed after 4 weeks. Violin plots for the SCWT according to the human donor scores. (E) Volcano plots of recipient’s mice differential mPFC genes associated (pFDR <0.1) with the human’s donor metagenome functions dUTP pyrophosphatase and (F) exodeoxyribonuclease V controlling for donor’s age, sex and education years. FC associated with a unit change in the expression of the corresponding bacterial function and the Benjamini-Hochberg adjusted p values (pFDR) are plotted for each gene. (G) Bar plot of the normalised variable importance measure (VIM) for the recipient’s mice mPFC genes associated with the human donor’s SCWT identified by machine learning using multiple random forest-based variable selection strategy with the Boruta algorithm with 5000 trees and 500 iterations. (H) Volcano plot of differential human donor’s KEGG metagenome functions associated with recipient’s mice Ms4a4a and (I) Slc16a12 genes. FC associated with a unit change in the expression of both genes and Benjamini-Hochberg adjusted p values (pFDR) are plotted for each metagenome function.

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