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Over the last five years, the effects of the gut microbiome on depression have gained scientific attention, resulting in a significant increase in research papers. The microbiota-gut-brain axis has been shown to control cognitive function and inhibitory behavior. Now, researchers at Trueta Hospital have studied how changes in the gut microbiome may lead to depression. Their research is published in the journal Cell Metabolism.

Study: Microbiota alterations in proline metabolism impact depression. ​​​​​​​Image Credit: Volodimir Zozulinskyi / Shutterstock

The study

To reveal the molecular mechanisms underlying the relationship between the gut microbiome and the brain during depression, can missing two birth control pills cause bleeding the researchers applied an integrative longitudinal multi-cohort and multi-omics approach, beginning by assessing the relationships of bacterial composition and functionality with depression.

A Patient Health Questionnaire 9 (PHQ-9) was administered to all cohorts, namely, non-depressed, mildly depressed, and majorly depressed. Read counts were modeled using a Dirichlet distribution to deal with 0 count values, and a centered log-ratio transformation was implemented. No significant differences in species richness, the Shannon index, or other alpha diversity measures were observed, but non-depressed individuals did show higher Fisher's alpha diversity indices. No differences were found between mild and major depression. The transformed data were subjected to a principal component analysis (PCA), revealing global variance patterns in microbiome profiles, identifying outliers, and revealing significant differences in microbiome composition between groups.

For each taxa a robust linear regression model was fitted between the PHQ-9 scores and the clr-transformed data, controlling for age, sex, body mass index, education, and antidepressant/anxiety medication. 30 species were found to be significantly associated with depression, and individuals with higher PHQ-9 scores showed increased levels of Parabacteroides spp. and Acidaminococcus spp. and lower levels or species from the Lachnospiraceae family as well as Bifidobacterium pseudolongum. No difference was found in the microbial profiles of individuals taking antidepressants or anxiolytics, but a small number of bacterial species belonging to the Firmicutes phylum were associated with antibiotic use – but not with PHQ-9 scores.

Following this, generalized linear models were fitted to reads from microbial genes mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG), revealing several pathways associated with PHQ-9 score. Pathways associated with arginine, proline, and histidine metabolism were negatively associated with depression. The catabolism of these pathways converges into glutamate, which fuels GABA synthesis. Bacterial glutamate metabolism, glutamatergic and GABAergic synapse were also significantly associated with host PHQ-9 scores. Following this, metabolic profiling of plasma and fecal samples was performed, and a machine learning variable selection strategy based on multiple random forest identified several metabolites linked to PHQ-9 scores. Several of the identified metabolites were linked to the TCA cycle, histidine metabolism, proline and glutamate metabolism. HPLC-MS based metabolic profiling of plasma samples in an independent cohort confirmed these findings, with the most consistent results showing a strong positive association of circulating proline with depression scores, but further enrichment analysis highlighted significant over-representation of pathways associated with the TCA cycle and oxidative phosphorylation, glutamate metabolism, and arginine, proline, and histidine catabolism, as well as overrepresentation of SLC, amino acid transporters, and the GABA synthesis/degradation pathway.

RNA sequencing of jejunum samples from an independent cohort was performed to identify the transcripts associated with proline consumption, with differential gene expression analyses performed following a trimmed mean of M value normalization. 1,547 out of 15,144 significant gene transcripts were associated with dietary proline.

Over-representation analyses then mapped those genes to Reactome and KEGG pathways, with redundant pathways collapsed into a single biological theme. Reactome-based analyses identified pathways involved in GABA receptor activation, synaptic interaction, axon guidance, extracellular matrix, muscle contraction MAPK signaling, and GPRC signaling. The KEGG-based analysis highlighted several pathways involved in neuron synapse, particularly GABAergic and glutamatergic synapse.

In order to explore further the effects of proline on depression, the researchers exposed mice to mild stressors and fed them either water or proline-fortified diets. Proline supplemented mice did not show a difference in body weight or consumption of water after six weeks, but they displayed longer immobility times in a validated model of despair behavior and a reduced sucrose intake, indicative of anhedonia.


The researchers have successfully identified several associations between changes in the gut microbiome and depression and explored the changes in gene expression in the gut microbiome and how these affect the changes in the catabolism of several metabolites. Finally, they have also proven that at least one of the affected metabolites has substantial effects on symptoms of depression in mice models. Further research may help healthcare workers provide individuals who are depressed with additional guidance.

Journal reference:
  • Mayneris-Perxachs et al., 2022, Cell Metabolism 34, 681–701. May 3, 2022 ª 2022 The Author(s). Published by Elsevier Inc. https://doi.org/10.1016/j.cmet.2022.04.001, https://www.cell.com/cell-metabolism/fulltext/S1550-4131(22)00128-0

Posted in: Men's Health News | Medical Research News | Medical Condition News | Women's Health News

Tags: Amino Acid, Anhedonia, Antibiotic, Antidepressant, Anxiety, Arginine, Body Mass Index, Brain, Cell, Cell Metabolism, Cognitive Function, Depression, Education, Gene, Gene Expression, Genes, Gut-Brain Axis, Healthcare, Histidine, Hospital, Machine Learning, Metabolic Profiling, Metabolism, Metabolites, Microbiome, Muscle, Neuron, Phosphorylation, Proline, Receptor, Research, RNA, RNA Sequencing, Synapse

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Written by

Sam Hancock

Sam completed his MSci in Genetics at the University of Nottingham in 2019, fuelled initially by an interest in genetic ageing. As part of his degree, he also investigated the role of rnh genes in originless replication in archaea.

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