2. Key Metabolic Changes in Depression
Advances in MS-based metabolomics techniques have been crucial in driving the progress of research into depression. Recent applications of MS-based metabolomics in depression biomarker discovery and elucidation of pathogenic mechanisms are summarized below.
2.1. Monoamine Neurotransmitters
The “monoamine hypothesis” is important in the study of depression, and the development of the majority of clinical antidepressants has been based on monoamine neurotransmitters
[13][43]. Although considerable progress has been made in this area, the underlying mechanisms remain unclear and treatments are increasingly controversial. Monoamine neurotransmitters can interact with other metabolic pathways in depression. The “monoamine (5-HT)-Glutamate/GABA long neural circuit”, proposed by Li, holds the view that monoaminergic and non-monoaminergic mechanisms form a long neural circuit that mediates rapid antidepressant effects
[14][44]. Li et al., using LC-MS/MS, studied changes in neurotransmitters and their related metabolites in GABAergic, serotonergic, and catecholaminergic pathways in the nucleus accumbens of CUMS-induced anhedonia-like rats
[15][45]. The level of 5-hydroxytryptamine in anhedonia-susceptible rats increased, while dopamine did not change significantly. Xu et al. found that gut microbiota (GM) can activate monoamines via stimulating the enteroendocrine cells to produce 5-hydroxytryptamine, dopamine, and norepinephrine, which can affect the central nervous system. The brain in turn can regulate gastrointestinal functions through the neuro-immune-endocrine system
[16][46]. Using LC-MS/MS, Zhong’s group showed that
Morinda officinalis oligosaccharides alleviated depression via the tryptophan-5-hydroxytryptophan-serotonin metabolic pathway in the GM
[17][47]. In addition, monoamine neurotransmitters are intertwined with numerous new depression pathways, such as inflammation, oxidative stress, neurotrophins, and neurogenesis. In-depth explanation and discussion can refer to some excellent works and reviews
[5][13][5,43].
2.2. Amino Acids
Amino acids and their metabolites are fundamental substrates and regulators in many metabolic pathways and some have been identified as biomarkers of depression. Untargeted GC-MS identified significant changes in
l-alanine,
l-glutamic acid, glycine,
l-methionine,
l-phenylalanine,
l-valine,
l-isoleucine, and
l-norleucine in the main stress-targeted tissues of CUMS-induced mice
[18][48]. High levels of glutamic acid, aspartic acid, and glycine and low levels of 3-hydroxykynurenine were quantified by LC-MS in serum of MDD patients, and the levels of glutamic acid and phenylalanine correlated with the severity of depression
[19][49]. Significant negative associations of the branched-chain amino acids valine and leucine with depression were identified using untargeted metabolomics
[20][50]. Increased glutamate, decreased dopamine, and altered trends in γ-aminobutyric acid in the habenula of CUMS-susceptible and -resilient rats were identified using LC-MS/MS
[21][51].
Disruption of the tryptophan pathway plays a crucial role in MDD. Tryptophan is metabolized alongside the kynurenine, serotonin, and microbial pathways. Brum et al. found that levels of all tryptophan catabolites were reduced in the plasma of patients with MDD, bipolar depression (BD), and schizophrenia (SCZ), but these metabolites could not be used to distinguish between the disorders
[22][52]. A similar conclusion was also reached by Liu et al.
[23][53]. Yun et al. studied the relationship between the tryptophan–kynurenine pathway and the painful physical symptoms of MDD
[24][54]. Patients with such symptoms exhibited higher kynurenine, quinolinic acid, and kynurenine/tryptophan ratios than those without. Tryptophan metabolism is central to communication between the GM and the brain in depression
[25][55]. LC-MS/MS showed that kynurenine and 3-hydroxycaninuric acid increased significantly along the gut–brain axis of depressive-like rats subjected to chronic restraint stress (CRS)
[26][56]. The tryptophan–kynurenine pathway is also linked to the inflammatory state of patients with MDD
[27][57]. Haroon et al. analyzed kynurenine pathway metabolites and inflammatory markers in the plasma and CSF of depressed patients
[28][58]. Kynurenine and kynurenine/tryptophan in plasma, and kynurenine, kynurenic acid, and quinolinic acid in CSF were closely related to plasma tumor necrosis factor. Pau et al. replicated and expanded upon these findings by evaluating more metabolites and suggesting that the levels of some peripheral kynurenine pathway metabolites might serve as proxies for central kynurenine pathway metabolites in patients with MDD
[29][59]. Zheng et al. also found that C-reactive protein and kynurenic acid/quinolinic acid are independently associated with white matter integrity in MDD
[30][60]. Some studies indicate that therapy can affect tryptophan metabolism. Tateishi et al. reported that levels of kynurenine, kynurenic acid, and kynurenine/tryptophan ratio in plasma of patients with treatment-resistant depression were unchanged after repetitive transcranial magnetic stimulation treatment
[31][61]; however, Ryan et al. reported that the kynurenic acid pathway was mobilized by electroconvulsive therapy
[32][62].
2.3. Lipids
Lipids are a broad class of biomolecules with essential roles in many cellular processes, including molecular signal transduction, energy storage, and cell membrane formation. Advanced MS-based lipidomics methods have deepened the understanding of the lipidome in the central and peripheral nervous systems and its associations with depression
[6]. Miao et al. identified lipid networks associated with the risk of depression using untargeted LC-MS lipid analysis
[33][63]. For example, lower levels of sphingomyelins and glycerophospholipids and higher levels of lysophospholipids were associated with the incidence and/or prevalence of depression. An LC-MS lipidomics study identified 13 differentially expressed lipids in the plasma of adult female MDD and BD patients and could distinguish between these conditions with medium confidence (area under the curve [AUC] was 0.860)
[34][64]. Similarly, a panel of 111 lipid species was capable of distinguishing SCZ from MDD (AUC = 0.920)
[35][65]. Glycerophospholipids are critical components of neuronal membranes and eukaryote cellular membranes. LC-MS lipid metabolite profiling in the hippocampus of PSD rats showed 50 key metabolites were reduced, and these were mainly involved in glycerophospholipid metabolism (particularly cardiolipin metabolism)
[36][66]. Glycerophospholipid metabolism was also associated with the pathogenesis of PSD in humans
[37][38][67,68]. Various lipidomics studies have confirmed that peripheral and central glycerophospholipid metabolism disorders are involved in the pathogenesis of depression via the microbiome–gut–brain axis
[39][40][41][42][69,70,71,72]. Jiang et al. used UHPLC-Q-TOF-MS to investigate plasma metabolite biomarkers in young MDD patients and identified phosphatidylcholine as a female-specific biomarker (AUC = 0.957)
[43][73]. Schumacher et al. found that ceramide concentration in the plasma of MDD patients correlated with the severity of MDD, and neutralization of ceramides abrogated depressive behavior in mice
[44][74]. Untargeted UHPLC-MS metabolomics revealed that phosphatidylserine (16:0/16:1) and phosphatidic acid (18:1/18:0) were significantly increased in plasma of MDD patients
[45][75].
2.4. Energy Metabolism
Many studies have shown that energy metabolism is impaired in patients with depression. This may point towards new treatments for the condition. Most of the body’s energy comes from the tricarboxylic acid cycle, oxidative phosphorylation, and glycolysis
[46][76]. Wang et al. demonstrated, using metabolomics, that the tricarboxylic acid cycle was inhibited in mice exposed to CSDS and in patients with first-episode depression
[47][77]. The altered metabolism of acylcarnitines may link mitochondrial dysfunction to depression via impairment of fatty acid β-oxidation
[48][78]. Lower levels of acetyl-
l-carnitine and medium- and long-chain acylcarnitines and higher levels of
l-carnitine and
l-carnitine/acetyl-
l-carnitine ratio were found in the plasma of depressed patients, but these differences disappeared after treatment
[49][50][79,80]. Acylcarnitine profiles also help to distinguish different phenotypic subtypes of MDD, such as core depression, anxious depression, and neurovegetative symptoms of melancholia
[51][81]. Given that glycogen is the main energy source for most higher organisms, Qin’s group used stable isotope-resolved metabolomics with a
13C
6-glucose tracer to reveal the blockage of the tricarboxylic acid cycle and abnormal activation of gluconeogenesis in rats with CUMS and in corticosteroid-induced PC12 cells
[52][53][54][55][56][82,83,84,85,86].
2.5. Gut Microbiota and Metabolomics
The relationship between the GM and depression is a particular focus of psychobiology research, but the underlying molecular mechanisms remain unclear
[57][87]. A combination of 16S rRNA gene sequencing and MS-based metabolomics is often used to investigate these GM mechanisms in patients with depression and in CUMS, CSDS, and CRS mouse models
[58][88]. Growing evidence from this toolkit of clinical studies and animal models suggests that GM compositions (e.g., the phylum Firmicutes and genera
Bacteroides and
Lactobacillus) and related metabolites (e.g., short-chain fatty acids and tryptophan metabolism) are disordered in depression along the brain–gut–microbiota axis. For example, Xie et al. found that two crucial tryptophan metabolism-related metabolites (tryptophan and 5-hydroxytryptophan) were reduced in the feces of CSDS mice, and these compounds were associated with
Lactobacillus [59][89]. Zhang et al. showed that
Bacteroides species enriched in the GM of MDD patients had differing effects on the susceptibility to depressive behaviors
[60][90]. This was partly explained by the different changes in tryptophan pathway metabolites and neurotransmitters along the gut–brain axis. The relationship between microbial metabolites in feces and neurotransmitters in the prefrontal cortex of depressed mice was also explored using targeted metabolomics
[61][91]. This suggested that the disruption of microbial metabolites may affect prefrontal cortex neurotransmitter levels, leading to depressive episodes. This same phenomenon—simultaneous changes in brain and gut metabolism in CUMS rats—was also observed by Hu et al.
[62][92]. The group used whole-genome shotgun metagenomic and untargeted metabolomic methods to identify disturbed microbial genes (in
Bacteroides,
Blautia, and
Eubacterium) and fecal metabolites (γ-aminobutyrate, phenylalanine, and tryptophan) in MDD patients
[63][93]. The antidepressant effect of chenodeoxycholic acid regulated by
Blautia and
Eubacterium has also been studied
[64][94].
Table 1 summarizes the GM-related metabolites that have been reported to be associated with depression.
Table 1.
Examples of metabolites associated with gut microbiota that have been reported to be associated with depression.
Gut Microbiome Profiling Method |
Gut Microbiota |
Metabolomics Method |
Metabolic Pathway |
Subject/Sample Type |
Reference |
16S rRNA gene sequencing |
Phylum Firmicutes and genus Lactobacillus |
Targeted metabolomics/UHPLC-MS/MS |
Tryptophan metabolism |
Mice (CSDS)/feces and hippocampus |
[59][89] |
16S rRNA gene sequencing and metagenomic analysis |
Lachnospiraceae |
Untargeted metabolomics/UPLC-Q-TOF-MS and targeted metabolomics/UPLC-MS/MS |
Glycerophospholipid metabolism and γ-aminobutyric acid |
Mice (CUMS)/feces, liver, and hippocampus |
[42][72] |
16S rRNA gene sequencing and metagenomic analysis |
Phylum Firmicutes |
Untargeted metabolomics/UPLC-Q-TOF-MS and targeted metabolomics/UPLC-MS/MS |
Glycerophospholipid metabolism, tryptophan pathway, and short-chain fatty acids |
Mice (CRS)/feces, serum, and hippocampus |
[41][71] |
16S rRNA gene sequencing |
Phylum Firmicutes |
Untargeted metabolomics/UPLC-Q-TOF-MS |
Inflammation-related metabolites |
MDD patients/serum and feces |
[65][95] |
16S rRNA gene sequencing |
Phylum Firmicutes |
Untargeted metabolomics/GC-MS and LC-MS |
Glycerophospholipid metabolism |
Cynomolgus macaque of depression/feces, peripheral, and brain tissue |
[39][69] |
16S rRNA gene sequencing |
Genus Allobaculum and family Ruminococcaceae |
Targeted metabolomics/LC-MS/MS and GC-MS |
Acetic acid, propionic acid, pentanoic acid, norepinephrine, 5-hydroxy indole acetic acid, and 5-hydroxy tryptamine |
Mice (CRS)/feces and hypothalamus |
[66][96] |
16S rRNA gene sequencing |
Ten genera (most of them belonged to phylum Firmicutes) |
Targeted metabolomics/GC-MS and untargeted metabolomics/LC-Q-Orbitrap/MS |
Short chain fatty acids |
Rats (PSD)/feces and prefrontal cortex |
[67][97] |
16S rRNA gene sequencing |
Phylum Firmicutes, genus Blautia, and Streptococcus |
Untargeted metabolomics/GC-MS |
Lipid metabolism |
Rats (PSD)/feces |
[68][98] |
16S rRNA gene sequencing |
Actinobacteria and Bacteroidetes |
Untargeted metabolomics/LC-Q-Orbitrap/MS and GC-MS |
Glycerophospholipids |
Mice (CSDS)/feces and prefrontal cortex |
[40][70] |
Whole-genome shotgun metagenomic |
Genus Bacteroides, genera Blautia, and Eubacterium |
Untargeted metabolomics/GC-MS |
Amino acid metabolism (γ-aminobutyrate, phenylalanine, and tryptophan) |
MDD patients/feces |
[63][93] |
Viral metagenomics |
Microviridae, Podoviridae, and Siphoviridae |
Targeted metabolomics/UPLC-MS/MS |
Tryptophan metabolism |
Mice (CRS)/feces |
[69][99] |
16S rDNA amplification sequencing |
Deferribacteres, Proteobacteria, Verrucomicrobia, Actinobacteria, Desulfovibrio, Clostridium_IV, Helicobacter, Pseudoflavonifractor, and Akkermansia |
Untargeted metabolomics/LC-MS/MS |
Lipid metabolites, glycerophospholipid metabolism Pathway, and the retrograde endocannabinoid signaling pathway |
Atherosclerosis co-depression mice/feces |
[70][100] |
16S rRNA gene sequencing |
Turicibacteraceae, Turicibacterales, and Turicibacter |
Targeted metabolomics/UPLC-MS/MS |
Bile acids metabolism |
MDD patients/blood and feces |
[71][101] |
Metagenomics sequencing |
Ruminococcus bromii, Lactococcus chungangensis, and Streptococcus gallolyticus |
Targeted metabolomics/HPLC-MS/MS |
Lipid, vitamin, and carbohydrate metabolism |
MDD patients/blood and feces |
[72][102] |
16S rRNA gene sequencing |
Bacteroides |
Untargeted metabolomics/UPLC-Q-TOF-MS and targeted metabolomics/UPLC-MS/MS |
Tryptophan pathway metabolites and neurotransmitters |
MDD patients/feces, serum, and tissue samples |
[60][90] |
16S rRNA gene sequencing |
Phylum Firmicute, Bacteroidetes, genus Faecalibacterium, Roseburia, Subdoligranulum, and Agathobacter |
Untargeted metabolomics/UPLC-Q-TOF-MS |
Alpha-linolenic acid metabolism, biosynthesis of unsaturated fatty acids, ATP-binding cassette transporters, and bile secretion |
Systemic lupus erythematosus patients with depression/feces |
[73][103] |
16S rRNA gene sequencing |
Streptococcus, Phascolarctobacterium, Akkermansia, Coprococcus, and Streptococcus |
Targeted metabolomics/LC-MS/MS |
Indole-3- carboxyaldehyde |
MDD patients/feces |
[74][104] |
16S ribosomal RNA gene sequencing |
Family Lachnospiraceae, Muribaculaceae, and Oscillospiraceae |
Untargeted metabolomics/LC-Q-Orbitrap/MS |
Lipid and amino acid metabolism |
Rats (CUMS, CRS, SD, and LH)/feces |
[58][88] |
16S rRNA gene sequencing |
Alistipes indistinctus, Bacteroides ovatus, and Alistipes senegalensis |
Untargeted metabolomics/LC-Q-Orbitrap/MS |
D-pinitol, indoxyl sulfate, trimethylaminen-oxide, and 3 alpha, 7 alpha-dihydroxy-12-oxocholanoic acid |
Rats (CUMS)/feces |
[75][105] |