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Liu, M.; Ma, W.; He, Y.; Sun, Z.; Yang, J. Key Metabolic Changes in Major Depressive Disorder. Encyclopedia. Available online: https://encyclopedia.pub/entry/51391 (accessed on 03 September 2024).
Liu M, Ma W, He Y, Sun Z, Yang J. Key Metabolic Changes in Major Depressive Disorder. Encyclopedia. Available at: https://encyclopedia.pub/entry/51391. Accessed September 03, 2024.
Liu, Mingxia, Wen Ma, Yi He, Zuoli Sun, Jian Yang. "Key Metabolic Changes in Major Depressive Disorder" Encyclopedia, https://encyclopedia.pub/entry/51391 (accessed September 03, 2024).
Liu, M., Ma, W., He, Y., Sun, Z., & Yang, J. (2023, November 10). Key Metabolic Changes in Major Depressive Disorder. In Encyclopedia. https://encyclopedia.pub/entry/51391
Liu, Mingxia, et al. "Key Metabolic Changes in Major Depressive Disorder." Encyclopedia. Web. 10 November, 2023.
Key Metabolic Changes in Major Depressive Disorder
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This entry is adapted from the peer-reviewed paper 10.3390/molecules28217430

Major depressive disorder (MDD) is a serious mental illness with a heavy social burden, but its underlying molecular mechanisms remain unclear. Mass spectrometry (MS)-based metabolomics is providing new insights into the heterogeneous pathophysiology, diagnosis, treatment, and prognosis of MDD by revealing multi-parametric biomarker signatures at the metabolite level.

major depressive disorder mass spectrometry metabolomics biomarkers

1. Introduction

Major depressive disorder (MDD) is a debilitating and widespread psychiatric illness characterized by enduring and substantial feelings of sadness, inferiority, and despair [1]. Notably, the World Health Organization has listed depression as the third leading cause of disease burden across the world and has predicted that the disease will rank first by 2030 [2]. However, due to the complicated pathogenesis of depression and the lack of pathophysiological biomarkers, the diagnosis and treatment of MDD using subjective evaluation and “trial-and-error” approaches often involve considerable error rates [3].
Metabolites are the downstream products of transcription and translation, and changes in those closest to a given phenotype can reflect many pathological or internal changes in biochemical pathways [4]. Metabolic disorders are considered to be an etiological factor in MDD, and metabolite analysis can certainly improve the understanding of the many pathological processes involved in MDD [5][6]. Metabolomics is the culmination of the cascade of “omics” technologies. It combines advanced analytical instrumentations with pattern recognition algorithms to reveal and monitor changes in metabolite profiles in subjects based on their disease status or response to medical or other interventions [7]. Advances in metabolomics have opened new avenues for exploring mechanisms related to MDD.
The main analytical platforms in metabolomics are nuclear magnetic resonance (NMR) and mass spectrometry (MS) [8]. NMR enables non-invasive analysis and relatively fast and straightforward metabolite annotation, but is less sensitive than MS. In-depth explanations and discussions of NMR-based metabolomics can be found in various excellent studies and reviews [9][10][11]. MS is widely used in metabolomics analyses. It combines rapidly developing separation technologies—primarily liquid chromatography (LC) and gas chromatography (GC)—to allow qualitative and quantitative analysis of multiple organic molecules in complex biological matrices (serum, plasma, urine, tissue, etc.) with high specificity, sensitivity, and throughput, and low sample consumption [12].

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]. 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]. 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]. 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]. 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]. 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].

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]. 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]. Significant negative associations of the branched-chain amino acids valine and leucine with depression were identified using untargeted metabolomics [20]. 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].
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]. A similar conclusion was also reached by Liu et al. [23]. Yun et al. studied the relationship between the tryptophan–kynurenine pathway and the painful physical symptoms of MDD [24]. 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]. 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]. The tryptophan–kynurenine pathway is also linked to the inflammatory state of patients with MDD [27]. Haroon et al. analyzed kynurenine pathway metabolites and inflammatory markers in the plasma and CSF of depressed patients [28]. 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]. Zheng et al. also found that C-reactive protein and kynurenic acid/quinolinic acid are independently associated with white matter integrity in MDD [30]. 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]; however, Ryan et al. reported that the kynurenic acid pathway was mobilized by electroconvulsive therapy [32].

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]. 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]. Similarly, a panel of 111 lipid species was capable of distinguishing SCZ from MDD (AUC = 0.920) [35]. 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]. Glycerophospholipid metabolism was also associated with the pathogenesis of PSD in humans [37][38]. 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]. 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]. 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]. 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].

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]. 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]. The altered metabolism of acylcarnitines may link mitochondrial dysfunction to depression via impairment of fatty acid β-oxidation [48]. 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]. Acylcarnitine profiles also help to distinguish different phenotypic subtypes of MDD, such as core depression, anxious depression, and neurovegetative symptoms of melancholia [51]. Given that glycogen is the main energy source for most higher organisms, Qin’s group used stable isotope-resolved metabolomics with a 13C6-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].

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]. 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]. 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]. Zhang et al. showed that Bacteroides species enriched in the GM of MDD patients had differing effects on the susceptibility to depressive behaviors [60]. 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]. 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]. 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]. The antidepressant effect of chenodeoxycholic acid regulated by Blautia and Eubacterium has also been studied [64]. 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]
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]
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]
16S rRNA gene
sequencing		 Phylum Firmicutes Untargeted
metabolomics/UPLC-Q-TOF-MS		 Inflammation-related metabolites MDD patients/serum and feces [65]
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]
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]
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]
16S rRNA gene
sequencing		 Phylum Firmicutes, genus Blautia, and Streptococcus Untargeted metabolomics/GC-MS Lipid metabolism Rats (PSD)/feces [68]
16S rRNA gene
sequencing		 Actinobacteria and Bacteroidetes Untargeted metabolomics/LC-Q-Orbitrap/MS and GC-MS Glycerophospholipids Mice (CSDS)/feces and prefrontal cortex [40]
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]
Viral metagenomics Microviridae, Podoviridae, and Siphoviridae Targeted metabolomics/UPLC-MS/MS Tryptophan metabolism Mice (CRS)/feces [69]
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]
16S rRNA gene
sequencing		 Turicibacteraceae, Turicibacterales, and Turicibacter Targeted metabolomics/UPLC-MS/MS Bile acids metabolism MDD patients/blood and feces [71]
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]
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]
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]
16S rRNA gene
sequencing		 Streptococcus, Phascolarctobacterium, Akkermansia, Coprococcus,
and Streptococcus		 Targeted metabolomics/LC-MS/MS Indole-3-
carboxyaldehyde		 MDD patients/feces [74]
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]
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]
Abbreviations: major depressive disorder (MDD), chronic social-defeat stress (CSDS), chronic unpredictable mild stress (CUMS), chronic restraint stress (CRS), post-stroke depression (PSD), social defeat (SD), and learned helplessness (LH).

References

  1. Beurel, E.; Toups, M.; Nemeroff, C.B. The bidirectional relationship of depression and inflammation: Double trouble. Neuron 2020, 107, 234–256.
  2. World Health Organization. Depression. Available online: https://www.who.int/news-room/fact-sheets/detail/ (accessed on 25 July 2021).
  3. Marwaha, S.; Palmer, E.; Suppes, T.; Cons, E.; Young, A.H.; Upthegrove, R. Novel and emerging treatments for major depression. Lancet 2023, 401, 141–153.
  4. Qiu, S.; Guo, S.F.; Yang, Q.; Xie, Y.Q.; Tang, S.Q.; Zhang, A.H. Innovation in identifying metabolites from complex metabolome-Highlights of recent analytical platforms and protocols. Front. Chem. 2023, 11, 1129717.
  5. Fries, G.R.; Saldana, V.A.; Finnstein, J.; Rein, T. Molecular pathways of major depressive disorder converge on the synapse. Mol. Psychiatry 2023, 28, 284–297.
  6. Pinto, B.; Conde, T.; Domingues, I.; Domingues, M.R. Adaptation of lipid profiling in depression disease and treatment: A critical review. Int. J. Mol. Sci. 2022, 23, 2032.
  7. Duan, J.J.; Xie, P. The potential for metabolomics in the study and treatment of major depressive disorder and related conditions. Expert Rev. Proteom. 2020, 17, 309–322.
  8. Letertre, M.P.M.; Dervilly, G.; Giraudeau, P. Combined nuclear magnetic resonance spectroscopy and mass spectrometryapproaches for metabolomics. Anal. Chem. 2021, 93, 500–518.
  9. Edison, A.S.; Colonna, M.; Gouveia, G.J.; Holderman, N.R.; Judge, M.T.; Shen, X.N.; Zhang, S.C. NMR: Unique strengths that enhance modern metabolomics research. Anal. Chem. 2021, 93, 478–499.
  10. Xie, X.F.; Shi, Y.; Ma, L.; Yang, W.Q.; Pu, J.C.; Shen, Y.Q.; Liu, Y.Y.; Zhang, H.P.; Lv, F.J.; Hu, L.B. Altered neurometabolite levels in the brains of patients with depression: A systematic analysis of magnetic resonance spectroscopy studies. J. Affect. Disord. 2023, 328, 95–102.
  11. Rydin, A.O.; Milaneschi, Y.; Quax, R.; Li, J.; Bosch, J.A.; Schoevers, R.A.; Giltay, E.J.; Penninx, B.W.J.H.; Lamers, F. A network analysis of depressive symptoms and metabolomics. Psychol. Med. 2023, 1–10, (Online ahead of print).
  12. Collins, S.L.; Koo, I.; Peters, J.M.; Smith, P.B.; Patterson, A.D. Current challenges and recent developments in mass spectrometry-based metabolomics. Annu. Rev. Anal. Chem. 2021, 14, 467–487.
  13. Jiang, Y.; Zou, D.; Li, Y.; Gu, S.; Dong, J.; Ma, X.; Xu, S.; Wang, F.; Huang, J.H. Monoamine neurotransmitters control basic emotions and affect major depressive disorders. Pharmaceuticals 2022, 15, 1203.
  14. Li, Y.F. A hypothesis of monoamine (5-HT) – Glutamate/GABA long neural circuit: Aiming for fast-onset antidepressant discovery. Pharmacol. Therapeut. 2020, 208, 107494.
  15. Li, Y.; Chen, Z.; Zhao, J.; Yu, H.; Chen, X.; He, Y.; Tian, Y.; Wang, Y.; Chen, C.; Cheng, K.; et al. Neurotransmitter and related metabolic profiling in the nucleus accumbens of chronic unpredictable mild stress-induced anhedonia-like rats. Front. Behav. Neurosci. 2022, 16, 862683.
  16. Xu, Q.Y.; Jiang, M.C.; Gu, S.M.; Zhang, X.L.; Feng, G.K.; Ma, X.J.; Xu, S.J.; Wu, E.X.; Huang, J.S.; Wang, F.S. Metabolomics changes in brain-gut axis after unpredictable chronic mild stress. Psychopharmacology 2022, 239, 729–743.
  17. Zhang, Z.W.; Gao, C.S.; Zhang, H.; Yang, J.; Wang, Y.P.; Pan, L.B.; Yu, H.; He, C.Y.; Luo, H.B.; Zhao, Z.X.; et al. Morinda officinalis oligosaccharides increase serotonin in the brain and ameliorate depression via promoting 5-hydroxytryptophan production in the gut microbiota. Acta Pharm. Sin. B 2022, 12, 3298–3312.
  18. Geng, C.; Guo, Y.; Wang, C.; Liao, D.; Han, W.; Zhang, J.; Jiang, P. Systematic impacts of chronic unpredictable mild stress on metabolomics in rats. Sci. Rep. 2020, 10, 700.
  19. Ho, C.S.H.; Tay, G.W.N.; Wee, H.N.; Ching, J. The utility of amino acid metabolites in the diagnosis of major depressive disorder and correlations with depression severity. Int. J. Mol. Sci. 2023, 24, 2231.
  20. Whipp, A.M.; Heinonen-Guzejev, M.; Pietiläinen, K.H.; van Kamp, I.; Kaprio, J. Branched-chain amino acids linked to depression in young adults. Front. Neurosci. 2022, 16, 935858.
  21. Tian, Y.; Wu, Z.; Wang, Y.; Chen, C.; He, Y.; Lan, T.; Li, Y.; Bai, M.; Yu, H.; Chen, X.; et al. Alterations of neurotransmitters and related metabolites in the habenula from CUMS-susceptible and -resilient rats. Biochem. Biophys. Res. Commun. 2021, 534, 422–428.
  22. Brum, M.; Nieberler, M.; Kehrwald, C.; Knopf, K.; Brunkhorst-Kanaan, N.; Etyemez, S.; Allers, K.A.; Bittner, R.A.; Slattery, D.A.; McNeill, R.V.; et al. Phase—And disorder-specific differences in peripheral metabolites of the kynurenine pathway in major depression, bipolar affective disorder and schizophrenia. World J. Biol. Psychiatry 2023, 24, 564–577.
  23. Liu, J.C.; Yu, H.; Li, R.; Zhou, C.H.; Shi, Q.Q.; Guo, L.; He, H. A preliminary comparison of plasma tryptophan metabolites and medium- and long-chain fatty acids in adult patients with major depressive disorder and schizophrenia. Medicina 2023, 59, 413.
  24. Yun, Y.; Zhang, Q.; Zhao, W.; Ma, T.; Fan, H.; Bai, L.; Ma, B.; Qi, S.; Wang, Z.; An, H.; et al. Relationship between the tryptophan-kynurenine pathway and painful physical symptoms in patients with major depressive disorder. J. Psychosom. Res. 2022, 163, 111069.
  25. Liaqat, H.; Parveen, A.; Kim, S.Y. Neuroprotective natural products’ regulatory effects on depression via gut-brain axis targeting tryptophan. Nutrients 2022, 14, 3270.
  26. Li, C.C.; Ye, F.; Xu, C.X.; Jiang, N.; Chang, Q.; Liu, X.M.; Pan, R.L. Tryptophan-kynurenine metabolic characterization in the gut and brain of depressive-like rats induced by chronic restraint stress. J. Affect. Disord. 2023, 328, 273–286.
  27. Arteaga-Henriquez, G.; Burger, B.; Weidinger, E.; Grosse, L.; Moll, N.; Schuetze, G.; Schwarz, M.; Wijkhuijs, A.; Op de Beeck, G.; Berghmans, R.; et al. Activation and deactivation steps in the tryptophan breakdown pathway in major depressive disorder: A link to the monocyte inflammatory state of patients. Prog. Neuropsychopharmacol. Biol. Psychiatry 2021, 107, 110226.
  28. Haroon, E.; Welle, J.R.; Woolwine, B.J.; Goldsmith, D.R.; Baer, W.; Patel, T.; Felger, J.C.; Miller, A.H. Associations among peripheral and central kynurenine pathway metabolites and inflammation in depression. Neuropsychopharmacology 2020, 45, 998–1007.
  29. Paul, E.R.; Schwieler, L.; Erhardt, S.; Boda, S.; Trepci, A.; Kampe, R.; Asratian, A.; Holm, L.; Yngve, A.; Dantzer, R.; et al. Peripheral and central kynurenine pathway abnormalities in major depression. Brain Behav. Immun. 2022, 101, 136–145.
  30. Zheng, H.; Teague, T.K.; Yeh, F.-C.; Burrows, K.; Figueroa-Hall, L.K.; Aupperle, R.L.; Khalsa, S.S.; Paulus, M.P.; Savitz, J. C-Reactive protein and the kynurenic acid to quinolinic acid ratio are independently associated with white matter integrity in major depressive disorder. Brain Behav. Immun. 2022, 105, 180–189.
  31. Tateishi, H.; Setoyama, D.; Kang, D.; Matsushima, J.; Kojima, R.; Fujii, Y.; Mawatari, S.; Kikuchi, J.; Sakemura, Y.; Fukuchi, J.; et al. The changes in kynurenine metabolites induced by rTMS in treatment-resistant depression: A pilot study. J. Psychiatr. Res. 2021, 138, 194–199.
  32. Ryan, K.M.; Allers, K.A.; McLoughlin, D.M.; Harkin, A. Tryptophan metabolite concentrations in depressed patients before and after electroconvulsive therapy. Brain Behav. Immun. 2020, 83, 153–162.
  33. Miao, G.; Deen, J.; Struzeski, J.B.; Chen, M.; Zhang, Y.; Cole, S.A.; Fretts, A.M.; Lee, E.T.; Howard, B.V.; Fiehn, O.; et al. Plasma lipidomic profile of depressive symptoms: A longitudinal study in a large sample of community-dwelling American Indians in the strong heart study. Mol. Psychiatr. 2023, 28, 2480–2489.
  34. Zhang, T.; Guo, L.; Li, R.; Wang, F.; Yang, W.M.; Yang, J.B.; Cui, Z.Q.; Zhou, C.H.; Chen, Y.H.; Yu, H.; et al. Alterations of plasma lipids in adult women with major depressive disorder and bipolar depression. Front. Psychiatry 2022, 13, 927817.
  35. Wang, F.; Guo, L.; Zhang, T.; Cui, Z.Q.; Wang, J.K.; Zhang, C.; Xue, F.; Zhou, C.H.; Li, B.J.; Tan, Q.R.; et al. Alterations in plasma lipidomic profiles in adult patients with schizophrenia and major depressive disorder. Medicina-Lithuania 2022, 58, 1509.
  36. Jiang, W.; Chen, J.; Gong, L.; Liu, F.; Zhao, H.; Mu, J. Alteration of glycerophospholipid metabolism in hippocampus of post-stroke depression rats. Neurochem. Res. 2022, 47, 2052–2063.
  37. Wen, L.L.; Yan, C.M.; Zheng, W.C.; Li, Y.; Wang, Y.H.; Qu, M. Metabolic alterations and related biological functions of post-stroke depression in ischemic stroke patients. Neuropsych. Dis. Treat. 2023, 19, 1555–1564.
  38. Mao, Q.; Tian, T.; Chen, J.; Guo, X.; Zhang, X.; Zou, T. Serum metabolic profiling of late-pregnant women with antenatal depressive symptoms. Front. Psychiatry 2021, 12, 679451.
  39. Zheng, P.; Wu, J.; Zhang, H.; Perry, S.W.; Yin, B.; Tan, X.; Chai, T.; Liang, W.; Huang, Y.; Li, Y.; et al. The gut microbiome modulates gut-brain axis glycerophospholipid metabolism in a region-specific manner in a nonhuman primate model of depression. Mol. Psychiatry 2021, 26, 2380–2392.
  40. Gong, X.; Huang, C.; Yang, X.; Chen, J.; Pu, J.; He, Y.; Xie, P. Altered fecal metabolites and colonic glycerophospholipids were associated with abnormal composition of gut microbiota in a depression model of mice. Front. Neurosci. 2021, 15, 701355.
  41. Tian, T.; Mao, Q.; Xie, J.; Wang, Y.; Shao, W.H.; Zhong, Q.; Chen, J.J. Multi-omics data reveals the disturbance of glycerophospholipid metabolism caused by disordered gut microbiota in depressed mice. J. Adv. Res. 2022, 39, 135–145.
  42. Xie, J.; Zhong, Q.; Wu, W.T.; Chen, J.J. Multi-omics data reveals the important role of glycerophospholipid metabolism in the crosstalk between gut and brain in depression. J. Transl. Med. 2023, 21, 93.
  43. Jiang, Y.; Qin, M.; Teng, T.; Li, X.; Yu, Y.; Wang, J.; Wu, H.; He, Y.; Zhou, X.; Xie, P. Identification of sex-specific plasma biomarkers using metabolomics for major depressive disorder in children and adolescents. Front. Psychiatry 2022, 13, 929207.
  44. Schumacher, F.; Edwards, M.J.; Mühle, C.; Carpinteiro, A.; Wilson, G.C.; Wilker, B.; Soddemann, M.; Keitsch, S.; Scherbaum, N.; Müller, B.W.; et al. Ceramide levels in blood plasma correlate with major depressive disorder severity and its neutralization abrogates depressive behavior in mice. J. Biol. Chem. 2022, 298, 102185.
  45. Homorogan, C.; Nitusca, D.; Enatescu, V.; Schubart, P.; Moraru, C.; Socaciu, C.; Marian, C. Untargeted plasma metabolomic profiling in patients with major depressive disorder using ultra-high performance liquid chromatography coupled with mass spectrometry. Metabolites 2021, 11, 466.
  46. Gu, X.; Ke, S.; Wang, Q.; Zhuang, T.; Xia, C.; Xu, Y.; Yang, L.; Zhou, M. Energy metabolism in major depressive disorder: Recent advances from omics technologies and imaging. Biomed. Pharmacother. 2021, 141, 111869.
  47. Wang, C.; Cui, C.; Xu, P.; Zhu, L.; Xue, H.; Chen, B.; Jiang, P. Targeting PDK2 rescues stress-induced impaired brain energy metabolism. Mol. Psychiatry 2023. (Online ahead of print).
  48. Liu, T.; Deng, K.; Xue, Y.; Yang, R.; Yang, R.; Gong, Z.; Tang, M. Carnitine and depression. Front. Nutr. 2022, 9, 853058.
  49. Ait Tayeb, A.E.K.; Colle, R.; El-Asmar, K.; Chappell, K.; Acquaviva-Bourdain, C.; David, D.J.; Trabado, S.; Chanson, P.; Feve, B.; Becquemont, L.; et al. Plasma acetyl-l-carnitine and l-carnitine in major depressive episodes: A case-control study before and after treatment. Psychol. Med. 2023, 53, 2307–2316.
  50. Ait Tayeb, A.E.K.; Colle, R.; Chappell, K.; El-Asmar, K.; Acquaviva-Bourdain, C.; David, D.J.; Trabado, S.; Chanson, P.; Feve, B.; Becquemont, L.; et al. Metabolomic profiles of 38 acylcarnitines in major depressive episodes before and after treatment. Psychol. Med. 2023. (Online ahead of print).
  51. Ahmed, A.T.; MahmoudianDehkordi, S.; Bhattacharyya, S.; Arnold, M.; Liu, D.; Neavin, D.; Moseley, M.A.; Thompson, J.W.; Williams, L.S.J.; Louie, G.; et al. Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes. J. Affect. Disord. 2020, 264, 90–97.
  52. Linghu, T.; Zhao, Y.; Wu, W.; Gao, Y.; Tian, J.; Qin, X. Novel targets for ameliorating energy metabolism disorders in depression through stable isotope-resolved metabolomics. Biochim. Biophys. Acta. Bioenerg. 2022, 1863, 148578.
  53. Ling-Hu, T.; Liu, S.B.; Gao, Y.; Han, Y.M.; Tian, J.S.; Qin, X.M. Stable isotope-resolved metabolomics reveals the abnormal brain glucose catabolism in depression based on chronic unpredictable mild stress rats. J. Proteome Res. 2021, 20, 3549–3558.
  54. Tian, J.S.; Zhao, Y.H.; Ling-Hu, T.; Wu, W.Z.; Wang, X.X.; Ji, C.; Zhao, W.D.; Han, Y.M.; Qin, X.M. A novel insight for high-rate and low-efficiency glucose metabolism in depression through stable isotope-resolved metabolomics in CUMS-induced rats. J. Affect. Disord. 2023, 331, 121–129.
  55. Tian, J.S.; Wu, W.Z.; Liu, S.B.; Ling-Hu, T.; Zhao, Y.H.; Gao, Y.; Qin, X.M. Stable isotope-resolved metabolomics studies on corticosteroid-induced PC12 cells: A strategy for evaluating glucose catabolism in an in vitro model of depression. J. Proteome Res. 2022, 21, 788–797.
  56. Ji, C.; Zhao, W.D.; Zheng, J.; Zhou, S.; Tian, J.S.; Han, Y.M.; Qin, X.M. Mechanism of the effect of Xiaoyao powder treatment on exercise capacity of depressed rats-A stable isotope tracer metabolomic study. J. Liq. Chromatogr. R. T. 2022, 45, 143–155.
  57. Liu, L.; Wang, H.; Chen, X.; Zhang, Y.; Zhang, H.; Xie, P. Gut microbiota and its metabolites in depression: From pathogenesis to treatment. EBioMedicine 2023, 90, 104527.
  58. Liu, X.; Li, X.; Teng, T.; Jiang, Y.; Xiang, Y.; Fan, L.; Yu, Y.; Zhou, X.; Xie, P. Comparative analysis of gut microbiota and fecal metabolome features among multiple depressive animal models. J. Affect. Disord. 2022, 314, 103–111.
  59. Xie, J.; Wu, W.T.; Chen, J.J.; Zhong, Q.; Wu, D.; Niu, L.; Wang, S.; Zeng, Y.; Wang, Y. Tryptophan metabolism as bridge between gut microbiota and brain in chronic social defeat stress-induced depression mice. Front. Cell. Infect. Microbiol. 2023, 13, 1121445.
  60. Zhang, Y.; Fan, Q.; Hou, Y.; Zhang, X.; Yin, Z.; Cai, X.; Wei, W.; Wang, J.; He, D.; Wang, G.; et al. Bacteroides species differentially modulate depression-like behavior via gut-brain metabolic signaling. Brain Behav. Immun. 2022, 102, 11–22.
  61. Xie, J.; Wang, Y.; Zhong, Q.; Bai, S.J.; Zhou, C.J.; Tian, T.; Chen, J.J. Associations between disordered microbial metabolites and changes of neurotransmitters in depressed mice. Front. Cell. Infect. Microbiol. 2022, 12, 906303.
  62. Hu, Z.Y.; Zhao, P.H.; Liao, A.M.; Pan, L.; Zhang, J.; Dong, Y.Q.; Huang, J.H.; He, W.W.; Ou, X.Q. Fermented wheat germ alleviates depression-like behavior in rats with chronic and unpredictable mild stress. Foods 2023, 12, 920.
  63. Yang, J.; Zheng, P.; Li, Y.; Wu, J.; Tan, X.M.; Zhou, J.J.; Sun, Z.L.; Chen, X.; Zhang, G.F.; Zhang, H.P.; et al. Landscapes of bacterial and metabolic signatures and their interaction in major depressive disorders. Sci. Adv. 2020, 6, eaba8555.
  64. Li, H.; Zhu, X.; Xu, J.; Li, L.; Kan, W.; Bao, H.; Xu, J.; Wang, W.; Yang, Y.; Chen, P.; et al. The FXR mediated anti-depression effect of CDCA underpinned its therapeutic potentiation for MDD. Int. Immunopharmacol. 2023, 115, 109626.
  65. Bai, S.J.; Xie, J.; Bai, H.L.; Tian, T.; Zou, T.; Chen, J.J. Gut microbiota-derived inflammation-related serum metabolites as potential biomarkers for major depressive disorder. J. Inflamm. Res. 2021, 14, 3755–3766.
  66. Wu, M.; Tian, T.; Mao, Q.; Zou, T.; Zhou, C.J.; Xie, J.; Chen, J.J. Associations between disordered gut microbiota and changes of neurotransmitters and short-chain fatty acids in depressed mice. Transl. Psychiat. 2020, 10, 350.
  67. Jiang, W.; Chen, J.; Gong, L.; Liu, F.; Zhao, H.; Yan, Z.; Li, Y.; Zhang, J.; Xiao, M.; Mu, J. Microbiota-derived short-chain fatty acids may participate in post-stroke depression by regulating host’s lipid metabolism. J. Psychiatr. Res. 2023, 161, 426–434.
  68. Jiang, W.; Gong, L.; Liu, F.; Ren, Y.; Mu, J. Alteration of gut microbiome and correlated lipid metabolism in post-sroke depression. Front. Cell. Infect. Microbiol. 2021, 11, 663967.
  69. Duan, J.; Wang, W.; Jiang, T.; Bai, X.; Liu, C. Viral metagenomics combined with metabolomics reveals the role of gut viruses in mouse model of depression. Front. Microbiol. 2022, 13, 1046894.
  70. Hu, K.; Liao, X.X.; Wu, X.Y.; Wang, R.; Hu, Z.W.; Liu, S.Y.; He, W.F.; Zhou, J.J. Effects of the lipid metabolites and the gut microbiota in ApoE-/- mice on atherosclerosis co-depression from the microbiota-gut-brain axis. Front. Mol. Biosci. 2022, 9, 786492.
  71. Sun, N.; Zhang, J.; Wang, J.; Liu, Z.; Wang, X.; Kang, P.; Yang, C.; Liu, P.; Zhang, K. Abnormal gut microbiota and bile acids in patients with first-episode major depressive disorder and correlation analysis. Psychiatry Clin. Neurosci. 2022, 76, 321–328.
  72. Zhao, H.; Jin, K.; Jiang, C.; Pan, F.; Wu, J.; Luan, H.; Zhao, Z.; Chen, J.; Mou, T.; Wang, Z.; et al. A pilot exploration of multi-omics research of gut microbiome in major depressive disorders. Transl. Psychiatry 2022, 12, 8.
  73. Yao, H.; Yang, H.; Wang, Y.; Xing, Q.; Yan, L.; Chai, Y. Gut microbiome and fecal metabolic alteration in systemic lupus erythematosus patients with depression. Front. Cell. Infect. Microbiol. 2022, 12, 1040211.
  74. Zhang, X.; Hou, Y.; Li, Y.; Wei, W.; Cai, X.; Shao, H.; Yuan, Y.; Zheng, X. Taxonomic and metabolic signatures of gut microbiota for assessing the severity of depression and anxiety in major depressive disorder patients. Neuroscience 2022, 496, 179–189.
  75. Li, Y.; Li, J.; Cheng, R.; Liu, H.; Zhao, Y.; Liu, Y.; Chen, Y.; Sun, Z.; Zhai, Z.; Wu, M.; et al. Alteration of the gut microbiome and correlated metabolism in a rat model of long-term depression. Front. Cell. Infect. Microbiol. 2023, 13, 1116277.
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Subjects: Biochemistry & Molecular Biology
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Mingxia Liu
,
Wen Ma
,
Yi He
,
Zuoli Sun
,
Jian Yang
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Revisions: 2 times (View History)
Update Date: 10 Nov 2023
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