Metabolomics Approach Reveals Integrated Metabolic Network Associated with Serotonin Deficiency

Abstract

Serotonin is an important neurotransmitter that broadly participates in various biological processes. While serotonin deficiency has been associated with multiple pathological conditions such as depression, schizophrenia, Alzheimer's disease and Parkinson's disease, the serotonin-dependent mechanisms remain poorly understood. This study therefore aimed to identify novel biomarkers and metabolic pathways perturbed by serotonin deficiency using metabolomics approach in order to gain new metabolic insights into the serotonin deficiency-related molecular mechanisms. Serotonin deficiency was achieved through pharmacological inhibition of tryptophan hydroxylase (Tph) using p-chlorophenylalanine (pCPA) or genetic knockout of the neuronal specific Tph2 isoform. This dual approach improved specificity for the serotonin deficiency-associated biomarkers while minimizing nonspecific effects of pCPA treatment or Tph2 knockout (Tph2-/-). Non-targeted metabolic profiling and a targeted pCPA dose-response study identified 21 biomarkers in the pCPA-treated mice while 17 metabolites in the Tph2-/- mice were found to be significantly altered compared with the control mice. These newly identified biomarkers were associated with amino acid, energy, purine, lipid and gut microflora metabolisms. Oxidative stress was also found to be significantly increased in the serotonin deficient mice. These new biomarkers and the overall metabolic pathways may provide new understanding for the serotonin deficiency-associated mechanisms under multiple pathological states.

Figure 1. Brain and serum serotonin concentrations of the a) pCPA-treated mice (n = 10) and b) Tph2-/- mice (n = 10).

Data are expressed as mean values ± standard deviation.

Figure 2. Multivariate statistical analysis results of serum metabolites in the pCPA-treated mice (n = 40) and the control mice (n = 40).

a) PCA scores plot (R²X = 0.447, Q² = 0.77) and b) OPLS-DA scores plot (R²X = 0.416, R²Y = 0.713, Q² = 0.57) in ESI+ mode, ^c) PCA scores plot (R²X = 0.455, Q² = 0.82) and d) OPLS-DA scores plot (R²X = 0.417, R²Y = 0.498, Q² = 0.66) in ESI- mode.

Figure 3. Heat map denoting fold changes (over normalized means) of the 21 biomarkers in mice injected with increasing dosages of pCPA and the control mice (n = 10).

Columns correspond to different mice groups, and rows correspond to the altered metabolites. Shades of red represent elevated levels of metabolite, and shades of green represent reduced levels of the metabolites.

Figure 4. Multivariate statistical analysis results of serum metabolites in the Tph2-/- mice (n = 20) and the control mice (n = 20).

a) PCA scores plot (R²X = 0.536, Q² = 0.84) and b) OPLS-DA scores plot (R²X = 0.518, R²Y = 0.453, Q² = 0.61) in ESI+ mode, c) PCA scores plot (R²X = 0.533, Q² = 0.86) and d) OPLS-DA scores plot (R²X = 0.493, R²Y = 0.467, Q² = 0.69) in ESI− mode.

Figure 5. An overview of the integrated metabolic pathway network in response to serotonin deficiency.

Red-labeled metabolites indicate up-regulation in mice with serotonin deficiency, while blue-labeled metabolites indicate the down-regulation compared with the control mice. Metabolite relationships were derived from HMDB and KEGG databases. Solid arrows represent direct metabolic reactions, and dashed arrows represent multiple reactions and indirect connections between two metabolites.