Tryptophan Depletion Modulates Tryptophanyl-tRNA Synthetase-Mediated High-Affinity Tryptophan Uptake into Human Cells

The high-affinity tryptophan uptake transport system in human cells

The L-tryptophan (Trp) transport system is highly selective for Trp with affinity in the nanomolar range. This transport system is augmented in human interferon (IFN)-γ-treated and indoleamine 2,3-dioxygenase 1 (IDO1)-expressing cells. Up-regulated cellular uptake of Trp causes a reduction in extracellular Trp and initiates immune suppression. Recent studies demonstrate that both IDO1 and tryptophanyl-tRNA synthetase (TrpRS), whose expression levels are up-regulated by IFN-γ, play a pivotal role in high-affinity Trp uptake into human cells. Furthermore, overexpression of tryptophan 2,3-dioxygenase (TDO2) elicits a similar effect as IDO1 on TrpRS-mediated high-affinity Trp uptake. In this review, we summarize recent findings regarding this Trp uptake system and put forward a possible molecular mechanism based on Trp deficiency induced by IDO1 or TDO2 and tryptophanyl-AMP production by TrpRS.

Tryptophan-Starved Human Cells Overexpressing Tryptophanyl-tRNA Synthetase Enhance High-Affinity Tryptophan Uptake via Enzymatic Production of Tryptophanyl-AMP

Our previous study demonstrated that L-tryptophan (Trp)-depleted cells display a marked enhancement in Trp uptake facilitated by extracellular tryptophanyl-tRNA synthetase (TrpRS). Here, we show that Trp uptake into TrpRS-overexpressing cells is also markedly elevated upon Trp starvation. These findings indicate that a Trp-deficient condition is critical for Trp uptake, not only into cells to which TrpRS protein has been added but also into TrpRS-overexpressing cells. We also show that overexpression of TrpRS mutants, which cannot synthesize tryptophanyl-AMP, does not promote Trp uptake, and that inhibition of tryptophanyl-AMP synthesis suppresses this uptake. Overall, these data suggest that tryptophanyl-AMP production by TrpRS is critical for high-affinity Trp uptake.

Combining Metabolomics and Interpretable Machine Learning to Reveal Plasma Metabolic Profiling and Biological Correlates of Alcohol-Dependent Inpatients: What About Tryptophan Metabolism Regulation?

Alcohol dependence (AD) is a condition of alcohol use disorder in which the drinkers frequently develop emotional symptoms associated with a continuous alcohol intake. AD characterized by metabolic disturbances can be quantitatively analyzed by metabolomics to identify the alterations in metabolic pathways. This study aimed to: i) compare the plasma metabolic profiling between healthy and AD-diagnosed individuals to reveal the altered metabolic profiles in AD, and ii) identify potential biological correlates of alcohol-dependent inpatients based on metabolomics and interpretable machine learning. Plasma samples were obtained from healthy (n = 42) and AD-diagnosed individuals (n = 43). The plasma metabolic differences between them were investigated using liquid chromatography-tandem mass spectrometry (AB SCIEX^® QTRAP 4500 system) in different electrospray ionization modes with scheduled multiple reaction monitoring scans. In total, 59 and 52 compounds were semi-quantitatively measured in positive and negative ionization modes, respectively. In addition, 39 metabolites were identified as important variables to contribute to the classifications using an orthogonal partial least squares-discriminant analysis (OPLS-DA) (VIP > 1) and also significantly different between healthy and AD-diagnosed individuals using univariate analysis (p-value < 0.05 and false discovery rate < 0.05). Among the identified metabolites, indole-3-carboxylic acid, quinolinic acid, hydroxy-tryptophan, and serotonin were involved in the tryptophan metabolism along the indole, kynurenine, and serotonin pathways. Metabolic pathway analysis revealed significant changes or imbalances in alanine, aspartate, glutamate metabolism, which was possibly the main altered pathway related to AD. Tryptophan metabolism interactively influenced other metabolic pathways, such as nicotinate and nicotinamide metabolism. Furthermore, among the OPLS-DA-identified metabolites, normetanephrine and ascorbic acid were demonstrated as suitable biological correlates of AD inpatients from our model using an interpretable, supervised decision tree classifier algorithm. These findings indicate that the discriminatory metabolic profiles between healthy and AD-diagnosed individuals may benefit researchers in illustrating the underlying molecular mechanisms of AD. This study also highlights the approach of combining metabolomics and interpretable machine learning as a valuable tool to uncover potential biological correlates. Future studies should focus on the global analysis of the possible roles of these differential metabolites and disordered metabolic pathways in the pathophysiology of AD.