Relationships of Ischemic Stroke Occurrence and Outcome with Gene Variants Encoding Enzymes of Tryptophan Metabolism

Tryptophan metabolism and ischemic stroke: An intricate balance

Ischemic stroke, which is characterized by hypoxia and ischemia, triggers a cascade of injury responses, including neurotoxicity, inflammation, oxidative stress, disruption of the blood-brain barrier, and neuronal death. In this context, tryptophan metabolites and enzymes, which are synthesized through the kynurenine and 5-hydroxytryptamine pathways, play dual roles. The delicate balance between neurotoxic and neuroprotective substances is a crucial factor influencing the progression of ischemic stroke. Neuroprotective metabolites, such as kynurenic acid, exert their effects through various mechanisms, including competitive blockade of N-methyl-D-aspartate receptors, modulation of α7 nicotinic acetylcholine receptors, and scavenging of reactive oxygen species. In contrast, neurotoxic substances such as quinolinic acid can hinder the development of vascular glucose transporter proteins, induce neurotoxicity mediated by reactive oxygen species, and disrupt mitochondrial function. Additionally, the enzymes involved in tryptophan metabolism play major roles in these processes. Indoleamine 2,3-dioxygenase in the kynurenine pathway and tryptophan hydroxylase in the 5-hydroxytryptamine pathway influence neuroinflammation and brain homeostasis. Consequently, the metabolites generated through tryptophan metabolism have substantial effects on the development and progression of ischemic stroke. Stroke treatment aims to restore the balance of various metabolite levels; however, precise regulation of tryptophan metabolism within the central nervous system remains a major challenge for the treatment of ischemic stroke. Therefore, this review aimed to elucidate the complex interactions between tryptophan metabolites and enzymes in ischemic stroke and develop targeted therapies that can restore the delicate balance between neurotoxicity and neuroprotection.

Systematic Insights into the Relationship between the Microbiota-Gut-Brain Axis and Stroke with the Focus on Tryptophan Metabolism

Stroke, as a serious cerebral vascular disease with high incidence and high rates of disability and mortality, has limited therapeutic options due to the narrow time window. Compelling evidence has highlighted the significance of the gut microbiota and gut-brain axis as critical regulatory factors affecting stroke. Along the microbiota-gut-brain axis, tryptophan metabolism further acquires increasing attention for its intimate association with central nervous system diseases. For the purpose of exploring the potential role of tryptophan metabolism in stroke and providing systematic insights into the intricate connection of the microbiota-gut-brain axis with the pathological procedure of stroke, this review first summarized the practical relationship between microbiota and stroke by compiling the latest case-control research. Then, the microbiota-gut-brain axis, as well as its interaction with stroke, were comprehensively elucidated on the basis of the basic anatomical structure and physiological function. Based on the crosstalk of microbiota-gut-brain, we further focused on the tryptophan metabolism from the three major metabolic pathways, namely, the kynurenine pathway, serotonin pathway, and microbial pathway, within the axis. Moreover, the effects of tryptophan metabolism on stroke were appreciated and elaborated here, which is scarcely found in other reviews. Hopefully, the systematic illustration of the mechanisms and pathways along the microbiota-gut-brain axis will inspire more translational research from metabolic perspectives, along with more attention paid to tryptophan metabolism as a promising pharmaceutical target in order to reduce the risk of stroke, mitigate the stroke progression, and ameliorate the stroke prognosis.

Combining metabolomics and network pharmacology to investigate the protective effect of Jiawei Xinglou Chengqi Granules in ischemic stroke

Jiawei Xinglou Chengqi Granule (JXCG) is an effective herbal medicine for the treatment of ischemic stroke (IS). JXCG has been shown to effectively ameliorate cerebral ischemic symptoms in clinical practice, but the underlying mechanisms are unclear. In this study, we investigated the mechanisms of action of JXCG in the treatment of IS by combining metabolomics with network pharmacology. The chemical composition of JXCG was analyzed using ultra-high performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS). Ultra-high performance liquid chromatography-tandem time-of-flight mass spectrometry (UHPLC-Q-TOF MS) untargeted metabolomics were used to identify differential metabolites within metabolic pathways. Network pharmacology was applied to mine potential targets of JXCG in the treatment of IS. The identified key targets were validated by constructing an integrated network of metabolomics and network pharmacology and by molecular docking using Cytoscape. The effect of JXCG on IS was evaluated in vivo, and the predicted targets and pathways of JXCG in IS therapy were assessed using immunoblotting. Combining metabolomics and network pharmacology, we identified the therapeutic targets of JXCG for IS. Notably, JXCG lessened neuronal damage and reduced cerebral infarct size in rats with IS. Western blot analysis showed that JXCG upregulated PRKCH and downregulated PRKCE and PRKCQ proteins. Our combined network pharmacology and metabolomics findings showed that JXCG may have therapeutic potential in the treatment of IS by targeting multiple factors and pathways.

Research on the Graphical Model Structure Characteristic of Strong Exogeneity Based on Twin Network Method and Its Application in Causal Inference

Strong exogeneity is an important assumption in the study of causal inference, but it is difficult to identify according to its definition. The twin network method provides a graphical model tool for analyzing the variable relationship, involving the actual world and the hypothetical world, which facilitates the investigating of strong exogeneity. In this paper, the graphical model structure characteristic of strong exogeneity is investigated based on the twin network method. Compared with other derivation methods of graphical diagnosis, the method based on the twin network is more concise, clearer, and easier to understand. Under the condition of strong exogeneity, it is easy to estimate the probability of causation based on observational data. As an example, the application of graphical model structure characteristic of strong exogeneity in causal inference in the context of lung cancer simple sets (LUCAS) is illustrated.