Tryptophan-kynurenine metabolism during acute alcohol withdrawal in patients with alcohol use disorder: The role of immune activation

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

Short-chain fatty acids suppresses astrocyte activation by amplifying Trp-AhR-AQP4 signaling in experimental autoimmune encephalomyelitis mice

The function of astrocytes in response to gut microbiota-derived signals has an important role in the pathophysiological processes of central nervous system (CNS) diseases. However, the specific effects of microbiota-derived metabolites on astrocyte activation have not been elucidated yet. Experimental autoimmune encephalomyelitis (EAE) was induced in female C57BL/6 mice as a classical MS model. The alterations of gut microbiota and the levels of short-chain fatty acids (SCFAs) were assessed after EAE induction. We observed that EAE mice exhibit low levels of Allobaculum, Clostridium_IV, Clostridium_XlVb, Lactobacillus genera, and microbial-derived SCFAs metabolites. SCFAs supplementation suppressed astrocyte activation by increasing the level of tryptophan (Trp)-derived AhR ligands that activating the AhR. The beneficial effects of SCFAs supplementation on the clinical scores, histopathological alterations, and the blood brain barrier (BBB)-glymphatic function were abolished by intracisterna magna injection of AAV-GFAP-shAhR. Moreover, SCFAs supplementation suppressed the loss of AQP4 polarity within astrocytes in an AhR-dependent manner. Together, SCFAs potentially suppresses astrocyte activation by amplifying Trp-AhR-AQP4 signaling in EAE mice. Our study demonstrates that SCFAs supplementation may serve as a viable therapy for inflammatory disorders of the CNS.

AI-based mining of biomedical literature: Applications for drug repurposing for the treatment of dementia

Neurodegenerative pathologies such as Alzheimer's disease, Parkinson's disease, Huntington's disease, Amyotrophic lateral sclerosis, Multiple sclerosis, HIV-associated neurocognitive disorder, and others significantly affect individuals, their families, caregivers, and healthcare systems. While there are no cures yet, researchers worldwide are actively working on the development of novel treatments that have the potential to slow disease progression, alleviate symptoms, and ultimately improve the overall health of patients. Huge volumes of new scientific information necessitate new analytical approaches for meaningful hypothesis generation. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases, such as PubMed. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. Here we focus on drugs that can be repurposed for dementia treatment as an outcome of neurodegenerative diseases. Therefore, we determined dementia-associated genes statistically highly ranked in other disease classes. Additionally, we report a mechanism for detecting genes common to multiple health conditions. These sets of genes were classified based on their presence in biological pathways, aiding in selecting candidates and biological processes that are exploitable with drug repurposing.

Author Summary

This manuscript outlines our project involving the application of AGATHA, an AI-based literature mining tool, to discover drugs with the potential for repurposing in the context of neurocognitive disorders. The primary objective is to identify connections between approved medications and specific health conditions through advanced statistical analysis, including techniques like Partial Least Squares Discriminant Analysis (PLSDA) and unsupervised clustering. The methodology involves grouping scientific terms related to different health conditions and genes, followed by building discrimination models to extract lists of disease-specific genes. These genes are then analyzed through pathway analysis to select candidates for drug repurposing.

Implications of Kynurenine Pathway Metabolism for the Immune System, Hypothalamic-Pituitary-Adrenal Axis, and Neurotransmission in Alcohol Use Disorder

In recent years, there has been a marked increase in interest in the role of the kynurenine pathway (KP) in mechanisms associated with addictive behavior. Numerous reports implicate KP metabolism in influencing the immune system, hypothalamic-pituitary-adrenal (HPA) axis, and neurotransmission, which underlie the behavioral patterns characteristic of addiction. An in-depth analysis of the results of these new studies highlights interesting patterns of relationships, and approaching alcohol use disorder (AUD) from a broader neuroendocrine-immune system perspective may be crucial to better understanding this complex phenomenon. In this review, we provide an up-to-date summary of information indicating the relationship between AUD and the KP, both in terms of changes in the activity of this pathway and modulation of this pathway as a possible pharmacological approach for the treatment of AUD.

Integrative Pharmacology in the Treatment of Substance Use Disorders

The detrimental physical, mental, and socioeconomic effects of substance use disorders (SUDs) have been apparent to the medical community for decades. However, it has become increasingly urgent in recent years to develop novel pharmacotherapies to treat SUDs. Currently, practitioners typically rely on monotherapy. Monotherapy has been shown to be superior to no treatment at all for most substance classes. However, many randomized controlled trials (RCTs) have revealed that monotherapy leads to poorer outcomes when compared with combination treatment in all specialties of medicine. The results of RCTs suggest that monotherapy frequently fails since multiple dysregulated pathways, enzymes, neurotransmitters, and receptors are involved in the pathophysiology of SUDs. As such, research is urgently needed to determine how various neurobiological mechanisms can be targeted by novel combination treatments to create increasingly specific yet exceedingly comprehensive approaches to SUD treatment. This article aims to review the neurobiology that integrates many pathophysiologic mechanisms and discuss integrative pharmacology developments that may ultimately improve clinical outcomes for patients with SUDs. Many neurobiological mechanisms are known to be involved in SUDs including dopaminergic, nicotinic, N-methyl-D-aspartate (NMDA), and kynurenic acid (KYNA) mechanisms. Emerging evidence indicates that KYNA, a tryptophan metabolite, modulates all these major pathophysiologic mechanisms. Therefore, achieving KYNA homeostasis by harmonizing integrative pathophysiology and pharmacology could prove to be a better therapeutic approach for SUDs. We propose KYNA-NMDA-α7nAChRcentric pathophysiology, the "conductor of the orchestra," as a novel approach to treat many SUDs concurrently. KYNA-NMDA-α7nAChR pathophysiology may be the "command center" of neuropsychiatry. To date, extant RCTs have shown equivocal findings across comparison conditions, possibly because investigators targeted single pathophysiologic mechanisms, hit wrong targets in underlying pathophysiologic mechanisms, and tested inadequate monotherapy treatment. We provide examples of potential combination treatments that simultaneously target multiple pathophysiologic mechanisms in addition to KYNA. Kynurenine pathway metabolism demonstrates the greatest potential as a target for neuropsychiatric diseases. The investigational medications with the most evidence include memantine, galantamine, and N-acetylcysteine. Future RCTs are warranted with novel combination treatments for SUDs. Multicenter RCTs with integrative pharmacology offer a promising, potentially fruitful avenue to develop novel therapeutics for the treatment of SUDs.

Kynurenine metabolite changes in individuals with alcohol use disorder: A systematic review and meta-analysis

OBJECTIVE

Growing evidence suggests an abnormal metabolism of kynurenine in individuals with alcohol use disorder (AUD). This systematic review and meta-analysis was aimed at assessing the possible differences in kynurenine metabolites between individuals with AUD and controls.

METHODS

We searched PubMed, Embase, and Web of Science databases and included any clinical studies comparing the peripheral blood levels of at least one metabolite, between individuals with AUD and controls without AUD. Random-effects meta-analyses were conducted to generate pooled standardized mean differences (SMD). Subgroup analyses and meta-regression analyses were conducted.

RESULTS

A total of seven eligible studies with 572 participants were included. The peripheral blood levels of kynurenine (SMD = 0.58; p = 0.004) along with the ratio of kynurenine and tryptophan (SMD = 0.73; p = 0.002) were higher in individuals with AUD, while kynurenic acid levels (SMD = -0.81; p = 0.003) were reduced in individuals with AUD compared to controls. The peripheral blood levels of tryptophan along with the ratio of kynurenic acid and kynurenine were unaltered. Subgroup analyses confirmed these results.

CONCLUSION

Our results suggested a shift in the tryptophan metabolism to the kynurenine pathway and a down-regulation of the potentially neuroprotective kynurenic acid in individuals with AUD.