TSPAN5 influences serotonin and kynurenine: pharmacogenomic mechanisms related to alcohol use disorder and acamprosate treatment response

Gene expression differences associated with alcohol use disorder in human brain

Excessive alcohol consumption is a leading cause of preventable death worldwide. To improve understanding of neurobiological mechanisms associated with alcohol use disorder (AUD) in humans, we compared gene expression data from deceased individuals with and without AUD across two addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC). Bulk RNA-seq data from NAc and DLPFC (N ≥50 with AUD, ≥46 non-AUD) were analyzed for differential gene expression using modified negative binomial regression adjusting for technical and biological covariates. The region-level results were meta-analyzed with those from an independent dataset (NNAc = 28 AUD, 29 non-AUD; NPFC = 66 AUD, 77 non-AUD). We further tested for heritability enrichment of AUD-related phenotypes, gene co-expression networks, gene ontology enrichment, and drug repurposing. We identified 176 differentially expressed genes (DEGs; 12 in both regions, 78 in NAc only, 86 in DLPFC only) for AUD in our new dataset. After meta-analyzing with published data, we identified 476 AUD DEGs (25 in both regions, 29 in NAc only, 422 in PFC only). Of these DEGs, 17 were significant when looked up in GWAS of problematic alcohol use or drinks per week. Gene co-expression analysis showed both concordant and unique gene networks across brain regions. We also identified 29 and 436 drug compounds that target DEGs from our meta-analysis in NAc and PFC, respectively. This study identified robust AUD-associated DEGs, contributing novel neurobiological insights into AUD and highlighting genes targeted by known drug compounds, generating opportunity for drug repurposing to treat AUD.

A systematic review and meta-analysis on the transcriptomic signatures in alcohol use disorder

Currently available clinical treatments on alcohol use disorder (AUD) exhibit limited efficacy and new druggable targets are required. One promising approach to discover new molecular treatment targets involves the transcriptomic profiling of brain regions within the addiction neurocircuitry, utilizing animal models and postmortem brain tissue from deceased patients with AUD. Unfortunately, such studies suffer from large heterogeneity and small sample sizes. To address these limitations, we conducted a cross-species meta-analysis on transcriptome-wide data obtained from brain tissue of patients with AUD and animal models. We integrated 36 cross-species transcriptome-wide RNA-expression datasets with an alcohol-dependent phenotype vs. controls, following the PRISMA guidelines. In total, we meta-analyzed 964 samples - 502 samples from the prefrontal cortex (PFC), 282 nucleus accumbens (NAc) samples, and 180 from amygdala (AMY). The PFC had the highest number of differentially expressed genes (DEGs) across rodents, monkeys, and humans. Commonly dysregulated DEGs suggest conserved cross-species mechanisms for chronic alcohol consumption/AUD comprising MAPKs as well as STAT, IRF7, and TNF. Furthermore, we identified numerous unique gene sets that might contribute individually to these conserved mechanisms and also suggest novel molecular aspects of AUD. Validation of the transcriptomic alterations on the protein level revealed interesting targets for further investigation. Finally, we identified a combination of DEGs that are commonly regulated across different brain tissues as potential biomarkers for AUD. In summary, we provide a compendium of genes that are assessable via a shiny app, and describe signaling pathways, and physiological and cellular processes that are altered in AUD that require future studies for functional validation.

IL17RB genetic variants are associated with acamprosate treatment response in patients with alcohol use disorder: A proteomics-informed genomics study

Acamprosate is a Food and Drug Administration (FDA) approved medication for the treatment of alcohol use disorder (AUD). However, only a subset of patients achieves optimal treatment outcomes. Currently, no biological measures are utilized to predict response to acamprosate treatment. We applied our established pharmaco-omics informed genomics strategy to identify potential biomarkers associated with acamprosate treatment response. Specifically, our previous open-label acamprosate clinical trial recruited 442 patients with AUD who were treated with acamprosate for three months. We first performed proteomics using baseline plasma samples to identify potential biomarkers associated with acamprosate treatment outcomes. Next, we applied our established "proteomics-informed genome-wide association study (GWAS)" research strategy, and identified 12 proteins, including interleukin-17 receptor B (IL17RB), associated with acamprosate treatment response.​ A GWAS for IL17RB concentrations identified several genome-wide significant signals. Specifically, the top hit single nucleotide polymorphism (SNP) rs6801605 with a minor allele frequency of 38% in the European American population mapped 4 kilobase (Kb) upstream of IL17RB, and intron 1 of the choline dehydrogenase (CHDH) gene on chromosome 3 (p: 4.8E-20). The variant genotype (AA) for the SNP rs6801605 was associated with lower IL17RB protein expression. In addition, we identified a series of genetic variants in IL17RB that were associated with acamprosate treatment outcomes. Furthermore, the variantgenotypes for all of those IL17RB SNPs were protective for alcohol relapse. Finally, we demonstrated that the basal level of mRNA expression of IL17RB was inversely correlated with those of nuclear factor-κB (NF-κB) subunits, and a significantly higher expression of NF-κB subunits was observed in AUD patients who relapsed to alcohol use. In summary, this study illustrates that IL17RB genetic variants might contribute to acamprosate treatment outcomes. This series of studies represents an important step toward generating functional hypotheses that could be tested to gain insight into mechanisms underlying acamprosate treatment response phenotypes. (The ClinicalTrials.gov Identifier: NCT00662571).

Molecular mechanisms involved in alcohol craving, IRF3, and endoplasmic reticulum stress: a multi-omics study

Alcohol use disorder (AUD) is the most prevalent substance use disorder worldwide. Acamprosate and naltrexone are anti-craving drugs used in AUD pharmacotherapy. However, molecular mechanisms underlying their anti-craving effect remain unclear. This study utilized a patient-derived induced pluripotent stem cell (iPSC)-based model system and anti-craving drugs that are used to treat AUD as "molecular probes" to identify possible mechanisms associated with alcohol craving. We examined the pathophysiology of craving and anti-craving drugs by performing functional genomics studies using iPSC-derived astrocytes and next-generation sequencing. Specifically, RNA sequencing performed using peripheral blood mononuclear cells from AUD patients with extreme values for alcohol craving intensity prior to treatment showed that inflammation-related pathways were highly associated with alcohol cravings. We then performed a genome-wide assessment of chromatin accessibility and gene expression profiles of induced iPSC-derived astrocytes in response to ethanol or anti-craving drugs. Those experiments identified drug-dependent epigenomic signatures, with IRF3 as the most significantly enriched motif in chromatin accessible regions. Furthermore, the activation of IRF3 was associated with ethanol-induced endoplasmic reticulum (ER) stress which could be attenuated by anti-craving drugs, suggesting that ER stress attenuation might be a target for anti-craving agents. In conclusion, we found that craving intensity was associated with alcohol consumption and treatment outcomes. Our functional genomic studies suggest possible relationships among craving, ER stress, IRF3 and the actions of anti-craving drugs.

Comparing stem cells, transdifferentiation and brain organoids as tools for psychiatric research

The inaccessibility of neurons coming directly from patients has hindered our understanding of mental illnesses at the cellular level. To overcome this obstacle, six different cellular approaches that carry the genetic vulnerability to psychiatric disorders are currently available: Olfactory Neuroepithelial Cells, Mesenchymal Stem Cells, Pluripotent Monocytes, Induced Pluripotent Stem Cells, Induced Neuronal cells and more recently Brain Organoids. Here we contrast advantages and disadvantages of each of these six cell-based methodologies. Neuronal-like cells derived from pluripotent monocytes are presented in more detail as this technique was recently used in psychiatry for the first time. Among the parameters used for comparison are; accessibility, need for reprograming, time to deliver differentiated cells, differentiation efficiency, reproducibility of results and cost. We provide a timeline on the discovery of these cell-based methodologies, but, our main goal is to assist researchers selecting which cellular approach is best suited for any given project. This manuscript also aims to help readers better interpret results from the published literature. With this goal in mind, we end our work with a discussion about the differences and similarities between cell-based techniques and postmortem research, the only currently available tools that allow the study of mental illness in neurons or neuronal-like cells coming directly from patients.

Epigenetic regulation of GABA catabolism in iPSC-derived neurons: The molecular links between FGF21 and histone methylation

OBJECTIVE

Fibroblast growth factor 21 (FGF21) analogs have been tested as potential therapeutics for substance use disorders. Prior research suggests that FGF21 administration might affect alcohol consumption and reward behaviors. Our recent report showed that plasma FGF21 levels were positively correlated with alcohol use in patients with alcohol use disorder (AUD). FGF21 has a short half-life (0.5-2 h) and crosses the blood-brain barrier. Therefore, we set out to identify molecular mechanisms for both the naïve form of FGF21 and a long-acting FGF21 molecule (PF-05231023) in induced pluripotent stem cell (iPSC)-derived forebrain neurons.

METHODS

We performed RNA-seq in iPSC-derived forebrain neurons treated with naïve FGF21 or PF-05231023 at physiologically relevant concentrations. We obtained plasma levels of FGF21 and GABA from our previous AUD clinical trial (n = 442). We performed ELISA for FGF21 in both iPSC-derived forebrain neurons and forebrain organoids. We determined protein interactions using co-immunoprecipitation. Finally, we applied ChIP assays to confirm the occupancy of REST, EZH2 and H3K27me3 by FGF21 using iPSC-derived forebrain neurons with and without drug exposure.

RESULTS

We identified 4701 and 1956 differentially expressed genes in response to naïve FGF21 or PF-05231023, respectively (FDR < 0.05). Notably, 974 differentially expressed genes overlapped between treatment with naïve FGF21 and PF-05231023. REST was the most important upstream regulator of differentially expressed genes. The GABAergic synapse pathway was the most significant pathway identified using the overlapping genes. We also observed a significant positive correlation between plasma FGF21 and GABA concentrations in AUD patients. In parallel, FGF21 and PF-05231023 significantly induced GABA levels in iPSC-derived neurons. Finally, functional genomics studies showed a drug-dependent occupancy of REST, EZH2, and H3K27me3 in the promoter regions of genes involved in GABA catabolism which resulted in transcriptional repression.

CONCLUSIONS

Our results highlight a significant role in the epigenetic regulation of genes involved in GABA catabolism related to FGF21 action. (The ClinicalTrials.gov Identifier: NCT00662571).

Acamprosate reduces ethanol intake in the rat by a combined action of different drug components

Alcohol misuse accounts for a sizeable proportion of the global burden of disease, and Campral® (acamprosate; calcium-bis-(N-acetylhomotaurinate)) is widely used as relapse prevention therapy. The mechanism underlying its effect has in some studies been attributed to the calcium moiety and not to the N-acetylhomotaurine part of the compound. We recently suggested that the dopamine elevating effect of acamprosate is mediated both by N-acetylhomotaurine and calcium in a glycine receptor dependent manner. Here we aimed to explore, by means of in vivo microdialysis, if our previous study using local administration was functionally relevant and if systemic administration of the sodium salt of N-acetylhomotaurine (sodium acamprosate; 200 mg/kg, i.p.) enhanced the effects of calcium chloride (CaCl2; 73.5 mg/kg, i.p.) on nucleus accumbens (nAc) dopamine and/or taurine levels in male Wistar rats. In addition, we investigated the impact of regular acamprosate and the combination of CaCl2 and N-acetylhomotaurine on the alcohol deprivation effect (ADE). Finally, we assessed if N-acetylhomotaurine potentiates the ethanol-intake reducing effect of CaCl2 in a two-bottle choice voluntary ethanol consumption model followed by an ADE paradigm. Systemic administration of regular acamprosate, sodium acamprosate and CaCl2 all trended to increase nAc dopamine whereas the combination of CaCl2 and sodium acamprosate produced a significant increase. Sodium acamprosate elevated extracellular taurine levels without additional effects of CaCl2. Ethanol intake was significantly reduced by systemic administration of CaCl2 without additional effects of the combination of CaCl2 and sodium acamprosate. Both acamprosate and CaCl2 combined with sodium acamprosate blocked the ADE following acute treatment. The data presented suggest that CaCl2 and N-acetylhomotaurine act in concert on a neurochemical level, but calcium appears to have the predominant effect on ethanol intake.

Machine Learning and Pharmacogenomics at the Time of Precision Psychiatry

Traditional medicine and biomedical sciences are reaching a turning point because of the constantly growing impact and volume of Big Data. Machine Learning (ML) techniques and related algorithms play a central role as diagnostic, prognostic, and decision-making tools in this field. Another promising area becoming part of everyday clinical practice is personalized therapy and pharmacogenomics. Applying ML to pharmacogenomics opens new frontiers to tailored therapeutical strategies to help clinicians choose drugs with the best response and fewer side effects, operating with genetic information and combining it with the clinical profile. This systematic review aims to draw up the state-of-the-art ML applied to pharmacogenomics in psychiatry. Our research yielded fourteen papers; most were published in the last three years. The sample comprises 9,180 patients diagnosed with mood disorders, psychoses, or autism spectrum disorders. Prediction of drug response and prediction of side effects are the most frequently considered domains with the supervised ML technique, which first requires training and then testing. The random forest is the most used algorithm; it comprises several decision trees, reduces the training set's overfitting, and makes precise predictions. ML proved effective and reliable, especially when genetic and biodemographic information were integrated into the algorithm. Even though ML and pharmacogenomics are not part of everyday clinical practice yet, they will gain a unique role in the next future in improving personalized treatments in psychiatry.

Plasma TNFSF10 levels associated with acamprosate treatment response in patients with alcohol use disorder

Acamprosate is an anti-craving drug used in alcohol use disorder (AUD) pharmacotherapy. However, only a subset of patients achieves optimal treatment outcomes. The identification of predictive biomarkers of acamprosate treatment response in patients with AUD would be a substantial advance in addiction medicine. We designed this study to use proteomics data as a quantitative biological trait as a step toward identifying inflammatory modulators that might be associated with acamprosate treatment outcomes. The NIAAA-funded Mayo Clinic Center for the Individualized Treatment of Alcoholism study had previously recruited 442 AUD patients who received 3 months of acamprosate treatment. However, only 267 subjects returned for the 3-month follow-up visit and, as a result, had treatment outcome information available. Baseline alcohol craving intensity was the most significant predictor of acamprosate treatment outcomes. We performed plasma proteomics using the Olink target 96 inflammation panel and identified that baseline plasma TNF superfamily member 10 (TNFSF10) concentration was associated with alcohol craving intensity and variation in acamprosate treatment outcomes among AUD patients. We also performed RNA sequencing using baseline peripheral blood mononuclear cells from AUD patients with known acamprosate treatment outcomes which revealed that inflammation-related pathways were highly associated with relapse to alcohol use during the 3 months of acamprosate treatment. These observations represent an important step toward advancing our understanding of the pathophysiology of AUD and molecular mechanisms associated with acamprosate treatment response. In conclusion, applying omics-based approaches may be a practical approach for identifying biologic markers that could potentially predict alcohol craving intensity and acamprosate treatment response.

HSP90 Mediates IFNγ-Induced Adaptive Resistance to Anti-PD-1 Immunotherapy

SIGNIFICANCE

This study reveals an HSP90-centric, iron-modulated mechanism that confers immunosuppression, offering potential therapeutic targets for interfering with acquired resistance to the most prevalent anticancer immunotherapies.

MiR-322-5p Alleviates Cell Injury and Impairment of Cognitive Function in Vascular Dementia by Targeting TSPAN5

PURPOSE

As the population ages, the incidence of clinical dementia has been rising around the world. It has been reported that microRNAs act as key diagnostic biomarkers and targets for various neurological conditions, including dementia. MiR-322-5p has been revealed to play an important role in multiple diseases. In this study, we aimed to investigate the role and regulatory mechanism of miR-322-5p in vascular dementia.

MATERIALS AND METHODS

In this study, neonatal rat neurons (NRNs) were subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) to induce cell injury. The animals were subjected to permanent bilateral occlusion of the carotid arteries (2-vessel occlusion, 2VO) to induce the model of chronic brain hypoperfusion.

RESULTS

MiR-322-5p expression was significantly downregulated in the neurons exposed to OGD/R and the hippocampi of 2VO rats. Overexpression of miR-322-5p ameliorated cell apoptosis and the inflammatory response in vitro. In a mechanistic study, miR-322-5p was confirmed to directly target and negatively regulate tetraspanin 5 (TSPAN5) in cultured NRNs. Moreover, overexpression of TSPAN5 could counteract the effects of miR-322-5p overexpression on cell apoptosis and the inflammatory response in OGD/R-treated neurons. More importantly, miR-322-5p improved cognitive ability and inhibited inflammatory production in 2VO rats.

CONCLUSION

Overall, the results suggest that miR-322-5p alleviates vascular dementia development by targeting TSPAN5. This discovery may provide a potential therapeutic target for dementia.

Genetic Variants Associated with Acamprosate Treatment Response in Alcohol Use Disorder Patients: A Multiple Omics Study

Background and Purpose

Acamprosate is an anti‐craving drug used for the pharmacotherapy of alcohol use disorder (AUD). However, only some patients achieve optimal therapeutic outcomes. This study was designed to explore differences in metabolomic profiles between patients who maintained sobriety and those who relapsed, to determine whether those differences provide insight into variation in acamprosate treatment response phenotypes.

Experimental Approach

We previously conducted an acamprosate trial involving 442 AUD patients, and 267 of these subjects presented themselves for a 3‐month follow‐up. The primary outcome was abstinence. Clinical information, genomic data and metabolomics data were collected. Baseline plasma samples were assayed using targeted metabolomics.

Key Results

Baseline plasma arginine, threonine, α‐aminoadipic acid and ethanolamine concentrations were associated with acamprosate treatment outcomes and baseline craving intensity, a measure that has been associated with acamprosate treatment response. We next applied a pharmacometabolomics‐informed genome‐wide association study (GWAS) strategy to identify genetic variants that might contribute to variations in plasma metabolomic profiles that were associated with craving and/or acamprosate treatment outcome. Gene expression data for induced pluripotent stem cell‐derived forebrain astrocytes showed that a series of genes identified during the metabolomics‐informed GWAS were ethanol responsive. Furthermore, a large number of those genes could be regulated by acamprosate. Finally, we identified a series of single nucleotide polymorphisms that were associated with acamprosate treatment outcomes.

Conclusion and Implications

These results serve as an important step towards advancing our understanding of disease pathophysiology and drug action responsible for variation in acamprosate response and alcohol craving in AUD patients.

Pharmacogenetics of Addiction Therapy

Drug addiction is a serious relapsing disease that has high costs to society and to the individual addicts. Treatment of these addictions is still in its nascency, with only a few examples of successful therapies. Therapeutic response depends upon genetic, biological, social, and environmental components. A role for genetic makeup in the response to treatment has been shown for several addiction pharmacotherapies with response to treatment based on individual genetic makeup. In this chapter, we will discuss the role of genetics in pharmacotherapies, specifically for cocaine, alcohol, and opioid dependences. The continued elucidation of the role of genetics should aid in the development of new treatments and increase the efficacy of existing treatments.

Multi-omics driven predictions of response to acute phase combination antidepressant therapy: a machine learning approach with cross-trial replication

Combination antidepressant pharmacotherapies are frequently used to treat major depressive disorder (MDD). However, there is no evidence that machine learning approaches combining multi-omics measures (e.g., genomics and plasma metabolomics) can achieve clinically meaningful predictions of outcomes to combination pharmacotherapy. This study examined data from 264 MDD outpatients treated with citalopram or escitalopram in the Mayo Clinic Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) and 111 MDD outpatients treated with combination pharmacotherapies in the Combined Medications to Enhance Outcomes of Antidepressant Therapy (CO-MED) study to predict response to combination antidepressant therapies. To assess whether metabolomics with functionally validated single-nucleotide polymorphisms (SNPs) improves predictability over metabolomics alone, models were trained/tested with and without SNPs. Models trained with PGRN-AMPS' and CO-MED's escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 76.6% (p < 0.01; AUC: 0.85) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs. Then, models trained solely with PGRN-AMPS' escitalopram/citalopram patients predicted response in CO-MED's combination pharmacotherapy patients with accuracies of 75.3% (p < 0.05; AUC: 0.84) without and 77.5% (p < 0.01; AUC: 0.86) with SNPs, demonstrating cross-trial replication of predictions. Plasma hydroxylated sphingomyelins were prominent predictors of treatment outcomes. To explore the relationship between SNPs and hydroxylated sphingomyelins, we conducted multi-omics integration network analysis. Sphingomyelins clustered with SNPs and metabolites related to monoamine neurotransmission, suggesting a potential functional relationship. These results suggest that integrating specific metabolites and SNPs achieves accurate predictions of treatment response across classes of antidepressants. Finally, these results motivate functional investigation into how sphingomyelins might influence MDD pathophysiology, antidepressant response, or both.

Selective Serotonin Reuptake Inhibitor Pharmaco-Omics: Mechanisms and Prediction

Selective serotonin reuptake inhibitors (SSRIs) are a standard of care for the pharmacotherapy of patients suffering from Major Depressive Disorder (MDD). However, only one-half to two-thirds of MDD patients respond to SSRI therapy. Recently, a "multiple omics" research strategy was applied to identify genetic differences between patients who did and did not respond to SSRI therapy. As a first step, plasma metabolites were assayed using samples from the 803 patients in the PGRN-AMPS SSRI MDD trial. The metabolomics data were then used to "inform" genomics by performing a genome-wide association study (GWAS) for plasma concentrations of the metabolite most highly associated with clinical response, serotonin (5-HT). Two genome-wide or near genome-wide significant single nucleotide polymorphism (SNP) signals were identified, one that mapped near the TSPAN5 gene and another across the ERICH3 gene, both genes that are highly expressed in the brain. Knocking down TSPAN5 and ERICH3 resulted in decreased 5-HT concentrations in neuroblastoma cell culture media and decreased expression of enzymes involved in 5-HT biosynthesis and metabolism. Functional genomic studies demonstrated that ERICH3 was involved in clathrin-mediated vesicle formation and TSPAN5 was an ethanol-responsive gene that may be a marker for response to acamprosate pharmacotherapy of alcohol use disorder (AUD), a neuropsychiatric disorder highly co-morbid with MDD. In parallel studies, kynurenine was the plasma metabolite most highly associated with MDD symptom severity and application of a metabolomics-informed pharmacogenomics approach identified DEFB1 and AHR as genes associated with variation in plasma kynurenine levels. Both genes also contributed to kynurenine-related inflammatory pathways. Finally, a multiply replicated predictive algorithm for SSRI clinical response with a balanced predictive accuracy of 76% (compared with 56% for clinical data alone) was developed by including the SNPs in TSPAN5, ERICH3, DEFB1 and AHR. In summary, application of a multiple omics research strategy that used metabolomics to inform genomics, followed by functional genomic studies, identified novel genes that influenced monoamine biology and made it possible to develop a predictive algorithm for SSRI clinical outcomes in MDD. A similar pharmaco-omic research strategy might be broadly applicable for the study of other neuropsychiatric diseases and their drug therapy.