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.

Machine Learning Identifies Smartwatch-Based Physiological Biomarker for Predicting Disruptive Behavior in Children: A Feasibility Study.

Objective: Parents frequently purchase and inquire about smartwatch devices to monitor child behaviors and functioning. This pilot study examined the feasibility and accuracy of using smartwatch monitoring for the prediction of disruptive behaviors. Methods: The study enrolled children (N = 10) aged 7-10 years hospitalized for the treatment of disruptive behaviors. The study team completed continuous behavioral phenotyping during study participation. The machine learning protocol examined severe behavioral outbursts (operationalized as episodes that preceded physical restraint) for preparing the training data. Supervised machine learning methods were trained with cross-validation to predict three behavior states-calm, playful, and disruptive. Results: The participants had a 90% adherence rate for per protocol smartwatch use. Decision trees derived conditional dependencies of heart rate, sleep, and motor activity to predict behavior. A cross-validation demonstrated 80.89% accuracy of predicting the child's behavior state using these conditional dependencies. Conclusion: This study demonstrated the feasibility of 7-day continuous smartwatch monitoring for children with severe disruptive behaviors. A machine learning approach characterized predictive biomarkers of impending disruptive behaviors. Future validation studies will examine smartwatch physiological biomarkers to enhance behavioral interventions, increase parental engagement in treatment, and demonstrate target engagement in clinical trials of pharmacological agents for young children.

Myocardial Recovery in Recent Onset Dilated Cardiomyopathy: Role of CDCP1 and Cardiac Fibrosis.

BACKGROUND: Dilated cardiomyopathy (DCM) is a major cause of heart failure and carries a high mortality rate. Myocardial recovery in DCM-related heart failure patients is highly variable, with some patients having little or no response to standard drug therapy. A genome-wide association study may agnostically identify biomarkers and provide novel insight into the biology of myocardial recovery in DCM. METHODS: A genome-wide association study for change in left ventricular ejection fraction was performed in 686 White subjects with recent-onset DCM who received standard pharmacotherapy. Genome-wide association study signals were subsequently functionally validated and studied in relevant cellular models to understand molecular mechanisms that may have contributed to the change in left ventricular ejection fraction. RESULTS: The genome-wide association study identified a highly suggestive locus that mapped to the 5'-flanking region of the CDCP1 (CUB [complement C1r/C1s, Uegf, and Bmp1] domain containing protein 1) gene (rs6773435; P=7.12×10-7). The variant allele was associated with improved cardiac function and decreased CDCP1 transcription. CDCP1 expression was significantly upregulated in human cardiac fibroblasts (HCFs) in response to the PDGF (platelet-derived growth factor) signaling, and knockdown of CDCP1 significantly repressed HCF proliferation and decreased AKT (protein kinase B) phosphorylation. Transcriptomic profiling after CDCP1 knockdown in HCFs supported the conclusion that CDCP1 regulates HCF proliferation and mitosis. In addition, CDCP1 knockdown in HCFs resulted in significantly decreased expression of soluble ST2 (suppression of tumorigenicity-2), a prognostic biomarker for heart failure and inductor of cardiac fibrosis. CONCLUSIONS: CDCP1 may play an important role in myocardial recovery in recent-onset DCM and mediates its effect primarily by attenuating cardiac fibrosis.

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).

PISTACHIo (PreemptIon of diSrupTive behAvior in CHIldren): real-time monitoring of sleep and behavior of children 3-7 years old receiving parent-child interaction therapy augment with artificial intelligence - the study protocol, pilot study.

BACKGROUND: Emotional behavior problems (EBP) are the most common and persistent mental health issues in early childhood. Early intervention programs are crucial in helping children with EBP. Parent-child interaction therapy (PCIT) is an evidence-based therapy designed to address personal difficulties of parent-child dyads as well as reduce externalizing behaviors. In clinical practice, parents consistently struggle to provide accurate characterizations of EBP symptoms (number, timing of tantrums, precipitating events) even from the week before in their young children. The main aim of the study is to evaluate feasibility of the use of smartwatches in children aged 3-7 years with EBP. METHODS: This randomized double-blind controlled study aims to recruit a total of 100 participants, consisting of 50 children aged 3-7 years with an EBP measure rated above the clinically significant range (T-score ≥ 60) (Eyberg Child Behavior Inventory-ECBI; Eyberg & Pincus, 1999) and their parents who are at least 18 years old. Participants are randomly assigned to the artificial intelligence-PCIT group (AI-PCIT) or the PCIT-sham biometric group. Outcome parameters include weekly ECBI and Pediatric Sleep Questionnaire (PSQ) as well as Child Behavior Checklist (CBCL) obtained weeks 1, 6, and 12 of the study. Two smartphone applications (Garmin connect and mEMA) and a wearable Garmin smartwatch are used collect the data to monitor step count, sleep, heart rate, and activity intensity. In the AI-PCIT group, the mEMA application will allow for the ecological momentary assessment (EMA) and will send behavioral alerts to the parent. DISCUSSION: Real-time predictive technologies to engage patients rely on daily commitment on behalf of the participant and recurrent frequent smartphone notifications. Ecological momentary assessment (EMA) provides a way to digitally phenotype in-the-moment behavior and functioning of the parent-child dyad. One of the study's goals is to determine if AI-PCIT outcomes are superior in comparison with standard PCIT. Overall, we believe that the PISTACHIo study will also be able to determine tolerability of smartwatches in children aged 3-7 with EBP and could participate in a fundamental shift from the traditional way of assessing and treating EBP to a more individualized treatment plan based on real-time information about the child's behavior. TRIAL REGISTRATION: The ongoing clinical trial study protocol conforms to the international Consolidated Standards of Reporting Trials (CONSORT) guidelines and is registered in clinicaltrials.gov (ID: NCT05077722), an international clinical trial registry.

Genetic predisposition to major depressive disorder differentially impacts alcohol consumption and high-risk drinking situations in men and women with alcohol use disorder.

Lifetime history of major depressive disorder (MDD) has a sex-specific association with pretreatment alcohol consumption in patients with alcohol dependence. Here, we investigated the association of genetic load for MDD estimated using a polygenic risk score (PRS) with pretreatment alcohol consumption assessed with Timeline Follow Back in a sample of 287 men and 156 women meeting DSM-IV-TR criteria for alcohol dependence. Preferred drinking situations were assessed using the Inventory of Drug Taking Situations (IDTS). Linear models were used to test for association of normalized alcohol consumption measures with the MDD-PRS, adjusting for ancestry, age, sex, and number of days sober at baseline. We fit models both with and without adjustment for MDD history and alcohol-use-related PRSs as covariates. Higher MDD-PRS was associated with lower 90-day total alcohol consumption in men (ß = -0.16, p = 0.0012) but not in women (ß = 0.11, p = 0.18). The association of MDD-PRS with IDTS measures was also sex-specific: higher MDD-PRS was associated with higher propensity to drink in temptation-related situations in women, while the opposite (negative association)was found in men. MDD-PRS was not associated with lifetime MDD history in our sample, and adjustment for lifetime MDD and alcohol-related PRSs did not impact the results. Our results suggest that genetic load for MDD impacts pretreatment alcohol consumption in a sex-specific manner, which is similar to, but independent from, the effect of history of MDD. The clinical implications of these findings and contributing biological and psychological factors should be investigated in future studies.

Network science approach elucidates integrative genomic-metabolomic signature of antidepressant response and lifetime history of attempted suicide in adults with major depressive disorder.

Background: Individuals with major depressive disorder (MDD) and a lifetime history of attempted suicide demonstrate lower antidepressant response rates than those without a prior suicide attempt. Identifying biomarkers of antidepressant response and lifetime history of attempted suicide may help augment pharmacotherapy selection and improve the objectivity of suicide risk assessments. Towards this goal, this study sought to use network science approaches to establish a multi-omics (genomic and metabolomic) signature of antidepressant response and lifetime history of attempted suicide in adults with MDD. Methods: Single nucleotide variants (SNVs) which associated with suicide attempt(s) in the literature were identified and then integrated with a) p180-assayed metabolites collected prior to antidepressant pharmacotherapy and b) a binary measure of antidepressant response at 8 weeks of treatment using penalized regression-based networks in 245 'Pharmacogenomics Research Network Antidepressant Medication Study (PGRN-AMPS)' and 103 'Combining Medications to Enhance Depression Outcomes (CO-MED)' patients with major depressive disorder. This approach enabled characterization and comparison of biological profiles and associated antidepressant treatment outcomes of those with (N = 46) and without (N = 302) a self-reported lifetime history of suicide attempt. Results: 351 SNVs were associated with suicide attempt(s) in the literature. Intronic SNVs in the circadian genes CLOCK and ARNTL (encoding the CLOCK:BMAL1 heterodimer) were amongst the top network analysis features to differentiate patients with and without a prior suicide attempt. CLOCK and ARNTL differed in their correlations with plasma phosphatidylcholines, kynurenine, amino acids, and carnitines between groups. CLOCK and ARNTL-associated phosphatidylcholines showed a positive correlation with antidepressant response in individuals without a prior suicide attempt which was not observed in the group with a prior suicide attempt. Conclusion: Results provide evidence for a disturbance between CLOCK:BMAL1 circadian processes and circulating phosphatidylcholines, kynurenine, amino acids, and carnitines in individuals with MDD who have attempted suicide. This disturbance may provide mechanistic insights for differential antidepressant pharmacotherapy outcomes between patients with MDD with versus without a lifetime history of attempted suicide. Future investigations of CLOCK:BMAL1 metabolic regulation in the context of suicide attempts may help move towards biologically-augmented pharmacotherapy selection and stratification of suicide risk for subgroups of patients with MDD and a lifetime history of attempted suicide.

Single cell transcriptomics reveals distinct transcriptional responses to oxycodone and buprenorphine by iPSC-derived brain organoids from patients with opioid use disorder.

The opioid epidemic represents a national crisis. Oxycodone is one of the most prescribed opioid medications in the United States, whereas buprenorphine is currently the most prescribed medication for opioid use disorder (OUD) pharmacotherapy. Given the extensive use of prescription opioids and the global opioid epidemic, it is essential to understand how opioids modulate brain cell type function at the single-cell level. We performed single nucleus RNA-seq (snRNA-seq) using iPSC-derived forebrain organoids from three male OUD subjects in response to oxycodone, buprenorphine, or vehicle for seven days. We utilized the snRNA-seq data to identify differentially expressed genes following drug treatment using the Seurat integrative analysis pipeline. We utilized iPSC-derived forebrain organoids and single-cell sequencing technology as an unbiased tool to study cell-type-specific and drug-specific transcriptional responses. After quality control filtering, we analyzed 25787 cells and identified sixteen clusters using unsupervised clustering analysis. Our results reveal distinct transcriptional responses to oxycodone and buprenorphine by iPSC-derived brain organoids from patients with OUD. Specifically, buprenorphine displayed a significant influence on transcription regulation in glial cells. However, oxycodone induced type I interferon signaling in many cell types, including neural cells in brain organoids. Finally, we demonstrate that oxycodone, but not buprenorphine activated STAT1 and induced the type I interferon signaling in patients with OUD. These data suggest that elevation of STAT1 expression associated with OUD might play a role in transcriptional regulation in response to oxycodone. In summary, our results provide novel mechanistic insight into drug action at single-cell resolution.

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.

Genome-wide association study for circulating FGF21 in patients with alcohol use disorder: Molecular links between the SNHG16 locus and catecholamine metabolism.

OBJECTIVE: Alcohol consumption can increase circulating levels of fibroblast growth factor 21 (FGF21). The effects of FGF21 in the central nervous system are associated with the regulation of catecholamines, neurotransmitters that play a crucial role in reward pathways. This study aims to identify genetic variants associated with FGF21 levels and evaluate their functional role in alcohol use disorder (AUD). METHODS: We performed a genome-wide association study (GWAS) using DNA samples from 442 AUD subjects recruited from the Mayo Clinic Center for the Individualized Treatment of Alcoholism Study. Plasma FGF21 levels were measured using Olink proximity extension immunoassays. Alcohol consumption at time of entry into the study was measured using the self-reported timeline followback method. Functional genomic studies were performed using HepG2 cells and induced pluripotent stem cell (iPSC)-derived brain organoids. RESULTS: Plasma FGF21 levels were positively correlated with recent alcohol consumption and gamma-glutamyl transferase levels, a commonly used marker for heavy alcohol use. One variant, rs9914222, located 5' of SNHG16 on chromosome 17 was associated with plasma FGF21 levels (p = 4.60E-09). This variant was also associated with AUD risk (ß: -3.23; p:0.0004). The rs9914222 SNP is an eQTL for SNHG16 in several brain regions, i.e., the variant genotype was associated with decreased expression of SNHG16. The variant genotype for the rs9914222 SNP was also associated with higher plasma FGF21 levels. Knockdown of SNHG16 in HepG2 cells resulted in increased FGF21 concentrations and decreased expression and enzyme activity for COMT, an enzyme that plays a key role in catecholamine metabolism. Finally, we demonstrated that ethanol significantly induced FGF21, dopamine, norepinephrine, and epinephrine concentrations in iPSC-derived brain organoids. CONCLUSIONS: GWAS for FGF21 revealed a SNHG16 genetic variant associated with FGF21 levels which are associated with recent alcohol consumption. Our data suggest that SNHG16 can regulate FGF21 concentrations and decrease COMT expression and enzyme activity which, in turn, have implications for the regulation of catecholamines. (The ClinicalTrials.gov Identifier: NCT00662571).

Multi-Omics Characterization of Early- and Adult-Onset Major Depressive Disorder.

Age at depressive onset (AAO) corresponds to unique symptomatology and clinical outcomes. Integration of genome-wide association study (GWAS) results with additional "omic" measures to evaluate AAO has not been reported and may reveal novel markers of susceptibility and/or resistance to major depressive disorder (MDD). To address this gap, we integrated genomics with metabolomics using data-driven network analysis to characterize and differentiate MDD based on AAO. This study first performed two GWAS for AAO as a continuous trait in (a) 486 adults from the Pharmacogenomic Research Network-Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS), and (b) 295 adults from the Combining Medications to Enhance Depression Outcomes (CO-MED) study. Variants from top signals were integrated with 153 p180-assayed metabolites to establish multi-omics network characterizations of early (

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.

Patient Satisfaction and Recommendations for Delivering a Group-Based Intensive Outpatient Program via Telemental Health During the COVID-19 Pandemic: Cross-sectional Cohort Study.

BACKGROUND: Although group-based intensive outpatient programs (IOPs) are a level of care commonly utilized by adults with serious mental illness, few studies have examined the acceptability of group-based IOPs that required rapid transition to a telemental health (TMH) format during the COVID-19 pandemic. OBJECTIVE: The aim of this study was to evaluate patient satisfaction and future recommendations for a group-based IOP that was transitioned to a TMH format during the COVID-19 pandemic. METHODS: A 17-item patient satisfaction questionnaire was completed by patients at discharge and covered 3 areas: IOP TMH satisfaction, future recommendations, and video technology challenges. Descriptive and content analyses were conducted for the quantitative and open-ended questions, respectively. RESULTS: A total of 76 patients completed the program in 2020. A subset of patients (n=40, 53%) responded to the survey at program discharge. The results indicated that the patients were satisfied overall with the TMH program format; 50% (n=20) of the patients preferred the program continue offering the TMH format, and the rest preferred returning to in-person formats after the pandemic. The patients indicated the elements of the program that they found most valuable and provided recommendations for future program improvement. CONCLUSIONS: Overall, adults with serious mental illness reported high satisfaction with the group-based IOP delivered via TMH. Health care systems may want to consider offering both TMH and in-person formats regardless of the state of the pandemic. Patients' feedback on future improvements should be considered to help ensure long-term success.

Chronic cortisol differentially impacts stem cell-derived astrocytes from major depressive disorder patients.

Major depressive disorder (MDD) is a prevalent psychiatric disorder, and exposure to stress is a robust risk factor for MDD. Clinical data and rodent models have indicated the negative impact of chronic exposure to stress-induced hormones like cortisol on brain volume, memory, and cell metabolism. However, the cellular and transcriptomic changes that occur in the brain after prolonged exposure to cortisol are less understood. Furthermore, the astrocyte-specific contribution to cortisol-induced neuropathology remains understudied. Here, we have developed an in vitro model of "chronic stress" using human induced pluripotent stem cell (iPSC)-derived astrocytes treated with cortisol for 7 days. Whole transcriptome sequencing reveals differentially expressed genes (DEGs) uniquely regulated in chronic cortisol compared to acute cortisol treatment. Utilizing this paradigm, we examined the stress response transcriptome of astrocytes generated from MDD patient iPSCs. The MDD-specific DEGs are related to GPCR ligand binding, synaptic signaling, and ion homeostasis. Together, these data highlight the unique role astrocytes play in the central nervous system and present interesting genes for future study into the relationship between chronic stress and MDD.

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.

Genetic contributions to alcohol use disorder treatment outcomes: a genome-wide pharmacogenomics study.

Naltrexone can aid in reducing alcohol consumption, while acamprosate supports abstinence; however, not all patients with alcohol use disorder (AUD) benefit from these treatments. Here we present the first genome-wide association study of AUD treatment outcomes based on data from the COMBINE and PREDICT studies of acamprosate and naltrexone, and the Mayo Clinic CITA study of acamprosate. Primary analyses focused on treatment outcomes regardless of pharmacological intervention and were followed by drug-stratified analyses to identify treatment-specific pharmacogenomic predictors of acamprosate and naltrexone response. Treatment outcomes were defined as: (1) time until relapse to any drinking (TR) and (2) time until relapse to heavy drinking (THR; ≥ 5 drinks for men, ≥4 drinks for women in a day), during the first 3 months of treatment. Analyses were performed within each dataset, followed by meta-analysis across the studies (N = 1083 European ancestry participants). Single nucleotide polymorphisms (SNPs) in the BRE gene were associated with THR (min p = 1.6E-8) in the entire sample, while two intergenic SNPs were associated with medication-specific outcomes (naltrexone THR: rs12749274, p = 3.9E-8; acamprosate TR: rs77583603, p = 3.1E-9). The top association signal for TR (p = 7.7E-8) and second strongest signal in the THR (p = 6.1E-8) analysis of naltrexone-treated patients maps to PTPRD, a gene previously implicated in addiction phenotypes in human and animal studies. Leave-one-out polygenic risk score analyses showed significant associations with TR (p = 3.7E-4) and THR (p = 2.6E-4). This study provides the first evidence of a polygenic effect on AUD treatment response, and identifies genetic variants associated with potentially medication-specific effects on AUD treatment response.

Alterations in acylcarnitines, amines, and lipids inform about the mechanism of action of citalopram/escitalopram in major depression.

Selective serotonin reuptake inhibitors (SSRIs) are the first-line treatment for major depressive disorder (MDD), yet their mechanisms of action are not fully understood and their therapeutic benefit varies among individuals. We used a targeted metabolomics approach utilizing a panel of 180 metabolites to gain insights into mechanisms of action and response to citalopram/escitalopram. Plasma samples from 136 participants with MDD enrolled into the Mayo Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomic Study (PGRN-AMPS) were profiled at baseline and after 8 weeks of treatment. After treatment, we saw increased levels of short-chain acylcarnitines and decreased levels of medium-chain and long-chain acylcarnitines, suggesting an SSRI effect on ß-oxidation and mitochondrial function. Amines-including arginine, proline, and methionine sulfoxide-were upregulated while serotonin and sarcosine were downregulated, suggesting an SSRI effect on urea cycle, one-carbon metabolism, and serotonin uptake. Eighteen lipids within the phosphatidylcholine (PC aa and ae) classes were upregulated. Changes in several lipid and amine levels correlated with changes in 17-item Hamilton Rating Scale for Depression scores (HRSD17). Differences in metabolic profiles at baseline and post-treatment were noted between participants who remitted (HRSD17 ≤ 7) and those who gained no meaningful benefits (<30% reduction in HRSD17). Remitters exhibited (a) higher baseline levels of C3, C5, alpha-aminoadipic acid, sarcosine, and serotonin; and (b) higher week-8 levels of PC aa C34:1, PC aa C34:2, PC aa C36:2, and PC aa C36:4. These findings suggest that mitochondrial energetics-including acylcarnitine metabolism, transport, and its link to ß-oxidation-and lipid membrane remodeling may play roles in SSRI treatment response.

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings.

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs). We then used unsupervised machine learning to identify specific depressive symptoms and thresholds of improvement that were predictive of antidepressant response by 4 weeks for a patient to achieve remission, response, or nonresponse by 8 weeks. Four depressive symptoms (depressed mood, guilt feelings and delusion, work and activities and psychic anxiety) and specific thresholds of change in each at 4 weeks predicted eventual outcome at 8 weeks to SSRI therapy with an average accuracy of 77% (p = 5.5E-08). The same four symptoms and prognostic thresholds derived from patients treated with SSRIs correctly predicted outcomes in 72% (p = 1.25E-05) of 1996 patients treated with other antidepressants in both inpatient and outpatient settings in independent publicly-available datasets. These predictive accuracies were higher than the accuracy of 53% for predicting SSRI response achieved using approaches that (i) incorporated only baseline clinical and sociodemographic factors, or (ii) used 4-week nonresponse status to predict likely outcomes at 8 weeks. The present findings suggest that PGMs providing interpretable predictions have the potential to enhance clinical treatment of depression and reduce the time burden associated with trials of ineffective antidepressants. Prospective trials examining this approach are forthcoming.

Acylcarnitine metabolomic profiles inform clinically-defined major depressive phenotypes.

BACKGROUND: Acylcarnitines have important functions in mitochondrial energetics and ß-oxidation, and have been implicated to play a significant role in metabolic functions of the brain. This retrospective study examined whether plasma acylcarnitine profiles can help biochemically distinguish the three phenotypic subtypes of major depressive disorder (MDD): core depression (CD+), anxious depression (ANX+), and neurovegetative symptoms of melancholia (NVSM+). METHODS: Depressed outpatients (n = 240) from the Mayo Clinic Pharmacogenomics Research Network were treated with citalopram or escitalopram for eight weeks. Plasma samples collected at baseline and after eight weeks of treatment with citalopram or escitalopram were profiled for short-, medium- and long-chain acylcarnitine levels using AbsoluteIDQ®p180-Kit and LC-MS. Linear mixed effects models were used to examine whether acylcarnitine levels discriminated the clinical phenotypes at baseline or eight weeks post-treatment, and whether temporal changes in acylcarnitine profiles differed between groups. RESULTS: Compared to ANX+, CD+ and NVSM+ had significantly lower concentrations of short- and long-chain acylcarnitines at both baseline and week 8. In NVSM+, the medium- and long-chain acylcarnitines were also significantly lower in NVSM+ compared to ANX+. Short-chain acylcarnitine levels increased significantly from baseline to week 8 in CD+ and ANX+, whereas medium- and long-chain acylcarnitines significantly decreased in CD+ and NVSM+. CONCLUSIONS: In depressed patients treated with SSRIs, ß-oxidation and mitochondrial energetics as evaluated by levels and changes in acylcarnitines may provide the biochemical basis of the clinical heterogeneity of MDD, especially when combined with clinical characteristics.