Blood and brain gene expression signatures of chronic intermittent ethanol consumption in mice

Evaluation of plasma-derived extracellular vesicles miRNAs and their connection with hippocampal mRNAs in alcohol use disorder

Alcohol use disorder (AUD) is a common mental illness with high morbidity and disability. The discovery of laboratory biomarkers has progressed slowly, resulting in suboptimal diagnosis and treatment of AUD. This study aimed to identify promising biomarkers, as well as the potential miRNA-mRNA networks associated with AUD pathogenesis. RNA sequencing was performed on plasma-derived small extracellular vesicles (sEVs) from AUD patients and healthy controls (HCs) to harvest miRNAs expression profiles. Machine learning (ML) models were built to screen characteristic miRNAs, whose target mRNAs were analyzed using TargetScan, miRanda and miRDB databases. Gene Expression Omnibus (GEO) datasets (GSE181804 and GSE180722) providing postmortem hippocampal gene expression profiles of AUD subjects were mined. A total of 247 differentially expressed (DE) plasma-derived sEVs miRNAs and 122 DE hippocampal mRNAs were obtained. Then, 22 overlapping sEVs miRNAs with high importance scores were gained by intersecting 5 ML models. As a result, we established a putative sEVs miRNA-hippocampal mRNA network that can effectively distinguish AUD patients from HCs. In conclusion, we proposed 5 AUD-representative sEVs miRNAs (hsa-miR-144-5p, hsa-miR-182-5p, hsa-miR-142-5p, hsa-miR-7-5p, and hsa-miR-15b-5p) that may participate in the pathogenesis of AUD by modulating downstream target hippocampal genes. These findings may provide novel insights into the diagnosis and treatment of AUD.

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review

BACKGROUND

Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets.

OBJECTIVE

This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression.

METHODS

This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies.

RESULTS

The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network.

CONCLUSIONS

The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.

The ubiquitin-proteasome system and learning-dependent synaptic plasticity - A 10 year update

Over 25 years ago, a seminal paper demonstrated that the ubiquitin-proteasome system (UPS) was involved in activity-dependent synaptic plasticity. Interest in this topic began to expand around 2008 following another seminal paper showing that UPS-mediated protein degradation controlled the "destabilization" of memories following retrieval, though we remained with only a basic understanding of how the UPS regulated activity- and learning-dependent synaptic plasticity. However, over the last 10 years there has been an explosion of papers on this topic that has significantly changed our understanding of how ubiquitin-proteasome signaling regulates synaptic plasticity and memory formation. Importantly, we now know that the UPS controls much more than protein degradation, is involved in plasticity underlying drugs of abuse and that there are significant sex differences in how ubiquitin-proteasome signaling is used for memory storage processes. Here, we aim to provide a critical 10-year update on the role of ubiquitin-proteasome signaling in synaptic plasticity and memory formation, including updated cellular models of how ubiquitin-proteasome activity could be regulating learning-dependent synaptic plasticity in the brain.

Sex-dependent factors of alcohol and neuroimmune mechanisms

Excessive alcohol use disrupts neuroimmune signaling across various cell types, including neurons, microglia, and astrocytes. The present review focuses on recent, albeit limited, evidence of sex differences in biological factors that mediate neuroimmune responses to alcohol and underlying neuroimmune systems that may influence alcohol drinking behaviors. Females are more vulnerable than males to the neurotoxic and negative consequences of chronic alcohol drinking, reflected by elevations of pro-inflammatory cytokines and inflammatory mediators. Differences in cytokine, microglial, astrocytic, genomic, and transcriptomic evidence suggest females are more reactive than males to neuroinflammatory changes after chronic alcohol exposure. The growing body of evidence supports that innate immune factors modulate synaptic transmission, providing a mechanistic framework to examine sex differences across neurocircuitry. Targeting neuroimmune signaling may be a viable strategy for treating AUD, but more research is needed to understand sex-specific differences in alcohol drinking and neuroimmune mechanisms.

Progenitor Cells Play a Role in Reinstatement of Ethanol Seeking in Adult Male and Female Ethanol Dependent Rats

Female and male glial fibrillary acidic protein-thymidine kinase (GFAP-TK) transgenic rats were made ethanol dependent via a six-week chronic intermittent ethanol vapor (CIE) and ethanol drinking (ED) procedure. During the last week of CIE, a subset of male and female TK rats was fed valcyte to ablate dividing progenitor cells and continued the diet until the end of this study. Following week six, all CIE rats experienced two weeks of forced abstinence from CIE-ED, after which they experienced relapse to drinking, extinction, and reinstatement of ethanol seeking sessions. CIE increased ED in female and male rats, with females having higher ethanol consumption during CIE and relapse sessions compared with males. In both sexes, valcyte reduced the levels of Ki-67-labeled progenitor cells in the subgranular zone of the dentate gyrus and did not alter the levels in the medial prefrontal cortex (mPFC). Valcyte increased ED during relapse, increased lever responses during extinction and, interestingly, enhanced latency to extinguish ethanol-seeking behaviors in males. Valcyte reduced the reinstatement of ethanol-seeking behaviors triggered by ethanol cues in females and males. Reduced seeking by valcyte was associated with the normalization of cytokines and chemokines in plasma isolated from trunk blood, indicating a role for progenitor cells in peripheral inflammatory responses. Reduced seeking by valcyte was associated with increases in tight junction protein claudin-5 and oligodendrogenesis in the dentate gyrus and reduction in microglial activity in the dentate gyrus and mPFC in females and males, demonstrating a role for progenitor cells in the dentate gyrus in dependence-induced endothelial and microglial dysfunction. These data suggest that progenitor cells born during withdrawal and abstinence from CIE in the dentate gyrus are aberrant and could play a role in strengthening ethanol memories triggered by ethanol cues via central and peripheral immune responses.

Chronic ethanol alters adrenergic receptor gene expression and produces cognitive deficits in male mice

Hyperkateifia and stress-induced alcohol cravings drive relapse in individuals with alcohol use disorder (AUD). The brain stress signal norepinephrine (also known as noradrenaline) tightly controls cognitive and affective behavior and was thought to be broadly dysregulated with AUD. The locus coeruleus (LC) is a major source of forebrain norepinephrine, and it was recently discovered that the LC sends distinct projections to addiction-associated regions suggesting that alcohol-induced noradrenergic changes may be more brain region-specific than originally thought. Here we investigated whether ethanol dependence alters adrenergic receptor gene expression in the medial prefrontal cortex (mPFC) and central amgydala (CeA), as these regions mediate the cognitive impairment and negative affective state of ethanol withdrawal. We exposed male C57BL/6J mice to the chronic intermittent ethanol vapor-2 bottle choice paradigm (CIE-2BC) to induce ethanol dependence, and assessed reference memory, anxiety-like behavior and adrenergic receptor transcript levels during 3-6 days of withdrawal. Dependence bidirectionally altered mouse brain α1 and β receptor mRNA levels, potentially leading to reduced mPFC adrenergic signaling and enhanced noradrenergic influence over the CeA. These brain region-specific gene expression changes were accompanied by long-term retention deficits and a shift in search strategy in a modified Barnes maze task, as well as greater spontaneous digging behavior and hyponeophagia. Current clinical studies are evaluating adrenergic compounds as a treatment for AUD-associated hyperkatefia, and our findings can contribute to the refinement of these therapies by increasing understanding of the specific neural systems and symptoms that may be targeted.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.