Translational Psychiatry

Background: Aberrations in neuro-immune, metabolic, and oxidative stress (NIMETOX) pathways are implicated in major depressive disorder (MDD). First-episode simple dysmood disorder (FE-SDMD) without metabolic syndrome offers a unique model to investigate early lipid alterations underlying NIMETOX pathophysiology. Methods: Plasma samples were collected from 88 university students (44 FE-SDMD, 44 healthy controls). Participants underwent comprehensive psychiatric and psychological assessments, including adverse childhood experiences (ACEs), negative life events (NLEs), depression, anxiety, suicidal behaviors, and insomnia. Untargeted lipid profiling was performed using LC-QTOF-MS, while indices of oxidative and nitrosative stress (ONS) and lecithin-cholesterol acyltransferase (LCAT) activity were assessed. Data was analyzed using machine learning approaches with recursive feature elimination and cross-validation. Results: FE-SDMD was characterized by increased ceramides (CER), diacylglycerides (DAG), triacylglycerides (TG), sphingomyelins (SM), bis-monoacylglycerol phosphates (BMP), cholestone, and fatty-acyl amino acids (FAAA). DAG, CER, and BMP were the strongest predictors of depression severity and physiosomatic symptoms, whereas cholestone, CER, and SM predicted suicidal behaviors. These lipid modules, together with lowered LCAT and increased ONS, explained substantial variance in depression severity (46.4%), physiosomatic symptoms (42.4%), cognitive-affective symptoms (37.9%), suicidal behaviors (30.1%), insomnia (32%), and anxiety (19.5%). ACEs and NLEs were strongly associated with CER (p<0.001), DAG (p<0.01), and cholestone (p<0.01). Conclusion: Early-stage MDD is characterized by distinct lipid dysregulations linked to psychosocial stress exposure, oxidative and nitrosative stress, and an indicant of impaired reverse cholesterol transport. These lipid modules may serve as early biomarkers and therapeutic targets in vulnerable populations.

Suicidal ideation in obsessive-compulsive disorder (OCD) is common and clinically significant, yet much of the existing literature conceptualizes suicide risk through the lens of comorbid depressive symptomatology. The present study examined whether other clinical features can identify clinically meaningful patterns associated with SI. Participants included 231 individuals with clinically significant OCD symptoms. SI was operationalized using Item 9 of the Beck Depression Inventory-II and binarized to reflect the presence or absence of suicidal thoughts. Depression severity scores were intentionally excluded from the predictive feature set, and three machine learning models (ElasticNet, Random Forest, and Explainable Boosting Machines) were evaluated using repeated nested cross-validation. All three algorithms showed comparable predictive performance. Given this overlap, the EBM was selected for interpretation due to its ability to model nonlinear relationships and interaction effects transparently. The model identified quality of life, obsessive-compulsive trait severity, somatic burden, and conscientiousness as prominent predictors of SI. Risk functions suggested nonlinear increases in estimated suicide risk at elevated levels of obsessive-compulsive traits and reduced quality of life. Additionally, interaction analyses indicated that severe obsessive-compulsive traits combined with elevated somatic burden were associated with higher estimated suicide risk than either factor alone. These findings suggest that interpretable machine learning can support clinically relevant phenotypic hypothesis generation. They also highlight somatic burden, functional impairment, obsessive-compulsive trait severity, and conscientiousness as potentially underappreciated targets for SI risk assessment in OCD, beyond the traditional focus on depressive comorbidity.

Problematic Sexual Behaviour (PSB) is defined as difficult to control recurrent sexual behaviours that continue despite adverse consequences, leading to social and functional impairment. There is debate whether PSB is a disorder of compulsion or addiction; PSB often co-occurs with neuropsychiatric disorders, but further elucidation regarding underlying biology is required. A deficiency in reward neurotransmitter systems (reward deficiency syndrome: RDS) may underlie a shared vulnerability to addiction. We conducted the first case-control genome wide association study (GWAS) of PSB in patients (n=448), and comparison participants with (n=196) and without PSB (n=1488). We used polygenic risk scores (PRS) to test genetic overlap with related psychiatric, behavioural and personality phenotypes. Three models were used: 1) All-PSB (patient + comparison) vs. controls, 2) Patient-PSB vs controls, and 3) RDS (yes/no). Results suggested genetic overlap of PSB with psychiatric conditions, with PRS for major depression, substance use, and others predicting PSB status. PRS for related behavioural phenotypes (e.g., externalizing, age at first sex, number of lifetime sexual partners) and personality traits also predicted PSB. The patient model showed stronger associations than the All-PSB model, and RDS had both shared and distinct genetics with PSB. As expected with the sample size, only suggestive hits were observed with single variant and gene-based tests. PSB may share genetic mechanisms with various conditions. Further research in larger cohorts is needed to better understand the underlying genetics and environmental factors involved, and to improve diagnostic classification, intervention and treatment prospects.

GABAergic deficit is associated with key neuropsychiatric disorders, such as major depressive disorder (MDD), anxiety, schizophrenia, and autism spectrum disorder (ASD). However, it is not known whether these disorders are causal to or a result of GABAergic dysfunction. We previously showed that the Solute carrier 38 member 1 (Slc38a1) accumulates glutamine in subpopulations of GABAergic neurons and sustains neurotransmitter GABA synthesis. Genetic inactivation of Slc38a1 in mice caused lowered GABA levels, altered synaptic vesicle morphology, slowed {gamma}-oscillations, and reduced cortical processing and plasticity, selectively at GABAergic synapses. We now demonstrate a significant reduction in learning and memory performance in the Morris water maze and increased signs of despair in the forced swim test in Slc38a1-/- mice compared to Slc38a1+/+ mice, implicating cognitive impairments and depressive-like behavior. Examination in the open field maze also indicates anxiety and/or reduced interest in exploration. There are no signs of impaired sociability or recognition of social novelty in the three-chambered test, speaking against involvement in schizophrenia- or ASD-like disorders. Metabolic phenotyping and measurement of the locomotion do not segregate the Slc38a1 genotypes, suggesting that the cognitive impairments, depressive-like behavior and anxiety are brain-dependent. Our data is further supported by a pathologic variant of Slc38a1 in a family with depression and suicidal behavior. Altogether, we demonstrate that dysfunction of Slc38a1-dependent GABA synthesis and the ensuing impaired {gamma}-oscillations underpin the pathogenesis of neurocognitive deficits, anxiety and depression.

Impaired learning that both novel and previously dangerous stimuli are safe (safety and extinction learning, respectively) are long standing, robust, and heritable features of anxiety disorders, representing potential endophenotypes. The computational mechanisms underpinning them have demonstrated associations with anxiety severity in recent studies. We undertook a pre-registered replication in a tenfold larger independent sample of twins (n = 925). Extinction learning rates were associated with anxiety severity ({rho}replication = -0.14, BFr0 = 1189. 67) but safety learning rates were not. Conversely, although safety learning rates showed modest heritability (h2safety = 0.16), extinction learning rates were not heritable. Accordingly, we were unable to identify genetic overlap between anxiety and either learning rate. Although this suggests neither learning rate is an anxiety endophenotype, we confirmed a cognitive-behavioral mechanism underpinning a robust marker of anxiety severity. Furthermore, we demonstrated heritability of a computationally modelled learning parameter, a key step towards establishing its biological basis.

Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods: We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results: Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions: Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

Background: Schizophrenia, bipolar disorder, and depression share substantial genetic liability. However, the molecular mechanisms underlying this shared architecture remain poorly characterized. In particular, the role of circulating proteins as potential mediators and therapeutic targets is not well understood. Methods: Based on large-scale genome-wide association studies, we constructed a latent psychiatric common factor using genomic structural equation modeling. We then performed proteome-wide Mendelian randomization to estimate the associations between circulating proteins and this shared liability, based on four independent proteomic cohorts. Protein-psychiatric common factor associations were prioritized through comprehensive sensitivity analyses and colocalization. We additionally performed tissue- and single-cell expression enrichment analyses and a systematic druggability assessment. Results: We identified 36 circulating proteins with evidence of association with the psychiatric common factor that withstood multiple sensitivity analyses. Several proteins showed distinct tissue-specific expression patterns, with enrichment in brain, immune, or liver tissues, highlighting convergent neuroimmune and systemic pathways. For instance, genetically predicted higher levels of MAPK3, FES, MRE11A, HS6ST3, OLFM1, BTN3A1, BTN3A2 and BTN3A3 were associated with increased psychiatric risk, whereas higher levels of CD40, ITIH3, and ITIH4 were associated with decreased risk. Druggability assessment identified CD40, MAPK3, FES, MRE11A and BTN3A1 as established or potential therapeutic targets. Conclusions: By integrating genetic, proteomic, and transcriptomic data, this study identifies circulating proteins that associated with the shared genetic effects on three major psychiatric disorders. These findings provide biologically grounded candidates for therapeutic targeting and offer insights into shared disease mechanisms.

Abstract Schizophrenia (SCZ) and bipolar disorder (BPD) are highly heritable psychiatric disorders with complex polygenic architectures. Genome-wide association studies (GWASs) have identified numerous common variant associations, but rarer variants detectable through whole-genome sequencing (WGS) remain underexplored. We conducted rare variant association analysis using WGS data from the Portuguese Island Collection (PIC), including 28 families with SCZ (n = 53) and 41 families with BPD (n = 83) cases, and population controls (n = 62). Following ANNOVAR and CADD annotation, burden analysis of deleterious variants showed that both affected and unaffected family members from SCZ and BPD pedigrees had significantly higher burdens of rare deleterious variants compared to controls (p < 0.0001), with no significant differences observed between affected and unaffected relatives, consistent with shared familial genetic liability. Polygenic Risk Score (PRS) analysis confirmed significant genetic contributions to both disorders within PIC. Association analyses were subsequently performed using SAIGE-GENE+ identifying 483 and 583 nominally significant (suggestive associations) gene sets (p-value [&le;] 0.05; FDR > 0.05) for SCZ and BPD, respectively, including gene sets related to neurotransmission, synaptic function and structure, neurodevelopment, and neuroinflammation as well as major signaling pathways. Cross disorder overlaps also identified shared suggestive enrichment of GABA and glutamate signaling, synaptic signaling, and Wnt signaling gene sets in both SCZ and BPD. These findings support shared rare variant burden within multiplex psychiatric families and highlight the role of gene-set based rare variant analysis in identifying neurobiological pathways relevant to SCZ and BPD. Keywords: WGS, Rare Variants, Schizophrenia, Bipolar Disorder

The ability to adjust to changing environments (cognitive flexibility) and optimal decision-making are pivotal brain functions that govern successful human behavior. Anxiety and depressive disorders are strongly pervasive psychiatric conditions across the lifespan that profoundly disrupt mechanisms of attention, working memory, and decision-making. Although existing task evidence documents impaired decision-making and flexibility outcomes for both anxiety and depression, there is a growing need to systematically evaluate the role of anxiety and depression and to quantitatively compare the effects of these disorders on these domains. In the present study, we conducted a meta-analysis of anxiety and depression on decision-making and cognitive flexibility. We utilized a random-effects approach, given that a large amount of between-subject heterogeneity was anticipated. Given the scope of this meta-analysis, we used the machine learning tool asReview to more efficiently conduct a meta-analytic search. Across all outcomes, results showed anxiety and depression were associated with reduced cognitive flexibility and decision-making. These effect sizes were then tested for significance using a fixed-effects (plural) model. Subgroup analyses revealed no significant differences between anxiety and depression for either decision-making or flexibility outcomes, consistent with a transdiagnostic perspective. Results are contextualized in light of the biopsychosocial model and potential transdiagnostic factors.

Background: Major depressive disorder (MDD) is characterized by disrupted information flow among brain regions. While effective connectivity (EC) captures these causal interactions, the underlying structural and molecular basis remain unclear. This study aims to investigate direction-specific EC alterations in MDD and their associations with laminar structural covariance (SC) and transcriptional and neurotransmitter profiles. Methods: Resting-state fMRI and structural MRI data were analyzed from the REST-meta-MDD consortium (Discovery, N=1627) and an independent cohort (Validation, N=226). We calculated the unsigned and signed EC using Liang Information Flow and laminar SC based on cortical depth, and compared them between MDD patients and healthy controls. The EC alterations were further associated with molecular profiles integrating gene expression (AHBA) and neurotransmitter receptors (PET/SPECT). Then, Chain mediation analyses were performed to map the hierarchical pathways from molecular basis to EC. Finally, we evaluated the clinical potential of EC in its therapeutic responses to medication and neuromodulation in a longitudinal dataset (N = 16 for medication, N = 11 for neuromodulation). Results: Our analysis revealed no significant changes in the EC of first-episode MDD but observed a hyper-driven cerebellar-cerebral EC pattern in recurrent MDD (RMDD), characterized by a direction-specific excitation-inhibition imbalance featuring enhanced inhibitory cerebellar output alongside a concurrent increase in both inhibitory input and excitatory output within sensorimotor/cognitive regions. These alterations were physically constrained by specific laminar SC patterns, particularly involving the middle cortical lamina. Moreover, the input EC changes in RMDD patients were primarily enriched in biological processes related to the modulation of chemical synaptic transmission, whereas output EC changes were linked to synapse structure regulation. These EC alterations were closely associated with serotonergic, GABAergic, and glutamatergic neurotransmitter systems. Importantly, we identified oligodendrocyte precursor cells (OPCs) as a key cellular mediator bridging microscale molecular features to macroscale connectional alterations in RMDD. These findings were reproducible in the validation dataset. Clinically, medication treatment primarily evoked a pattern of decreased input coupled with increased output, whereas neuromodulation elicited a reciprocal shift characterized by enhanced input and attenuated output. Conclusions: These findings underscore a direction-specific gene-neurotransmitter-cell type-laminar SC-EC pathological model in RMDD. By integrating multi-scale biological mechanisms with clinical phenotypes, this study highlights the potential of directional EC as a biomarker for stratifying refractory depression and guiding precision therapeutics.

The persistence of a left-handed minority of slightly over 10% of the population is enigmatic because it is associated with stigma, increased psychopathology, and cognitive deficits. In a community sample of 9,352 individuals (age range 8-21 years) with neurobehavioral assessments, left-handers (N=1,281, 673 male) indeed showed greater psychopathology and performed more poorly than right-handers (N=8,076, 3,839 male) on tests of executive function, memory, complex cognition, and social cognition, while excelling in motor speed. Furthermore, the variance was higher and within-individual variability (WIV) - the extent to which scores in the different domains varied within individuals - was higher in left-handers. Since low WIV indicates even distribution of abilities while high WIV reflects specialization in circumscribed areas, the finding indicates that left-handers are "neurocognitive specialists". This combination of behavioral traits could confer resilience against natural selection pressures and help explain preponderance of left-handers in highly specialized professions requiring specific talents. Our findings encourage more research on left-handers, who are currently excluded from multiple brain behavior studies.

The gut microbiome has been implicated in alcohol use disorder (AUD), but its relationship to drinking intensity and treatment response remains poorly understood. We conducted a longitudinal multi-omics analysis of stool samples collected at baseline and endpoint (after 12 weeks) from 122 participants enrolled in a double-blind, placebo-controlled trial of dutasteride for AUD. Gut microbiome composition was characterized using 16S rRNA gene sequencing, and fecal metabolites were measured by LC-MS-based metabolomics. At baseline, drinking intensity was associated with increasingly lower microbial richness. Genera in the class Clostridia emerged as key microbial hubs associated with drinking intensity in an age- and sex-dependent manner. Drinking intensity promoted co-enrichment of [Ruminococcus] gnavus group and [Clostridium] inocuum group with amino acid catabolites, as well as the co-depletion of diverse Clostridia taxa and lipid metabolites. Dutasteride treatment and drinking reduction had minimal impact on gut microbiome composition. Random forest models integrating baseline clinical, microbiome, and metabolome data improved the classification of clinically meaningful drinking reduction compared to models using clinical data alone. These findings show that a coupled baseline gut microbiome-metabolome signature is associated with drinking intensity and future treatment response in AUD, highlighting the potential for multi-omics integration to inform precision treatment approaches.

Background: Alcohol use disorder (AUD) is associated with altered gene expression across diverse cell types in reward-related brain regions, including the ventral tegmental area (VTA), which is rich in dopaminergic neurons. The VTA plays a central role in reward processing, learning, and memory; however, cell type-specific gene expression changes within the VTA remain uncharacterized. Methods: We applied single-nucleus RNA sequencing (snRNA-seq) to profile transcriptomic alterations associated with AUD in the VTA. Postmortem VTA samples from four individuals of European ancestry [two with AUD (one male, one female) and two matched controls (one male, one female)] were analyzed using the 10X Genomics Chromium Fixed RNA Profiling protocol. Differentially expressed genes (DEGs) were identified using Seurat, and enriched KEGG pathways was assessed by gene set enrichment analysis. Results: Nuclei were classified into six major cell types: astrocytes, endothelial cells, mature neurons, microglia, oligodendrocytes, and oligodendrocyte precursor cells (OPCs). At thresholds of P < 0.05 and |fold change| > 2.0, we identified 547 DEGs in astrocytes, 727 DEGs in endothelial cells, 715 DEGs in mature neurons, 421 DEGs in microglia, 263 DEGs in oligodendrocytes, and 432 DEGs in OPCs. DEGs across VTA cell types were enriched for pathways related to mitochondrial function, neurodegeneration, and synaptic signaling. Notably, DEGs in mature neurons were enriched for addiction-related pathways. Further subdivision of mature neurons into dopaminergic, GABAergic, glutamatergic, and unclassified subtypes revealed 526, 930, 896, and 569 DEGs, respectively. Neuronal DEGs indicate a convergence on mitochondrial/oxidative phosphorylation and neurodegeneration-related pathways across subtypes, whereas addiction- and synapse-related pathways show dopaminergic neuron-specific enrichment. Conclusions: This study provides the first cell type-resolved transcriptomic profiling of the human VTA, revealing AUD-associated gene expression alterations across neuronal, glial, and endothelial cells. The observed cell type-specific changes in synaptic plasticity and addiction-related genes offer new insights into molecular mechanisms underlying AUD pathophysiology.

Brain similarity networks (BSNs), extracted from structural magnetic resonance imaging, provide a validated framework for studying brain network organization and encode neurodevelopmental information relevant for psychiatric disorders. Recently, a neurodevelopmental hypothesis has been proposed for bipolar disorder (BD), where evidence demonstrates neuroprogression phenotypes differing from controls. BSNs offer a promising framework for investigating BD's neural correlates but remain largely underexplored. Parallelly, graph neural networks (GNNs) have emerged as suitable deep learning models for exploiting network-level information. This study aimed to investigate BSNs for discriminating subjects with BD from controls within a GNN framework using the multi-site StratiBip network, composed of 605 controls and 501 subjects with BD. Leveraging advanced analysis tools, we developed a multi-site classification framework including: i) the state-of-the-art MIND algorithm for computing morphometric similarity (MS) networks based on gray matter volumes (GMV), ii) MS integration with age, sex, and GMV, iii) a leave-one-site-out cross-validation for multi-site model generalizability evaluation. The best model achieved a mean multi-site accuracy of 68%. Explainability analyses revealed meaningful MS patterns in the basal ganglia, frontal and temporal lobes, and a particularly relevant integration with age. This study provides interpretable insights into the role of MS in BD and unveils evidence supporting ageing-related processes as a significant component of BD pathophysiology.

ImportancePrenatal exposure to gestational diabetes mellitus (GDM) has been associated with adverse metabolic, neurodevelopmental, and psychiatric outcomes in offspring. However, whether GDM-exposed youth exhibit heterogeneous neuroanatomical patterns remains unclear. ObjectiveTo identify distinct cortical thickness subtypes among GDM-exposed youth and examine their associations with anthropometric, neurocognitive, psychiatric/behavioral and neuroimaging measures both cross-sectionally and longitudinally. Design, Setting, and ParticipantsThis cohort study used the Adolescent Brain Cognitive Development (ABCD)(R)data, a multisite longitudinal population study. Subtype and Stage Inference (SuStaIn), an unsupervised machine learning framework, was applied to cross-sectional structural MRI data to identify cortical thickness patterns in 573 GDM-exposed youth and 2854 healthy controls. Posthoc longitudinal analyses included 1,853 observations from a subset of GDM-exposed youth with 1-, 2-, and 4-year follow-up visits to examine subtype differences in developmental trajectories over time. Exposure(s)Prenatal exposure to GDM. Main Outcome(s) and Measure(s)The primary outcomes included identification of cortical thickness subtypes and their inferred regional ordering patterns. Secondary outcomes included subtype-specific anthropometric, neurocognitive, psychiatric/behavioral and neuroimaging measures. ResultsThe GDM-exposed sample had a mean age of 119.02 {+/-} 7.34 months and was 47.5% female. Two cortical thickness subtypes were identified. Between subtypes, Subtype 1 (63.2%) was characterized by earlier inferred insula involvement and was associated with greater height (d = 0.36, pFDR < 0.001) and weight (d = 0.26, pFDR = 0.007), whereas Subtype 2 exhibited earlier inferred frontal involvement and nominally higher Attention-Deficit/Hyperactivity Disorder (ADHD) prevalence (d = 0.08, p = 0.036), steeper longitudinal cortical thinning across all six cortical regions of interest ({beta} range: -0.05 to -0.13, all pFDR < 0.05), and a smaller decline in Obsessive-Compulsive Disorder (OCD) prevalence over time ({beta} = -1.02, pFDR = 0.049). Conclusions and RelevanceGDM exposure was associated with two distinct offspring cortical thickness subtypes, each showing different inferred regional ordering patterns and clinical associations. One subtype showed an insula-cingulate-predominant pattern associated with anthropometric measures, whereas the other showed a frontal-predominant pattern associated with nominally higher psychiatric measures and faster cortical thinning over time.

Background: The biological mechanisms linking generalized anxiety disorder (GAD) and COVID-19 remain poorly understood, despite substantial evidence of their comorbidity. To address this gap, we examined genetic and epigenetic factors underlying their co-occurrence. Methods: In a multi-ancestry sample of 893 participants, we conducted genome-wide and epigenome-wide analyses of GAD and COVID-19 severity. Integrating large-scale genome-wide datasets and information regarding methylation quantitative trait loci, complementary analytic approaches were used to identify regional methylation patterns, assess genetically regulated DNA methylation in blood and brain tissue, and evaluate causal loci shared between GAD and COVID-19. Results: GAD was associated with epigenome-wide significant variation in loci involved in chromatin regulation and synaptic signaling. Conversely, COVID-19-related epigenetic signals were enriched in immune-inflammatory and host-response pathways. Mild COVID-19 was epigenetically related to endothelial-inflammatory signals, while severe COVID-19 was linked to epigenetic changes implicated in myeloid and thrombo-inflammatory pathways. Epigenetic signals shared between GAD and COVID-19 implicated processes related to stress adaptation and tissue homeostasis. Genetically informed analyses identified 60 shared loci, including MAPT, ZFP57, and FBXL18, indicating pleiotropy between GAD and COVID-19 in genetically regulated DNA methylation variation. Brain-specific analyses further highlighted convergence in additional loci (i.e., MICB and HLA-DPB1), suggesting neuroimmune mechanisms underlying GAD-COVID-19 shared methylation patterns. Conclusions: These findings support that GAD and COVID-19 share epigenetic and genetic architecture involving pathways related to vascular integrity, immune function, and cellular adaptation, highlighting a potential neuroimmune basis for their co-occurrence.

Antidepressants are widely prescribed for major depressive disorder, yet only one-third of patients achieve remission after initial treatment. Previous genome-wide association studies (GWAS) of clinically assessed antidepressant response combined multiple antidepressant classes, potentially obscuring class-specific effects. This study focused on selective serotonin reuptake inhibitors (SSRIs), often first-line due to better tolerability. Data from 15 cohorts across four ancestries were integrated: European (N = 3887; 11 studies), East Asian (N = 1068; 4), African (N = 277; 1), and Admixed American (N = 250; 1). GWAS of non-remission and percentage improvement were conducted within cohorts, followed by ancestry-specific meta-analyses and trans-ancestry meta-regression. Single nucleotide polymorphism (SNP)-based heritability was estimated in European samples. Polygenic scores were used for leave-one-out prediction and to assess shared genetic architecture with psychiatric traits. Gene-level and gene-set enrichment analyses were also performed. No genome-wide significant variants were identified for either outcome in any ancestry-specific or trans-ancestry analyses. However, trans-ancestry meta-regression yielded eight independent loci with suggestive associations (p < 1 x 10-5) for non-remission and 17 for percentage improvement. Gene-set analyses revealed nominal enrichment of the serotonergic synapse pathway for non-remission. SNP-based heritability estimates were not significantly different from zero for either outcome. Better SSRI response was nominally associated with lower genetic predisposition to major depressive disorder, post-traumatic stress disorder, and schizophrenia. This study represents the largest trans-ancestry GWAS of SSRI response, highlighting emerging biological signals. Limited power emphasises the need for larger and ancestrally diverse cohorts to better characterise the genetic architecture of antidepressant response.

BackgroundResponding appropriately to threats is critical for survival. Dysregulated defensive responses are core features of psychiatric illnesses including panic disorder and post-traumatic stress disorder. Carbon dioxide (CO2) inhalation evokes defensive behaviors in both humans and mice. Here we investigated the role of acid-sensing ion channels (ASICs) in CO2-evoked jumping in mice. MethodsDefensive behaviors (jumping and freezing) were assessed in response to CO2 inhalation and basolateral amygdala (BLA) acidification. We tested the role of ASICs using global knockout mice and Asic1aloxP/loxP mice transduced with AAV-CMV-Cre or AAV-CaMKII-Cre in the BLA. Effects of CO2 on single neuron firing and local field potentials were studied via BLA microwire arrays. ResultsASIC1A disruption increased CO2-evoked jumping while reducing freezing, paralleled by increased BLA c-Fos induction. Acidification of the BLA recapitulated these effects. Virus-mediated ASIC1A disruption in BLA did not resolve the locus of ASIC1A action in jumping. CO2 inhalation suppressed firing in most BLA neurons, though a small number increased firing. ASIC1A disruption enhanced CO2-induced suppression of narrow waveform neurons (putative interneurons), and facilitated excitation of wide waveform neurons (putative principal neurons). Additionally, CO2 produced concentration-dependent broadband power suppression with selective theta enhancement, effects that were augmented by ASIC1A disruption. ConclusionsTogether, these findings suggest that ASIC1A promotes interneuron activity during acidosis and that its loss may reduce inhibition of principal neuron output, shifting defensive responses from freezing toward jumping. These results advance our understanding of how brain pH and ASICs regulate defensive behavior, with potential implications for understanding dysregulated defensive responses.

Cognitive impairments significantly impact the daily life of people with Down syndrome (DS). Overinhibition mediated by interneurons in the central nervous system was proposed as a key pathophysiological mechanism. Previous studies demonstrated cognitive rescue in the Ts65Dn mouse model using 5IA, a negative allosteric modulator of the 5 subunit-containing GABAA receptors. Here, we evaluated the effect of this drug in a mouse model carrying a more accurate duplication of the orthologous region to the human chromosome 21, namely the Dp(16)1Yey mouse model. First, we expanded the phenotypic characterization of Dp(16)1Yey mice using translationally more relevant behavioral tests. We confirmed spatial memory deficits in Dp(16)1Yey mice in the Barnes maze, and highlighted robust learning deficits in the pattern dissociation task and impairments in motor coordination. Next, we evaluated the effect of 5IA treatment on cognitive and motor performance. While 5IA treatment improved motor coordination in the Dp(16)1Yey mice, it failed to restore cognitive performance in the Barnes maze or in the pattern dissociation task. These findings could suggest divergent pathophysiological mechanisms between the Dp(16)1Yey and the Ts65Dn models. Potentially, it could explain the limited efficacy of similar pharmacological intervention in clinical trials for DS. Further preclinical studies should prioritize refined behavioral paradigms and probably the use of more complex DS models to enhance the translational potential of candidate therapies.

BackgroundAn overwhelming body of evidence suggests neuroimmune dysfunction as a key underlying mechanism of FASD-associated adverse CNS outcomes. While few studies have highlighted the lingering effects of prenatal alcohol exposure (PAE) on producing specific immune factors, others suggest a primed neuroimmune state in adulthood, in which a proinflammatory bias is unmasked following subsequent immune activation in later-life. However, the PAE-induced neuroimmune landscape in adulthood remains poorly defined. We hypothesized that PAE induces long-term changes in gene expression linked to neuroimmune function that may be brain region-specific. MethodsUsing long-read next-generation RNA sequencing of brain tissues from a previously established model of a moderate PAE in mice, we compared across six regions: medial prefrontal cortex (mPFC), anterior cingulate cortex (ACC), hypothalamus, hippocampus, midbrain, and medulla. A comprehensive bioinformatics analysis investigated PAE-induced changes, dysregulated gene pathways, and transcriptional regulators with a focus on neuroimmune function. ResultsOur data identified at least 60 differentially expressed genes per brain region, many of which were associated with neuroimmune function. Upregulation of multiple proinflammatory factors and pathways was observed, suggesting ongoing baseline neuroimmune activation, potentially involving PXR, TNF, TLR4, the complement pathway, and various cytokine and chemokine signaling. A comparative analysis identified multiple upstream transcriptional regulators across multiple brain regions, including MECP2, TCF7L2, and IL-4. Importantly, this unbiased analysis revealed heterogeneity across brain regions in the activation of canonical immune pathways and highlighted previously unprecedented roles of pathways such as PXR, matrix metalloproteases, and cytokine signaling (e.g., IL-15, IL-27, IL-17) in PAE. ConclusionsPAE creates a unique inflammatory signature in the adult brain, even in the absence of secondary injury, with novel patterns of region-specific changes in genes implicated in glial-immune function. These data identified potential immune targets to elucidate the mechanisms underlying behavioral dysfunction and provide a framework for future therapeutic interventions.