Translational Psychiatry

Background: Metabolomic studies of depression have yielded heterogeneous findings, potentially because metabolic correlates differ across symptoms and metabolic states. We examined symptom-specific metabolomic associations and whether body mass index (BMI) modifies these relationships. Methods: We analyzed 83,717 Estonian Biobank participants (70.6% female) with 249 Nightingale metabolite measures and 14 lifetime depressive symptoms. Logistic regression models progressively adjusted for sociodemographic, lifestyle, medication, and BMI factors. BMI-related attenuation and metabolite x BMI interactions were evaluated, followed by self-organizing map analyses of broader metabolic context. Results: Before BMI adjustment, 660 metabolite-symptom associations were Bonferroni-significant; 136 were significant after BMI adjustment, including 105 retained associations. Weight-related associations showed the strongest BMI dependence: none of 199 weight-gain associations and 2 of 115 weight-loss associations were retained. Among 691 preselected metabolite-symptom pairs, 211 (30.5%) showed significant metabolite x BMI interactions after false discovery rate correction. Six systemic metabolic profiles were identified, but only 3 of 211 BMI-sensitive pairs showed additional profile-dependent heterogeneity. Conclusions: Circulating metabolic correlates of depressive symptoms are heterogeneous and strongly dependent on symptom phenotype and BMI-related metabolic context. These findings suggest that metabolic biomarkers in depression should be interpreted in relation to both symptom presentation and metabolic state rather than as uniform correlates of the disorder.

Music-based interventions (MIs) have shown promise in autism spectrum disorder (ASD), yet the molecular mechanisms underlying their effects remain poorly understood. The transcriptomic response to music exposure in individuals with ASD and healthy controls (HCs) was analyzed using buccal swab samples and a targeted NanoString gene expression panel. The discovery cohort included baseline and post-intervention samples, whereas the validation cohort added a 25-minute intermediate sample to assess transcriptional dynamics. Thirty-three candidate genes were identified in the discovery cohort, of which 20 (61%) remained significantly differentially expressed in the independent validation cohort, including 13 that withstood multiple-testing correction. Replicated genes included several key regulators of innate immunity and inflammatory signaling, such as CXCL8, MYD88, RELA, PTGS2, PTPRC, SELL, LYN, and LTBR. Functional enrichment analyses revealed robust innate immune and inflammatory pathways, characterized by TLR/IL-1-MYD88 signaling, cytokine activity, leukocyte activation, and antimicrobial responses. Co-expression network analysis identified coordinated remodeling of immune transcriptional programs in ASD. Consensus feature selection identified a two-gene signature that accurately discriminated pre- and post-musical stimulation samples in ASD but not in HCs. The same signature detected transcriptomic responses after only 25 minutes of music exposure by discriminating baseline and intermediate samples, with intermediate expression levels between baseline and post-intervention values, suggesting a progressive duration-dependent response. These findings demonstrate a reproducible transcriptional response to music in ASD across independent cohorts implicating neuroimmune pathways. The resulting transcriptional signature supports the development of minimally invasive biomarkers for monitoring MIs in neurodevelopmental disorders.

Individuals with autism spectrum disorder (ASD) and co-occurring mood or psychotic disorders face substantially elevated suicide-attempt risk, but prior research is largely limited to population-level incidence and cross-sectional risk-factor studies, leaving unclear whether individual-level, prospective prediction is possible in this population. We conducted a retrospective prediction model study using Epic Cosmos electronic health record (EHR) data from 322,879 individuals with ASD and co-occurring mood or psychotic disorders to predict suicide attempts at the next clinical encounter using only information available at or before the preceding encounter. Five machine-learning algorithms were compared, with performance evaluated across discrimination, calibration, and clinical utility. All algorithms achieved strong discrimination using the full predictor set (AUROC 0.87-0.91). A parsimonious feature set (22 predictors) matched or exceeded full-model performance, with extreme gradient boosting (XGBoost) achieving the highest discrimination and standardized net benefit post-ablation (AUROC = 0.90; AUPRC = 0.34), though net benefit was comparable for elastic net and logistic regression. A prior suicide attempt was the strongest individual predictor. Decision-curve analysis indicated that model-guided decisions provided greater net benefit than treating all or no individuals across a broad range of clinically plausible thresholds. These findings demonstrate that near-term suicide-attempt risk can be predicted with high decision utility in this high-risk population using data already captured in routine care, suggesting a feasible basis for future EHR-based clinical decision-support tools.

Background: Antidepressant selection in major depressive disorder remains trial-and-error, and no biomarker identifies which drug will work for an individual patient. Because neuroplasticity is a convergent mechanism of antidepressant response, we tested whether it could serve as a drug-specific biomarker in patient-derived neurons. Methods: We developed the Neuroplasticity Score (NPS), integrating structural and synaptic features from high-content imaging of patient-derived cortical neurons, and validated it against graded brain-derived neurotrophic factor (BDNF) exposure in neurons from 16 healthy donors. NPS was then applied unmodified to 75 antidepressant treatment episodes from 44 patients across two independent retrospective cohorts: 40 episodes from 20 STAR*D participants (Cohort 1; USA multi-site) and 35 episodes from 24 participants at Sheba Medical Center and Geha Mental Health Center (Cohort 2; Israel). Results: NPS increased with escalating BDNF dose (Spearman rho=0.83; P<.001). Response rate rose monotonically across NPS tertiles in both cohorts (trend P=.001 and P<.001). Across all 75 episodes, 84.6% of episodes in the highest NPS tertile responded, compared to 48.0% overall. NPS discriminated responding from non-responding episodes with an area under the receiver operating characteristic curve of 0.81 in both cohorts. The pooled odds ratio per 1-SD increase was 6.05 (95% CI, 2.80-13.05; I-squared=26%). Within patients contributing both a responding and a non-responding episode, the highest-scoring drug was the one that worked in 86% of patients (P<.001). Conclusions: Higher antidepressant-induced neuroplasticity in a patient's own neurons corresponded to a higher likelihood of response to that specific drug across two independent cohorts.

Early-life adversity during critical developmental windows markedly increases vulnerability to neuropsychiatric disorders later in life. Although adult behavioral and neurobiological alterations following early stress are well documented, the early emerging phenotypes and their underlying cellular and molecular mechanisms during juvenile development remain poorly understood. In this study, we utilized the translational Scarcity-Adversity Model (SAM) to investigate early behavioral alterations and hippocampal neuroplasticity in juvenile offspring. We characterized the behavioral profile of juvenile subjects exposed to SAM to identify early manifestations of vulnerability. Next, we evaluated hippocampal neurogenesis by quantifying neural progenitor cell proliferation immediately following stress exposure (at postnatal day 13, PND 13) and the short-term survival of newborn neurons (1 week post-labeling) in the dentate gyrus of juvenile offspring. Furthermore, given the functional compartmentalization of the hippocampus, we measured the protein expression of mature BDNF (mBDNF) and its precursor proBDNF across both dorsal and ventral domains. At the behavioral level, SAM-exposed juvenile offspring exhibited increased immobility in the forced swim test alongside impaired spatial memory in the novel object location task. Regarding hippocampal neurogenesis, SAM exposure did not alter immediate progenitor proliferation at PND 13; however, it produced a significant, male-specific reduction in newborn neuron survival, concomitant with a trend toward decreased mBDNF levels in males. Together, these results provide critical insight into the early neurodevelopmental trajectories triggered by early-life adversity, identifying early behavioral impairments and male-specific neuroplastic deficits as key juvenile markers of vulnerability preceding adult psychopathology.

Major Depressive Disorder (MDD) shows marked gender differences in prevalence and molecular signatures. Transcriptomic studies of post-mortem human brain tissue have reported alterations in the expression of synapse-related genes in MDD, including gender-specific patterns. But it remains unclear whether transcriptional changes observed in the brains of women with MDD are detectable in peripheral blood and conserved in experimental stress models. Whole-blood RNA sequencing was performed in women with MDD (n = 6) and matched healthy controls (n = 4). Differentially expressed genes (DEGs) were compared with previously reported female-specific blood and post-mortem brain transcriptomic datasets where selected overlapping synapse-associated genes were evaluated in the hippocampus and prefrontal cortex of female mice exposed to Chronic Variable Mild Stress (CVMS). Peripheral blood analysis identified DEGs enriched for synaptic organization, neuronal structure, and ion transport pathways. A substantial proportion of DEGs overlapped with previously reported datasets from peripheral blood, female MDD brain transcriptomic studies, and genes showing exclusive/enriched expression in the normal human brain. Network-based prioritization identified seven synapse-associated genes (SHANK2, SHANK3, CACNG8, GPHN, PICK1, NRXN2 and DNM2) for further analysis. In the female CVMS model, several of these genes showed altered expression in the hippocampus and/or prefrontal cortex, alongside behavioural changes and reduced dendritic spine density. These findings highlight shared transcriptional signals across human blood and human brain datasets, as well as in the mouse brain. However, larger studies are required to confirm and validate these observations.

Background: Pregnancy involves profound immune adaptations that may contribute to the development of postpartum depression (PPD); however, the relevant biomarkers and critical time window remain unclear. Therefore, we investigated whether third-trimester and postpartum cytokines and kynurenine pathway (KP) metabolites predict PPD and whether these biological signatures are related to postpartum brain morphology. Methods: Circulating cytokines and KP metabolites were assessed in 140 pregnant women during the third trimester and postpartum. Partial least squares (PLS) regression models were used to identify multivariate biological profiles predictive of PPD, followed by classification and regression tree (CART) analyses to evaluate predictive performance. In a neuroimaging subsample (n=61), voxel-based morphometry examined associations among PPD status, significant PLS-derived components, selected individual biomarkers, and postpartum grey matter volume. Results: A third-trimester cytokine signature including 21 cytokines significantly predicted PPD with high specificity. A third-trimester KP profile, driven primarily by the kynurenic acid/quinolinic acid ratio, also predicted PPD, and the combination of cytokine and KP components improved overall predictive performance to 93.3%. Postpartum biomarkers showed substantially weaker associations with PPD. The cytokine component was differentially associated with postpartum grey matter volume according to PPD status, with opposite associations observed in occipital regions between women who did and did not develop PPD. KP measures also showed distinct associations within sensorimotor regions based on PPD status Conclusions: Late pregnancy immune-inflammatory alterations are associated with subsequent PPD and postpartum brain morphology, supporting the potential utility of pregnancy-derived biological signatures for identifying women at increased risk for PPD.

Background: Major depressive disorder (MDD) is clinically heterogeneous, but relationships among cortical hemodynamic abnormalities, peripheral neuroimmune, metabolic, and oxidative stress (NIMETOX) processes, and clinical phenome features remain unclear. We applied a systems-biology approach integrating functional near-infrared spectroscopy (fNIRS), peripheral NIMETOX biomarkers, and multidimensional clinical phenomes. Methods: Patients with MDD (n = 86) and demographically matched healthy controls (HC, n = 40) underwent 89-channel high-density fNIRS during resting-state and verbal fluency task (VFT) conditions. Temporal hemodynamic variability (Difference) and area under the concentration-time curve (AUC) were derived from oxygenated hemoglobin (HbO), deoxygenated hemoglobin (HbR), and total hemoglobin (HbT) signals. NIMETOX assessments included inflammatory biomarkers, generalized and lipophilic antioxidant defenses, oxidized lipids, lipidomics, and 16S rRNA amplicon sequencing of the stool microbiome. Results: MDD showed reduced VFT Difference and AUC across HbO, HbR, and HbT. Difference reductions were widespread across prefrontal, frontal eye field, language-related, and sensorimotor cortices, whereas AUC abnormalities were more restricted and resting-state alterations were limited. VFT differences significantly differentiated MDD from HC, with further improvement after integrating NIMETOX biomarkers. Lower antioxidants, carnitines/acetylcarnitines, polyunsaturated and even-chain saturated fatty acids, inflammation, indices of gut dysbiosis and leaky gut, and adverse childhood experiences explained substantial variance in HbO, HbR, and HbT Difference. Integrated fNIRS-NIMETOX profiles explained substantial variance in overall illness severity (cross-validated R2= 0.62) and physiosomatic symptoms (cross-validated R2= 0.48). Conclusions: Systems-biology integration identified a multilevel MDD phenotype linking peripheral NIMETOX dysregulation with task-evoked cortical hemodynamic abnormalities and clinical phenome heterogeneity, supporting an interconnected gut-NIMETOX-brain pathophysiology. Key words: major depressive disorder, biomarkers, redox mechanisms, lipophilic antioxidants, oxidized lipids

Tourette disorder (TD) is a childhood-onset neurodevelopmental disorder for which no molecular diagnostic markers currently exist. Here, we used whole-blood transcriptome profiling to identify transcriptional signatures associated with TD and its underlying biology. Comparison of 24 TD cases and 24 controls revealed 19 differentially expressed genes. Classification models based on elastic net and support vector machine algorithms achieved receiver operating characteristic area-under-the-curve (ROC-AUC) values exceeding 0.80 under leave-one-out cross-validation, demonstrating robust discriminatory performance. Collectively, the expression pattern of the 19 genes distinguished affected and unaffected individuals with high accuracy using both classifiers. Among the 19 genes, all 14 protein-coding genes are expressed in the brain, and 8 have established links to neurodevelopmental or neurological processes. Several genes, including GIGYF1, KCNH1, NLGN2, and OTUD7A, have previously been implicated in autism spectrum disorder and related neurodevelopmental conditions, whereas IFI27, NLGN2, POLE2, and TPPP are associated with neurological or behavioral phenotypes in mouse loss-of-function models. These results suggest that peripheral blood transcriptional alterations mirror biologically processes relevant to TD pathophysiology and provide a framework for the development of transcriptome-based biomarkers and functional studies of tic disorders.

Background: Major depressive disorder (MDD) is associated with brain dysfunction and age-related diseases. Epigenetic clocks based on DNA methylation patterns estimate biological aging and aim to predict morbidity and mortality. Although MDD has been linked to epigenetic age acceleration (EAA) cross-sectionally, it is unknown whether EAA is associated with MDD after trauma. Methods: We included 706 participants (66% non-white, 64% female) from the multi-ancestry AURORA study, enrolled at emergency departments (ED) within 72 hours after trauma, from whom three age-adjusted epigenetic clocks were measured at enrollment: PhenoAge, GrimAge, and DunedinPACE. Functional MRI and assessment for probable MDD were conducted two weeks and six months later, respectively. Logistic and linear regression models tested the association of EAA with MDD and functional neuroimaging endpoints. Results: A total of 660 participants had available assessments of probable MDD six months after trauma, and from those 160 screened positively (24.2%) after trauma. EAA calculated by GrimAge (OR = 1.44, 95% CI = 1.12 - 1.85, p-adj= 0.012) and DunedinPACE (OR = 1.46, 95% CI = 1.15 - 1.85, p-adj = 0.005), but not PhenoAge (p-adj= 0.239), was significantly associated with MDD 6-months after trauma. Associations remained significant after adjusting for additional confounders, including prior trauma and disability (p= 0.008 for GrimAge, p= 0.010 for DunedinPACE). Subgroup analysis based on a median age split revealed these effects were only observed in participants 35 years of age or older. DunedinPACE was significantly associated with altered connectivity of fourteen brain network pairs after FDR correction (n = 217). Conclusions: EAA is associated with a higher risk of MDD after trauma and with concomitant brain connectivity changes. Calculation of GrimAge and DunedinPACE at the time of ED presentation may help recognize patients at risk for MDD, potentially improving personalized care after trauma.

Major depressive disorder (MDD) is a highly prevalent psychiatric disorder that remains challenging to treat effectively. Difficulty predicting who will develop the disorder and which individuals will respond to treatment is most likely due to our limited mechanistic understanding and the lack of reliable biomarkers. The current literature suggests that lipid profiles differ in MDD; however, most studies do not integrate other omics layers to elucidate their potential causal relationship at the molecular level. Using phenotypic, genetic, epigenetic and mass-spectrometry lipid data from a Scottish-based cohort of approximately 1,000 participants, we examined associations with individual lipid species. We identified 77 common genetic variant associations ({beta} range = -0.46 to 0.69, p < 3.1x10-10) and 82 genome-wide DNA methylation associations ({beta} range = -1.88 to 1.39, p < 2.2x10-10). We identified 23 lipid species associated with lifetime MDD ({beta} range = -0.23 to 0.2, p < 0.05), including lipid classes such as lysophosphatidylcholine (LPC), diacylglycerol (DG) and phosphatidylcholine (PC). Our integration analysis showed that genetic variants and DNA methylation at sites annotated to the FADS gene cluster were linked with lipid species that were also associated with lifetime MDD status, including LPC(14:1) ({beta} = 0.15, p = 0.03) and PC(19:0)/(20:4) ({beta} = -0.14, p < 0.05). Our findings provide further support for the role of the FADS gene cluster in lipid metabolism and its association with MDD, while introducing DNA methylation as an additional molecular layer supporting this link.

Suicidal behavior results from complex behavioral and biological changes. Previous cross-sectional studies indicate that proinflammatory immunobiological factors are often increased in close temporal proximity to a suicide attempt. Suicidal individuals may also exhibit a biological trait vulnerability to stress and inflammation, due to persistent epigenetic modifications. We enrolled 130 individuals with major depressive disorder (MDD), 83 with suicidal behavior at intake, and followed them for 12 months with up to eight clinical assessments. Quantification of plasma inflammatory markers and metabolites was performed by high-sensitivity electrochemiluminescence and Ultra High-Performance-Liquid-Mass Spectrometry (UPLC-MS), respectively. Epigenetic changes were identified using Illumina EPIC arrays. We identified 15 genes with altered DNA-methylation associated with suicidal behavior and attempts at baseline. Childhood trauma predicted lifetime suicide attempts and was associated with altered methylation of seven genes. Increased neutrophils and lower plasma serotonin at baseline predicted future suicide attempts over the following year (neutrophil estimate = 0.42, P = 0.016; serotonin OR = 0.58, 95% CI: 0.39-1.13). Utilizing biomarkers from baseline and epigenetic data from the genes with highest predictive values (STBD1 ,PRDM8, and TRIM15), we achieved an area under the curve (AUC) of 0.84 for suicide attempts over the year. Suicidal behavior in MDD was associated with specific epigenetic signatures. Several of the identified genes, such as MAD1L1, have been implicated in psychiatric disease, suicidal behavior and the immune response. These findings support the usefulness of epigenetic and immunometabolic blood markers for identifying suicidal individuals in clinical settings, potentially enhancing preventative efforts.

BackgroundAdversity during pregnancy is associated with alterations in offspring brain development. DNA methylation (DNAm) is a type of epigenetic modification is a putative mechanism for the intergenerational transfer of prenatal adversity on developmental outcomes. We examined the effects of prenatal social disadvantage (PSD) and prenatal psychosocial stress (PSS) on offspring epigenome-wide DNAm at four time points from infancy to age 4, and tested whether persistent DNAm signals mediated associations between PSD, PSS, and child psychopathology at ages 4-6. MethodsLongitudinal DNAm data (birth, Y1, Y2, Y3) was derived from salivary tissue of 281 infants (43.7% female) in the eLABE study. We examined epigenome-wide associations between DNAm and PSD and PSS. Linear models were adjusted for age, child sex, child race, maternal tobacco smoking, cell type composition, and batch effects. Mediation analyses tested whether birth DNAm mediated associations between prenatal exposures and internalizing and externalizing symptoms at ages 4-6. ResultsPSD was associated with 47 FDR-significant CpGs at birth and 3 at year 1. PSS was associated with 3 FDR-significant CpGs at birth. Thirteen CpGs showed PSD-associated significance across all four timepoints, including two CpGs associated with genes involved with neuronal maturation (BCL11B) and one CpG associated with a gene implicated in brain vascular health (ZNF474). Exploratory mediation analyses revealed an indirect effect of methylation at ZNF474 (cg19980369; p=0.028; pFDR=0.360) on the association between PSD and year 4-6 externalizing symptoms. ConclusionsPSD was associated with epigenetic signatures at birth, with a subset of associations persisting across early childhood and converging on cellular stress response biology. PSS showed minimal epigenetic associations, suggesting differential biological embedding of structural versus psychological dimensions of adversity.

Background: Genetic and observational studies have identified metabolic factors potentially contributing to the risk of Major Depression (MD). However, studies to date have concentrated on common genetic variants, many of which are in linkage disequilibrium. This makes it difficult to localise variants to a single gene. To overcome this, we used short read whole-genome sequenced datasets (srWGS) to conduct a targeted rare variant association analysis of the FADS region, which has previously been implicated in metabolic effects on MD. Methods: Using srWGS data from 740,373 individuals from All of Us (AoU) and the UK Biobank (UKB) we tested for associations between rare variants in the FADS region and MD. Additionally we conducted association tests between rare FADS variants and five blood-based metabolites implicated by previous studies of MD. Finally, we applied Mendelian randomisation to identify causal effects of metabolites on MD using rare-variants. Results: We found FADS rare variants were significantly associated with all tested metabolites, but not with MD. Using Wald-Ratio MR we found a putative causal effect of Docosahexaenoic acid (DHA) on MD using a rare-variant instrument in the FADS2 gene. Conclusion: Our study represents the largest rare-variant analysis of MD to date. However, the lack of associations between FADS rare-variants and MD suggests our analysis is underpowered, or that genetic effects on MD are conferred substantially by higher frequency variants. Rare-variant MR suggested a causal effect of DHA on MD, and implicates the FADS2 gene.

Background: Circadian rhythm disturbances (CRDs) are transdiagnostic risk factors across mental disorders. We examined CRD-associated phenotypes, their heritability, and clinical, functional and genetic correlates, cross-sectionally, in young adults from the Brisbane Longitudinal Twin Study. Methods: We assessed 2,773 community-based participants (25.6{+/-}4.1 years; 57.8% female). Six CRD-associated phenotypes (seasonality, hypersomnia, social jetlag, delayed sleep, evening preference, sleep inertia) were included. Associations of CRD-load (number of CRDs) with clinical and functional outcomes, and with polygenic risk scores (PRS) for mental health, physical health, and sleep/circadian traits, were tested using linear, negative binomial, or logistic regression, with family-clustered standard errors. Heritability of CRD-phenotypes was estimated using structural equation modelling. Results: Almost half the sample (49.2%) reported experiencing [&ge;]1 CRD-phenotype and 20.5% reported [&ge;]2. Four CRD-phenotypes (hypersomnia, social jetlag, delayed sleep, evening preference) were significantly heritable (h2=0.19-0.48). Higher CRD-load was associated with greater psychological distress ({beta}=0.96, p<0.001), more psychological symptoms (incidence rate ratio [IRR]=1.07-1.40), higher odds of a full-threshold mental disorder (OR=1.28, p<0.001), and greater functional impairment (days-in-bed, IRR=1.55; days-out-of-role, IRR=1.43). In genotyped participants (n=2,301), CRD-load was associated with mood-disorder PRS (major depression [OR=1.14, p=0.003] and bipolar disorder [OR=1.14, p=0.002]) but not PRSs for schizophrenia, attention deficit hyperactivity disorder, autism, neuroticism or anxiety. Trends towards increased PRSs for metabolic traits did not survive Bonferroni-correction. Conclusions: CRD-load was associated with clinical symptoms, functional impairment, and mood-related genetic liability. These findings support further evaluation of brief assessments of circadian-associated phenotypes for identifying young people at greater clinical burden and informing circadian-focused prevention and early intervention strategies.

Post-traumatic stress disorder (PTSD) has a significant genetic component (h2 = 24-72%). While the majority of prior studies have utilized common variants, recent research suggests rare variants also contribute to PTSD liability. Here, we examined the role of rare copy number variants (rCNVs; MAF < 1%) from multi-ancestry whole-genome sequencing data in the All of Us Research Program, using an electronic health record-defined PTSD phenotype. Genome-wide burden analyses utilizing all rCNV lengths revealed significant associations for deletion count across European-like (EUR-like; beta = 0.005, SE = 0.001, P = 5.99 x 10-6, FDR P = 3.59 x 10-5 ) and African-like (AFR-like; beta = 0.003, SE = 0.001, P = 0.005, FDR P =0.02) ancestries. We conducted rCNV burden analyses within and across EUR-like (N case = 1,179, N control = 33,689), AFR-like (N case = 504, N control = 12,216), and Admixed American (AMR-like) (N case = 418, N control = 11,935) ancestries using three separate domains of gene-sets previously implicated in neuropsychiatric disorders: neurodevelopmental disorders (NDD; N = 53), abnormal behavior mouse mutant-derived (N = 146), and PTSD (N = 5). Within-ancestry analyses across all three domains yielded one significant brain-expressed gene-set within the EUR-like cohort. Meta-analyzing across ancestries identified 11 NDD gene-sets enriched for rCNV count (FDR P < 0.05). rCNVs with lengths greater than 10kb were examined, but observed no significant signals. Future research using larger samples and detailed PTSD phenotypes, integrated with functional genomic data and brain-region-specific expression profiles, is needed to fully map the disorder's genetic landscape.

Autism spectrum disorder (ASD) is one of the most common neurodevelopmental disorders. The absence of a clear aetiology strengthens the need to identify quantitative biomarkers capable of diagnosing the condition and assessing the severity of symptoms. We implemented a normative modelling framework based on EEG effective connectivity (EC), leveraging recordings collected in three centers, in typical development (TD) and ASD participants with age spanning from 5 to 19 years. Deviations from the normative evolution were tested as ASD biomarkers, compared with standard EC metrics. Normative EC metrics supported accurate classification of ASD vs TD participants (0.82 AUC), reaching 0.95 AUC when combined with behavioral subscales, significantly outperforming a classification based on behavioral data alone. In ASD participants, selected metrics in the Temporo-Parietal-Frontal network predicted social responsiveness scale (SRS) values with high significance (SRS Total R^2=0.42, p=0.0005). These results suggest that normative EC values constitute robust and generalizable ASD biomarkers.

Background Classic psychedelics are serotonergic agents increasingly recognized for their ability to produce rapid and long-lasting therapeutic effects in several neuropsychiatric conditions, yet the molecular mechanisms that translate acute serotonergic perturbation into long-term brain adaptation remain poorly understood. Psychedelic action is commonly attributed to enhanced neuronal plasticity, but emerging evidence suggests broader engagement of stress-responsive and homeostatic processes across neural and non-neural cell types. Methods To define the temporal structure of psychedelic-induced transcriptional responses, we profiled gene expression in the prefrontal cortex of mice at early and delayed time points following a single administration of lysergic acid diethylamide (LSD). Differentially expressed genes were then contextualized through pathway-level and cross-dataset comparisons with established models of adaptive and chronic stress. Results LSD induced a coherent, temporally organized transcriptional program extending beyond immediate neuronal activation. Early responses reflected adaptive metabolic and stress-related signaling, whereas later ones involved circadian and neuroendocrine regulation. A persistent transcriptional core spanning both time points indicated sustained regulation of metabolic, circadian, and stress-responsive pathways. Comparative analyses showed that, although LSD-induced transcriptional profile shares features with general stress responses, its preferentially aligns with adaptive, hormetic stress paradigms rather than maladaptive chronic stress. Conclusions These findings indicate that in the prefrontal cortex LSD activates adaptive stress transcriptional programs consistent with hormetic adaptation, providing a molecular framework to interpret the enduring effects of psychedelics beyond synaptic plasticity.

Background: Autism is a lifelong neurodevelopmental condition, but the biological processes shaping aging are poorly understood. DNA methylation-based measures index physiological decline and mortality risk independently of chronological age. We tested whether biological aging differs in autistic adults, whether differences are system-specific, and which factors predict within-autism variation. Methods: Saliva DNA methylation (Illumina EPIC) was assayed in 37 autistic adults (31 male, 6 female; mean age 29.8 years), followed from early childhood, spanning a broad ability range including intellectual disability, and 188 non-autistic adults from four public datasets, processed jointly. Fifteen measures were calculated: four global (Horvath, GrimAge, DunedinPACE, SystemsAge) and 11 system-specific SystemsAge subscores. Models adjusted for age, sex, and epithelial-cell proportion. Results: Global aging measures did not differ between groups (all q > .34). However, autistic adults had higher Brain ({beta} = 0.81), Blood ({beta} = 0.65), and Liver SystemsAge ({beta} = 0.58; all q < .01), with Brain and Blood robust across sensitivity analyses. Within autism, aging measures were unrelated to symptom severity, IQ, co-occurring diagnoses, or medication use. Instead, elevations were associated with psychosocial circumstances, including a lack of structured daytime activity, greater loneliness, and lower quality of life. Conclusions: Differences in aging biology in autism appeared system-specific rather than global, with no evidence of generalized epigenetic age acceleration. This vulnerability appeared more closely tied to psychosocial factors than core clinical features. System-specific DNAm measures may therefore provide a more sensitive framework for identifying biological heterogeneity and potentially modifiable contributors to healthy aging in autistic adulthood.

Biologically informed polygenic scores (PGSs) may help interpret genetic associations with treatment outcomes. Here, we computed neurotransmitter-based PGSs (NeuroPGSs) for patients with major depressive disorder (MDD) and assessed their association with SSRI treatment outcomes. By targeting genetic variants involved in key neurotransmission pathways, we developed PGSs for serotonergic (5228), gamma-aminobutyric acid (GABA, 4103), dopaminergic (7061), and glutamatergic (5690) variants. We leveraged data from two cohorts of patients who received SSRIs for at least 4 weeks: the International SSRI Pharmacogenomics Consortium (ISPC) and the Pharmacogenomics Research Network Antidepressants Medication Pharmacogenomics Study (PGRN-AMPS). Treatment response was defined as [&ge;]50% reduction from baseline depression symptoms, measured using 17-item Hamilton Rating Scale for Depression (HRSD-17) in ISPC and 16-item Quick Inventory of Depressive Symptomatology (QIDS-C16) in PGRN-AMPS. Remission was defined as HRSD-17 [&le;]7 (ISPC) or QIDS-C16[&le;]5 (PGRN-AMPS). The association between each NeuroPGS and antidepressant response or remission was evaluated using logistic regression models, adjusting for age, sex and five genetic principal components, with statistical significance determined at Benjamini Hochberg-adjusted p<0.05. The GABAergic-based PGS was associated with SSRI response (aOR per SD = 1.45, 95%CI: 1.14-1.85). Patients with the highest GABAergic PGS (10th decile) had 2.59-fold higher odds of response (95%CI: 1.46-4.60) compared to those with the lowest decile (1st decile). These exploratory findings support further evaluation of NeuroPGSs; independent replication and assessment of incremental predictive value are needed before clinical application. Keywords: Polygenic score; Neurotransmitters; GABAergic; depression; SSRIs; pharmacogenomics