Major depressive disorders (MDD) are projected to become the leading cause of disease burden worldwide by 2030, but diverse etiological mechanisms mean that most patients respond poorly to first-line antidepressant treatments. Current rodent models of MDD are primarily based on exposure to a single genetic or environmental risk factor, which does not reflect the multifactorial and polygenic nature of MDD. We recently generated a polygenic mouse model of MDD through selective breeding following moderate stress induced by the Tail Suspension Test (TST), named H-TST. Here, we selected animals exhibiting high immobility during the Forced Swim Test (FST) to generate a new stable polygenic model of MDD, called H-FST. Unlike our previous H-TST model, H-FST mice did not exhibit any anxiety- or anhedonia-like behaviors, nor did they display any sleep disturbances, suggesting these mouse lines may model the phenotypic heterogeneity of MDD. Moreover, H-TST and H-FST mice showed opposite response after administration of various antidepressant treatments. The gene expression level in the prefrontal cortex of H-TST and H-FST mice revealed little overlap in the genes and biological pathways associated with depressive-like behaviors, and we showed opposite dysregulation of the synaptic imbalance between excitatory and inhibitory pathways. By comparing gene expression between our models and subtypes of people with MDD, we identified biomarkers that accurately predict treatment response in an independent cohort of patients. Overall, the H-TST and H-FST mouse models appropriately reproduce various clinical subtypes of MDD and are useful tools for identifying biomarkers that predict treatment response in humans.

Bipolar disorder's (BD) clinical heterogeneity has an unresolved genetic basis. We meta-analyzed genome-wide association studies (GWAS) of 16 BD subphenotypes in 226,032 individuals from 57 cohorts (38,022 cases); 10 advanced to multivariate and multi-trait analyses. Four factors (compulsive, psychotic, dysregulated, internalizing) explained 82.8% of shared genetic variance. BD1 and BD2 loaded on distinct factors despite a high genetic correlation; 87.0% of common-factor loci were significant in neither subtype. Unipolar mania aligned with psychosis over internalizing, and was distinguishable from BD1, and rapid cycling showed heritable cross-domain liability. We identified 356 risk loci, 158 novel, including the first univariate-GWAS associations for psychosis, unipolar mania, rapid cycling and schizoaffective disorder-and 249 credible genes (89 high-confidence), 12 with approved-drug or clinical-phase annotations. Cell-type association showed a midbrain dopaminergic-GABAergic gradient along the psychotic factor. BD's genetic architecture appears hierarchical-a general liability resolving into dimensions of course and comorbidity, beyond subtypes.

ABSTRACT Major depressive disorders (MDD) are predicted to become the first cause of burden of disease worldwide in 2030, but 30% of patients still do not respond to antidepressants. Current rodent models of MDD mainly result either from one genetic or one environmental risk factor exposure, not recapitulating the multifactorial and polygenic nature of MDD. We recently generated a polygenic mouse model of MDD from selective breeding after mild stress in the Tail Suspension Test (TST), named H-TST. Here, we selected animals exhibiting high immobility during the Forced Swim Test (FST) to generate a new stable polygenic model of MDD, called H-FST. Unlike our previous H-TST model, H-FST mice did not exhibit any anxiety-or anhedonia-like behaviors, nor did they display any sleep disturbances. Moreover, H-TST and H-FST mice showed opposite response after administration of various antidepressant treatments. The gene expression level in the prefrontal cortex of H-TST and H-FST mice revealed little overlap in genes and biological pathways associated with depressive-like behaviors and opposite dysregulation of excitatory/inhibitory synaptic imbalance. Finally, these two models allowed in humans the identification biomarkers of treatment response specific of clinical subgroup of patients.

Polygenic risk scores (PRSs) for several psychiatric disorders have been associated with the clinical presentation of bipolar disorder (BD). PRSs have also been suggested to moderate the associations between childhood maltreatment and BD severity. In this study, we investigated how PRSs for BD, schizophrenia, major depressive disorders (MDD) and attention-deficit/hyperactivity disorder (ADHD) might disentangle the clinical and dimensional heterogeneity of BD in a sample of 852 affected individuals. We used logistic and linear regressions, moderation and mediation models to test the associations between PRSs, dimensions in childhood/adulthood and clinical indicators of severity of BD. All models were adjusted for age, sex, BD type and depressive symptoms. None of the PRSs were significantly associated with the clinical expression of BD when considered in terms of mode of onset, course, or psychiatric comorbidities. Nevertheless, the PRS-ADHD significantly and positively correlated with the levels of childhood maltreatment, childhood ADHD symptoms, and of some adulthood measures (affective lability, impulsivity and hostility) with p values ranging from 3.10−8–4.10−4. None of the PRSs moderated the effects of childhood maltreatment on the clinical or dimensional variables. Mediation model suggested paths from both PRS-ADHD and PRS-MDD to childhood ADHD symptoms and childhood maltreatment. The links between PRS-ADHD to all adulthood dimensions were mediated by childhood ADHD symptoms (p < 0.002). In turn, some adulthood dimensions (mainly affect intensity and affective lability) were associated with the clinical severity of BD, as defined by rapid cycling, suicide attempts and anxiety disorders. In conclusion, this study disentangles the associations between the genetic liability for four psychiatric disorders and the clinical/dimensional heterogeneity of BD. We suggest a continuum from the genetic risk for ADHD and MDD through dimensions in childhood/adulthood to a severe/complex clinical expression of BD.

Introduction: Immunochemotherapy remains the cornerstone of treatment for Mantle Cell Lymphoma (MCL). However, 25% of patients experience early progression, with survival rates of less than two years. Current prognostic tools, such as the MCL International Prognostic Index (MIPI), and poor prognostic histological and genetic features are insufficient for stratifying patients into individualized therapeutic strategies. This study aimed to identify biomarkers for high-risk MCL patients using an integrated analysis of clinical and biological factors. Methods: We analyzed data from 299 patients enrolled in the LyMa phase 3 trial, with a focus on high-risk patients, defined by refractoriness to immunochemotherapy or relapse within 12 months post-autologous stem cell transplantation. We used optical genome mapping (OGM) on frozen samples, alongside whole-exome sequencing (WES), RNA sequencing, and DNA methylation arrays analyses on FFPE tumor biopsies to identify genetic, transcriptomic and epigenetic alterations. Machine learning models, including random forest analysis and Partial Least-Squares Discriminant Analysis (PLS-DA), were employed to predict high-risk MCL status. Results: Among the 299 patients, 31 (10.4%) were identified as high-risk (HR) with a median overall survival of 8.5 months after relapse. HR patients exhibited significantly higher levels of LDH, higher-risk MIPI scores (45% vs. 16%, p<0.001), Ki-67 >30% (71% vs. 31%, p<0.001) and blastoid/pleomorphic histology (32% vs. 9%, p<0.001). In multivariate analysis, only high-risk MIPI score, and Ki-67 >30% were associated with HR MCL. These factors were insufficient to specifically capture HR patients, as one-third of long-term responders would have been misidentified as high-risk. The high-risk (HR) subgroup displayed a greater burden of complex genetic alterations, with significantly increased frequencies of TP53 alterations (OR 25.4, p < 0.001), CDKN2A deletions (OR 4.5, p = 0.015), RB1 deletions (OR 4.9, p = 0.024), MYC gains (OR 5.8, p = 0.047), and MIR17HG gains (OR 11.8, p = 0.013). To improve predictive accuracy, an integrative analysis combining well-established prognostic markers with gene alterations assessed by WES, was performed. Random forest analysis achieved a test accuracy of 91% when predicting HR MCL status, with a ROC AUC of 96%. The sensitivity was 84% and the specificity was 96%, with a misclassification rate of 14%. The most influential features included the Ki-67 index, histological subtype, TP53 alterations, MIPI score, and gains of MYC and MIR17HG. Unsupervised Uniform Manifold Approximation and Projection (UMAP) analysis of gene expression profiling on 49 FFPE samples, including 15 HR MCLs, showed that HR MCLs tended to cluster together, but the distinction was not perfect. Supervised analyses, using PLS-DA, indicated potential overfitting, suggesting that transcriptomic signals alone are insufficient for perfect discrimination. In contrast, DNA methylation analysis of 29 FFPE samples, including 12 HR MCLs, revealed a distinct epigenetic signature that robustly discriminated HR MCLs from control cases. Supervised approaches (PLS-DA) identified differentially methylated probes (DMPs, n=225) that perfectly discriminated HR MCL from controls. Importantly, this epigenetic signature was validated in an independent cohort (Barcelona cohort, n=64). To explore the genome-wide impact of DNA methylation on gene expression, we performed correlation analyses between promoter methylation and transcriptomic data across all protein-coding genes. A subset of genes showed significant correlations, with a predominant inverse relationship in HR cases, absent in controls, indicating that promoter hypermethylation may drive transcriptional deregulation in this subgroup. Notably, CHL1, a tumor suppressor, and KLHL6, associated with chemoresistance, demonstrated strong inverse correlations between methylation and expression, supporting their involvement in HR MCL pathogenesis. Conclusion: This study provides an integrated characterization of high-risk MCL, identifying a novel epigenetic signature that outperform traditional prognostic markers. Our baseline epigenetic approach may enhance patient stratification and support the development of personalized therapies. These results support the combined analysis of genetic and epigenetic features to capture MCL's full biological complexity.

Treatment of schizophrenia relies heavily on the use of antipsychotic drugs. Their efficacy is at present determined by lengthy trial-and-error approach, calling for more efficient strategies based on personalized medicine. Here, we present a prospective study of 116 first-episode-psychosis (FEP) patients from the OPTiMiSE cohort, aiming to identify blood epigenomic biomarkers predicting response to amisulpride and to shed light on involved mechanisms by linking the observed methylation patterns to genetic variation and gene expression. The analysis of 210 paired (baseline and follow-up) blood methylomes revealed 67 regions stably differentially methylated between good and bad responders and 197 regions with response-specific dynamics. The former were primarily enriched in functions related to neurotransmission and synapse assembly, the latter in immunity and inflammation. Baseline methylation values of three of these candidate regions, situated within HOXA, HTR2A and PRR5 genes, were selected as good predictors (10x cross-validated Matthews correlation coefficient = 0.81) of amisulpride response in our cohort. Screening for associations between the methylation of the selected regions and the genetic variants (SNPs) in a 1MBp surroundings revealed a high degree of genetic control for HTR2A, but not for HOXA or PRR5 regions. Whereas we detected multiple correlations between methylation and gene expression, few were temporally stable, such as the correlation between HOXA5 and SKAP2 expression, a gene affecting susceptibility to schizophrenia. Our findings demonstrate the strengths of prospective design in response-biomarker research and suggest that epigenetic variation associated with antipsychotic response is shaped by both the environmental and genetic factors.

Dysregulation of inflammatory mediators and complement cascade proteins has been implicated in psychosis. In the current study, we aimed to investigate the relationship between complement cascade proteins and inflammatory cytokines in blood from people at clinical high risk (CHR) for psychosis and at first episode of psychosis (FEP). Baseline blood samples from two cohorts of CHR participants [NEURAPRO (n = 153) and STEP (n = 146)], and one cohort of FEP patients [OPTiMiSE (n = 226)] were included. The blood levels of three Inflammatory markers including Interleukin (IL)-6, Tumour necrosis factor-alpha (TNF-α) and C-reactive protein (CRP) along with about 30 complement proteins were considered for the analyses. First, we evaluated the interrelationship between the inflammatory markers and then using regression models, we investigated their association with complement proteins. We detected positive associations among all three inflammatory markers IL-6, TNF-α, and CRP in CHR individuals, whereas in FEP positive association was observed only between IL-6 and TNF-α. Regression models showed strong positive associations for complement proteins C3, C4A, C4B, C5, CFB and CFI with all three inflammatory markers in both CHR cohorts. This indicates the presence of a complement related pro-inflammatory tone at risk of developing psychosis. In contrast, in the FEP cohort, complement proteins C1QA, C3, C5, FCN-2, and MASP2 showed an inverse association with TNF-α, and no association found with IL-6 or CRP. These results suggest a switch in the immune activity in the peripheral circulation of FEP compared to CHR. These novel findings propose that complement protein-targeted anti-inflammatory therapy could be effective at CHR state and hence could be used for early intervention in psychosis.

Identifying biological markers to guide treatment decisions in first-episode psychosis (FEP) is essential for improving patient outcomes. This longitudinal study investigated DNA methylation (DNAm) patterns and DNAm-derived cell-type proportions (CTP) in blood and associated them with response to risperidone treatment, a second-generation antipsychotic drug, in antipsychotic-naïve FEP patients. We also explored longitudinal changes in DNAm associated with risperidone treatment. We profiled DNAm in 114 individuals before (anFEP) and after two months of risperidone treatment using microarrays. The main results were compared with 115 healthy controls and validated in an independent cohort of subjects with schizophrenia (n = 26) with one-month follow-up data. We identified 302 differentially methylated positions (DMPs) associated with treatment response, measured by changes in the Positive and Negative Syndrome Scale score, of which 16 were validated in the independent cohort. Sixteen differentially methylated regions (DMRs) were associated with response, with one (in SIPA1L3) being validated. A decrease in B-cell proportions was correlated with symptom improvement in both cohorts. Additionally, four DMPs associated with risperidone treatment were identified: two related to the psychotic state and two specifically to risperidone treatment. DNAm-derived CTP showed alterations in anFEP compared with controls, particularly in the neutrophil-to-lymphocyte ratio, which normalized after treatment. These findings suggest that DNAm, particularly in B-cells, may be a promising marker for monitoring response to risperidone treatment in schizophrenia. Our longitudinal study revealed novel and known genes that may be regulated by risperidone and could be used as response markers to improve prognosis in schizophrenia and FEP.

Background: Polygenic scores (PGSs) hold the potential to identify patients who respond favorably to specific psychiatric treatments. However, their biological interpretation remains unclear. In this study, we developed pathway-specific PGSs (PSPGSs) for lithium response and assessed their association with clinical lithium response in patients with bipolar disorder. Methods: Using sets of genes involved in pathways affected by lithium, we developed 9 PSPGSs and evaluated their associations with lithium response in the International Consortium on Lithium Genetics (ConLi+Gen) (N = 2367), with validation in combined PsyCourse (Pathomechanisms and Signatures in the Longitudinal Course of Psychosis) (N = 105) and BipoLife (N = 102) cohorts. The association between each PSPGS and lithium response—defined both as a continuous ALDA score and a categorical outcome (good vs. poor responses)—was evaluated using regression models, with adjustment for confounders. The cutoff for a significant association was p < .05 after multiple testing correction. Results: The PGSs for acetylcholine, GABA (gamma-aminobutyric acid), and mitochondria were associated with response to lithium in both categorical and continuous outcomes. However, the PGSs for calcium channel, circadian rhythm, and GSK (glycogen synthase kinase) were associated only with the continuous outcome. Each score explained 0.29% to 1.91% of the variance in the categorical and 0.30% to 1.54% of the variance in the continuous outcomes. A multivariate model combining PSPGSs that showed significant associations in the univariate analysis (combined PSPGS) increased the percentage of variance explained (R2) to 3.71% and 3.18% for the categorical and continuous outcomes, respectively. Associations for PGSs for GABA and circadian rhythm were replicated. Patients with the highest genetic loading (10th decile) for acetylcholine variants were 3.03 times more likely (95% CI, 1.95 to 4.69) to show a good lithium response (categorical outcome) than patients with the lowest genetic loading (1st decile). Conclusions: PSPGSs achieved predictive performance comparable to the conventional genome-wide PGSs, with the added advantage of biological interpretability using a smaller list of genetic variants.

Bipolar disorder is a leading contributor to the global burden of disease1. Despite high heritability (60-80%), the majority of the underlying genetic determinants remain unknown2. We analysed data from participants of European, East Asian, African American and Latino ancestries (n = 158,036 cases with bipolar disorder, 2.8 million controls), combining clinical, community and self-reported samples. We identified 298 genome-wide significant loci in the multi-ancestry meta-analysis, a fourfold increase over previous findings3, and identified an ancestry-specific association in the East Asian cohort. Integrating results from fine-mapping and other variant-to-gene mapping approaches identified 36 credible genes in the aetiology of bipolar disorder. Genes prioritized through fine-mapping were enriched for ultra-rare damaging missense and protein-truncating variations in cases with bipolar disorder4, highlighting convergence of common and rare variant signals. We report differences in the genetic architecture of bipolar disorder depending on the source of patient ascertainment and on bipolar disorder subtype (type I or type II). Several analyses implicate specific cell types in the pathophysiology of bipolar disorder, including GABAergic interneurons and medium spiny neurons. Together, these analyses provide additional insights into the genetic architecture and biological underpinnings of bipolar disorder.

Importance:The clinical heterogeneity of bipolar disorder (BD) is a major obstacle to improving diagnosis, predicting patient outcomes, and developing personalized treatments. A genetic approach is needed to deconstruct the disorder and uncover its fundamental biology. Previous genetic studies focusing on broad diagnostic categories have been limited in their ability to parse this complexity. Objective:To test the hypothesis that clinically distinct subphenotypes of BD are associated with different underlying common variant genetic architectures. Design Setting and Participants:This multicenter study included a primary genome-wide association study (GWAS) of up to 23,819 bipolar disorder (BD) cases and 163,839 controls. These results were integrated via multi-trait analysis of GWAS (MTAG) with external summary statistics for BD (59,287 cases; 781,022 controls) and schizophrenia (SCZ; 53,386 cases; 77,258 controls). Sample overlap was statistically accounted for. Main Outcomes and Measures:The primary outcomes were the genetic dimensions underlying BD heterogeneity, differentiated by single nucleotide polymorphism (SNP)-heritability (h 2 SNP ), genetic correlations, genomic loci ( P ≤5×10 -8 ), and functional, cell-type, and gene-expression pathway analyses. Results:We identified four genetically-informed dimensions of BD: Severe Illness, Core Mania, Externalizing/Impulsive Comorbidity, and Internalizing/Affective Comorbidity. The analyses yielded up to 181 subphenotype-associated loci, 53 of which are novel. The Severe Illness Dimension was characterized by a unique neuro-immune signature (a protective association with HLA-DMB , P =2.50×10 -273 ) evident only when leveraging SCZ genetic data. The Internalizing/Affective dimension was associated with neurodevelopmental genes (e.g., DCC ). Notably, the rapid-cycling subphenotype showed a unique signature of strong negative selection, a finding not observed in other subphenotypes. Conclusions and Relevance:The clinical heterogeneity of bipolar disorder appears to be defined by a complex and multi-layered genetic architecture. The presented findings provide an empirical framework that may advance psychiatric nosology beyond its current diagnostic boundaries. These results may also inform future research to identify targets for personalized interventions. The delineation of these genetically-informed dimensions offers specific, biologically-grounded hypotheses for subsequent therapeutic discovery. Establishing such a framework is an essential step toward refining diagnostic criteria and developing more effective, personalized treatments. This work lays the foundation for a transition from a uniform treatment model to the paradigm of precision psychiatry. Key Points:Question: What are the distinct genetic architectures underlying the clinical heterogeneity of bipolar disorder?Findings: In this genetic study of 23,819 bipolar disorder (BD) cases and 163,839 controls, clinical heterogeneity mapped onto four genetically-informed dimensions. A severe illness dimension was defined by a neuro-immune signature ( HLA-DMB ) shared with schizophrenia. An affective comorbidity dimension was distinguished by neurodevelopmental pathways involving axonal guidance ( DCC ). Notably, the rapid-cycling phenotype showed evidence of purifying selection, suggesting influence by rare, highly penetrant alleles. Meaning: These findings provide a data-driven biological framework for bipolar disorder, guiding future research toward patient stratification and targeted therapeutics.

BACKGROUND:Bipolar disorder (BD) is a complex and heterogeneous psychiatric disorder. It has been suggested that neurodevelopmental factors contribute to the etiology of BD, but a specific neurodevelopmental phenotype (NDP) of the disorder has not been identified. Our objective was to define and characterize an NDP in BD and validate its associations with clinical outcomes, polygenic risk scores, and treatment responses. METHODS:We analyzed the FondaMental Advanced Centers of Expertise for Bipolar Disorders cohort of 4468 patients with BD, a validation cohort of 101 patients with BD, and 2 independent replication datasets of 274 and 89 patients with BD. Using factor analyses, we identified a set of criteria for defining NDP. Next, we developed a scoring system for NDP load and assessed its association with prognosis, neurological soft signs, polygenic risk scores for neurodevelopmental disorders, and responses to treatment using multiple regressions, adjusted for age and gender with bootstrap replications. RESULTS:Our study established an NDP in BD consisting of 9 clinical features: advanced paternal age, advanced maternal age, childhood maltreatment, attention-deficit/hyperactivity disorder, early onset of BD, early onset of substance use disorders, early onset of anxiety disorders, early onset of eating disorders, and specific learning disorders. Patients with higher NDP load showed a worse prognosis and increased neurological soft signs. Notably, these individuals exhibited a poorer response to lithium treatment. Furthermore, a significant positive correlation was observed between NDP load and polygenic risk score for attention-deficit/hyperactivity disorder, suggesting potential overlapping genetic factors or pathophysiological mechanisms between BD and attention-deficit/hyperactivity disorder. CONCLUSIONS:The proposed NDP constitutes a promising clinical tool for patient stratification in BD.

Background Multiple genetic and environmental risk factors play a role in the development of both schizophrenia-spectrum disorders and affective psychoses. How they act in combination is yet to be clarified.Methods We analyzed 573 first episode psychosis cases and 1005 controls, of European ancestry. Firstly, we tested whether the association of polygenic risk scores for schizophrenia, bipolar disorder, and depression (PRS-SZ, PRS-BD, and PRS-D) with schizophrenia-spectrum disorder and affective psychosis differed when participants were stratified by exposure to specific environmental factors. Secondly, regression models including each PRS and polyenvironmental measures, including migration, paternal age, childhood adversity and frequent cannabis use, were run to test potential polygenic by polyenvironment interactions.Results In schizophrenia-spectrum disorder vs controls comparison, PRS-SZ was the strongest genetic predictor, having a nominally larger effect in nonexposed to strong environmental factors such as frequent cannabis use (unexposed vs exposed OR 2.43 and 1.35, respectively) and childhood adversity (3.04 vs 1.74). In affective psychosis vs controls, the relative contribution of PRS-D appeared to be stronger in those exposed to environmental risk. No evidence of interaction was found between any PRS with polyenvironmental score.Conclusions Our study supports an independent role of genetic liability and polyenvironmental risk for psychosis, consistent with the liability threshold model. Whereas schizophrenia-spectrum disorders seem to be mostly associated with polygenic risk for schizophrenia, having an additive effect with well-replicated environmental factors, affective psychosis seems to be a product of cumulative environmental insults alongside a higher genetic liability for affective disorders.

Background:Lithium (Li) remains the treatment of choice for bipolar disorders (BP). Its mood-stabilizing effects help reduce the long-term burden of mania, depression and suicide risk in patients with BP. It also has been shown to have beneficial effects on disease-associated conditions, including sleep and cardiovascular disorders. However, the individual responses to Li treatment vary within and between diagnostic subtypes of BP (e.g. BP-I and BP-II) according to the clinical presentation. Moreover, long-term Li treatment has been linked to adverse side-effects that are a cause of concern and non-adherence, including the risk of developing chronic medical conditions such as thyroid and renal disease. In recent years, studies by the Consortium on Lithium Genetics (ConLiGen) have uncovered a number of genetic factors that contribute to the variability in Li treatment response in patients with BP. Here, we leveraged the ConLiGen cohort (N=2,064) to investigate the genetic basis of Li effects in BP. For this, we studied how Li response and linked genes associate with the psychiatric symptoms and polygenic load for medical comorbidities, placing particular emphasis on identifying differences between BP-I and BP-II. Results:We found that clinical response to Li treatment, measured with the Alda scale, was associated with a diminished burden of mania, depression, substance and alcohol abuse, psychosis and suicidal ideation in patients with BP-I and, in patients with BP-II, of depression only. Our genetic analyses showed that a stronger clinical response to Li was modestly related to lower polygenic load for diabetes and hypertension in BP-I but not BP-II. Moreover, our results suggested that a number of genes that have been previously linked to Li response variability in BP differentially relate to the psychiatric symptomatology, particularly to the numbers of manic and depressive episodes, and to the polygenic load for comorbid conditions, including diabetes, hypertension and hypothyroidism. Conclusions:Taken together, our findings suggest that the effects of Li on symptomatology and comorbidity in BP are partially modulated by common genetic factors, with differential effects between BP-I and BP-II.

Treatments are only partially effective in major depressive disorders (MDD) but no biomarker exists to predict symptom improvement in patients. Animal models are essential tools in the development of antidepressant medications, but while recent genetic studies have demonstrated the polygenic contribution to MDD, current models are limited to either mimic the effect of a single gene or environmental factor. We developed in the past a model of depressive-like behaviors in mice (H/Rouen), using selective breeding based on behavioral reaction after an acute mild stress in the tail suspension test. Here, we propose a new mouse model of depression (H-TST) generated from a more complex genetic background and based on the same selection process. We first demonstrated that H/Rouen and H-TST mice had similar phenotypes and were more sensitive to glutamate-related antidepressant medications than selective serotonin reuptake inhibitors. We then conducted an exome sequencing on the two mouse models and showed that they had damaging variants in 174 identical genes, which have also been associated with MDD in humans. Among these genes, we showed a higher expression level of Tmem161b in brain and blood of our two mouse models. Changes in TMEM161B expression level was also observed in blood of MDD patients when compared with controls, and after 8-week treatment with duloxetine, mainly in good responders to treatment. Altogether, our results introduce H/Rouen and H-TST as the two first polygenic animal models of MDD and demonstrate their ability to identify biomarkers of the disease and to develop rapid and effective antidepressant medications.

T-lymphoblastic lymphoma (T-LBL) and thymoma are two rare primary tumors of the thymus deriving either from T-cell precursors or from thymic epithelial cells, respectively. Some thymoma subtypes (AB, B1, and B2) display numerous reactive terminal deoxynucleotidyl transferase-positive (TdT+) T-cell precursors masking epithelial tumor cells. Therefore, the differential diagnosis between T-LBL and TdT+ T-lymphocyte-rich thymoma could be challenging, especially in the case of needle biopsy. To distinguish between T-LBL and thymoma-associated lymphoid proliferations, we analyzed the global DNA methylation using two different technologies, namely MeDIP array and EPIC array, in independent samples series [17 T-LBLs compared with one TdT+ lymphocyte-rich thymoma (B1 subtype) and three normal thymi, and seven lymphocyte-rich thymomas compared with 24 T-LBLs, respectively]. In unsupervised principal component analysis (PCA), T-LBL and thymoma samples clustered separately. We identified differentially methylated regions (DMRs) using MeDIP-array and EPIC-array datasets and nine overlapping genes between the two datasets considering the top 100 DMRs including ZIC1, TSHZ2, CDC42BPB, RBM24, C10orf53, and MACROD2. In order to explore the DNA methylation profiles in larger series, we defined a classifier based on these six differentially methylated gene promoters, developed an MS-MLPA assay, and demonstrated a significant differential methylation between thymomas (hypomethylated; n = 48) and T-LBLs (hypermethylated; n = 54) (methylation ratio median 0.03 versus 0.66, respectively; p < 0.0001), with MACROD2 methylation status the most discriminating. Using a machine learning strategy, we built a prediction model trained with the EPIC-array dataset and defined a cumulative score taking into account the weight of each feature. A score above or equal to 0.4 was predictive of T-LBL and conversely. Applied to the MS-MLPA dataset, this prediction model accurately predicted diagnoses of T-LBL and thymoma. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Cholinergic striatal interneurons (ChIs) express the vesicular glutamate transporter 3 (VGLUT3) which allows them to regulate the striatal network with glutamate and acetylcholine (ACh). In addition, VGLUT3-dependent glutamate increases ACh vesicular stores through vesicular synergy. A missense polymorphism, VGLUT3-p.T8I, was identified in patients with substance use disorders (SUDs) and eating disorders (EDs). A mouse line was generated to understand the neurochemical and behavioral impact of the p.T8I variant. In VGLUT3T8I/T8I male mice, glutamate signaling was unchanged but vesicular synergy and ACh release were blunted. Mutant male mice exhibited a reduced DA release in the dorsomedial striatum but not in the dorsolateral striatum, facilitating habit formation and exacerbating maladaptive use of drug or food. Increasing ACh tone with donepezil reversed the self-starvation phenotype observed in VGLUT3T8I/T8I male mice. Our study suggests that unbalanced dopaminergic transmission in the dorsal striatum could be a common mechanism between SUDs and EDs.

In bipolar disorders, abnormalities of sleep patterns and of circadian rhythms of activity are observed during mood episodes, but also persist during euthymia. Shared vulnerabilities between mood disorders and abnormalities of sleep patterns and circadian rhythms of activity have been suggested. This exploratory study investigated the association between polygenic risk scores for bipolar disorder and major depressive disorder, actigraphy estimates of sleep patterns, and circadian rhythms of activity in a sample of 62 euthymic individuals with bipolar disorder. The polygenic risk score - bipolar disorder and polygenic risk score - major depressive disorder were calculated for three stringent thresholds of significance. Data reduction was applied to aggregate actigraphy measures into dimensions using principal component analysis. A higher polygenic risk score - major depressive disorder was associated with more fragmented sleep, while a higher polygenic risk score - bipolar disorder was associated with a later peak of circadian rhythms of activity. These results remained significant after adjustment for age, sex, bipolar disorder subtype, body mass index, current depressive symptoms, current tobacco use, and medications prescribed at inclusion, but not after correction for multiple testing. In conclusion, the genetic vulnerabilities to major depression and to bipolar disorder might be associated with different abnormalities of sleep patterns and circadian rhythms of activity. The results should be replicated in larger and independent samples.