Jonathan Flint

Summary: The human basal ganglia (BG), subcortical nuclei fundamental to motor regulation and cognitive modulation, is constructed from neurons produced during gestation in the adjacent ganglionic eminences (GEs). GEs are transient structures in the ventral prenatal brain that also generate GABAergic inhibitory neurons which migrate to destinations in the BG, cortex and other destinations. This study aims to elucidate the epigenomic and 3D-genomic dynamics involved in the specification and maturation of GEs and GE-derived neurons, using single-nucleus methyl-3C sequencing (snm3C-seq), highly-multiplexed spatial transcriptomics, and chromatin+RNA single-molecule imaging. Our multi-modal data support a heterogeneous temporal progression across GE subregions, with the lateral GE (LGE) showing declining neurogenic activity in mid-gestation and caudal GE (CGE) exhibiting ongoing developmental progression through infancy. We identified regulatory programs that specify subtypes of BG principal cells, medium spiny neurons (MSN), via synchronized maturation of the 3D-epigenome. In infant brains, we found a transient short-range enriched (SE) chromatin conformation during the transition between oligodendrocyte progenitors (OPCs) and oligodendrocytes (ODCs), and a temporary shift toward Long-range Enriched (LE) chromatin conformation in projection neurons, extending previous works showing the differentiation of neurons and glial cells is associated with permanent SE and LE conformation, respectively. Lastly, we found that gene regulatory regions active in MSNs were enriched in loci associated with genetic risk for neuropsychiatric disease. Our study delineates the highly complex, lineage-specific 3D genomic dynamics in ventral progenitors and basal ganglia populations of the perinatal human brain. Highlights: Joint 3D genome and DNA methylome analysis of ventral brain progenitor zones Heterogeneous developmental progressions of the ganglionic eminences Distinct development dynamics and regulatory landscape of MSNs and interneurons Transient remodeling of the 3D-genome in neurons and oligodendrocyte progenitors

Identifying significant associations between genetic loci and psychiatric disorders is dependent on very large sample sizes. Methods for diagnosing diseases on this scale, such as the use of self-assessment questionnaires and data from electronic health records, incorporate heritable variation unrelated to the disease of interest into the diagnosis. Consequently, genetic mapping will identify loci unrelated to the target disease while missing some that are related, and genetic correlations cannot be used to infer the genetic relationships between diseases and between cohorts. Furthermore, shared biases between different disorders appear as shared etiology. As sample sizes grow, such confounders propagate, and findings based on their presence are replicated and extended. Here, we draw attention to the problem, make suggestions for flagging affected cohorts, and discuss future data collection and machine learning approaches to mitigate the effects of heritable confounders in psychiatric disorders.

Objectives: Mixed features in major depressive disorder (MDD)—the presence of manic symptoms during major depressive episodes (MDEs)—are associated with adverse clinical outcomes. We investigated the prevalence and clinical correlates of mixed features among Korean women with recurrent MDD. Method: Data ( n = 3000) were drawn from the Korean Mood Disorder Genetic Study‐Depression, a nationwide case‐control study recruiting women aged ≥30 years with recurrent MDD from 47 hospitals across South Korea. Structured clinical interviews assessing depressive symptoms, family history, comorbidities, and psychosocial functioning were administered. Mixed features during MDEs were defined per Diagnostic and Statistical Manual of Mental Disorders (DSM)‐5 criteria. Generalized linear models (GLMs) examined associations between mixed features and clinical correlates. Results: The worst‐MDE‐based prevalence of mixed features was 4.6%. A family history of manic episodes (odds ratio [OR] = 3.36, 95% confidence interval [CI]: 1.67–6.36) and of MDEs (OR = 1.90, 95% CI: 1.41–2.55) was significantly associated with mixed features. Five worst‐MDE symptoms were independently associated with mixed features: increased weight (OR = 2.19, 95% CI: 1.16–4.09), late insomnia (OR = 1.84, 95% CI: 1.07–3.41), slow or mixed thoughts (OR = 1.93, 95% CI: 1.18–3.33), suicidal ideation (OR = 1.96, 95% CI: 1.04–3.94), and diurnal mood variation (OR = 1.94, 95% CI: 1.34–2.81). Mixed features were associated with mood‐stabilizer use (lifetime use OR = 2.21, 95% CI: 1.38–3.42; current use OR = 2.53, 95% CI: 1.58–3.92), panic disorder (OR = 2.10, 95% CI: 1.46–2.99), premenstrual syndrome symptoms (rate ratio [RR] = 1.22, 95% CI: 1.08–1.37), and damage by suicide attempts (RR = 1.66, 95% CI: 1.17–2.07). Conclusions: Although less prevalent in this clinically ascertained sample than in Western samples, mixed features in recurrent MDD exhibit distinct clinical patterns. This finding underscores the importance of screening for mixed features, particularly when associated depressive symptoms are present, to inform early detection and treatment planning.

Despite biomarker-guided treatment strategies, clinical outcomes among patients with type 2 (T2)-high asthma remain heterogeneous, with some patients responding poorly to T2-targeted biologic therapies. We developed a contrastive machine learning method for patient stratification based on whole-blood DNA methylation (DNAm), applying it to pediatric asthma cohorts of Latino (discovery; n=1,016) and African American (replication; n=429) children. The resulting DNAm stratification score revealed a continuum of clinical severity and drug response within the T2-high asthma endotype. Molecular profiling of high-score asthma patients identified eosinophil-specific hypermethylation—validated in an independent Canadian adult cohort using purified eosinophil DNAm—as well as upregulation of canonical T2-associated genes. Transcriptomic analysis of elevated DNAm scores within T2-high patients further uncovered a gene signature linked to B-cell lineage activity, predominantly reflecting plasma cell activity orthogonal to canonical T2 inflammation programs. This defines a previously unrecognized sub-endotype, which we term T2-high asthma with Altered B Cell response (T2ABC). In a randomized controlled trial of the anti-IgE biologic omalizumab in primarily White adult T2-high asthmatic patients (n=300), the T2ABC gene expression signature was prognostic of poor outcomes, including a 24% mean increase in disease exacerbation rates compared to the trial baseline (P=0.004), which could not be explained by treatment or placebo assignment. Patients treated with omalizumab showed better outcomes than patients in the placebo arm within the T2ABC-low group (P=0.019) but not within the T2ABC-high group (P=0.48), suggesting that IgE blockade does not adequately target the pathogenic mechanisms active in T2ABC-high disease. Single-cell transcriptomic analysis demonstrated that the T2ABC signature reflects heightened activity of non-IgE plasma cells, consistent with the presence of additional antibody isotype responses in a form of severe asthma arising within a T2-high immunologic context. Our findings, replicated and validated across four ancestrally and ethnically diverse pediatric and adult cohorts, support the use of DNAm- and transcriptome-based patient stratification to refine drug development, eligibility, and administration strategies for improving precision in T2 asthma therapy.

Genetic studies of psychiatric disorders have typically assumed that all genetic effects contribute additively to disease liability. However, it is likely that psychiatric disorders have unrecognized subtypes, where synergistic sets of risk variants co-occur within certain cases more than expected under additivity. The existence of synergistic sets induces a structured form of statistical interactions called coordinated epistasis. We test for these interactions in five psychiatric disorders and find evidence for synergistic sets, and by extension, disorder subtypes. We further find that synergistic sets contributing to comorbidities are mostly disorder-specific, despite high genetic correlations between disorders, supporting current diagnostic distinctions between disorders. Finally, we find that genetic risk factors shared across disorders identify a cross-disorder subtype that is likely the result of heritable confounders, rather than disorder-specific etiology. Our results show that genetic risk effects for psychiatric disorders act in sets, implying the existence of subtypes, and re-interpret the importance of shared genetic effects in understanding disease biology and classification.

We present the first mouse pangenome consisting of 17 high-quality inbred mouse strain genomes with complete annotation. This collection includes 12 widely used classical laboratory strains and 5 wild-derived strains. We have fully resolved previously incomplete genomic regions, including the major histocompatibility complex (MHC), the defensin cluster, T-cell receptor, and Ly49 complexes. Hundreds of non-reference genes identified in previous publications not found in GRCm39, like Defa1, Raet1a , and Klra20 ( Ly49T ), were localised in the new reference genomes. We conducted the first genome-wide scan of variable number tandem repeats (VNTRs) within the coding regions of mice, identifying over 400 genes with VNTR polymorphisms up to more than 600 repeat copies and repeat units reaching 990 nucleotides. Our strain-specific annotations enhance RNA-Seq analyses, as demonstrated in PWK/PhJ, where we observed a 5.1% improvement in read mapping and expression level differences in 2.1% of coding genes compared to using GRCm39.

Major depressive disorder (MDD) often goes undiagnosed due to the absence of clear biomarkers. We sought to identify voice biomarkers for MDD and separate biomarkers indicative of MDD predisposition from biomarkers reflecting current depressive symptoms. Using a two-stage meta-analytic design to remove confounds, we tested the association between features representing vocal pitch and MDD in a multisite case-control cohort study of Chinese women with recurrent depression. Sixteen features were replicated in an independent cohort, with absolute association coefficients (beta values) from the combined analysis ranging from 0.24 to 1.07, indicating moderate to large effects. The statistical significance of these associations remained robust, with P values ranging from 7.2 × 10^–6 to 6.8 × 10^–58. Eleven features were significantly associated with current depressive symptoms. Using genotype data, we found that this association was driven in part by a genetic correlation with MDD. Significant voice features, reflecting a slower pitch change and a lower pitch, achieved an AUC-ROC of 0.90 (sensitivity of 0.85 and specificity of 0.81) in MDD classification. Our results return vocal features to a more central position in clinical and research work on MDD.