Xiaohong Gong

Major depressive disorder (MDD) and bipolar disorder (BD) share numerous overlapping symptoms and clinical features, both classified as mood disorders (MD). Despite the proposal of several transdiagnostic subtypes, the dynamic features and network-level neurobiological mechanisms underlying these subtypes remain poorly understood. In the current study, data from two independent cohorts were used to investigate the spatiotemporal features of brain activity. Cohort1 included 112 MD patients and 142 healthy controls (HC) for cross-sectional comparison, while Cohort2 comprised 423 MD patients who underwent MRI scanning before and after one week in-hospital treatment. Initially, two subtypes were identified based on their amplitude of low-frequency fluctuation (ALFF) maps, and their brain spatiotemporal dynamics were further explored using co-activation patterns (CAPs). Four recurrent CAP states were identified: CAP2 and CAP3 were primarily associated with default mode network (DMN), while CAP1 and CAP4 were associated with frontoparietal network (FPN). Results showed that Subtype1 exhibited higher occurrence, persistence, and transition probabilities of DMN-related states, along with lower dynamics in FPN-related states. In contrast, Subtype2 showed higher occurrence and persistence in FPN-related states. Further analysis revealed that these dynamic features tended to revert toward normal states after one week treatment. Moreover, the dynamic characteristics in Subtype1 were associated with higher suicidality and lower agitation symptoms, while those in Subtype2 were correlated with somatic anxiety and systemic symptoms. Our study provides insights into the spatiotemporal dynamics of brain intrinsic states in different subtypes of MD, revealing the variations in MD subtypes and treatment response from a dynamic perspective and offering a pathway for further elucidation of their underlying biological mechanisms.

The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological characteristics using multi-omics data. A total of 807 participants were included in this study, comprising 327 individuals with MDD and 480 healthy controls (HC). The amplitude of low-frequency fluctuations (ALFF), a functional neuroimaging feature, was extracted for each participant and used to identify MDD subtypes through machine learning clustering. Multi-omics data, including profiles of genetic, epigenetics, metabolomics, and pro-inflammatory cytokines, were obtained. Comparative analyses of multi-omics data were conducted between each MDD subtype and HC to explore the molecular underpinnings involved in each subtype. We identified three neuroimaging-based MDD subtypes, each characterized by unique ALFF pattern alterations compared to HC. Multi-omics analysis showed a strong genetic predisposition for Subtype 1, primarily enriched in neuronal development and synaptic regulation pathways. This subtype also exhibited the most severe depressive symptoms and cognitive decline compared to the other subtypes. Subtype 2 is characterized by immuno-inflammation dysregulation, supported by elevated IL-1 beta levels, altered epigenetic inflammatory measures, and differential metabolites correlated with IL-1 beta levels. No significant biological markers were identified for Subtype 3. Our results identify neuroimaging-based MDD subtypes and delineate the distinct biological features of each subtype. This provides a proof of concept for mechanism-targeted therapy in MDD, highlighting the importance of personalized treatment approaches based on neurobiological and molecular profiles.

Background The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological characteristics using multi-omics data. Method A total of 807 participants were included in this study, comprising 327 individuals with MDD and 480 healthy controls (HC). The amplitude of low-frequency fluctuations (ALFF), a functional neuroimaging feature, was extracted for each participant and used to identify MDD subtypes through machine learning clustering. Multi-omics data, including profiles of genetic, epigenetics, metabolomics, and pro-inflammatory cytokines, were obtained. Comparative analyses of multi-omics data were conducted between each MDD subtype and HC to explore the molecular underpinnings involved in each subtype. Results We identified three neuroimaging-based MDD subtypes, each characterized by unique ALFF pattern alterations compared to HC. Multi-omics analysis showed a strong genetic predisposition for Subtype 1, primarily enriched in neuronal development and synaptic regulation pathways. This subtype also exhibited the most severe depressive symptoms and cognitive decline compared to the other subtypes. Subtype 2 is characterized by immuno-inflammation dysregulation, supported by elevated IL-1β levels, altered epigenetic inflammatory measures, and differential metabolites correlated with IL-1β levels. No significant biological markers were identified for Subtype 3. Conclusion Our results identify neuroimaging-based MDD subtypes and delineate the distinct biological features of each subtype. This provides a proof of concept for mechanism-targeted therapy in MDD, highlighting the importance of personalized treatment approaches based on neurobiological and molecular profiles.

Alcohol consumption is a heritable behavior seriously endangers human health. However, genetic studies on alcohol consumption primarily focuses on common variants, while insights from rare coding variants are lacking. Here we leverage whole exome sequencing data across 304,119 white British individuals from UK Biobank to identify protein-coding variants associated with alcohol consumption. Twenty-five variants are associated with alcohol consumption through single variant analysis and thirteen genes through gene-based analysis, ten of which have not been reported previously. Notably, the two unreported alcohol consumption-related genes GIGYF1 and ANKRD12 show enrichment in brain function-related pathways including glial cell differentiation and are strongly expressed in the cerebellum. Phenome-wide association analyses reveal that alcohol consumption-related genes are associated with brain white matter integrity and risk of digestive and neuropsychiatric diseases. In summary, this study enhances the comprehension of the genetic architecture of alcohol consumption and implies biological mechanisms underlying alcohol-related adverse outcomes.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by core symptoms that consist of social deficits and repetitive behaviors. Unfortunately, no effective medication is available thus far to target the core symptoms of ASD, since the pathogenesis remains largely unknown. To investigate the pathogenesis of the core symptoms in ASD, we constructed Shank1 P1812L-knock-in (KI) mice corresponding to a recurrent ASD-related mutation, SHANK1 P1806L, to achieve construct validity and face validity. Shank1 P1812L-KI heterozygous (HET) mice presented with social deficits and repetitive behaviors without the presence of confounding comorbidities. HET mice also exhibited downregulation of metabotropic glutamate receptor (mGluR1) and associated signals, along with structural abnormalities in the dendritic spines and postsynaptic densities. Combined with findings from Shank1 R882H-KI mice, our study confirms that mGluR1-mediated signaling dysfunction is a pivotal mechanism underlying the core symptoms of ASD. Interestingly, Shank1 P1812L-KI homozygous (HOM) mice manifested behavioral signs of impaired long-term memory rather than autistic-like core traits; thus, their phenotype was markedly different from that of Shank1 P1812L-KI HET mice. Correspondingly, at the molecular level, Shank1 P1812L-KI HOM displayed upregulation of AMPA receptor (GluA2)-related signals. The different patterns of protein changes in HOM and HET mice may explain the differences in behaviors. Our study emphasizes the universality of mGluR1-signaling hypofunction in the pathogenesis of the core symptoms in ASD, providing a potential target for therapeutic drugs. The precise correspondence between genotype and phenotype, as shown in HOM and HET mice, indicates the importance of reproducing disease-related genotypes in mouse models.

The genetic etiology and underlying mechanism of autism spectrum disorder (ASD) remain elusive. SHANK family genes (SHANK1/2/3) are well known ASD-related genes. However, little is known about how SHANK missense mutations contribute to ASD. Here, we aimed to clarify the molecular mechanism of and the multilevel neuropathological features induced by Shank1 mutations in knock-in (KI) mice. In this study, by sequencing the SHANK1 gene in a cohort of 615 ASD patients and 503 controls, we identified an ASD-specific recurrent missense mutation, c.2621 G > A (p.R874H). This mutation demonstrated strong pathogenic potential in in vitro experiments, and we generated the corresponding Shank1 R882H-KI mice. Shank1 R882H-KI mice displayed core symptoms of ASD, namely, social disability and repetitive behaviors, without confounding comorbidities of abnormal motor function and heightened anxiety. Brain structural changes in the frontal cortex, hippocampus and cerebellar cortex were observed in Shank1 R882H-KI mice via structural magnetic resonance imaging. These key brain regions also showed severe and consistent downregulation of mGluR1-IP3R1-calcium signaling, which subsequently affected the release of intracellular calcium. Corresponding cellular structural and functional changes were present in Shank1 R882H-KI mice, including decreased spine size, reduced spine density, abnormal morphology of postsynaptic densities, and impaired hippocampal long-term potentiation and basal excitatory transmission. These findings demonstrate the causative role of SHANK1 in ASD and elucidate the underlying biological mechanism of core symptoms of ASD. We also provide a reliable model of ASD with core symptoms for future studies, such as biomarker identification and therapeutic intervention studies.