Exploring the interplay between core and mood symptoms in schizophrenia: A network analysis.

BACKGROUND: Schizophrenia is a complex neuropsychiatric disorder characterized by positive symptoms, negative symptoms, cognitive deficits, and co-occurring mood symptoms. Network analysis offers a novel approach to investigate the intricate relationships between these symptom dimensions, potentially informing personalized treatment strategies. METHODS: A cross-sectional study was conducted from November 2019 to October 2021, involving 1285 inpatients with schizophrenia in Liaoning Province, China. Symptom severity was assessed using the Positive and Negative Syndrome Scale (PANSS), Hamilton Depression Rating Scale (HAMD-17), Hamilton Anxiety Rating Scale (HAMA-14), and Montreal Cognitive Assessment (MoCA). Network analysis was conducted to investigate the network structure, central symptoms, and bridge symptoms. RESULTS: The network analysis uncovered profound interconnectivity between core symptoms and the anxiety-depression community. Central symptoms, such as psychic anxiety, poor rapport, delusions, and attention, were identified as potential therapeutic targets. Bridge symptoms, including insomnia, depressed mood, anxiety-somatic, conceptual disorganization, and stereotyped thinking, emerged as key nodes facilitating interactions between symptom communities. The stability and reliability of the networks were confirmed through bootstrapping procedures. DISCUSSION: The findings highlight the complex interplay between schizophrenia symptoms, emphasizing the importance of targeting affective symptoms and cognitive impairment in treatment. The identification of central and bridge symptoms suggests potential pathways for personalized interventions aimed at disrupting self-reinforcing symptom cycles. The study underscores the need for a transdiagnostic, personalized approach to schizophrenia treatment.

Gender differences in plasma S100B levels of patients with major depressive disorder.

BACKGROUND: Low concentrations of S100B have neurotrophic effects and can promote nerve growth and repair, which plays an essential role in the pathophysiological and histopathological alterations of major depressive disorder (MDD) during disease development. Studies have shown that plasma S100B levels are altered in patients with MDD. In this study, we investigated whether the plasma S100B levels in MDD differ between genders. METHODS: We studied 235 healthy controls (HCs) (90 males and 145 females) and 185 MDD patients (65 males and 120 females). Plasma S100B levels were detected via multifactor assay. The Mahalanobis distance method was used to detect the outliers of plasma S100B levels in the HC and MDD groups. The Kolmogorov-Smirnov test was used to test the normality of six groups of S100B samples. The Mann-Whitney test and Scheirer-Ray-Hare test were used for the comparison of S100B between diagnoses and genders, and the presence of a relationship between plasma S100B levels and demographic details or clinical traits was assessed using Spearman correlation analysis. RESULTS: All individuals in the HC group had plasma S100B levels that were significantly greater than those in the MDD group. In the MDD group, males presented significantly higher plasma S100B levels than females. In the male group, the plasma S100B levels in the HC group were significantly higher than those in the MDD group, while in the female group, no significant difference was found between the HC and MDD groups. In the male MDD subgroup, there was a positive correlation between plasma S100B levels and years of education. In the female MDD subgroup, there were negative correlations between plasma S100B levels and age and suicidal ideation. CONCLUSIONS: In summary, plasma S100B levels vary with gender and are decreased in MDD patients, which may be related to pathological alterations in glial cells.

Characterizing the distinct imaging phenotypes, clinical behavior, and genetic vulnerability of brain maturational subtypes in mood disorders.

BACKGROUND: Mood disorders are characterized by great heterogeneity in clinical manifestation. Uncovering such heterogeneity using neuroimaging-based individual biomarkers, clinical behaviors, and genetic risks, might contribute to elucidating the etiology of these diseases and support precision medicine. METHODS: We recruited 174 drug-naïve and drug-free patients with major depressive disorder and bipolar disorder, as well as 404 healthy controls. T1 MRI imaging data, clinical symptoms, and neurocognitive assessments, and genetics were obtained and analyzed. We applied regional gray matter volumes (GMV) and quantile normative modeling to create maturation curves, and then calculated individual deviations to identify subtypes within the patients using hierarchical clustering. We compared the between-subtype differences in GMV deviations, clinical behaviors, cell-specific transcriptomic associations, and polygenic risk scores. We also validated the GMV deviations based subtyping analysis in a replication cohort. RESULTS: Two subtypes emerged: subtype 1, characterized by increased GMV deviations in the frontal cortex, cognitive impairment, a higher genetic risk for Alzheimer's disease, and transcriptionally associated with Alzheimer's disease pathways, oligodendrocytes, and endothelial cells; and subtype 2, displaying globally decreased GMV deviations, more severe depressive symptoms, increased genetic vulnerability to major depressive disorder and transcriptionally related to microglia and inhibitory neurons. The distinct patterns of GMV deviations in the frontal, cingulate, and primary motor cortices between subtypes were shown to be replicable. CONCLUSIONS: Our current results provide vital links between MRI-derived phenotypes, spatial transcriptome, genetic vulnerability, and clinical manifestation, and uncover the heterogeneity of mood disorders in biological and behavioral terms.

Alterations in the fecal microbiota of methamphetamine users with bad sleep quality during abstinence.

BACKGROUND: Methamphetamine (MA) abuse has resulted in a plethora of social issues. Sleep disturbance is a prominent issue about MA addiction, which serve as a risk factor for relapse, and the gut microbiota could play an important role in the pathophysiological mechanisms of sleep disturbances. Therefore, improving sleep quality can be beneficial for treating methamphetamine addiction, and interventions addressing the gut microbiota may represent a promising approach. METHOD: We recruited 70 MA users to investigate the associations between sleep quality and fecal microbiota by the Pittsburgh Sleep Quality Index (PSQI), which was divided into MA-GS (PSQI score < 7, MA users with good sleep quality, n = 49) and MA-BS group (PSQI score ≥ 7, MA users with bad sleep quality, n = 21). In addition, we compared the gut microbiota between the MA-GS and healthy control (HC, n = 38) groups. 16S rRNA sequencing was applied to identify the gut bacteria. RESULT: The study revealed that the relative abundances of the Thermoanaerobacterales at the order level differed between the MA-GS and MA-BS groups. Additionally, a positive correlation was found between the relative abundance of the genus Sutterella and daytime dysfunction. Furthermore, comparisons between MA users and HCs revealed differences in beta diversity and relative abundances of various bacterial taxa. CONCLUSION: In conclusion, the study investigated alterations in the gut microbiota among MA users. Furthermore, we demonstrated that the genus Sutterella changes may be associated with daytime dysfunction, suggesting that the genus Sutterella may be a biomarker for bad sleep quality in MA users.

SP6 controls human cytotrophoblast fate decisions and trophoblast stem cell establishment by targeting MSX2 regulatory elements.

The commitment and differentiation of human placental progenitor cytotrophoblast (CT) cells are crucial for a successful pregnancy, but the underlying mechanism remains poorly understood. Here, we identified the transcription factor (TF), specificity protein 6 (SP6), as a human species-specific trophoblast lineage TF expressed in human placental CT cells. Using pluripotent stem cells as a model, we demonstrated that SP6 controls CT generation and the establishment of trophoblast stem cells (TSCs) and identified msh homeobox 2 (MSX2) as the downstream effector in these events. Mechanistically, we showed that SP6 interacts with histone acetyltransferase P300 to alter the landscape of H3K27ac at targeted regulatory elements, thereby favoring transcriptional activation and facilitating CT cell fate decisions and TSC maintenance. Our results established SP6 as a regulator of the human trophoblast lineage and implied its role in placental development and the pathogenies of placental diseases.

Extended Conjugation Strategy Enabling Red-Shifted and Efficient Emission of Orange-Red Thermally Activated Delayed Fluorescence Materials.

In account of the energy gap law, the development of efficient narrow-band gap thermally activated delayed fluorescence (TADF) materials remains a major challenge for the application of organic light-emitting diodes (OLEDs). The orange-red TADF materials are commonly designed with either large π-conjugated systems or strong intramolecular donor-acceptor (D-A) interactions for red-shift emission and small singlet-triplet energy gap (ΔEST). There are rare reports on the simultaneous incorporation of these two strategies on the same material systems. Herein, two orange-red emitters named 1P2D-BP and 2P2D-DQ have been designed by extending the conjugation degree of the center acceptor DQ and increasing the number distribution of the peripheral donor PXZ units, respectively. The emission peak of 1P2D-BP is red-shifted to 615 nm compared to 580 nm for 2P2D-DQ, revealing the pronounced effect of the conjugation extension on the emission band gap. In addition, the distorted molecular structure yields a small ΔEST of 0.02 eV, favoring the acquisition of a high exciton utilization through an efficient reverse intersystem crossing process. As a result, orange-red OLEDs with both 1P2D-BP and 2P2D-DQ have achieved an external quantum efficiency (EQE) of more than 17%. In addition, the efficient white OLED based on 1P2D-BP is realized through precise exciton assignment and energy transport modulation, showing an EQE of 23.6% and a color rendering index of 82. The present work provides an important reference for the design of high-efficiency narrow-band gap materials in the field of solid-state lighting.

Associations between impulsivity and fecal microbiota in individuals abstaining from methamphetamine.

INTRODUCTION: Methamphetamine (MA) abuse is a major public problem, and impulsivity is both a prominent risk factor and a consequence of addiction. Hence, clarifying the biological mechanism of impulsivity may facilitate the understanding of addiction to MA. The microbiota-gut-brain axis was suggested to underlie a biological mechanism of impulsivity induced by MA. METHODS: We therefore recruited 62 MA addicts and 50 healthy controls (HCs) to investigate the alterations in impulsivity and fecal microbiota and the associations between them in the MA group. Thereafter, 25 MA abusers who abstained from MA for less than 3 months were followed up for 2 months to investigate the relationship between impulsivity and microbiota as abstinence became longer. 16S rRNA sequencing was conducted for microbiota identification. RESULTS: Elevated impulsivity and dysbiosis characterized by an increase in opportunistic pathogens and a decrease in probiotics were identified in MA abusers, and both the increased impulsivity and disrupted microbiota tended to recover after longer abstinence from MA. Impulsivity was related to microbiota, and the effect of MA abuse on impulsivity was mediated by microbiota. CONCLUSION: Our findings potentially highlighted the importance of abstention and implicated the significant role of the microbiota-gut-brain axis in the interrelationship between microbiota and behaviors, as well as the potential of microbiota as a target for intervention of impulsivity.

State- and trait-related dysfunctions in bipolar disorder across different mood states: a graph theory study.

BACKGROUND: The interplay between state- and trait-related disruptions in structural networks remains unclear in bipolar disorder (BD), but graph theory can offer insights into global and local network changes. We sought to use diffusion-tensor imaging (DTI) and graph theory approaches to analyze structural topological properties across distinct mood states and identify high-risk individuals by examining state- and trait-related impairments in BD. METHODS: We studied changes in white matter network among patients with BD and healthy controls, exploring relationships with clinical variables. Secondary analysis involved comparing patients with BD with unaffected people at high genetic risk for BD. RESULTS: We included 152 patients with BD, including 52 with depressive BD (DBD), 64 with euthymic BD (EBD) and 36 with manic BD (MBD); we also included 75 healthy controls. Secondary analyses involved 27 unaffected people at high genetic risk for BD. Patients with DBD and MBD exhibited significantly lower global efficiencies than those with EBD and healthy controls, with patients with DBD showing the lowest global efficiencies. In addition, patients with DBD displayed impaired local efficiency and normalized clustering coefficient (γ). At a global level, γ correlated negatively with depression and anxiety. Compared with healthy controls, and across mood states, patients with BD showed abnormal shortest path lengths in the frontolimbic circuit, a trend mirrored among those at high genetic risk for BD. LIMITATIONS: Considerations include medication effects, absence of recorded BD episode counts and the cross-sectional nature of the study. CONCLUSION: Mood-specific whole-brain network metrics could serve as potential biomarkers in BD for transitions between mood states. Moreover, these findings contribute to evidence of trait-related frontolimbic circuit irregularities, shedding light on underlying pathophysiological mechanisms in BD.

Integrative omics analysis reveals epigenomic and transcriptomic signatures underlying brain structural deficits in major depressive disorder.

Several lines of evidence support the involvement of transcriptomic and epigenetic mechanisms in the brain structural deficits of major depressive disorder (MDD) separately. However, research in these two areas has remained isolated. In this study, we proposed an integrative strategy that combined neuroimaging, brain-wide gene expression, and peripheral DNA methylation data to investigate the genetic basis of gray matter abnormalities in MDD. The MRI T1-weighted images and Illumina 850 K DNA methylation microarrays were obtained from 269 patients and 416 healthy controls, and brain-wide transcriptomic data were collected from Allen Human Brain Atlas. The between-group differences in gray matter volume (GMV) and differentially methylated CpG positions (DMPs) were examined. The genes with their expression patterns spatially related to GMV changes and genes with DMPs were overlapped and selected. Using principal component regression, the associations between DMPs in overlapped genes and GMV across individual patients were investigated, and the region-specific correlations between methylation status and gene expression were examined. We found significant associations between the decreased GMV and DMPs methylation status in the anterior cingulate cortex, inferior frontal cortex, and fusiform face cortex regions. These DMPs genes were primarily enriched in the neurodevelopmental and synaptic transmission process. There was a significant negative correlation between DNA methylation and gene expression in genes associated with GMV changes of the frontal cortex in MDD. Our findings suggest that GMV abnormalities in MDD may have a transcriptomic and epigenetic basis. This imaging-transcriptomic-epigenetic integrative analysis provides spatial and biological links between cortical morphological deficits and peripheral epigenetic signatures in MDD.

Identifying misdiagnosed bipolar disorder using support vector machine: feature selection based on fMRI of follow-up confirmed affective disorders.

Nearly a quarter of bipolar disorder (BD) patients were misdiagnosed as major depressive disorder (MDD) patients, which cannot be corrected until mania/hypomania develops. It is important to recognize these obstacles so that the appropriate treatment can be initiated. Thus, we sought to distinguish patients with BD from MDD, especially to identify misdiagnosed BD before mania/hypomania, and further explore potential trait features that allow accurate differential diagnosis independent of state matters. Functional magnetic resonance imaging scans were performed at baseline on 92 MDD patients and 48 BD patients. The MDD patients were then followed up for more than two years. After follow-up, 23 patients transformed into BD (tBD), and 69 patients whose diagnoses remained unchanged were eligible for unipolar depression (UD). A support vector machine classifier was trained on the amygdala-based functional connectivity (FC) of 48 BD and 50 UD patients using a novel region-based feature selection. Then, the classifier was tested on the dataset, encompassing tBD and the remaining UD. It performed well for known BD and UD and can also distinguish tBD from UD with an accuracy of 81%, sensitivity of 82.6%, specificity of 79%, and AUC of 74.6%, respectively. Feature selection results revealed that ten regions within the cortico-limbic neural circuit contributed most to classification. Furthermore, in the FC comparisons among diseases, BD and tBD shared almost overlapped FC patterns in the cortico-limbic neural circuit, and both of them presented pronounced differences in most regions within the circuit compared with UD. The FC values of the most discriminating brain regions had no prominent correlations with the severity of depression, anxiety, and mania/hypomania (FDR correction). It suggests that BD possesses some trait features in the cortico-limbic neural circuit, rendering it dichotomized by the classifier based on known-diagnosis data.

Functional and structural alterations in different durations of untreated illness in the frontal and parietal lobe in major depressive disorder.

Major depressive disorder (MDD) is one of the most disabling illnesses that profoundly restricts psychosocial functions and impairs quality of life. However, the treatment rate of MDD is surprisingly low because the availability and acceptability of appropriate treatments are limited. Therefore, identifying whether and how treatment delay affects the brain and the initial time point of the alterations is imperative, but these changes have not been thoroughly explored. We investigated the functional and structural alterations of MDD for different durations of untreated illness (DUI) using regional homogeneity (ReHo) and voxel-based morphometry (VBM) with a sample of 125 treatment-naïve MDD patients and 100 healthy controls (HCs). The MDD patients were subgrouped based on the DUI, namely, DUI ≤ 1 M, 1 < DUI ≤ 6 M, 6 < DUI ≤ 12 M, and 12 < DUI ≤ 48 M. Subgroup comparison (MDD with different DUIs) was applied to compare ReHo and grey matter volume (GMV) extracted from clusters of regions with significant differences (the pooled MDD patients relative to HCs). Correlations and mediation effects were analysed to estimate the relationships between the functional and structural neuroimaging changes and clinical characteristics. MDD patients exhibited decreased ReHo in the left postcentral gyrus and precentral gyrus and reduced GMV in the left middle frontal gyrus and superior frontal gyrus relative to HCs. The initial functional abnormalities were detected after being untreated for 1 month, whereas this duration was 3 months for GMV reduction. Nevertheless, a transient increase in ReHo was observed after being untreated for 3 months. No significant differences were discovered between HCs and MDD patients with a DUI less than 1 month or among MDD patients with different DUIs in either ReHo or GMV. Longer DUI was related to reduced ReHo with GMV as mediator in MDD patients. We identified disassociated functional and anatomical alterations in treatment-naïve MDD patients at different time points in distinct brain regions at the early stage of the disease. Additionally, we also discovered that GMV mediated the relationship between a longer DUI and diminished ReHo in MDD patients, disclosing the latent deleterious and neuro-progressive implications of DUI on both the structure and function of the brain and indicating the necessity of early treatment of MDD.

Metabolome subtyping reveals multi-omics characteristics and biological heterogeneity in major psychiatric disorders.

Growing evidence suggests that major psychiatric disorders (MPDs) share common etiologies and pathological processes. However, the diagnosis is currently based on descriptive symptoms, which ignores the underlying pathogenesis and hinders the development of clinical treatments. This highlights the urgency of characterizing molecular biomarkers and establishing objective diagnoses of MPDs. Here, we collected untargeted metabolomics, proteomics and DNA methylation data of 327 patients with MPDs, 131 individuals with genetic high risk and 146 healthy controls to explore the multi-omics characteristics of MPDs. First, differential metabolites (DMs) were identified and we classified MPD patients into 3 subtypes based on DMs. The subtypes showed distinct metabolomics, proteomics and DNA methylation signatures. Specifically, one subtype showed dysregulation of complement and coagulation proteins, while the DNA methylation showed abnormalities in chemical synapses and autophagy. Integrative analysis in metabolic pathways identified the important roles of the citrate cycle, sphingolipid metabolism and amino acid metabolism. Finally, we constructed prediction models based on the metabolites and proteomics that successfully captured the risks of MPD patients. Our study established molecular subtypes of MPDs and elucidated their biological heterogeneity through a multi-omics investigation. These results facilitate the understanding of pathological mechanisms and promote the diagnosis and prevention of MPDs.

The gut microbiota as a potential biomarker for methamphetamine use disorder: evidence from two independent datasets.

Background: Methamphetamine use disorder (MUD) poses a considerable public health threat, and its identification remains challenging due to the subjective nature of the current diagnostic system that relies on self-reported symptoms. Recent studies have suggested that MUD patients may have gut dysbiosis and that gut microbes may be involved in the pathological process of MUD. We aimed to examine gut dysbiosis among MUD patients and generate a machine-learning model utilizing gut microbiota features to facilitate the identification of MUD patients. Method: Fecal samples from 78 MUD patients and 50 sex- and age-matched healthy controls (HCs) were analyzed by 16S rDNA sequencing to identify gut microbial characteristics that could help differentiate MUD patients from HCs. Based on these microbial features, we developed a machine learning model to help identify MUD patients. We also used public data to verify the model; these data were downloaded from a published study conducted in Wuhan, China (with 16 MUD patients and 14 HCs). Furthermore, we explored the gut microbial features of MUD patients within the first three months of withdrawal to identify the withdrawal period of MUD patients based on microbial features. Results: MUD patients exhibited significant gut dysbiosis, including decreased richness and evenness and changes in the abundance of certain microbes, such as Proteobacteria and Firmicutes. Based on the gut microbiota features of MUD patients, we developed a machine learning model that demonstrated exceptional performance with an AUROC of 0.906 for identifying MUD patients. Additionally, when tested using an external and cross-regional dataset, the model achieved an AUROC of 0.830. Moreover, MUD patients within the first three months of withdrawal exhibited specific gut microbiota features, such as the significant enrichment of Actinobacteria. The machine learning model had an AUROC of 0.930 for identifying the withdrawal period of MUD patients. Conclusion: In conclusion, the gut microbiota is a promising biomarker for identifying MUD and thus represents a potential approach to improving the identification of MUD patients. Future longitudinal studies are needed to validate these findings.

Functional connectome through the human life span.

The functional connectome of the human brain represents the fundamental network architecture of functional interdependence in brain activity, but its normative growth trajectory across the life course remains unknown. Here, we aggregate the largest, quality-controlled multimodal neuroimaging dataset from 119 global sites, including 33,809 task-free fMRI and structural MRI scans from 32,328 individuals ranging in age from 32 postmenstrual weeks to 80 years. Lifespan growth charts of the connectome are quantified at the whole cortex, system, and regional levels using generalized additive models for location, scale, and shape. We report critical inflection points in the non-linear growth trajectories of the whole-brain functional connectome, particularly peaking in the fourth decade of life. Having established the first fine-grained, lifespan-spanning suite of system-level brain atlases, we generate person-specific parcellation maps and further show distinct maturation timelines for functional segregation within different subsystems. We identify a spatiotemporal gradient axis that governs the life-course growth of regional connectivity, transitioning from primary sensory cortices to higher-order association regions. Using the connectome-based normative model, we demonstrate substantial individual heterogeneities at the network level in patients with autism spectrum disorder and patients with major depressive disorder. Our findings shed light on the life-course evolution of the functional connectome and serve as a normative reference for quantifying individual variation in patients with neurological and psychiatric disorders.

Cognitive function and cardiovascular health in the elderly: network analysis based on hypertension, diabetes, cerebrovascular disease, and coronary heart disease.

Introduction: Cognitive decline in the elderly population is a growing concern, and vascular factors, such as hypertension, diabetes, cerebrovascular disease, and coronary heart disease, have been associated with cognitive impairments. This study aims to provide deeper insights into the structure of cognitive function networks under these different vascular factors and explore their potential associations with specific cognitive domains. Methods: Cognitive function was assessed using a modified Chinese version of the mini-mental state examination (MMSE) scale, and intensity centrality and side weights were estimated by network modeling. The network structure of cognitive function was compared across subgroups by including vascular factors as subgroup variables while controlling for comorbidities and confounders. Results: The results revealed that cerebrovascular disease and coronary heart disease had a more significant impact on cognitive function. Cerebrovascular disease was associated with weaker centrality in memory and spatial orientation, and a sparser cognitive network structure. Coronary heart disease was associated with weaker centrality in memory, repetition, executive function, recall, attention, and calculation, as well as a sparser cognitive network structure. The NCT analyses further highlighted significant differences between the cerebrovascular disease and coronary heart disease groups compared to controls in terms of overall network structure and connection strength. Conclusion: Our findings suggest that specific cognitive domains may be more vulnerable to impairments in patients with cerebrovascular disease and coronary heart disease. These insights could be used to improve the accuracy and sensitivity of cognitive screening in these patient populations, inform personalized cognitive intervention strategies, and provide a better understanding of the potential mechanisms underlying cognitive decline in patients with vascular diseases.

Sex effects on differentiating patients with major depressive disorder from bipolar disorder in depressive state: A fMRI study with follow-up.

BACKGROUND: Bipolar disorder (BD) is difficult to discriminate from major depressive disorder (MDD) before the appearance of mania or hypomania. This study was designed to identify whether patients with MDD and those who converted to BD are distinguishable using dynamic amplitude low-frequency fluctuations (dALFF) and describe the sex effects on the identification of the two disorders. METHODS: We compared the dALFF values of 35 BD patients who converted from MDD during the 2-year follow-up, 99 MDD patients, and 130 healthy controls (HCs) using two-way ANOVA. Pearson's correlation was used to compare dALFF in dysfunctional brain regions and clinical characteristics. RESULTS: A main effect of diagnosis was discovered in the frontal and occipital gyrus. For the main effect of sex, both the left middle occipital gyrus and the medial part of the superior frontal gyrus had higher dALFF values in males compared to females. An interaction of sex and diagnosis effect was observed in the right precentral gyrus. Male MDD patients exhibited a higher dALFF value than male BD patients. Additionally, we discovered a higher dALFF value in females than in males in BD patients. WCST scores were positively associated with dALFF values in the frontal and occipital gyrus in MDD patients. Meanwhile, dALFF values in the occipital gyrus positively correlated with WCST in female MDD patients only. LIMITATION: Most of the participants were on medication and the sample size was small. CONCLUSIONS: Our study is the first to find the non-neglectable role of sex effects in differentiating BD and MDD at an early stage.

SEC61G assists EGFR-amplified glioblastoma to evade immune elimination.

Amplification of chromosome 7p11 (7p11) is the most common alteration in primary glioblastoma (GBM), resulting in gains of epidermal growth factor receptor (EGFR) copy number in 50 to 60% of GBM tumors. However, treatment strategies targeting EGFR have thus far failed in clinical trials, and the underlying mechanism remains largely unclear. We here demonstrate that EGFR amplification at the 7p11 locus frequently encompasses its neighboring genes and identifies SEC61G as a critical regulator facilitating GBM immune evasion and tumor growth. We found that SEC61G is always coamplified with EGFR and is highly expressed in GBM. As an essential subunit of the SEC61 translocon complex, SEC61G promotes translocation of newly translated immune checkpoint ligands (ICLs, including PD-L1, PVR, and PD-L2) into the endoplasmic reticulum and promotes their glycosylation, stabilization, and membrane presentation. Depletion of SEC61G promotes the infiltration and cytolytic activity of CD8+ T cells and thus inhibits GBM occurrence. Further, SEC61G inhibition augments the therapeutic efficiency of EGFR tyrosine kinase inhibitors in mice. Our study demonstrates a critical role of SEC61G in GBM immune evasion, which provides a compelling rationale for combination therapy of EGFR-amplified GBMs.

Soluble CD80 oral delivery by recombinant Lactococcus suppresses tumor growth by enhancing antitumor immunity.

CD80 is an important co-stimulatory molecule that participates in the immune response. Soluble CD80 can induce T cell activation and overcome PDL1-mediated immune suppression. In this study, we aimed to construct recombinant Lactococcus lactis for oral delivery of the soluble CD80 (hsCD80) protein or the fusion protein containing the cholera toxin B subunit (CTB) and hsCD80 (CTB-hsCD80) under the control of the nisin-inducible expression system. The recombinant L. lactis expressed and secreted hsCD80 or CTB-hsCD80 fusion proteins after induction by nisin in vitro and in the enteric cavity. Additionally, the CTB-hsCD80 fusion protein showed uptake by intestinal epithelial cells, was cleaved by the furin protease, and was released as free hsCD80 protein into the blood circulation. Orally administered hsCD80 and CTB-hsCD80 containing L. lactis increased the proportion of activated T cells in the spleen and intestinal epithelium, inhibited tumor growth, and prolonged the survival of tumor-bearing mice. The hsCD80-containing L. lactis showed greater therapeutic effects on primary colonic adenoma in APCmin/- mice and completely suppressed tumor growth. Further, recombinant CTB-hsCD80 in L. lactis was more efficient than hsCD80-containing bacteria in inhibiting the growth of xenografted colon cancer and melanoma cells. hsCD80 engineered probiotics may serve as a promising new approach for antitumor immunotherapy, especially for colorectal cancer.

Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial.

BACKGROUND: It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. METHODS: This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. RESULTS: At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). CONCLUSIONS: Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov (NCT03451734).

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous disorder that typically emerges in adolescence and can occur throughout adulthood. Studies aimed at quantitatively uncovering the heterogeneity of individual functional connectome abnormalities in MDD and identifying reproducibly distinct neurophysiological MDD subtypes across the lifespan, which could provide promising insights for precise diagnosis and treatment prediction, are still lacking. METHODS: Leveraging resting-state functional magnetic resonance imaging data from 1148 patients with MDD and 1079 healthy control participants (ages 11-93), we conducted the largest multisite analysis to date for neurophysiological MDD subtyping. First, we characterized typical lifespan trajectories of functional connectivity strength based on the normative model and quantitatively mapped the heterogeneous individual deviations among patients with MDD. Then, we identified neurobiological MDD subtypes using an unsupervised clustering algorithm and evaluated intersite reproducibility. Finally, we validated the subtype differences in baseline clinical variables and longitudinal treatment predictive capacity. RESULTS: Our findings indicated great intersubject heterogeneity in the spatial distribution and severity of functional connectome deviations among patients with MDD, which inspired the identification of 2 reproducible neurophysiological subtypes. Subtype 1 showed severe deviations, with positive deviations in the default mode, limbic, and subcortical areas and negative deviations in the sensorimotor and attention areas. Subtype 2 showed a moderate but converse deviation pattern. More importantly, subtype differences were observed in depressive item scores and the predictive ability of baseline deviations for antidepressant treatment outcomes. CONCLUSIONS: These findings shed light on our understanding of different neurobiological mechanisms underlying the clinical heterogeneity of MDD and are essential for developing personalized treatments for this disorder.