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The coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has highlighted the pivotal role of the immune response in determining the progression and severity of viral infections. In this paper, we review the most recent studies on the complicated dynamics between SARS-CoV-2 and the host immune system, highlight the importance of understanding these dynamics in developing effective treatments and formulate potent management strategies for COVID-19. We describe the activation of the host's innate immunity and the subsequent adaptive immune response following infection with SARS-CoV-2. In addition, the review emphasizes the immune evasion strategies of the SARS-CoV-2, including inhibition of interferon production and induction of cytokine storms, along with the resulting clinical outcomes. Finally, we assess the efficacy of current treatment strategies, including antiviral drugs, monoclonal antibodies, and anti-inflammatory treatments, and discuss their role in providing immunity and preventing severe disease.
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Interactions between bacteria and cyanobacteria influence the occurrence and development of harmful cyanobacterial blooms (HCBs). Bloom-forming cyanobacteria and cyanotoxin-degrading bacteria are essential in HCBs, nonetheless, their interactions and the underlying mechanisms remain unclear. To address this gap, a typical microcystin-LR (MC-LR)-degrading bacterium and a toxic Microcystis aeruginosa strain were co-cultivated to investigate their interactions. The cyanobacterial growth was enhanced by 24.8 %-44.3 % in the presence of the bacterium in the first 7 days, and the cyanobacterium enhanced the bacterial growth by 59.2 %-117.5 % throughout the growth phases, suggesting a mutualistic relationship between them. The presence of the bacterium increased cyanobacterial intracellular MC-LR content on days 4, 8, and 10 while reducing the extracellular MC-LR concentration, revealing the dual roles of the bacterium in enhancing cyanotoxin production and degrading cyanotoxins. The bacterium alleviated the oxidative stress, which may be crucial in promoting cyanobacterial growth. Critical functional genes related to cyanobacterial photosynthesis and MC-LR synthesis, and bacterial MC-LR degradation were up-regulated in the presence of the bacterium and cyanobacterium, respectively. Moreover, extracellular polymeric substances (EPS) were produced at the cell interface, implying EPS play a role in cyanobacterial-bacterial interactions. This study is the first to unveil the interaction mechanisms between cyanotoxin-degrading bacteria and bloom-forming cyanobacteria, shedding light on the dynamics of HCBs.
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OBJECTIVE: Increasing evidence has shown that the microbiota-gut-brain axis (MGB) is involved in the mechanism of major depressive disorder (MDD). However, the relationship between the gut microbiome and brain function in MDD patients has not been determined. Here, we intend to identify specific changes in the gut microbiome and brain function in first-episode, drug-naïve MDD patients and then explore the associations between the two omics to elucidate how the MGB axis plays a role in MDD development. METHODS: We recruited 38 first-episode, drug-naïve MDD patients and 37 healthy controls (HC). The composition of the fecal microbiome and neural spontaneous activity alterations were examined using 16S rRNA gene amplicon sequencing analysis and regional homogeneity (ReHo). Spearman correlation analyses were conducted to assess the associations between the gut microbiome and brain function. RESULTS: Compared with HC, MDD patients exhibited distinct alterations in the gut microbiota and elevated ReHo in the frontal regions. In the MDD group, a positive relationship was noted between the relative abundance of Blautia and the HAMD-17 and HAMA scores, as well as between the relative abundance of Oxalobacteraceae and the HAMD-17 score. The relative abundances of Porphyromonadaceae and Parabacteroides were negatively correlated with the ReHo values of frontal regions. LIMITATIONS: Our study utilized a cross-sectional design, and the number of subjects was relatively small. CONCLUSION: We found that some specific gut microbiomes were associated with frontal function, and others were associated with clinical symptoms in MDD patients, which may support the role of the MGB axis underlying MDD.
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Eje Cerebro-Intestino , Trastorno Depresivo Mayor , Microbioma Gastrointestinal , Humanos , Trastorno Depresivo Mayor/microbiología , Trastorno Depresivo Mayor/fisiopatología , Microbioma Gastrointestinal/fisiología , Femenino , Masculino , Adulto , Eje Cerebro-Intestino/fisiología , Heces/microbiología , Encéfalo/fisiopatología , ARN Ribosómico 16S/genética , Imagen por Resonancia Magnética , Adulto Joven , Estudios de Casos y ControlesRESUMEN
BACKGROUND: Major depressive disorder (MDD) is progressively recognized as a stress-related disorder characterized by aberrant brain network dynamics, encompassing both structural and functional domains. Yet, the intricate interplay between these dynamic networks and their molecular underpinnings remains predominantly unexplored. METHODS: Both structural and functional networks were constructed using multimodal neuroimaging data from 183 MDD patients and 300 age- and gender-matched healthy controls (HC). structural-functional connectivity (SC-FC) coupling was evaluated at both the connectome- and nodal-levels. Methylation data of five HPA axis key genes, including NR3C1, FKBP5, CRHBP, CRHR1, and CRHR2, were analyzed using Illumina Infinium Methylation EPIC BeadChip. RESULTS: We observed a significant reduction in SC-FC coupling at the connectome-level in patients with MDD compared to HC. At the nodal level, we found an imbalance in SC-FC coupling, with reduced coupling in cortical regions and increased coupling in subcortical regions. Furthermore, we identified 23 differentially methylated CpG sites on the HPA axis, following adjustment for multiple comparisons and control of age, gender, and medication status. Notably, three CpG sites on NR3C1 (cg01294526, cg19457823, and cg23430507), one CpG site on FKBP5 (cg25563198), one CpG site on CRHR1 (cg26656751), and one CpG site on CRHR2 (cg18351440) exhibited significant associations with SC-FC coupling in MDD patients. CONCLUSIONS: These findings provide valuable insights into the connection between micro-scale epigenetic changes in the HPA axis and SC-FC coupling at macro-scale connectomes. They unveil the mechanisms underlying increased susceptibility to MDD resulting from chronic stress and may suggest potential pharmacological targets within the HPA-axis for MDD treatment.
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Encéfalo , Conectoma , Metilación de ADN , Trastorno Depresivo Mayor , Epigénesis Genética , Humanos , Trastorno Depresivo Mayor/fisiopatología , Trastorno Depresivo Mayor/genética , Trastorno Depresivo Mayor/diagnóstico por imagen , Femenino , Masculino , Adulto , Encéfalo/fisiopatología , Encéfalo/diagnóstico por imagen , Persona de Mediana Edad , Proteínas de Unión a Tacrolimus/genética , Sistema Hipotálamo-Hipofisario/fisiopatología , Sistema Hipotálamo-Hipofisario/metabolismo , Receptores de Hormona Liberadora de Corticotropina/genética , Imagen por Resonancia Magnética , Sistema Hipófiso-Suprarrenal/fisiopatología , Receptores de Glucocorticoides/genética , Estudios de Casos y ControlesRESUMEN
Pattern recognition receptors are an essential part of the immune system, which detect pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs) and help shape both innate and adaptive immune responses. When dsDNA is present, cyclic GMP-AMP Synthase (cGAS) produces a second messenger called cyclic GMP-AMP (cGAMP), which then triggers an adaptor protein called STING, and eventually activates the expression of type I interferon (IFN) and pro-inflammatory cytokines in immune cells. The cGAS-STING signaling pathway has been receiving a lot of attention lately as a key immune-surveillance mediator. In this review, we summarize the present circumstances of the cGAS-STING signaling pathway in viral infections and inflammatory diseases, as well as autoimmune diseases. Modulation of the cGAS-STING signaling pathway provides potential strategies for treating viral infections, inflammatory diseases, and autoimmune diseases.
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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.
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Esquizofrenia , Humanos , Esquizofrenia/fisiopatología , Adulto , Masculino , Femenino , Estudios Transversales , Persona de Mediana Edad , Disfunción Cognitiva/etiología , Disfunción Cognitiva/fisiopatología , China , Depresión/fisiopatología , Ansiedad/fisiopatología , Síntomas Afectivos/fisiopatología , Síntomas Afectivos/etiología , Adulto JovenRESUMEN
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.
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Trastorno Depresivo Mayor , Subunidad beta de la Proteína de Unión al Calcio S100 , Humanos , Trastorno Depresivo Mayor/sangre , Masculino , Femenino , Subunidad beta de la Proteína de Unión al Calcio S100/sangre , Adulto , Factores Sexuales , Persona de Mediana Edad , Caracteres Sexuales , Biomarcadores/sangre , Estudios de Casos y ControlesRESUMEN
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.
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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.
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Diferenciación Celular , Proteínas de Homeodominio , Trofoblastos , Humanos , Trofoblastos/metabolismo , Trofoblastos/citología , Femenino , Proteínas de Homeodominio/metabolismo , Proteínas de Homeodominio/genética , Embarazo , Placenta/metabolismo , Placenta/citología , Linaje de la Célula , Placentación , Factores de Transcripción/metabolismo , Factores de Transcripción/genética , Células Madre/metabolismo , Células Madre/citología , Secuencias Reguladoras de Ácidos Nucleicos/genética , Células Madre Pluripotentes/metabolismo , Células Madre Pluripotentes/citologíaRESUMEN
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.
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Trastornos Relacionados con Anfetaminas , Heces , Microbioma Gastrointestinal , Metanfetamina , Calidad del Sueño , Humanos , Microbioma Gastrointestinal/efectos de los fármacos , Microbioma Gastrointestinal/fisiología , Metanfetamina/efectos adversos , Masculino , Adulto , Heces/microbiología , Femenino , ARN Ribosómico 16S/genética , Adulto Joven , Trastornos del Sueño-Vigilia/microbiologíaRESUMEN
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.
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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.
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Trastornos Relacionados con Anfetaminas , Metanfetamina , Microbiota , Humanos , Metanfetamina/efectos adversos , ARN Ribosómico 16S/genética , Conducta ImpulsivaRESUMEN
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.
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Trastorno Bipolar , Trastorno Depresivo Mayor , Humanos , Trastorno Bipolar/diagnóstico por imagen , Manía , Trastorno Depresivo Mayor/diagnóstico por imagen , Imagen por Resonancia Magnética , Estudios de Seguimiento , Máquina de Vectores de Soporte , Trastornos del HumorRESUMEN
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.
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Trastorno Depresivo Mayor , Humanos , Trastorno Depresivo Mayor/diagnóstico por imagen , Trastorno Depresivo Mayor/genética , Epigenómica , Multiómica , Encéfalo/diagnóstico por imagen , Perfilación de la Expresión GénicaRESUMEN
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.
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Trastorno Bipolar , Sustancia Blanca , Humanos , Trastorno Bipolar/diagnóstico por imagen , Trastorno Bipolar/genética , Estudios Transversales , Encéfalo , Sustancia Blanca/diagnóstico por imagen , Imagen de Difusión Tensora , Imagen por Resonancia Magnética/métodosRESUMEN
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.
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Trastorno Depresivo Mayor , Humanos , Calidad de Vida , Imagen por Resonancia Magnética/métodos , Encéfalo , Sustancia Gris/diagnóstico por imagen , Lóbulo Parietal , Lóbulo Frontal/diagnóstico por imagenRESUMEN
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.
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Trastornos Mentales , Multiómica , Humanos , Metaboloma , Trastornos Mentales/genética , Metabolómica/métodos , ProteómicaRESUMEN
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.
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Microbioma Gastrointestinal , Humanos , Microbioma Gastrointestinal/genética , Disbiosis/microbiología , ARN Ribosómico 16S/genética , Bacterias/genética , Heces/microbiología , BiomarcadoresRESUMEN
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.
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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.