Network modules linking expression and methylation in prefrontal cortex of schizophrenia

Traversing the epigenetic landscape: DNA methylation from retina to brain in development and disease

DNA methylation plays a crucial role in development, aging, degeneration of various tissues and dedifferentiated cells. This review explores the multifaceted impact of DNA methylation on the retina and brain during development and pathological processes. First, we investigate the role of DNA methylation in retinal development, and then focus on retinal diseases, detailing the changes in DNA methylation patterns in diseases such as diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Since the retina is considered an extension of the brain, its unique structure allows it to exhibit similar immune response mechanisms to the brain. We further extend our exploration from the retina to the brain, examining the role of DNA methylation in brain development and its associated diseases, such as Alzheimer's disease (AD) and Huntington's disease (HD) to better understand the mechanistic links between retinal and brain diseases, and explore the possibility of communication between the visual system and the central nervous system (CNS) from an epigenetic perspective. Additionally, we discuss neurodevelopmental brain diseases, including schizophrenia (SZ), autism spectrum disorder (ASD), and intellectual disability (ID), focus on how DNA methylation affects neuronal development, synaptic plasticity, and cognitive function, providing insights into the molecular mechanisms underlying neurodevelopmental disorders.

Multiomics Analysis of the Molecular Response to Glucocorticoids: Insights Into Shared Genetic Risk From Psychiatric to Medical Disorders

BACKGROUND

Alterations in the effects of glucocorticoids have been implicated in mediating some of the negative health effects associated with chronic stress, including increased risk for psychiatric disorders and cardiovascular and metabolic diseases. In this study, we investigated how genetic variants influence gene expression and DNA methylation in response to glucocorticoid receptor (GR) activation and their association with disease risk.

METHODS

We measured DNA methylation (n = 199) and gene expression (n = 297) in peripheral blood before and after GR activation with dexamethasone, with matched genotype data available for all samples. A comprehensive molecular quantitative trait locus (QTL) analysis was conducted, mapping GR-response methylation (me)QTLs, GR-response expression (e)QTLs, and GR-response expression quantitative trait methylation (eQTMs). A multilevel network analysis was employed to map the complex relationships between the transcriptome, epigenome, and genetic variation.

RESULTS

We identified 3772 GR-response meCpGs corresponding to 104,828 local GR-response meQTLs that did not strongly overlap with baseline meQTLs. eQTM and eQTL analyses revealed distinct genetic influences on gene expression and DNA methylation. Multilevel network analysis uncovered GR-response network trio QTLs, characterized by SNP-CpG-transcript combinations where meQTLs act as both eQTLs and eQTMs. GR-response trio variants were enriched in a genome-wide association study for psychiatric, respiratory, autoimmune, and cardiovascular diseases and conferred a higher relative heritability per SNP than GR-response meQTL and baseline QTL SNPs.

CONCLUSIONS

Genetic variants modulating the molecular effects of glucocorticoids are associated with psychiatric as well as medical diseases and not uncovered in baseline QTL analyses.

Cortico-striatal differences in the epigenome in attention-deficit/ hyperactivity disorder

While epigenetic modifications have been implicated in ADHD through studies of peripheral tissue, to date there has been no examination of the epigenome of the brain in the disorder. To address this gap, we mapped the methylome of the caudate nucleus and anterior cingulate cortex in post-mortem tissue from fifty-eight individuals with or without ADHD. While no single probe showed adjusted significance in differential methylation, several differentially methylated regions emerged. These regions implicated genes involved in developmental processes including neurogenesis and the differentiation of oligodendrocytes and glial cells. We demonstrate a significant association between differentially methylated genes in the caudate and genes implicated by GWAS not only in ADHD but also in autistic spectrum, obsessive compulsive and bipolar affective disorders through GWAS. Using transcriptomic data available on the same subjects, we found modest correlations between the methylation and expression of genes. In conclusion, this study of the cortico-striatal methylome points to gene and gene pathways involved in neurodevelopment, consistent with studies of common and rare genetic variation, as well as the post-mortem transcriptome in ADHD.

Multi-omics data integration methods and their applications in psychiatric disorders

To study mental illness and health, in the past researchers have often broken down their complexity into individual subsystems (e.g., genomics, transcriptomics, proteomics, clinical data) and explored the components independently. Technological advancements and decreasing costs of high throughput sequencing has led to an unprecedented increase in data generation. Furthermore, over the years it has become increasingly clear that these subsystems do not act in isolation but instead interact with each other to drive mental illness and health. Consequently, individual subsystems are now analysed jointly to promote a holistic understanding of the underlying biological complexity of health and disease. Complementing the increasing data availability, current research is geared towards developing novel methods that can efficiently combine the information rich multi-omics data to discover biologically meaningful biomarkers for diagnosis, treatment, and prognosis. However, clinical translation of the research is still challenging. In this review, we summarise conventional and state-of-the-art statistical and machine learning approaches for discovery of biomarker, diagnosis, as well as outcome and treatment response prediction through integrating multi-omics and clinical data. In addition, we describe the role of biological model systems and in silico multi-omics model designs in clinical translation of psychiatric research from bench to bedside. Finally, we discuss the current challenges and explore the application of multi-omics integration in future psychiatric research. The review provides a structured overview and latest updates in the field of multi-omics in psychiatry.

Gene Expression and Epigenetic Regulation in the Prefrontal Cortex of Schizophrenia

Schizophrenia pathogenesis remains challenging to define; however, there is strong evidence that the interaction of genetic and environmental factors causes the disorder. This paper focuses on transcriptional abnormalities in the prefrontal cortex (PFC), a key anatomical structure that determines functional outcomes in schizophrenia. This review summarises genetic and epigenetic data from human studies to understand the etiological and clinical heterogeneity of schizophrenia. Gene expression studies using microarray and sequencing technologies reported the aberrant transcription of numerous genes in the PFC in patients with schizophrenia. Altered gene expression in schizophrenia is related to several biological pathways and networks (synaptic function, neurotransmission, signalling, myelination, immune/inflammatory mechanisms, energy production and response to oxidative stress). Studies investigating mechanisms driving these transcriptional abnormalities focused on alternations in transcription factors, gene promoter elements, DNA methylation, posttranslational histone modifications or posttranscriptional regulation of gene expression mediated by non-coding RNAs.

Construction of Economic Data Management System Based on BP Neural Network

In order to further understand the economic data management system and technology, in-depth research was conducted in the state of people's nervous system feeling. The method of building open platform algorithm to optimize and modify weight rule 2BP grid construction was used to study. According to the basic principle, the BP neural network which is more suitable for economic data management system was constructed. At the same time, to construct economic database resources, neural network system was mainly to simplify and abstract or simulate the human brain nervous system, which is not completely the same, but can also map the basic characteristics of many functions of the human brain. Through the analysis of the economic data of the neural network, the neural network is widely used in the economic data management, which not only improves the management level of enterprises, but also improves the benefits and profits of enterprises. Besides, it has application effect in predicting economic early warning risk analysis cost control strategy management enterprise credit evaluation and enterprise competitiveness evaluation.

Developments in Biological Mechanisms and Treatments for Negative Symptoms and Cognitive Dysfunction of Schizophrenia

The causal mechanisms and treatment for the negative symptoms and cognitive dysfunction in schizophrenia are the main issues attracting the attention of psychiatrists over the last decade. The first part of this review summarizes the pathogenesis of schizophrenia, especially the negative symptoms and cognitive dysfunction from the perspectives of genetics and epigenetics. The second part describes the novel medications and several advanced physical therapies (e.g., transcranial magnetic stimulation and transcranial direct current stimulation) for the negative symptoms and cognitive dysfunction that will optimize the therapeutic strategy for patients with schizophrenia in future.

DNA methylation differences in cortical grey and white matter in schizophrenia

Aim: Identify grey- and white-matter-specific DNA-methylation differences between schizophrenia (SCZ) patients and controls in postmortem brain cortical tissue. Materials & methods: Grey and white matter were separated from postmortem brain tissue of the superior temporal and medial frontal gyrus from SCZ (n = 10) and control (n = 11) cases. Genome-wide DNA-methylation analysis was performed using the Infinium EPIC Methylation Array (Illumina, CA, USA). Results: Four differentially methylated regions associated with SCZ status and tissue type (grey vs white matter) were identified within or near KLF9, SFXN1, SPRED2 and ALS2CL genes. Gene-expression analysis showed differential expression of KLF9 and SFXN1 in SCZ. Conclusion: Our data show distinct differences in DNA methylation between grey and white matter that are unique to SCZ, providing new leads to unravel the pathogenesis of SCZ.

DNA methylation under the major depression pathway predicts pediatric quality of life four-month post-pediatric mild traumatic brain injury

BACKGROUND

Major depression has been recognized as the most commonly diagnosed psychiatric complication of mild traumatic brain injury (mTBI). Moreover, major depression is associated with poor outcomes following mTBI; however, the underlying biological mechanisms of this are largely unknown. Recently, genomic and epigenetic factors have been increasingly implicated in the recovery following TBI.

RESULTS

This study leveraged DNA methylation within the major depression pathway, along with demographic and behavior measures (features used in the clinical model) to predict post-concussive symptom burden and quality of life four-month post-injury in a cohort of 110 pediatric mTBI patients and 87 age-matched healthy controls. The results demonstrated that including DNA methylation markers in the major depression pathway improved the prediction accuracy for quality of life but not persistent post-concussive symptom burden. Specifically, the prediction accuracy (i.e., the correlation between the predicted value and observed value) of quality of life was improved from 0.59 (p = 1.20 × 10^-3) (clinical model) to 0.71 (p = 3.89 × 10^-5); the identified cytosine-phosphate-guanine sites were mainly in the open sea regions and the mapped genes were related to TBI in several molecular studies. Moreover, depression symptoms were a strong predictor (with large weights) for both post-concussive symptom burden and pediatric quality of life.

CONCLUSION

This study emphasized that both molecular and behavioral manifestations of depression symptoms played a prominent role in predicting the recovery process following pediatric mTBI, suggesting the urgent need to further study TBI-caused depression symptoms for better recovery outcome.