Epigenome-wide association study of long-term psychosocial stress in older adults

Long-term psychosocial stress is strongly associated with negative physical and mental health outcomes, as well as adverse health behaviours; however, little is known about the role that stress plays on the epigenome. One proposed mechanism by which stress affects DNA methylation is through health behaviours. We conducted an epigenome-wide association study (EWAS) of cumulative psychosocial stress (n = 2,689) from the Health and Retirement Study (mean age = 70.4 years), assessing DNA methylation (Illumina Infinium HumanMethylationEPIC Beadchip) at 789,656 CpG sites. For identified CpG sites, we conducted a formal mediation analysis to examine whether smoking, alcohol use, physical activity, and body mass index (BMI) mediate the relationship between stress and DNA methylation. Nine CpG sites were associated with psychosocial stress (all p < 9E-07; FDR q < 0.10). Additionally, health behaviours and/or BMI mediated 9.4% to 21.8% of the relationship between stress and methylation at eight of the nine CpGs. Several of the identified CpGs were in or near genes associated with cardiometabolic traits, psychosocial disorders, inflammation, and smoking. These findings support our hypothesis that psychosocial stress is associated with DNA methylation across the epigenome. Furthermore, specific health behaviours mediate only a modest percentage of this relationship, providing evidence that other mechanisms may link stress and DNA methylation.

Direction-aware functional class scoring enrichment analysis of infinium DNA methylation data

Infinium Methylation BeadChip arrays remain one of the most popular platforms for epigenome-wide association studies, but tools for downstream pathway analysis have their limitations. Functional class scoring (FCS) is a group of pathway enrichment techniques that involve the ranking of genes and evaluation of their collective regulation in biological systems, but the implementations described for Infinium methylation array data do not retain direction information, which is important for mechanistic understanding of genomic regulation. Here, we evaluate several candidate FCS methods that retain directional information. According to simulation results, the best-performing method involves the mean aggregation of probe limma t-statistics by gene followed by a rank-ANOVA enrichment test using the mitch package. This method, which we call 'LAM,' outperformed an existing over-representation analysis method in simulations, and showed higher sensitivity and robustness in an analysis of real lung tumour-normal paired datasets. Using matched RNA-seq data, we examine the relationship of methylation differences at promoters and gene bodies with RNA expression at the level of pathways in lung cancer. To demonstrate the utility of our approach, we apply it to three other contexts where public data were available. First, we examine the differential pathway methylation associated with chronological age. Second, we investigate pathway methylation differences in infants conceived with in vitro fertilization. Lastly, we analyse differential pathway methylation in 19 disease states, identifying hundreds of novel associations. These results show LAM is a powerful method for the detection of differential pathway methylation complementing existing methods. A reproducible vignette is provided to illustrate how to implement this method.

Knowledge-guided learning methods for integrative analysis of multi-omics data

Integrative analysis of multi-omics data has the potential to yield valuable and comprehensive insights into the molecular mechanisms underlying complex diseases such as cancer and Alzheimer's disease. However, a number of analytical challenges complicate multi-omics data integration. For instance, -omics data are usually high-dimensional, and sample sizes in multi-omics studies tend to be modest. Furthermore, when genes in an important pathway have relatively weak signal, it can be difficult to detect them individually. There is a growing body of literature on knowledge-guided learning methods that can address these challenges by incorporating biological knowledge such as functional genomics and functional proteomics into multi-omics data analysis. These methods have been shown to outperform their counterparts that do not utilize biological knowledge in tasks including prediction, feature selection, clustering, and dimension reduction. In this review, we survey recently developed methods and applications of knowledge-guided multi-omics data integration methods and discuss future research directions.

Optimal variable identification for accurate detection of causal expression Quantitative Trait Loci with applications in heart-related diseases

Gene expression plays a pivotal role in various diseases, contributing significantly to their mechanisms. Most GWAS risk loci are in non-coding regions, potentially affecting disease risk by altering gene expression in specific tissues. This expression is notably tissue-specific, with genetic variants substantially influencing it. However, accurately detecting the expression Quantitative Trait Loci (eQTL) is challenging due to limited heritability in gene expression, extensive linkage disequilibrium (LD), and multiple causal variants. The single variant association approach in eQTL analysis is limited by its susceptibility to capture the combined effects of multiple variants, and a bias towards common variants, underscoring the need for a more robust method to accurately identify causal eQTL variants. To address this, we developed an algorithm, CausalEQTL, which integrates L 0 +L 1 penalized regression with an ensemble approach to localize eQTL, thereby enhancing prediction performance precisely. Our results demonstrate that CausalEQTL outperforms traditional models, including LASSO, Elastic Net, Ridge, in terms of power and overall performance. Furthermore, analysis of heart tissue data from the GTEx project revealed that eQTL sites identified by our algorithm provide deeper insights into heart-related tissue eQTL detection. This advancement in eQTL mapping promises to improve our understanding of the genetic basis of tissue-specific gene expression and its implications in disease. The source code and identified causal eQTLs for CausalEQTL are available on GitHub: https://github.com/zhc-moushang/CausalEQTL.

MIMOSA: a resource consisting of improved methylome prediction models increases power to identify DNA methylation-phenotype associations

Although DNA methylation (DNAm) has been implicated in the pathogenesis of numerous complex diseases, from cancer to cardiovascular disease to autoimmune disease, the exact methylation sites that play key roles in these processes remain elusive. One strategy to identify putative causal CpG sites and enhance disease etiology understanding is to conduct methylome-wide association studies (MWASs), in which predicted DNA methylation that is associated with complex diseases can be identified. However, current MWAS models are primarily trained using the data from single studies, thereby limiting the methylation prediction accuracy and the power of subsequent association studies. Here, we introduce a new resource, MWAS Imputing Methylome Obliging Summary-level mQTLs and Associated LD matrices (MIMOSA), a set of models that substantially improve the prediction accuracy of DNA methylation and subsequent MWAS power through the use of a large summary-level mQTL dataset provided by the Genetics of DNA Methylation Consortium (GoDMC). Through the analyses of GWAS (genome-wide association study) summary statistics for 28 complex traits and diseases, we demonstrate that MIMOSA considerably increases the accuracy of DNA methylation prediction in whole blood, crafts fruitful prediction models for low heritability CpG sites, and determines markedly more CpG site-phenotype associations than preceding methods. Finally, we use MIMOSA to conduct a case study on high cholesterol, pinpointing 146 putatively causal CpG sites.

Human brain organoid: trends, evolution, and remaining challenges

Advanced brain organoids provide promising platforms for deciphering the cellular and molecular processes of human neural development and diseases. Although various studies and reviews have described developments and advancements in brain organoids, few studies have comprehensively summarized and analyzed the global trends in this area of neuroscience. To identify and further facilitate the development of cerebral organoids, we utilized bibliometrics and visualization methods to analyze the global trends and evolution of brain organoids in the last 10 years. First, annual publications, countries/regions, organizations, journals, authors, co-citations, and keywords relating to brain organoids were identified. The hotspots in this field were also systematically identified. Subsequently, current applications for brain organoids in neuroscience, including human neural development, neural disorders, infectious diseases, regenerative medicine, drug discovery, and toxicity assessment studies, are comprehensively discussed. Towards that end, several considerations regarding the current challenges in brain organoid research and future strategies to advance neuroscience will be presented to further promote their application in neurological research.

Unveiling DNA methylation in Alzheimer's disease: a review of array-based human brain studies

The intricacies of Alzheimer's disease pathogenesis are being increasingly illuminated by the exploration of epigenetic mechanisms, particularly DNA methylation. This review comprehensively surveys recent human-centered studies that investigate whole genome DNA methylation in Alzheimer's disease neuropathology. The examination of various brain regions reveals distinctive DNA methylation patterns that associate with the Braak stage and Alzheimer's disease progression. The entorhinal cortex emerges as a focal point due to its early histological alterations and subsequent impact on downstream regions like the hippocampus. Notably, ANK1 hypermethylation, a protein implicated in neurofibrillary tangle formation, was recurrently identified in the entorhinal cortex. Further, the middle temporal gyrus and prefrontal cortex were shown to exhibit significant hypermethylation of genes like HOXA3, RHBDF2, and MCF2L, potentially influencing neuroinflammatory processes. The complex role of BIN1 in late-onset Alzheimer's disease is underscored by its association with altered methylation patterns. Despite the disparities across studies, these findings highlight the intricate interplay between epigenetic modifications and Alzheimer's disease pathology. Future research efforts should address methodological variations, incorporate diverse cohorts, and consider environmental factors to unravel the nuanced epigenetic landscape underlying Alzheimer's disease progression.

Serum signature of antibodies to Toxoplasma gondii, rubella virus, and cytomegalovirus in females with bipolar disorder: A cross-sectional study

BACKGROUND AND AIM

Immunity alterations have been observed in bipolar disorder (BD). However, whether serum positivity of antibodies to Toxoplasma gondii (T gondii), rubella, and cytomegalovirus (CMV) shared clinical relevance with BD, remains controversial. This study aimed to investigate this association.

METHODS

Antibody seropositivity of IgM and IgG to T gondii, rubella virus, and CMV of females with BD and controls was extracted based on medical records from January 2018 to January 2023. Family history, type of BD, onset age, and psychotic symptom history were also collected.

RESULTS

585 individuals with BD and 800 healthy controls were involved. Individuals with BD revealed a lower positive rate of T gondii IgG in the 10-20 aged group (OR = 0.10), and a higher positive rate of rubella IgG in the 10-20 (OR = 5.44) and 20-30 aged group (OR = 3.15). BD with family history preferred a higher positive rate of T gondii IgG (OR = 24.00). Type-I BD owned a decreased positive rate of rubella IgG (OR = 0.37) and an elevated positive rate of CMV IgG (OR = 2.12) compared to type-II BD, while BD with early onset showed contrast results compared to BD without early onset (Rubella IgG, OR = 2.54; CMV IgG, OR = 0.26). BD with psychotic symptom history displayed a lower positive rate of rubella IgG (OR = 0.50).

LIMITATIONS

Absence of male evidence and control of socioeconomic status and environmental exposure.

CONCLUSIONS

Differential antibody seropositive rates of T gondii, rubella, and cytomegalovirus in BD were observed.

Gene-gene interaction analysis for age at onset of bipolar disorder in a Korean population

BACKGROUND

Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data.

METHODS

The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions.

RESULTS

The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes.

CONCLUSIONS

Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary.

Unraveling the Enigma of Organismal Death: Insights, Implications, and Unexplored Frontiers

Significant knowledge gaps exist regarding the responses of cells, tissues, and organs to organismal death. Examining the survival mechanisms influenced by metabolism and environment, this research has the potential to transform regenerative medicine, redefine legal death, and provide insights into life's physiological limits, paralleling inquiries in embryogenesis.

Identification of State Markers in Anorexia Nervosa: Replication and Extension of Inflammation-Associated Biomarkers Using Multiplex Profiling

Background

Proteomics offers potential for detecting and monitoring anorexia nervosa (AN) and its variant, atypical AN (atyp-AN). However, research has been limited by small protein panels, a focus on adult AN, and lack of replication.

Methods

In this study, we performed Olink multiplex profiling of 92 inflammation-related proteins in females with AN/atyp-AN (n = 64), all of whom were ≤90% of expected body weight, and age-matched healthy control individuals (n = 44).

Results

Five proteins differed significantly between the primary AN/atyp-AN group and the healthy control group (lower levels: HGF, IL-18R1, TRANCE; higher levels: CCL23, LIF-R). The expression levels of 3 proteins (lower IL-18R1, TRANCE; higher LIF-R) were uniquely disrupted in participants with AN in our primary model. No unique expression levels emerged for atyp-AN. In the total sample, 12 proteins (ADA, CD5, CD6, CXCL1, FGF-21, HGF, IL-12B, IL18, IL-18R1, SIRT2, TNFSF14, TRANCE) were positively correlated with body mass index and 5 proteins (CCL11, FGF-19, IL8, LIF-R, OPG) were negatively correlated with body mass index in our primary models.

Conclusions

Our results replicate the results of a previous study that demonstrated a dysregulated inflammatory status in AN and extend those results to atyp-AN. Of the 17 proteins correlated with body mass index, 11 were replicated from a previous study that used similar methods, highlighting the promise of inflammatory protein expression levels as biomarkers of AN disease monitoring. Our findings underscore the complexity of AN and atyp-AN by highlighting the inability of the identified proteins to differentiate between these 2 subtypes, thereby emphasizing the heterogeneous nature of these disorders.

Matrix metalloproteinases targeting in prostate cancer

Prostate cancer (PCa) is one of the most common tumors affecting men all over the world. PCa has brought a huge health burden to men around the world, especially for elderly men, but its pathogenesis is unclear. In prostate cancer, epigenetic inheritance plays an important role in the development, progression, and metastasis of the disease. An important role in cancer invasion and metastasis is played by matrix metalloproteinases (MMPs), zinc-dependent proteases that break down extracellular matrix. We review two important forms of epigenetic modification and the role of matrix metalloproteinases in tumor regulation, both of which may be of significant value as novel biomarkers for early diagnosis and prognosis monitoring. The author considers that both mechanisms have promising therapeutic applications for therapeutic agent research in prostate cancer, but that efforts should be made to mitigate or eliminate the side effects of drug therapy in order to maximize quality of life of patients. The understanding of epigenetic modification, MMPs, and their inhibitors in the functional regulation of prostate cancer is gradually advancing, it will provide a new technical means for the prevention of prostate cancer, early diagnosis, androgen-independent prostate cancer treatment, and drug research.

DNA methylation and its potential roles in common oral diseases

Oral diseases are among the most common diseases worldwide and are associated with systemic illnesses, and the rising occurrence of oral diseases significantly impacts the quality of life for many individuals. It is crucial to detect and treat these conditions early to prevent them from advancing. DNA methylation is a fundamental epigenetic process that contributes to a variety of diseases including various oral diseases. Taking advantage of its reversibility, DNA methylation becomes a viable therapeutic target by regulating various cellular processes. Understanding the potential role of this DNA alteration in oral diseases can provide significant advances and more opportunities for diagnosis and therapy. This article will review the biology of DNA methylation, and then mainly discuss the key findings on DNA methylation in oral cancer, periodontitis, endodontic disease, oral mucosal disease, and clefts of the lip and/or palate in the background of studies on global DNA methylation and gene-specific DNA methylation.

Cardiovascular mortality in bipolar disorder: Population-based cohort study

BACKGROUND

Limited evidence base on cause-specific excess cardiovascular disease (CVD) mortality in bipolar disorder (BD) is a barrier to developing preventive interventions aimed at reducing the persistent mortality gap in BD.

OBJECTIVE

To investigate cause-specific CVD mortality in BD.

METHODS

We identified all individuals aged 15+ years during 2004-2018 with a diagnosis of BD using Finnish nationwide routine data. Standardised mortality ratios (SMR) with 95% confidence intervals (CI) were calculated using the mortality rates in the general population as weights.

RESULTS

53,273 individuals with BD (57% women; median age at BD diagnosis, 40 years), were followed up for 428,426 person-years (median, 8.2 years). There were 5988 deaths due to any cause, of which 26% were due to CVD. The leading cause of absolute excess CVD mortality was coronary artery disease (CAD). The leading causes of relative excess mortality were cardiomegaly (SMR, 4.51; 95% CI, 3.58-5.43), venous thromboembolism (3.03; 2.26-3.81), cardiomyopathy (2.46; 1.95-2.97), and hypertensive heart disease (2.12; 1.71-2.54). The leading causes of absolute CVD mortality showed markedly lower relative excess, including CAD (1.47; 1.34-1.61), ischaemic stroke (1.31; 1.06-1.54), and acute myocardial infarction (1.12; 0.98-1.25). Due to the higher relative excess mortality, structural and functional heart disorders contributed as much as atherosclerotic and ischaemic disorders to the absolute excess mortality.

CONCLUSIONS

Cardiomyopathy and hypertensive heart disease as the leading causes of relative excess mortality emphasise the contribution of structural and functional heart disorders to the overall excess mortality alongside coronary artery disease. Interventions targeted at these modifiable causes of death should be priorities in the prevention of premature excess CVD mortality in BD.

Estimating the direct effects of the genetic liabilities to bipolar disorder, schizophrenia, and behavioral traits on suicide attempt using a multivariable Mendelian randomization approach

Bipolar disorder (BD) and schizophrenia (SZ) are associated with higher odds of suicide attempt (SA). In this study, we aimed to explore the effect of BD and SZ genetic liabilities on SA, also considering the contribution of behavioral traits, socioeconomic factors, and substance use disorders. Leveraging large-scale genome-wide association data from the Psychiatric Genomics Consortium (PGC) and the UK Biobank (UKB), we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the putative causal effect of BD (41,917 cases, 371,549 controls) and SZ (53,386 cases, 77,258 controls) on SA (26,590 cases, 492,022 controls). Then, we assessed the putative causal effect of BD and SZ on behavioral traits, socioeconomic factors, and substance use disorders. Considering the associations identified, we evaluated the direct causal effect of behavioral traits, socioeconomic factors, and substance use disorders on SA using a multivariable MR approach. The genetic liabilities to BD and SZ were associated with higher odds of SA (BD odds ratio (OR) = 1.24, p = 3.88 × 10-12; SZ OR = 1.09, p = 2.44 × 10-20). However, while the effect of mental distress (OR = 1.17, p = 1.02 × 10-4) and risk-taking (OR = 1.52, p = 0.028) on SA was independent of SZ genetic liability, the BD-SA relationship appeared to account for the effect of these risk factors. Similarly, the association with loneliness on SA was null after accounting for the effect of SZ genetic liability. These findings highlight the complex interplay between genetic risk of psychiatric disorders and behavioral traits in the context of SA, suggesting the need for a comprehensive mental health assessment for high-risk individuals.

Individualized Texture Similarity Network in Schizophrenia

BACKGROUND

Structural covariance network disruption has been considered an important pathophysiological indicator for schizophrenia. Here, we introduced a novel individualized structural covariance network measure, referred to as a texture similarity network (TSN), and hypothesized that the TSN could reliably reveal unique intersubject heterogeneity and complex dysconnectivity patterns in schizophrenia.

METHODS

The TSN was constructed by measuring the covariance of 180 three-dimensional voxelwise gray-level co-occurrence matrix feature maps between brain areas in each participant. We first tested the validity and reproducibility of the TSN in characterizing the intersubject variability in 2 longitudinal test-retest healthy cohorts. The TSN was further applied to elucidate intersubject variability and dysconnectivity patterns in 10 schizophrenia case-control datasets (609 schizophrenia cases vs. 579 controls) as well as in a first-episode depression dataset (69 patients with depression vs. 69 control participants).

RESULTS

The test-retest analysis demonstrated higher TSN intersubject than intrasubject variability. Moreover, the TSN reliably revealed higher intersubject variability in both chronic and first-episode schizophrenia, but not in depression. The TSN also reproducibly detected coexistent increased and decreased TSN strength in widespread brain areas, increased global small-worldness, and the coexistence of both structural hyposynchronization in the central networks and hypersynchronization in peripheral networks in patients with schizophrenia but not in patients with depression. Finally, aberrant intersubject variability and covariance strength patterns revealed by the TSN showed a missing or weak correlation with other individualized structural covariance network measures, functional connectivity, and regional volume changes.

CONCLUSIONS

These findings support the reliability of a TSN in revealing unique structural heterogeneity and complex dysconnectivity in patients with schizophrenia.

Relationships between trauma types and psychotic symptoms: A network analysis of patients with psychotic disorders in a large, multi-country study in East Africa

BACKGROUND

The link between trauma exposure and psychotic disorders is well-established. Further, specific types of trauma may be associated with specific psychotic symptoms. Network analysis is an approach that can advance our understanding of the associations across trauma types and psychotic symptoms.

METHODS

We conducted a network analysis with data from 16,628 adult participants (mean age [standard deviation] = 36.3 years [11.5]; 55.8% males) with psychotic disorders in East Africa recruited between 2018 and 2023. We used the Life Events Checklist and the Mini International Neuropsychiatric Interview to determine whether specific trauma types experienced over the life course and specific psychotic symptoms were connected. We used an Ising model to estimate the network connections and bridge centrality statistics to identify nodes that may influence trauma types and psychotic symptoms.

RESULTS

The trauma type "exposure to a war zone" had the highest bridge strength, betweenness, and closeness. The psychotic symptom "odd or unusual beliefs" had the second highest bridge strength. Exposure to a war zone was directly connected to visual hallucinations, odd or unusual beliefs, passivity phenomena, and disorganized speech. Odd or unusual beliefs were directly connected to transportation accidents, physical assault, war, and witnessing sudden accidental death.

CONCLUSION

Specific trauma types and psychotic symptoms may interact bidirectionally. Screening for psychotic symptoms in patients with war-related trauma and evaluating lifetime trauma in patients with odd or unusual beliefs in clinical care may be considered points of intervention to limit stimulating additional psychotic symptoms and trauma exposure. This work reaffirms the importance of trauma-informed care for patients with psychotic disorders.

The Role of Glia Telomere Dysfunction in the Pathogenesis of Central Nervous System Diseases

Maintaining the telomere length is decisive for the viability and homeostasis process of all the cells of an organism, including human glial cells. Telomere shortening of microglial cells has been widely associated with the onset and progression of neurodegenerative diseases such as Parkinson's and Alzheimer's disease. Additionally, traumatic brain injury appears to have a positive correlation with the telomere-shortening process of microglia, and telomere length can be used as a non-invasive biomarker for the clinical management of these patients. Moreover, telomere involvement through telomerase reactivation and homologous recombination also known as the alternative lengthening of telomeres (ALT) has been described in gliomagenesis pathways, and particular focus has been given in the translational significance of these mechanisms in gliomas diagnosis and prognostic classification. Finally, glia telomere shortening is implicated in some psychiatric diseases. Given that telomere dysfunction of glial cells is involved in the central nervous system (CNS) disease pathogenesis, it represents a promising drug target that could lead to the incorporation of new tools in the medicinal arsenal for the management of so far incurable conditions.

Effects of diazepam on hippocampal blood flow in people at clinical high risk for psychosis

Elevated hippocampal perfusion has been observed in people at clinical high risk for psychosis (CHR-P). Preclinical evidence suggests that hippocampal hyperactivity is central to the pathophysiology of psychosis, and that peripubertal treatment with diazepam can prevent the development of psychosis-relevant phenotypes. The present experimental medicine study examined whether diazepam can normalize hippocampal perfusion in CHR-P individuals. Using a randomized, double-blind, placebo-controlled, crossover design, 24 CHR-P individuals were assessed with magnetic resonance imaging (MRI) on two occasions, once following a single oral dose of diazepam (5 mg) and once following placebo. Regional cerebral blood flow (rCBF) was measured using 3D pseudo-continuous arterial spin labeling and sampled in native space using participant-specific hippocampus and subfield masks (CA1, subiculum, CA4/dentate gyrus). Twenty-two healthy controls (HC) were scanned using the same MRI acquisition sequence, but without administration of diazepam or placebo. Mixed-design ANCOVAs and linear mixed-effects models were used to examine the effects of group (CHR-P placebo/diazepam vs. HC) and condition (CHR-P diazepam vs. placebo) on rCBF in the hippocampus as a whole and by subfield. Under the placebo condition, CHR-P individuals (mean [±SD] age: 24.1 [±4.8] years, 15 F) showed significantly elevated rCBF compared to HC (mean [±SD] age: 26.5 [±5.1] years, 11 F) in the hippocampus (F(1,41) = 24.7, pFDR < 0.001) and across its subfields (all pFDR < 0.001). Following diazepam, rCBF in the hippocampus (and subfields, all pFDR < 0.001) was significantly reduced (t(69) = -5.1, pFDR < 0.001) and normalized to HC levels (F(1,41) = 0.4, pFDR = 0.204). In conclusion, diazepam normalized hippocampal hyperperfusion in CHR-P individuals, consistent with evidence implicating medial temporal GABAergic dysfunction in increased vulnerability for psychosis.

The genetics of trichotillomania and excoriation disorder: A systematic review

BACKGROUND

Trichotillomania (TTM) and excoriation disorder (ED) are impairing obsessive-compulsive related disorders that are common in the general population and for which there are no clear first-line medications, highlighting the need to better understand the underlying biology of these disorders to inform treatments. Given the importance of genetics in obsessive-compulsive disorder (OCD), evaluating genetic factors underlying TTM and ED may advance knowledge about the pathophysiology of these body-focused repetitive behaviors.

AIM

In this systematic review, we summarize the available evidence on the genetics of TTM and ED and highlight gaps in the field warranting further research.

METHOD

We systematically searched Embase, PsycInfo, PubMed, Medline, Scopus, and Web of Science for original studies in genetic epidemiology (family or twin studies) and molecular genetics (candidate gene and genome-wide) published up to June 2023.

RESULTS

Of the 3536 records identified, 109 studies were included in this review. These studies indicated that genetic factors play an important role in the development of TTM and ED, some of which may be shared across the OCD spectrum, but there are no known high-confidence specific genetic risk factors for either TTM or ED.

CONCLUSIONS

Our review underscores the need for additional genome-wide research conducted on the genetics of TTM and ED, for instance, genome-wide association and whole-genome/whole-exome DNA sequencing studies. Recent advances in genomics have led to the discovery of risk genes in several psychiatric disorders, including related conditions such as OCD, but to date, TTM and ED have remained understudied.

Polygenic Risk of Mental Disorders and Subject-Specific School Grades

BACKGROUND

Education is essential for socioeconomic security and long-term mental health; however, mental disorders are often detrimental to the educational trajectory. Genetic correlations between mental disorders and educational attainment do not always align with corresponding phenotypic associations, implying heterogeneity in the genetic overlap.

METHODS

We unraveled this heterogeneity by investigating associations between polygenic risk scores for 6 mental disorders and fine-grained school outcomes: school grades in language and mathematics in ninth grade and high school, as well as educational attainment by age 25, using nationwide-representative data from established cohorts (N = 79,489).

RESULTS

High polygenic liability of attention-deficit/hyperactivity disorder was associated with lower grades in language and mathematics, whereas high polygenic risk of anorexia nervosa or bipolar disorder was associated with higher grades in language and mathematics. Associations between polygenic risk and school grades were mixed for schizophrenia and major depressive disorder and neutral for autism spectrum disorder.

CONCLUSIONS

Polygenic risk scores for mental disorders are differentially associated with language and mathematics school grades.

Associations between soluble urokinase plasminogen activator receptor (suPAR) concentration and psychiatric disorders - A systematic review and meta-analysis

BACKGROUND

There is some evidence of an association between inflammation in the pathogenesis of mental disorders. Soluble urokinase plasminogen activator receptor (suPAR) is a biomarker of chronic inflammation, which provides a more stable index of systemic inflammation than more widely used biomarkers. This review aims to synthesise studies that measured suPAR concentrations in individuals with a psychiatric disorder, to determine if these concentrations are altered in comparison to healthy participants.

METHOD

Comprehensive literature searches from inception to October 2023 were conducted of five relevant databases (PubMed, Web of Science, Embase, Scopus, APA PsychInfo). Random-effects meta-analyses were performed to compare the standardised mean difference of blood suPAR levels (i.e. plasma or serum) for individuals with any psychiatric disorder relative to controls. Separate meta-analyses of suPAR levels were conducted for individuals with schizophrenia or other psychotic disorder and depressive disorder. Risk of bias was assessed using the Newcastle Ottawa Scale. Post-hoc sensitivity analyses included excluding studies at high risk of bias, and analyses of studies that measured suPAR concentrations either in serum or in plasma separately.

RESULTS

The literature search identified 149 records. Ten full-text studies were screened for eligibility and 9 studies were included for review. Primary analyses revealed no significant difference in suPAR levels between individuals with any psychiatric disorder compared to controls (k = 7, SMD = 0.42, 95 % CI [-0.20, 1.04]). However, those with depressive disorder had elevated suPAR levels relative to controls (k = 3, SMD = 0.61, 95 % CI [0.34, 0.87]). Similarly, secondary analyses showed no evidence of a significant difference in suPAR levels in individuals with any psychiatric disorder when studies at high risk of bias were excluded (k = 6, SMD = 0.54, 95 % CI [-0.14, 1.22]), but elevated suPAR concentrations for those with schizophrenia or other psychotic disorder were found (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]). Furthermore, studies that analysed plasma suPAR concentrations found elevated plasma suPAR levels in individuals with any psychiatric disorder relative to controls (k = 5, SMD = 0.84, 95 % CI [0.38, 1.29]), while studies measuring serum suPAR levels in any psychiatric disorder did not find a difference (k = 2, SMD = -0.61, 95 % CI [-1.27, 0.04]). For plasma, elevated suPAR concentrations were also identified for those with schizophrenia or other psychotic disorder (k = 3, SMD = 0.98, 95 % CI [0.39, 1.58]).

DISCUSSION

When studies measuring either only serum or only plasma suPAR were considered, no significant difference in suPAR levels were observed between psychiatric disorder groups, although significantly elevated suPAR levels were detected in those with moderate to severe depressive disorder. However, plasma suPAR levels were significantly elevated in those with any psychiatric disorder relative to controls, while no difference in serum samples was found. A similar finding was reported for schizophrenia or other psychotic disorder. The plasma findings suggest that chronic inflammatory dysregulation may contribute to the pathology of schizophrenia and depressive disorder. Future longitudinal studies are required to fully elucidate the role of this marker in the psychopathology of these disorders.

Spatial Dynamic Subspaces Encode Sex-Specific Schizophrenia Disruptions in Transient Network Overlap and Their Links to Genetic Risk

BACKGROUND

Schizophrenia research reveals sex differences in incidence, symptoms, genetic risk factors, and brain function. However, a knowledge gap remains regarding sex-specific schizophrenia alterations in brain function. Schizophrenia is considered a dysconnectivity syndrome, but the dynamic integration and segregation of brain networks are poorly understood. Recent advances in resting-state functional magnetic resonance imaging allow us to study spatial dynamics, the phenomenon of brain networks spatially evolving over time. Nevertheless, estimating time-resolved networks remains challenging due to low signal-to-noise ratio, limited short-time information, and uncertain network identification.

METHODS

We adapted a reference-informed network estimation technique to capture time-resolved networks and their dynamic spatial integration and segregation for 193 individuals with schizophrenia and 315 control participants. We focused on time-resolved spatial functional network connectivity, an estimate of network spatial coupling, to study sex-specific alterations in schizophrenia and their links to genomic data.

RESULTS

Our findings are consistent with the dysconnectivity and neurodevelopment hypotheses and with the cerebello-thalamo-cortical, triple-network, and frontoparietal dysconnectivity models, helping to unify them. The potential unification offers a new understanding of the underlying mechanisms. Notably, the posterior default mode/salience spatial functional network connectivity exhibits sex-specific schizophrenia alteration during the state with the highest global network integration and is correlated with genetic risk for schizophrenia. This dysfunction is reflected in regions with weak functional connectivity to corresponding networks.

CONCLUSIONS

Our method can effectively capture spatially dynamic networks, detect nuanced schizophrenia effects including sex-specific ones, and reveal the intricate relationship of dynamic information to genomic data. The results also underscore the clinical potential of dynamic spatial dependence and weak connectivity.

Impact of healthy lifestyle on the incidence and progression trajectory of mental disorders: A prospective study in the UK Biobank

BACKGROUND

Healthier lifestyle decreased the risk of mental disorders (MDs) such as depression and anxiety. However, research on the effects of a comprehensive healthy lifestyle on their progression is lacking.

METHODS

385,704 individuals without baseline MDs from the UK Biobank cohort were included. A composite healthy lifestyle score was computed by assessing alcohol intake, smoking status, television viewing time, physical activity, sleep duration, fruit and vegetable intake, oily fish intake, red meat intake, and processed meat intake. Follow-up utilized hospital and death register records. Multistate model was used to examine the role of healthy lifestyle on the progression of specific MDs, while a piecewise Cox regression model was utilized to assess the influence of healthy lifestyle across various phases of disease progression.

RESULTS

Higher lifestyle score reduced risks of transitions from baseline to anxiety and depression, as well as from anxiety and depression to comorbidity, with corresponding hazard ratios (HR) and 95 % confidence intervals (CI) of 0.94 (0.93, 0.95), 0.90 (0.89, 0.91), 0.94 (0.91, 0.98), and 0.95 (0.92, 0.98), respectively. Healthier lifestyle decreased the risk of transitioning from anxiety to comorbidity within 2 years post-diagnosis, with HR 0.93 (0.88, 0.98). Higher lifestyle scores at 2-4 years and 4-6 years post-depression onset were associated with reduced risk of comorbidity, with HR 0.93 (0.87, 0.99) and 0.92 (0.86, 0.99), respectively.

LIMITATION

The generalizability to other ethnic groups is limited.

CONCLUSION

This study observed a protective role of holistic healthy lifestyle in the trajectory of MDs and contributed to identifying critical progression windows.

Maternal selenium deficiency during pregnancy in association with autism and ADHD traits in children: The Odense Child Cohort

BACKGROUND

Selenoproteins regulate pathways controlling neurodevelopment, e.g., redox signaling and thyroid hormone metabolism. However, studies investigating maternal selenium in relation to child neurodevelopmental disorders are scarce.

METHODS

719 mother-child pairs from the prospective population-based Odense Child Cohort study in Denmark were included. Three selenium biomarkers, i.e. concentrations of serum selenium, selenoprotein P (SELENOP), and activity of glutathione peroxidase 3 (GPX3), along with serum copper, zinc and iron were measured in early third trimester (at 28.9+/-0.8 weeks of pregnancy). ADHD and ASD traits in children were assessed systematically using the established Child Behaviour Checklist at 5 years of age, based on a Danish reference cohort with cut-off at 90th percentile. Multivariable regression models adjusted for biologically relevant confounders were applied.

RESULTS

155 of 719 (21.6 %) children had ASD traits and 59 of 719 (8.2 %) children had traits of ADHD at 5 years of age. In crude and adjusted models, all three selenium biomarkers associated inversely with ADHD traits. For ADHD, fully adjusted OR for 10 μg/L increment in selenium was 0.76 (95 % CI 0.60, 0.94), for one mg/L increment in SELENOP was 0.73 (0.56, 0.95), and for 10 U/L increment in GPx3 was 0.93 (0.87,1.00). Maternal total selenium was inversely associated with child ASD traits, OR per 10 μg/L increment was 0.85 (0.74, 0,98). SELENOP and GPx3 were not associated with ASD traits. The associations were specific to selenium, as other trace elements such as copper, zinc, or iron were not associated with the outcomes.

CONCLUSIONS

The results provide coherent evidence for selenium deficiency as a risk factor for ADHD and ASD traits in an environment with borderline supply, the causality of which should be elucidated in a randomized controlled trial.

Polygenic risk for schizophrenia and bipolar disorder in relation to cardiovascular biomarkers

Individuals with schizophrenia and bipolar disorder are at an increased risk of cardiovascular disease (CVD), and a range of biomarkers related to CVD risk have been found to be abnormal in these patients. Common genetic factors are a putative underlying mechanism, alongside lifestyle factors and antipsychotic medication. However, the extent to which the altered CVD biomarkers are related to genetic factors involved in schizophrenia and bipolar disorder is unknown. In a sample including 699 patients with schizophrenia, 391 with bipolar disorder, and 822 healthy controls, we evaluated 8 CVD risk biomarkers, including BMI, and fasting plasma levels of CVD biomarkers from a subsample. Polygenic risk scores (PGRS) were obtained from genome-wide associations studies (GWAS) of schizophrenia and bipolar disorder from the Psychiatric Genomics Consortium. The CVD biomarkers were used as outcome variables in linear regression models including schizophrenia and bipolar disorder PGRS as predictors, age, sex, diagnostic category, batch and 10 principal components as covariates, controlling for multiple testing by Bonferroni correction for the number of independent tests. Bipolar disorder PGRS was significantly (p = 0.03) negatively associated with BMI after multiple testing correction, and schizophrenia PGRS was nominally negatively associated with BMI. There were no other significant associations between bipolar or schizophrenia PGRS, and other investigated CVD biomarkers. Despite a range of abnormal CVD risk biomarkers in psychotic disorders, we only found a significant negative association between bipolar disorder PGRS and BMI. This has previously been shown for schizophrenia PGRS and BMI, and warrants further exploration.

β-Hydroxybutyrate Improves the Redox Status, Cytokine Production and Phagocytic Potency of Glucose-Deprived HMC3 Human Microglia-like Cells

Brain glucose deprivation is a component of the pathophysiology of ischemia, glucose transporter1 (GLUT1) deficiency, neurological disorders and occurs transiently in diabetes. Microglia, the neuroimmune cells must function effectively to offer immune defence and debris removal in low-energy settings. Brain glucose deprivation may compromise microglial functions further escalating the disease pathology and deteriorating the overall mental health. In the current study, HMC3 human microglia-like cells were cultured in vitro and exposed to glucose deprivation to investigate the effects of glucose deprivation on phenotypic state, redox status, secretion of cytokines and phagocytic capabilities of HMC3 cells. However, HMC3 cells were able to proliferate in the absence of glucose but showed signs of redox imbalance and mitochondrial dysfunction, as demonstrated by decreased MTT reduction and Mito Tracker™ staining of cells, along with a concomitant reduction in NOX2 protein, superoxide, and nitrite levels. Reduced levels of secreted TNF and IL-1β were the signs of compromised cytokine secretion by glucose-deprived HMC3 microglia-like cells. Moreover, glucose-deprived HMC3 cells also showed reduced phagocytic activity as assessed by fluorescently labelled latex beads-based functional phagocytosis assay. β-hydroxybutyrate (BHB) supplementation restored the redox status, mitochondrial health, cytokine secretion, and phagocytic activity of glucose-deprived HMC3 microglia-like cells. Overall, impaired brain glucose metabolism may hinder microglia's capacity to release diffusible immune factors and perform phagocytosis. This could escalate the mental health issues in neurological diseases where brain glucose metabolism is compromised. Moreover, nutritional ketosis or exogenous ketone supplementation such as BHB may be utilized as a potential metabolic therapies for these conditions.

Long-term Environmental Enrichment Normalizes Schizophrenia-like Abnormalities and Promotes Hippocampal Slc6a4 Promoter Demethylation in Mice Submitted to a Two-hit Model

Here, we explored the impact of prolonged environmental enrichment (EE) on behavioral, neurochemical, and epigenetic changes in the serotonin transporter gene in mice subjected to a two-hit schizophrenia model. The methodology involved administering the viral mimetic PolyI:C to neonatal Swiss mice as a first hit during postnatal days (PND) 5-7, or a sterile saline solution as a control. At PND21, mice were randomly assigned either to standard environment (SE) or EE housing conditions. Between PND35-44, the PolyI:C-treated group was submitted to various unpredictable stressors, constituting the second hit. Behavioral assessments were conducted on PND70, immediately after the final EE exposure. Following the completion of behavioral assessments, we evaluated the expression of proteins in the hippocampus that are indicative of microglial activation, such as Iba-1, as well as related to neurogenesis, including doublecortin (Dcx). We also performed methylation analysis on the serotonin transporter gene (Slc6a4) to investigate alterations in serotonin signaling. The findings revealed that EE for 50 days mitigated sensorimotor gating deficits and working memory impairments in two-hit mice and enhanced their locomotor and exploratory behaviors. EE also normalized the overexpression of hippocampal Iba-1 and increased the expression of hippocampal Dcx. Additionally, we observed hippocampal demethylation of the Slc6a4 gene in the EE-exposed two-hit group, indicating epigenetic reprogramming. These results contribute to the growing body of evidence supporting the protective effects of long-term EE in counteracting behavioral disruptions caused by the two-hit schizophrenia model, pointing to enhanced neurogenesis, diminished microglial activation, and epigenetic modifications of serotonergic pathways as underlying mechanisms.

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and somatic traits

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and somatic traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and somatic traits were calculated in European-ancestry (EUR; n = 5691) participants and, when discovery datasets were available, for African-ancestry (AFR; n = 4918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGSMDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGSBMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and somatic traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

Instrumental variable and colocalization analyses identify endotrophin and HTRA1 as potential therapeutic targets for coronary artery disease

Coronary artery disease (CAD) remains a leading cause of disease burden globally, and there is a persistent need for new therapeutic targets. Instrumental variable (IV) and genetic colocalization analyses can help identify novel therapeutic targets for human disease by nominating causal genes in genome-wide association study (GWAS) loci. We conducted cis-IV analyses for 20,125 genes and 1,746 plasma proteins with CAD using molecular trait quantitative trait loci variant (QTLs) data from three different studies. 19 proteins and 119 genes were significantly associated with CAD risk by IV analyses and demonstrated evidence of genetic colocalization. Notably, our analyses validated well-established targets such as PCSK9 and ANGPTL4 while also identifying HTRA1 and endotrophin (a cleavage product of COL6A3) as proteins whose levels are causally associated with CAD risk. Further experimental studies are needed to confirm the causal role of the genes and proteins identified through our multiomic cis-IV analyses on human disease.

Schizophrenia genomics: genetic complexity and functional insights

Determining the causes of schizophrenia has been a notoriously intractable problem, resistant to a multitude of investigative approaches over centuries. In recent decades, genomic studies have delivered hundreds of robust findings that implicate nearly 300 common genetic variants (via genome-wide association studies) and more than 20 rare variants (via whole-exome sequencing and copy number variant studies) as risk factors for schizophrenia. In parallel, functional genomic and neurobiological studies have provided exceptionally detailed information about the cellular composition of the brain and its interconnections in neurotypical individuals and, increasingly, in those with schizophrenia. Taken together, these results suggest unexpected complexity in the mechanisms that drive schizophrenia, pointing to the involvement of ensembles of genes (polygenicity) rather than single-gene causation. In this Review, we describe what we now know about the genetics of schizophrenia and consider the neurobiological implications of this information.

Causality between six psychiatric disorders and digestive tract cancers risk: a two-sample Mendelian randomization study

Associations between psychiatric disorders and digestive tract cancers have been proposed. However, the causal link between these factors remains unclear. This study pioneers Mendelian randomization (MR) analysis to explore the genetic link between psychiatric disorders and digestive tract cancers risk. We analysed data on six psychiatric disorders [schizophrenia, bipolar disorder, major depressive disorder (MDD), attention deficit hyperactivity disorder, autism spectrum disorder, and panic disorder (PD)] and digestive tract cancers [esophagus cancer (EC), gastric cancer (GC), and colorectal cancer (CRC)] from genome-wide association studies databases. Using instrumental variables identified from significant single nucleotide polymorphism associations, we employed the inverse variance weighted (IVW) method alongside the weighted median (WM) method and MR-Egger regression. The results revealed no causal link between psychiatric disorders and the risk of EC or GC. Psychiatric disorders were not identified as risk factors for CRC. Notably, PD demonstrated a lower CRC risk (OR = 0.79, 95% CI 0.66-0.93, P = 0.01). This MR analysis underscores the lack of a causal association between psychiatric disorders and digestive tract cancers risk while suggesting a potential protective effect of PD against CRC.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

A functional schizophrenia-associated genetic variant near the TSNARE1 and ADGRB1 genes

Recent collaborative genome-wide association studies (GWAS) have identified >200 independent loci contributing to risk for schizophrenia (SCZ). The genes closest to these loci have diverse functions, supporting the potential involvement of multiple relevant biological processes, yet there is no direct evidence that individual variants are functional or directly linked to specific genes. Nevertheless, overlap with certain epigenetic marks suggest that most GWAS-implicated variants are regulatory. Based on the strength of association with SCZ and the presence of regulatory epigenetic marks, we chose one such variant near TSNARE1 and ADGRB1, rs4129585, to test for functional potential and assay differences that may drive the pathogenicity of the risk allele. We observed that the variant-containing sequence drives reporter expression in relevant neuronal populations in zebrafish. Next, we introduced each allele into human induced pluripotent cells and differentiated four isogenic clones homozygous for the risk allele and five clones homozygous for the non-risk allele into neural progenitor cells. Employing RNA sequencing, we found that the two alleles yield significant transcriptional differences in the expression of 109 genes at a false discovery rate (FDR) of <0.05 and 259 genes at a FDR of <0.1. We demonstrate that these genes are highly interconnected in pathways enriched for synaptic proteins, axon guidance, and regulation of synapse assembly. Exploration of genes near rs4129585 suggests that this variant does not regulate TSNARE1 transcripts, as previously thought, but may regulate the neighboring ADGRB1, a regulator of synaptogenesis. Our results suggest that rs4129585 is a functional common variant that functions in specific pathways likely involved in SCZ risk.

Maternal high-fat diet-induced microbiota changes are associated with alterations in embryonic brain metabolites and adolescent behaviour

The developing central nervous system is highly sensitive to nutrient changes during the perinatal period, emphasising the potential impact of alterations of maternal diet on offspring brain development and behaviour. A growing body of research implicates the gut microbiota in neurodevelopment and behaviour. Maternal overweight and obesity during the perinatal period has been linked to changes in neurodevelopment, plasticity and affective disorders in the offspring, with implications for microbial signals from the maternal gut. Here we investigate the impact of maternal high-fat diet (mHFD)-induced changes in microbial signals on offspring brain development, and neuroimmune signals, and the enduring effects on behaviour into adolescence. We first demonstrate that maternal caecal microbiota composition at term pregnancy (embryonic day 18: E18) differs significantly in response to maternal diet. Moreover, mHFD resulted in the upregulation of microbial genes in the maternal intestinal tissue linked to alterations in quinolinic acid synthesis and elevated kynurenine levels in the maternal plasma, both neuronal plasticity mediators related to glutamate metabolism. Metabolomics of mHFD embryonic brains at E18 also detected molecules linked to glutamate-glutamine cycle, including glutamic acid, glutathione disulphide and kynurenine. During adolescence, the mHFD offspring exhibited increased locomotor activity and anxiety-like behaviour in a sex-dependent manner, along with upregulation of glutamate-related genes compared to controls. Overall, our results demonstrate that maternal exposure to high-fat diet results in microbiota changes, behavioural imprinting, altered brain metabolism and glutamate signalling during critical developmental windows during the perinatal period.

MRBEE: A bias-corrected multivariable Mendelian randomization method

Mendelian randomization (MR) is an instrumental variable approach used to infer causal relationships between exposures and outcomes, which is becoming increasingly popular because of its ability to handle summary statistics from genome-wide association studies. However, existing MR approaches often suffer the bias from weak instrumental variables, horizontal pleiotropy and sample overlap. We introduce MRBEE (MR using bias-corrected estimating equation), a multivariable MR method capable of simultaneously removing weak instrument and sample overlap bias and identifying horizontal pleiotropy. Our extensive simulations and real data analyses reveal that MRBEE provides nearly unbiased estimates of causal effects, well-controlled type I error rates and higher power than comparably robust methods and is computationally efficient. Our real data analyses result in consistent causal effect estimates and offer valuable guidance for conducting multivariable MR studies, elucidating the roles of pleiotropy, and identifying total 42 horizontal pleiotropic loci missed previously that are associated with myopia, schizophrenia, and coronary artery disease.

Critical reasoning on the co-expression module QTL in the dorsolateral prefrontal cortex

Expression quantitative trait locus (eQTL) analysis is a popular method of gaining insight into the function of regulatory variation. While cis-eQTL resources have been instrumental in linking genome-wide association study variants to gene function, complex trait heritability may be additionally mediated by other forms of gene regulation. Toward this end, novel eQTL methods leverage gene co-expression (module-QTL) to investigate joint regulation of gene modules by single genetic variants. Here we broadly define a "module-QTL" as the association of a genetic variant with a summary measure of gene co-expression. This approach aims to reduce the multiple testing burden of a trans-eQTL search through the consolidation of gene-based testing and provide biological context to eQTLs shared between genes. In this article we provide an in-depth examination of the co-expression module eQTL (module-QTL) through literature review, theoretical investigation, and real-data application of the module-QTL to three large prefrontal cortex genotype-RNA sequencing datasets. We find module-QTLs in our study that are disease associated and reproducible are not additionally informative beyond cis- or trans-eQTLs for module genes. Through comparison to prior studies, we highlight promises and limitations of the module-QTL across study designs and provide recommendations for further investigation of the module-QTL framework.

Human brain proteome-wide association study provides insights into the genetic components of protein abundance in obesity

BACKGROUNDS

Genome-wide association studies have identified multiple genetic variants associated with obesity. However, most obesity-associated loci were waiting to be translated into new biological insights. Given the critical role of brain in obesity development, we sought to explore whether obesity-associated genetic variants could be mapped to brain protein abundances.

METHODS

We performed proteome-wide association studies (PWAS) and colocalization analyses to identify genes whose cis-regulated brain protein abundances were associated with obesity-related traits, including body fat percentage, trunk fat percentage, body mass index, visceral adipose tissue, waist circumference, and waist-to-hip ratio. We then assessed the druggability of the identified genes and conducted pathway enrichment analysis to explore their functional relevance. Finally, we evaluated the effects of the significant PWAS genes at the brain transcriptional level.

RESULTS

By integrating human brain proteomes from discovery (ROSMAP, N = 376) and validation datasets (BANNER, N = 198) with genome-wide summary statistics of obesity-related phenotypes (N ranged from 325,153 to 806,834), we identified 51 genes whose cis-regulated brain protein abundance was associated with obesity. These 51 genes were enriched in 11 metabolic processes, e.g., small molecule metabolic process and metabolic pathways. Fourteen of the 51 genes had high drug repurposing value. Ten of the 51 genes were also associated with obesity at the transcriptome level, suggesting that genetic variants likely confer risk of obesity by regulating mRNA expression and protein abundance of these genes.

CONCLUSIONS

Our study provides new insights into the genetic component of human brain protein abundance in obesity. The identified proteins represent promising therapeutic targets for future drug development.

DeepFace: Deep-learning-based framework to contextualize orofacial-cleft-related variants during human embryonic craniofacial development

Orofacial clefts (OFCs) are among the most common human congenital birth defects. Previous multiethnic studies have identified dozens of associated loci for both cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP). Although several nearby genes have been highlighted, the "casual" variants are largely unknown. Here, we developed DeepFace, a convolutional neural network model, to assess the functional impact of variants by SNP activity difference (SAD) scores. The DeepFace model is trained with 204 epigenomic assays from crucial human embryonic craniofacial developmental stages of post-conception week (pcw) 4 to pcw 10. The Pearson correlation coefficient between the predicted and actual values for 12 epigenetic features achieved a median range of 0.50-0.83. Specifically, our model revealed that SNPs significantly associated with OFCs tended to exhibit higher SAD scores across various variant categories compared to less related groups, indicating a context-specific impact of OFC-related SNPs. Notably, we identified six SNPs with a significant linear relationship to SAD scores throughout developmental progression, suggesting that these SNPs could play a temporal regulatory role. Furthermore, our cell-type specificity analysis pinpointed the trophoblast cell as having the highest enrichment of risk signals associated with OFCs. Overall, DeepFace can harness distal regulatory signals from extensive epigenomic assays, offering new perspectives for prioritizing OFC variants using contextualized functional genomic features. We expect DeepFace to be instrumental in accessing and predicting the regulatory roles of variants associated with OFCs, and the model can be extended to study other complex diseases or traits.

Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

Exploration on the potential efficacy and mechanism of methyl salicylate glycosides in the treatment of schizophrenia based on bioinformatics, molecular docking and dynamics simulation

The etiological and therapeutic complexities of schizophrenia (SCZ) persist, prompting exploration of anti-inflammatory therapy as a potential treatment approach. Methyl salicylate glycosides (MSGs), possessing a structural parent nucleus akin to aspirin, are being investigated for their therapeutic potential in schizophrenia. Utilizing bioinformation mining, network pharmacology, molecular docking and dynamics simulation, the potential value and mechanism of MSGs (including MSTG-A, MSTG-B, and Gaultherin) in the treatment of SCZ, as well as the underlying pathogenesis of the disorder, were examined. 581 differentially expressed genes related to SCZ were identified in patients and healthy individuals, with 349 up-regulated genes and 232 down-regulated genes. 29 core targets were characterized by protein-protein interaction (PPI) network, with the top 10 core targets being BDNF, VEGFA, PVALB, KCNA1, GRIN2A, ATP2B2, KCNA2, APOE, PPARGC1A and SCN1A. The pathogenesis of SCZ primarily involves cAMP signaling, neurodegenerative diseases and other pathways, as well as regulation of ion transmembrane transport. Molecular docking analysis revealed that the three candidates exhibited binding activity with certain targets with binding affinities ranging from -4.7 to -109.2 kcal/mol. MSTG-A, MSTG-B and Gaultherin show promise for use in the treatment of SCZ, potentially through their ability to modulate the expression of multiple genes involved in synaptic structure and function, ion transport, energy metabolism. Molecular dynamics simulation revealed good binding abilities between MSTG-A, MSTG-B, Gaultherin and ATP2B2. It suggests new avenues for further investigation in this area.

Differential interactions between gene expressions and stressors across the lifespan in major depressive disorder

BACKGROUND

Both genetic predispositions and exposures to stressors have collectively contributed to the development of major depressive disorder (MDD). To deep dive into their roles in MDD, our study aimed to examine which susceptible gene expression interacts with various dimensions of stressors in the MDD risk among a large population cohort.

METHODS

Data analyzed were from a longitudinal community-based cohort from Southwest Montreal, Canada (N = 1083). Latent profile models were used to identify distinct patterns of stressors for the study cohort. A transcriptome-wide association study (TWAS) method was performed to examine the interactive effects of three dimensions of stressors (threat, deprivation, and cumulative lifetime stress) and gene expression on the MDD risk in a total of 48 tissues from GTEx. Additional analyses were also conducted to further explore and specify these associations including colocalization, and fine-mapping analyses, in addition to enrichment analysis investigations based on TWAS.

RESULTS

We identified 3321 genes linked to MDD at the nominal p-value <0.05 and found that different patterns of stressors can amplify the genetic susceptibility to MDD. We also observed specific genes and pathways that interacted with deprivation and cumulative lifetime stressors, particularly in specific brain tissues including basal ganglia, prefrontal cortex, brain amygdala, brain cerebellum, brain cortex, and the whole blood. Colocalization analysis also identified these genes as having a high probability of sharing MDD causal variants.

LIMITATIONS

The study cohort was composed exclusively of individuals of Caucasians, which restricts the generalizability of the findings to other ethnic population groups.

CONCLUSIONS

The findings of the study unveiled significant interactions between potential tissue-specific gene expression × stressors in the MDD risk and shed light on the intricate etiological attributes of gene expression and specific stressors across the lifespan in MDD. These genetic and environmental attributes in MDD corroborate the vulnerability-stress theory and direct future stress research to have a closer examination of genetic predisposition and potential involvements of omics studies to specify the intricate relationships between genes and stressful environments.

Polygenic prediction of major depressive disorder and related traits in African ancestries UK Biobank participants

Genome-Wide Association Studies (GWAS) over-represent European ancestries, neglecting all other ancestry groups and low-income nations. Consequently, polygenic risk scores (PRS) more accurately predict complex traits in Europeans than African Ancestries groups. Very few studies have looked at the transferability of European-derived PRS for behavioural and mental health phenotypes to Africans. We assessed the comparative accuracy of depression PRS trained on European and African Ancestries GWAS studies to predict major depressive disorder (MDD) and related traits in African ancestry participants from the UK Biobank. UK Biobank participants were selected based on Principal component analysis clustering with an African genetic similarity reference population, MDD was assessed with the Composite International Diagnostic Interview (CIDI). PRS were computed using PRSice2 software using either European or African Ancestries GWAS summary statistics. PRS trained on European ancestry samples (246,363 cases) predicted case control status in Africans of the UK Biobank with similar accuracies (R2 = 2%, β = 0.32, empirical p-value = 0.002) to PRS trained on far much smaller samples of African Ancestries participants from 23andMe, Inc. (5045 cases, R² = 1.8%, β = 0.28, empirical p-value = 0.008). This suggests that prediction of MDD status from Africans to Africans had greater efficiency relative to discovery sample size than prediction of MDD from Europeans to Africans. Prediction of MDD status in African UK Biobank participants using GWAS findings of likely causal risk factors from European ancestries was non-significant. GWAS of MDD in European ancestries are inefficient for improving polygenic prediction in African samples; urgent MDD studies in Africa are needed.

Independent inheritance of cognition and bipolar disorder in a family sample

Cognitive deficits in people with bipolar disorder (BD) may be the result of the illness or its treatment, but they could also reflect genetic risk factors shared between BD and cognition. We investigated this question using empirical genetic relationships within a sample of patients with BD and their unaffected relatives. Participants with bipolar I, II, or schizoaffective disorder ("narrow" BD, n = 69), related mood disorders ("broad" BD, n = 135), and their clinically unaffected relatives (n = 227) completed five cognitive tests. General cognitive function (g) was quantified via principal components analysis (PCA). Heritability and genetic correlations were estimated with SOLAR-Eclipse. Participants with "narrow" or "broad" diagnoses showed deficits in g, although affect recognition was unimpaired. Cognitive performance was significantly heritable (h2 = 0.322 for g, p < 0.005). Coheritability between psychopathology and g was small (0.0184 for narrow and 0.0327 for broad) and healthy relatives of those with BD were cognitively unimpaired. In this family sample, cognitive deficits were present in participants with BD but were not explained by substantial overlaps in genetic determinants of mood and cognition. These findings support the view that cognitive deficits in BD are largely the result of the illness or its treatment.

Whole-exome sequencing identifies high-confidence genes for tic disorders in a Chinese Han population

BACKGROUND

Tic disorder (TD) is a polygenic neurodevelopmental disorder with high susceptibility. However, identifying high-confidence risk genes has been challenging due to poor replication across multiple studies.

METHODS

Whole-exome sequencing was performed on 390 TD patients and 372 unaffected individuals in a Chinese Han population. Analysis of variance, burden analysis and in silico prediction were used to identify candidate genes for TD. To facilitate data analysis and to focus on high-confidence genes, we defined a panel of 160 genes as known causal or candidate TD genes from previous studies. Gene enrichment and protein-protein interaction analysis were utilized to detect potential novel TD risk genes.

RESULTS

Totally, 14 variants across 12 known TD candidate genes were considered potential susceptibility variants. Ten variants across 10 known TD candidate genes were identified as potential disease-causing variants. Burden analysis identified variants of 28 known genes were significantly excess in TD patients. In addition, 354 previously unproven TD genes are over-represented in patients. Genes enriched in the PI3K-Akt signaling, sphingolipid metabolism and serotonergic synaptic pathways, as well as those interacting with FN1, were considered potential new candidate genes for TD.

CONCLUSIONS

This is the largest WES study focusing on TD patients in a Chinese Han population. Several variants recurring in our cohort were identified as high-confidence risk loci for TD. Moreover, we provided potential new risk genes that may be prioritized for further investigation.

Genetic neurodevelopmental clustering and dyslexia

Dyslexia is a learning difficulty with neurodevelopmental origins, manifesting as reduced accuracy and speed in reading and spelling. It is substantially heritable and frequently co-occurs with other neurodevelopmental conditions, particularly attention deficit-hyperactivity disorder (ADHD). Here, we investigate the genetic structure underlying dyslexia and a range of psychiatric traits using results from genome-wide association studies of dyslexia, ADHD, autism, anorexia nervosa, anxiety, bipolar disorder, major depressive disorder, obsessive compulsive disorder, schizophrenia, and Tourette syndrome. Genomic Structural Equation Modelling (GenomicSEM) showed heightened support for a model consisting of five correlated latent genomic factors described as: F1) compulsive disorders (including obsessive-compulsive disorder, anorexia nervosa, Tourette syndrome), F2) psychotic disorders (including bipolar disorder, schizophrenia), F3) internalising disorders (including anxiety disorder, major depressive disorder), F4) neurodevelopmental traits (including autism, ADHD), and F5) attention and learning difficulties (including ADHD, dyslexia). ADHD loaded more strongly on the attention and learning difficulties latent factor (F5) than on the neurodevelopmental traits latent factor (F4). The attention and learning difficulties latent factor (F5) was positively correlated with internalising disorders (.40), neurodevelopmental traits (.25) and psychotic disorders (.17) latent factors, and negatively correlated with the compulsive disorders (-.16) latent factor. These factor correlations are mirrored in genetic correlations observed between the attention and learning difficulties latent factor and other cognitive, psychological and wellbeing traits. We further investigated genetic variants underlying both dyslexia and ADHD, which implicated 49 loci (40 not previously found in GWAS of the individual traits) mapping to 174 genes (121 not found in GWAS of individual traits) as potential pleiotropic variants. Our study confirms the increased genetic relation between dyslexia and ADHD versus other psychiatric traits and uncovers novel pleiotropic variants affecting both traits. In future, analyses including additional co-occurring traits such as dyscalculia and dyspraxia will allow a clearer definition of the attention and learning difficulties latent factor, yielding further insights into factor structure and pleiotropic effects.

Air pollutants, genetic susceptibility, and incident schizophrenia in later life: A prospective study in the UK Biobank

OBJECTIVE

Air pollution has been linked to multiple psychiatric disorders, but little is known on its long-term association with schizophrenia. The interaction between air pollution and genetic susceptibility on incident schizophrenia has never been reported. We aimed to explore the associations between long-term air pollution exposure and late-onset schizophrenia and evaluate whether genetic susceptibility could modify the association.

METHODS

This population-based prospective cohort study included 437,802 middle-aged and elderly individuals free of schizophrenia at baseline in the UK Biobank. Land use regression models were applied in the estimation of the annual average concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter (PM2.5), and inhalable particulate matter (PM10) at residence. The associations between air pollutants and schizophrenia were evaluated by using Cox proportional hazard models. A polygenic risk score of schizophrenia was constructed for exploring potential interaction of air pollutants with genetic susceptibility.

RESULTS

An interquartile range increase in PM2.5, PM10, NO2, and NOx was associated with the hazard ratios (HR) for incident schizophrenia at 1.19, 1.16, 1.22, and 1.09, respectively. The exposure-response curves for the association of air pollution with incident schizophrenia were approximately linear. There are additive interactions of air pollution score (APS), PM10, NO2, and NOx with genetic risk. Specifically, compared with participants with low genetic susceptibility and low APS, the HR was 3.23 for individuals with high genetic risk and high APS, among which 0.49 excess risk could be attributed to the additive interaction, accounting for 15 % of the schizophrenia risk.

CONCLUSION

This large-scale, prospective cohort study conveys the first-hand evidence that long-term air pollution exposure could elevate schizophrenia incidence in later life, especially for individuals with higher genetic risks. The findings highlight the importance of improving air quality for preventing the late-onset schizophrenia in an aging era, especially among those with high genetic risks.

Multi-objective context-guided consensus of a massive array of techniques for the inference of Gene Regulatory Networks

BACKGROUND AND OBJECTIVE

Gene Regulatory Network (GRN) inference is a fundamental task in biology and medicine, as it enables a deeper understanding of the intricate mechanisms of gene expression present in organisms. This bioinformatics problem has been addressed in the literature through multiple computational approaches. Techniques developed for inferring from expression data have employed Bayesian networks, ordinary differential equations (ODEs), machine learning, information theory measures and neural networks, among others. The diversity of implementations and their respective customization have led to the emergence of many tools and multiple specialized domains derived from them, understood as subsets of networks with specific characteristics that are challenging to detect a priori. This specialization has introduced significant uncertainty when choosing the most appropriate technique for a particular dataset. This proposal, named MO-GENECI, builds upon the basic idea of the previous proposal GENECI and optimizes consensus among different inference techniques, through a carefully refined multi-objective evolutionary algorithm guided by various objective functions, linked to the biological context at hand.

METHODS

MO-GENECI has been tested on an extensive and diverse academic benchmark of 106 gene regulatory networks from multiple sources and sizes. The evaluation of MO-GENECI compared its performance to individual techniques using key metrics (AUROC and AUPR) for gene regulatory network inference. Friedman's statistical ranking provided an ordered classification, followed by non-parametric Holm tests to determine statistical significance.

RESULTS

MO-GENECI's Pareto front approximation facilitates easy selection of an appropriate solution based on generic input data characteristics. The best solution consistently emerged as the winner in all statistical tests, and in many cases, the median precision solution showed no statistically significant difference compared to the winner.

CONCLUSIONS

MO-GENECI has not only demonstrated achieving more accurate results than individual techniques, but has also overcome the uncertainty associated with the initial choice due to its flexibility and adaptability. It is shown intelligently to select the most suitable techniques for each case. The source code is hosted in a public repository at GitHub under MIT license: https://github.com/AdrianSeguraOrtiz/MO-GENECI. Moreover, to facilitate its installation and use, the software associated with this implementation has been encapsulated in a Python package available at PyPI: https://pypi.org/project/geneci/.