Cross-Species Convergence of Brain Transcriptomic and Epigenomic Findings in Posttraumatic Stress Disorder: A Systematic Review

Introduction

Posttraumatic stress disorder (PTSD) is a complex multifactorial disorder influenced by the interaction of genetic and environmental factors. Analyses of epigenomic and transcriptomic modifications may help to dissect the biological factors underlying the gene-environment interplay in PTSD. To date, most human PTSD epigenetics studies have used peripheral tissue, and these findings have complex and poorly understood relationships to brain alterations. Studies examining brain tissue may help characterize the brain-specific transcriptomic and epigenomic profiles of PTSD. In this review, we compiled and integrated brain-specific molecular findings of PTSD from humans and animals.

Methods

A systematic literature search according to the PRISMA criteria was performed to identify transcriptomic and epigenomic studies of PTSD, focusing on brain tissue from human postmortem samples or animal-stress paradigms.

Results

Gene- and pathway-level convergence analyses revealed PTSD-dysregulated genes and biological pathways across brain regions and species. A total of 243 genes converged across species, with 17 of them significantly enriched for PTSD. Chemical synaptic transmission and signaling by G-protein-coupled receptors were consistently enriched across omics and species.

Discussion

Our findings point out dysregulated genes highly replicated across PTSD studies in humans and animal models and suggest a potential role for the corticotropin-releasing hormone/orexin pathway in PTSD's pathophysiology. Further, we highlight current knowledge gaps and limitations and recommend future directions to address them.

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies.

THUNDER: A reference-free deconvolution method to infer cell type proportions from bulk Hi-C data

Hi-C data provide population averaged estimates of three-dimensional chromatin contacts across cell types and states in bulk samples. Effective analysis of Hi-C data entails controlling for the potential confounding factor of differential cell type proportions across heterogeneous bulk samples. We propose a novel unsupervised deconvolution method for inferring cell type composition from bulk Hi-C data, the Two-step Hi-c UNsupervised DEconvolution appRoach (THUNDER). We conducted extensive simulations to test THUNDER based on combining two published single-cell Hi-C (scHi-C) datasets. THUNDER more accurately estimates the underlying cell type proportions compared to reference-free methods (e.g., TOAST, and NMF) and is more robust than reference-dependent methods (e.g. MuSiC). We further demonstrate the practical utility of THUNDER to estimate cell type proportions and identify cell-type-specific interactions in Hi-C data from adult human cortex tissue samples. THUNDER will be a useful tool in adjusting for varying cell type composition in population samples, facilitating valid and more powerful downstream analysis such as differential chromatin organization studies. Additionally, THUNDER estimated contact profiles provide a useful exploratory framework to investigate cell-type-specificity of the chromatin interactome while experimental data is still rare.

Bayesian modeling of skewed X inactivation in genetically diverse mice identifies a novel Xce allele associated with copy number changes

Female mammals are functional mosaics of their parental X-linked gene expression due to X chromosome inactivation (XCI). This process inactivates one copy of the X chromosome in each cell during embryogenesis and that state is maintained clonally through mitosis. In mice, the choice of which parental X chromosome remains active is determined by the X chromosome controlling element (Xce), which has been mapped to a 176-kb candidate interval. A series of functional Xce alleles has been characterized or inferred for classical inbred strains based on biased, or skewed, inactivation of the parental X chromosomes in crosses between strains. To further explore the function structure basis and location of the Xce, we measured allele-specific expression of X-linked genes in a large population of F1 females generated from Collaborative Cross (CC) strains. Using published sequence data and applying a Bayesian "Pólya urn" model of XCI skew, we report two major findings. First, inter-individual variability in XCI suggests mouse epiblasts contain on average 20-30 cells contributing to brain. Second, CC founder strain NOD/ShiLtJ has a novel and unique functional allele, Xceg, that is the weakest in the Xce allelic series. Despite phylogenetic analysis confirming that NOD/ShiLtJ carries a haplotype almost identical to the well-characterized C57BL/6J (Xceb), we observed unexpected patterns of XCI skewing in females carrying the NOD/ShiLtJ haplotype within the Xce. Copy number variation is common at the Xce locus and we conclude that the observed allelic series is a product of independent and recurring duplications shared between weak Xce alleles.

Robust Hi-C Maps of Enhancer-Promoter Interactions Reveal the Function of Non-coding Genome in Neural Development and Diseases

Genome-wide mapping of chromatin interactions at high resolution remains experimentally and computationally challenging. Here we used a low-input "easy Hi-C" protocol to map the 3D genome architecture in human neurogenesis and brain tissues and also demonstrated that a rigorous Hi-C bias-correction pipeline (HiCorr) can significantly improve the sensitivity and robustness of Hi-C loop identification at sub-TAD level, especially the enhancer-promoter (E-P) interactions. We used HiCorr to compare the high-resolution maps of chromatin interactions from 10 tissue or cell types with a focus on neurogenesis and brain tissues. We found that dynamic chromatin loops are better hallmarks for cellular differentiation than compartment switching. HiCorr allowed direct observation of cell-type- and differentiation-specific E-P aggregates spanning large neighborhoods, suggesting a mechanism that stabilizes enhancer contacts during development. Interestingly, we concluded that Hi-C loop outperforms eQTL in explaining neurological GWAS results, revealing a unique value of high-resolution 3D genome maps in elucidating the disease etiology.

Genome-wide association study identifies eight risk loci and implicates metabo-psychiatric origins for anorexia nervosa

Characterized primarily by a low body-mass index, anorexia nervosa is a complex and serious illness1, affecting 0.9-4% of women and 0.3% of men2-4, with twin-based heritability estimates of 50-60%5. Mortality rates are higher than those in other psychiatric disorders6, and outcomes are unacceptably poor7. Here we combine data from the Anorexia Nervosa Genetics Initiative (ANGI)8,9 and the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) and conduct a genome-wide association study of 16,992 cases of anorexia nervosa and 55,525 controls, identifying eight significant loci. The genetic architecture of anorexia nervosa mirrors its clinical presentation, showing significant genetic correlations with psychiatric disorders, physical activity, and metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of common variants associated with body-mass index. These results further encourage a reconceptualization of anorexia nervosa as a metabo-psychiatric disorder. Elucidating the metabolic component is a critical direction for future research, and paying attention to both psychiatric and metabolic components may be key to improving outcomes.

LEGAL AND ETHICAL IMPLICATIONS OF CRISPR APPLICATIONS IN PSYCHIATRY

Gene-environment interactions play a key role in how psychiatric disorders manifest and develop. Psychiatric genetics researchers are making progress in identifying genomic correlates of many disorders. And recently, the field of genetics has given rise to a technology that many claim will revolutionize the biological sciences and propel the field into a transformative phase: the powerful gene-editing tool known as CRISPR-Cas9. This Article illustrates which psychiatric conditions are likely to make an attractive target for CRISPR as the technology evolves and CRISPR therapies becomes a viable tool to manage or prevent disorders in a clinical setting. We examine the potential scientific and clinical challenges of applying CRISPR in the mental health context, along with the regulatory, ethical, and legal issues that might arise as a consequence of these applications.

Integrative functional genomic analysis of human brain development and neuropsychiatric risks

To broaden our understanding of human neurodevelopment, we profiled transcriptomic and epigenomic landscapes across brain regions and/or cell types for the entire span of prenatal and postnatal development. Integrative analysis revealed temporal, regional, sex, and cell type-specific dynamics. We observed a global transcriptomic cup-shaped pattern, characterized by a late fetal transition associated with sharply decreased regional differences and changes in cellular composition and maturation, followed by a reversal in childhood-adolescence, and accompanied by epigenomic reorganizations. Analysis of gene coexpression modules revealed relationships with epigenomic regulation and neurodevelopmental processes. Genes with genetic associations to brain-based traits and neuropsychiatric disorders (including MEF2C, SATB2, SOX5, TCF4, and TSHZ3) converged in a small number of modules and distinct cell types, revealing insights into neurodevelopment and the genomic basis of neuropsychiatric risks.

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on ∼150,000 individuals give a higher accuracy than LDSC estimates based on ∼400,000 individuals (from combined meta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.

Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression and imply that a continuous measure of risk underlies the clinical phenotype.

Activity-dependent p25 generation regulates synaptic plasticity and Aβ-induced cognitive impairment

Cyclin-dependent kinase 5 regulates numerous neuronal functions with its activator, p35. Under neurotoxic conditions, p35 undergoes proteolytic cleavage to liberate p25, which has been implicated in various neurodegenerative diseases. Here, we show that p25 is generated following neuronal activity under physiological conditions in a GluN2B- and CaMKIIα-dependent manner. Moreover, we developed a knockin mouse model in which endogenous p35 is replaced with a calpain-resistant mutant p35 (Δp35KI) to prevent p25 generation. The Δp35KI mice exhibit impaired long-term depression and defective memory extinction, likely mediated through persistent GluA1 phosphorylation at Ser845. Finally, crossing the Δp35KI mice with the 5XFAD mouse model of Alzheimer's disease (AD) resulted in an amelioration of β-amyloid (Aβ)-induced synaptic depression and cognitive impairment. Together, these results reveal a physiological role of p25 production in synaptic plasticity and memory and provide new insights into the function of p25 in Aβ-associated neurotoxicity and AD-like pathology.

The genomics of schizophrenia: update and implications

Schizophrenia is strongly familial yet rarely (if ever) exhibits classical Mendelian inheritance patterns. The advent of large-scale genotyping and sequencing projects has yielded large data sets with higher statistical power in an effort to uncover new associations with schizophrenia. Here, we review the challenges in dissecting the genetics of schizophrenia and provide an update of the current understanding of the underlying genomics. We discuss the breadth of susceptibility alleles, including those that may occur with low frequency and high disease risk, such as the 22q11.2 hemideletion, as well as alleles that may occur with greater frequency but convey a lower risk of schizophrenia, such as variants in genes encoding subunits of the voltage-gated L-type calcium channel. Finally, we provide an overview of the clinical implications for the diagnosis and treatment of schizophrenia based on progress in understanding the underlying genetic basis.