Transcriptome association studies of neuropsychiatric traits in African Americans implicate PRMT7 in schizophrenia

Critical Roles of Protein Arginine Methylation in the Central Nervous System

A remarkable post-transitional modification of both histones and non-histone proteins is arginine methylation. Methylation of arginine residues is crucial for a wide range of cellular process, including signal transduction, DNA repair, gene expression, mRNA splicing, and protein interaction. Arginine methylation is modulated by arginine methyltransferases and demethylases, like protein arginine methyltransferase (PRMTs) and Jumonji C (JmjC) domain containing (JMJD) proteins. Symmetric dimethylarginine and asymmetric dimethylarginine, metabolic products of the PRMTs and JMJD proteins, can be changed by abnormal expression of these proteins. Many pathologies including cancer, inflammation and immune responses have been closely linked to aberrant arginine methylation. Currently, the majority of the literature discusses the substrate specificity and function of arginine methylation in the pathogenesis and prognosis of cancers. Numerous investigations on the roles of arginine methylation in the central nervous system (CNS) have so far been conducted. In this review, we display the biochemistry of arginine methylation and provide an overview of the regulatory mechanism of arginine methyltransferases and demethylases. We also highlight physiological functions of arginine methylation in the CNS and the significance of arginine methylation in a variety of neurological diseases such as brain cancers, neurodegenerative diseases and neurodevelopmental disorders. Furthermore, we summarize PRMT inhibitors and molecular functions of arginine methylation. Finally, we pose important questions that require further research to comprehend the roles of arginine methylation in the CNS and discover more effective targets for the treatment of neurological diseases.

PRMT7 can prevent neurovascular uncoupling, blood-brain barrier permeability, and mitochondrial dysfunction in repetitive and mild traumatic brain injury

Mild traumatic brain injury (TBI) comprises the largest percentage of TBI-related injuries, with pathophysiological and functional deficits that persist in a subset of TBI patients. In our three-hit paradigm of repetitive and mild traumatic brain injury (rmTBI), we observed neurovascular uncoupling via decreased red blood cell velocity, microvessel diameter, and leukocyte rolling velocity 3 days post-rmTBI via intra-vital two-photon laser scanning microscopy. Furthermore, our data suggest increased blood-brain barrier (BBB) permeability (leakage), with corresponding decrease in junctional protein expression post-rmTBI. Mitochondrial oxygen consumption rates (measured via Seahorse XFe24) were also altered 3 days post-rmTBI, along with disrupted mitochondrial dynamics of fission and fusion. Overall, these pathophysiological findings correlated with decreased protein arginine methyltransferase 7 (PRMT7) protein levels and activity post-rmTBI. Here, we increased PRMT7 levels in vivo to assess the role of the neurovasculature and mitochondria post-rmTBI. In vivo overexpression of PRMT7 using a neuronal specific AAV vector led to restoration of neurovascular coupling, prevented BBB leakage, and promoted mitochondrial respiration, altogether to suggest a protective and functional role of PRMT7 in rmTBI.

The genetic architecture of schizophrenia: review of large-scale genetic studies

Schizophrenia is a complex and often chronic psychiatric disorder with high heritability. Diagnosis of schizophrenia is still made clinically based on psychiatric symptoms; no diagnostic tests or biomarkers are available. Pathophysiology-based diagnostic scheme and treatments are also not available. Elucidation of the pathogenesis is needed for development of pathology-based diagnostics and treatments. In the past few decades, genetic research has made substantial advances in our understanding of the genetic architecture of schizophrenia. Rare copy number variations (CNVs) and rare single-nucleotide variants (SNVs) detected by whole-genome CNV analysis and whole-genome/-exome sequencing analysis have provided the great advances. Common single-nucleotide polymorphisms (SNPs) detected by large-scale genome-wide association studies have also provided important information. Large-scale genetic studies have been revealed that both rare and common genetic variants play crucial roles in this disorder. In this review, we focused on CNVs, SNVs, and SNPs, and discuss the latest research findings on the pathogenesis of schizophrenia based on these genetic variants. Rare variants with large effect sizes can provide mechanistic hypotheses. CRISPR-based genetics approaches and induced pluripotent stem cell technology can facilitate the functional analysis of these variants detected in patients with schizophrenia. Recent advances in long-read sequence technology are expected to detect variants that cannot be detected by short-read sequence technology. Various studies that bring together data from common variant and transcriptomic datasets provide biological insight. These new approaches will provide additional insight into the pathophysiology of schizophrenia and facilitate the development of pathology-based therapeutics.

A novel multi-class classification model for schizophrenia, bipolar disorder and healthy controls using comprehensive transcriptomic data

Two common psychiatric disorders, schizophrenia (SCZ) and bipolar disorder (BP), confer lifelong disability and collectively affect 2% of the world population. Because the diagnosis of psychiatry is based only on symptoms, developing more effective methods for the diagnosis of psychiatric disorders is a major international public health priority. Furthermore, SCZ and BP overlap considerably in terms of symptoms and risk genes. Therefore, the clarity of the underlying etiology and pathology remains lacking for these two disorders. Although many studies have been conducted, a classification model with higher accuracy and consistency was found to still be necessary for accurate diagnoses of SCZ and BP. In this study, a comprehensive dataset was combined from five independent transcriptomic studies. This dataset comprised 120 patients with SCZ, 101 patients with BP, and 149 healthy subjects. The partial least squares discriminant analysis (PLS-DA) method was applied to identify the gene signature among multiple groups, and 341 differentially expressed genes (DEGs) were identified. Then, the disease relevance of these DEGs was systematically performed, including (α) the great disease relevance of the identified signature, (β) the hub genes of the protein-protein interaction network playing a key role in psychiatric disorders, and (γ) gene ontology terms and enriched pathways playing a key role in psychiatric disorders. Finally, a popular multi-class classifier, support vector machine (SVM), was applied to construct a novel multi-class classification model using the identified signature for SCZ and BP. Using the independent test sets, the classification capacity of this multi-class model was assessed, which showed this model had a strong classification ability.

Prmt7 Downregulation in Mouse Spermatogonia Functions through miR-877-3p/Col6a3

Protein arginine methyltransferases 7 (Prmt7) is expressed in male germ cells, including primordial germ cells, gonocytes, and spermatogonia. Our previous study demonstrated that Prmt7 downregulation reduced the proliferation of GC-1 cells (a cell line of mouse immortalized spermatogonia). However, how Prmt7 regulates spermatogonial proliferation through miRNA and the target gene remains elusive. Here, we experimentally reduced the Prmt7 expression in the GC-1 cells and subjected them to miRNA sequencing to explore the miRNA profile and its Prmt7-responsive members. In total, 48 differentially expressed miRNAs (DEmiRNAs), including 36 upregulated and 12 downregulated miRNAs, were identified. After verifying the validity of sequencing results through qRT-PCR assays in randomly selected DEmiRNAs, we predicted the target genes of these DEmiRNAs. Next, we combined DEmiRNA target genes and previously identified differentially expressed genes between Prmt7 knockdown and control groups of GC-1 cells, which resulted in seven miRNA/target gene pairs. Among these miRNA/target gene pairs, we further detected the expression of Col6a3 (collagen type VI alpha 3) as the target gene of mmu-miR-877-3p. The results suggested that Prmt7 downregulation in mouse spermatogonia might function through miR-877-3p/Col6a3. Overall, these findings provide new insights into the role of Prmt7 in male germ cell development through miRNA and target genes.

Potential diagnostic biomarkers for schizophrenia

Schizophrenia (SCH) is a complex and severe mental disorder with high prevalence, disability, mortality and carries a heavy disease burden, the lifetime prevalence of SCH is around 0.7%-1.0%, which has a profound impact on the individual and society. In the clinical practice of SCH, key problems such as subjective diagnosis, experiential treatment, and poor overall prognosis are still challenging. In recent years, some exciting discoveries have been made in the research on objective biomarkers of SCH, mainly focusing on genetic susceptibility genes, metabolic indicators, immune indices, brain imaging, electrophysiological characteristics. This review aims to summarize the biomarkers that may be used for the prediction and diagnosis of SCH.

PRMT7: A Pivotal Arginine Methyltransferase in Stem Cells and Development

Protein arginine methylation is a posttranslational modification catalyzed by protein arginine methyltransferases (PRMTs), which play critical roles in many biological processes. To date, nine PRMT family members, namely, PRMT1, 2, 3, 4, 5, 6, 7, 8, and 9, have been identified in mammals. Among them, PRMT7 is a type III PRMT that can only catalyze the formation of monomethylarginine and plays pivotal roles in several kinds of stem cells. It has been reported that PRMT7 is closely associated with embryonic stem cells, induced pluripotent stem cells, muscle stem cells, and human cancer stem cells. PRMT7 deficiency or mutation led to severe developmental delay in mice and humans, which is possibly due to its crucial functions in stem cells. Here, we surveyed and summarized the studies on PRMT7 in stem cells and development in mice and humans and herein provide a discussion of the underlying molecular mechanisms. Furthermore, we also discuss the roles of PRMT7 in cancer, adipogenesis, male reproduction, cellular stress, and cellular senescence, as well as the future perspectives of PRMT7-related studies. Overall, PRMT7 mediates the proliferation and differentiation of stem cells. Deficiency or mutation of PRMT7 causes developmental delay, including defects in skeletal muscle, bone, adipose tissues, neuron, and male reproduction. A better understanding of the roles of PRMT7 in stem cells and development as well as the underlying mechanisms will provide information for the development of strategies for in-depth research of PRMT7 and stem cells as well as their applications in life sciences and medicine.

Genetic contributions to bipolar disorder: current status and future directions

Bipolar disorder (BD) is a highly heritable mental disorder and is estimated to affect about 50 million people worldwide. Our understanding of the genetic etiology of BD has greatly increased in recent years with advances in technology and methodology as well as the adoption of international consortiums and large population-based biobanks. It is clear that BD is also highly heterogeneous and polygenic and shows substantial genetic overlap with other psychiatric disorders. Genetic studies of BD suggest that the number of associated loci is expected to substantially increase in larger future studies and with it, improved genetic prediction of the disorder. Still, a number of challenges remain to fully characterize the genetic architecture of BD. First among these is the need to incorporate ancestrally-diverse samples to move research away from a Eurocentric bias that has the potential to exacerbate health disparities already seen in BD. Furthermore, incorporation of population biobanks, registry data, and electronic health records will be required to increase the sample size necessary for continued genetic discovery, while increased deep phenotyping is necessary to elucidate subtypes within BD. Lastly, the role of rare variation in BD remains to be determined. Meeting these challenges will enable improved identification of causal variants for the disorder and also allow for equitable future clinical applications of both genetic risk prediction and therapeutic interventions.

Genetic architecture of schizophrenia: a review of major advancements

Schizophrenia is a severe psychiatric disorder with high heritability. Consortia efforts and technological advancements have led to a substantial increase in knowledge of the genetic architecture of schizophrenia over the past decade. In this article, we provide an overview of the current understanding of the genetics of schizophrenia, outline remaining challenges, and summarise future directions of research. World-wide collaborations have resulted in genome-wide association studies (GWAS) in over 56 000 schizophrenia cases and 78 000 controls, which identified 176 distinct genetic loci. The latest GWAS from the Psychiatric Genetics Consortium, available as a pre-print, indicates that 270 distinct common genetic loci have now been associated with schizophrenia. Polygenic risk scores can currently explain around 7.7% of the variance in schizophrenia case-control status. Rare variant studies have implicated eight rare copy-number variants, and an increased burden of loss-of-function variants in SETD1A, as increasing the risk of schizophrenia. The latest exome sequencing study, available as a pre-print, implicates a burden of rare coding variants in a further nine genes. Gene-set analyses have demonstrated significant enrichment of both common and rare genetic variants associated with schizophrenia in synaptic pathways. To address current challenges, future genetic studies of schizophrenia need increased sample sizes from more diverse populations. Continued expansion of international collaboration will likely identify new genetic regions, improve fine-mapping to identify causal variants, and increase our understanding of the biology and mechanisms of schizophrenia.

Environmental Risk Factors for Schizophrenia and Bipolar Disorder and Their Relationship to Genetic Risk: Current Knowledge and Future Directions

Schizophrenia (SZ) and bipolar disorder (BD) are severe psychiatric disorders which result from complex interplay between genetic and environmental factors. It is well-established that they are highly heritable disorders, and considerable progress has been made identifying their shared and distinct genetic risk factors. However, the 15-40% of risk that is derived from environmental sources is less definitively known. Environmental factors that have been repeatedly investigated and often associated with SZ include: obstetric complications, infections, winter or spring birth, migration, urban living, childhood adversity, and cannabis use. There is evidence that childhood adversity and some types of infections are also associated with BD. Evidence for other risk factors in BD is weaker due to fewer studies and often smaller sample sizes. Relatively few environmental exposures have ever been examined for SZ or BD, and additional ones likely remain to be discovered. A complete picture of how genetic and environmental risk factors confer risk for these disorders requires an understanding of how they interact. Early gene-by-environment interaction studies for both SZ and BD often involved candidate genes and were underpowered. Larger samples with genome-wide data and polygenic risk scores now offer enhanced prospects to reveal genetic interactions with environmental exposures that contribute to risk for these disorders. Overall, although some environmental risk factors have been identified for SZ, few have been for BD, and the extent to which these account for the total risk from environmental sources remains unknown. For both disorders, interactions between genetic and environmental risk factors are also not well understood and merit further investigation. Questions remain regarding the mechanisms by which risk factors exert their effects, and the ways in which environmental factors differ by sex. Concurrent investigations of environmental and genetic risk factors in SZ and BD are needed as we work toward a more comprehensive understanding of the ways in which these disorders arise.

Genotype-Based Gene Expression in Colon Tissue-Prediction Accuracy and Relationship with the Prognosis of Colorectal Cancer Patients

Colorectal cancer (CRC) survival has environmental and inherited components. The expression of specific genes can be inferred based on individual genotypes-so called expression quantitative trait loci. In this study, we used the PrediXcan method to predict gene expression in normal colon tissue using individual genotype data from 91 CRC patients and examined the correlation ρ between predicted and measured gene expression levels. Out of 5434 predicted genes, 58% showed a negative ρ value and only 16% presented a ρ higher than 0.10. We subsequently investigated the association between genotype-based gene expression in colon tissue for genes with ρ > 0.10 and survival of 4436 CRC patients. We identified an inverse association between the predicted expression of ARID3B and CRC-specific survival for patients with a body mass index greater than or equal to 30 kg/m2 (HR (hazard ratio) = 0.66 for an expression higher vs. lower than the median, p = 0.005). This association was validated using genotype and clinical data from the UK Biobank (HR = 0.74, p = 0.04). In addition to the identification of ARID3B expression in normal colon tissue as a candidate prognostic biomarker for obese CRC patients, our study illustrates the challenges of genotype-based prediction of gene expression, and the advantage of reassessing the prediction accuracy in a subset of the study population using measured gene expression data.