From genetics to biology: advancing mental health research in the Genomics ERA

Epigenetics, Nutrition, and the Brain: Improving Mental Health through Diet

The relationship between nutrition and brain health is intricate. Studies suggest that nutrients during early life impact not only human physiology but also mental health. Although the exact molecular mechanisms that depict this relationship remain unclear, there are indications that environmental factors such as eating, lifestyle habits, stress, and physical activity, influence our genes and modulate their function by epigenetic mechanisms to shape mental health outcomes. Epigenetic mechanisms act as crucial link between genes and environmental influences, proving that non-genetic factors could have enduring effects on the epigenome and influence health trajectories. We review studies that demonstrated an epigenetic mechanism of action of nutrition on mental health, focusing on the role of specific micronutrients during critical stages of brain development. The methyl-donor micronutrients of the one-carbon metabolism, such as choline, betaine, methionine, folic acid, VitB6 and VitB12 play critical roles in various physiological processes, including DNA and histone methylation. These micronutrients have been shown to alter gene function and susceptibility to diseases including mental health and metabolic disorders. Understanding how micronutrients influence metabolic genes in humans can lead to the implementation of early nutritional interventions to reduce the risk of developing metabolic and mental health disorders later in life.

Gene and schizophrenia in the pregenome and postgenome-wide association studies era: a bibliometric analysis and network visualization

This study aimed to perform a bibliometric analysis on genetic studies in schizophrenia in the pregenome-wide association studies (GWAS) and post-GWAS era. We searched the literature on genes and schizophrenia using the Scopus database. The documents increased with time, especially after the human genome project and International HapMap Project, with the highest citation in 2008. The top occurrence author keywords were discovered to be different in the pre-GWAS and post-GWAS eras, reflecting the progress of genetic studies connected to schizophrenia. Emerging keywords highlighted a trend towards an application of precision medicine, showing an interplay of environmental exposures as well as genetic factors in schizophrenia pathogenesis, progression, and response to therapy. In conclusion, the gene and schizophrenia literature has grown rapidly after the human genome project, and the temporal variation in the author keywords pattern reflects the trend of genetic studies related to schizophrenia in the pre-GWAS and post-GWAS era.

Modeling common and rare genetic risk factors of neuropsychiatric disorders in human induced pluripotent stem cells

Recent genome-wide association studies (GWAS) and whole-exome sequencing of neuropsychiatric disorders, especially schizophrenia, have identified a plethora of common and rare disease risk variants/genes. Translating the mounting human genetic discoveries into novel disease biology and more tailored clinical treatments is tied to our ability to causally connect genetic risk variants to molecular and cellular phenotypes. When combined with the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated (Cas) nuclease-mediated genome editing system, human induced pluripotent stem cell (hiPSC)-derived neural cultures (both 2D and 3D organoids) provide a promising tractable cellular model for bridging the gap between genetic findings and disease biology. In this review, we first conceptualize the advances in understanding the disease polygenicity and convergence from the past decade of iPSC modeling of different types of genetic risk factors of neuropsychiatric disorders. We then discuss the major cell types and cellular phenotypes that are most relevant to neuropsychiatric disorders in iPSC modeling. Finally, we critically review the limitations of iPSC modeling of neuropsychiatric disorders and outline the need for implementing and developing novel methods to scale up the number of iPSC lines and disease risk variants in a systematic manner. Sufficiently scaled-up iPSC modeling and a better functional interpretation of genetic risk variants, in combination with cutting-edge CRISPR/Cas9 gene editing and single-cell multi-omics methods, will enable the field to identify the specific and convergent molecular and cellular phenotypes in precision for neuropsychiatric disorders.

RCL1 copy number variants are associated with a range of neuropsychiatric phenotypes

Mendelian and early-onset severe psychiatric phenotypes often involve genetic variants having a large effect, offering opportunities for genetic discoveries and early therapeutic interventions. Here, the index case is an 18-year-old boy, who at 14 years of age had a decline in cognitive functioning over the course of a year and subsequently presented with catatonia, auditory and visual hallucinations, paranoia, aggression, mood dysregulation, and disorganized thoughts. Exome sequencing revealed a stop-gain mutation in RCL1 (NM_005772.4:c.370 C > T, p.Gln124Ter), encoding an RNA 3'-terminal phosphate cyclase-like protein that is highly conserved across eukaryotic species. Subsequent investigations across two academic medical centers identified eleven additional cases of RCL1 copy number variations (CNVs) with varying neurodevelopmental or psychiatric phenotypes. These findings suggest that dosage variation of RCL1 contributes to a range of neurological and clinical phenotypes.

Analysis of Four Polymorphisms Located at the Promoter of the Estrogen Receptor Alpha ESR1 Gene in a Population With Gender Incongruence

INTRODUCTION

Gender incongruence defines a state in which individuals feel discrepancy between the sex assigned at birth and their gender. Some of these people make a social transition from male to female (trans women) or from female to male (trans men). By contrast, the word cisgender describes a person whose gender identity is consistent with their sex assigned at birth.

AIM

To analyze the implication of the estrogen receptor α gene (ESR1) in the genetic basis of gender incongruence.

MAIN OUTCOME MEASURES

Polymorphisms rs9478245, rs3138774, rs2234693, rs9340799.

METHOD

We carried out the analysis of 4 polymorphisms located at the promoter of the ESR1 gene (C1 = rs9478245, C2 = rs3138774, C3 = rs2234693, and C4 = rs9340799) in a population of 273 trans women, 226 trans men, and 537 cis gender controls. For SNP polymorphisms, the allele and genotype frequencies were analyzed by χ² test. The strength of the SNP associations with gender incongruence was measured by binary logistic regression. For the STR polymorphism, the mean number of repeats were analyzed by the Mann-Whitney U test. Measurement of linkage disequilibrium and haplotype frequencies were also performed.

RESULTS

The C2 median repeats were shorter in the trans men population. Genotypes S/S and S/L for the C2 polymorphism were overrepresented in the trans men group (P = .012 and P = .003 respectively). We also found overtransmission of the A/A genotype (C4) in the trans men population (P = .017), while the A/G genotype (C4) was subrepresented (P = .009]. The analyzed polymorphisms were in linkage disequilibrium. In the trans men population, the T(C1)-L(C2)-C(C3)-A(C4) haplotype was overrepresented (P = .019) while the T(C1)-L(C2)-C(C3)-G(C4) was subrepresented (P = .005).

CONCLUSION

The ESR1 is associated with gender incongruence in the trans men population. Fernández R, Delgado-Zayas E,RamírezK, et al. Analysis of Four Polymorphisms Located at the Promoter of the Estrogen Receptor Alpha ESR1 Gene in a Population With Gender Incongruence. Sex Med 2020;8:490-500.

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI's ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.

From Schizophrenia Genetics to Disease Biology: Harnessing New Concepts and Technologies

Schizophrenia (SZ) is a severe mental disorder afflicting around 1% of the population. It is highly heritable but with complex genetics. Recent research has unraveled a plethora of risk loci for SZ. Accordingly, our conceptual understanding of SZ genetics has been rapidly evolving, from oligogenic models towards polygenic or even omnigenic models. A pressing challenge to the field, however, is the translation of the many genetic findings of SZ into disease biology insights leading to more effective treatments. Bridging this gap requires the integration of genetic findings and functional genomics using appropriate cellular models. Harnessing new technologies, such as the development of human induced pluripotent stem cells (hiPSC) and the CRISPR/Cas-based genome/epigenome editing approach are expected to change our understanding of SZ disease biology to a fundamentally higher level. Here, we discuss some new developments.