A Genome-Wide Association Study Suggests Novel Loci Associated with a Schizophrenia-Related Brain-Based Phenotype

Unraveling molecular mechanisms underlying low-temperature adaptation in Laguncularia racemosa

Laguncularia racemosa (L.) C.F. Gaertn is a controversial species in China, in terms of being a pioneer species for mangrove restoration and a putative invasive species occupying natural habitats. The tolerance to chilling stress allows L. racemosa to adapt to extreme climate change. However, little is known about the molecular-level chilling resistance mechanisms in L. racemosa, which restricts our understanding of its biological features and invasion potential. In this study, L. racemosa seedlings were treated with freezing temperature (0 °C) at four durations (0 h, 3 h, 12 h and 24 h of recovery after treatment), and both physiological and transcriptional regulations underlying chilling stress resistance were investigated. Chilling stress caused damage to the cell membrane system and reduced photosynthesis efficiency of L. racemosa seedlings. To combat the adverse impacts, plasma membrane biosynthesis and antioxidant processes were substantially enhanced. After 24 h of recovery, the seedlings nearly recovered to normal growth condition, except for the processes related to photosynthesis, indicating their vigorous adaptation to short-term chilling stress. Importantly, the individuals from higher latitude displayed better adaptation to chilling injury than those from lower latitude, highlighting the role of long-term heredity × environment interactions in promoting the chilling resistance capacity of L. racemosa. These features allow L. racemosa to survive in extremely cold weather, but may also increase its risk of invasion into intertidal ecosystems. Together, our findings present a comprehensive view of the chilling-adaptative mechanisms of L. racemosa, which provide clues for better evaluating the invasive potential of L. racemosa.

A Latent Gaussian Copula Model for Mixed Data Analysis in Brain Imaging Genetics

Recent advances in imaging genetics make it possible to combine different types of data including medical images like functional magnetic resonance imaging (fMRI) and genetic data like single nucleotide polymorphisms (SNPs) for comprehensive diagnosis of mental disorders. Understanding complex interactions among these heterogeneous data may give rise to a new perspective, while at the same time demand statistical models for their integration. Various graphical models have been proposed for the study of interaction or association networks with continuous, binary, and count data as well as the mixture of them. However, limited efforts have been made for the multinomial case, for instance, SNP data. Our goal is therefore to fill the void by developing a graphical model for the integration of fMRI image and SNP data, which can provide deeper understanding of the unknown neurogenetic mechanism. In this article, we propose a latent Gaussian copula model for mixed data containing multinomial components. We assume that the discrete variable is obtained by discretizing a latent (unobserved) continuous variable and then create a semi-rank based estimator of the graph structure. The simulation results demonstrate that the proposed latent correlation has more steady and accurate performance than several existing methods in detecting graph structure. When applying to a real schizophrenia data consisting of SNP array and fMRI image collected by the Mind Clinical Imaging Consortium (MCIC), the proposed method reveals a set of distinct SNP-brain associations, which are verified to be biologically significant. The proposed model is statistically promising in handling mixed types of data including multinomial components, which can find widespread applications. To promote reproducible research, the R code is available at https://github.com/Aiying0512/LGCM.

Cannabinoid Receptor 1 associates to different molecular complexes during GABAergic neuron maturation

CB₁ cannabinoid receptor is widely expressed in the central nervous system of animals from late prenatal development to adulthood. Appropriate activation and signaling of CB₁ cannabinoid receptors in cortical interneurons are crucial during perinatal/postnatal ages and adolescence, when long-lasting changes in brain activity may elicit subsequent appearance of disorders in the adult brain. Here we used an optimized immunoprecipitation protocol based on specific antibodies followed by shot-gun proteomics to find CB₁ interacting partners in postnatal rat GABAergic cortical neurons in vitro at two different stages of maturation. Besides describing new proteins associated with CB₁ like dihydrolipoyllysine-residue acetyltransferase component of pyruvate dehydrogenase complex (DLAT), fatty acid synthase (FASN), tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), voltage-dependent anion channel 1 (VDAC1), myosin phosphatase Rho-interacting protein (MPRIP) or usher syndrome type-1C protein-binding protein 1 (USHBP1), we show that the signaling complex of CB₁ is different between maturational stages. Interestingly, the CB₁ signaling complex is enriched at the more immature stage in mitochondrial associated proteins and metabolic molecular functions, whereas at more mature stage, CB₁ complex is increased in maturation and synaptic-associated proteins. We describe also interacting partners specifically immunoprecipitated with either N-terminal or C-terminal CB₁ directed antibodies. Our results highlight new players that may be affected by altered cannabinoid signaling at this critical window of postnatal cortical development.

Genome-wide association study identifies susceptibility loci of brain atrophy to NFIA and ST18 in Alzheimer's disease

To identify genetic variants influencing cortical atrophy in Alzheimer's disease (AD), we performed genome-wide association studies (GWAS) of mean cortical thicknesses in 17 AD-related brain. In this study, we used neuroimaging and genetic data of 919 participants from the Alzheimer's Disease Neuroimaging Initiative cohort, which include 268 cognitively normal controls, 488 mild cognitive impairment, 163 AD individuals. We performed GWAS with 3,041,429 single nucleotide polymorphisms (SNPs) for cortical thickness. The results of GWAS indicated that rs10109716 in ST18 (ST18 C2H2C-type zinc finger transcription factor) and rs661526 in NFIA (nuclear factor I A) genes are significantly associated with mean cortical thicknesses of the left inferior frontal gyrus and left parahippocampal gyrus, respectively. The rs661526 regulates the expression levels of NFIA in the substantia nigra and frontal cortex and rs10109716 regulates the expression levels of ST18 in the thalamus. These results suggest a crucial role of identified genes for cortical atrophy and could provide further insights into the genetic basis of AD.

[The IMAGEN cohort: perspectives and problems of longitudinal research]

BACKGROUND

Genetic risk factors for major mental disorders identified in psychiatric research show a substantial overlap. Therefore, it has been suggested that neurobiological research should focus on intermediate phenotypes that reflect shared aspects of different mental disorders due to overlapping genetic effects and environmental factors. Longitudinal studies are required to assess the interaction between genetic variability and modifying environmental factors and to investigate the effects on intermediate phenotypes and (mediated by them) on the expression of individual mental disorders.

OBJECTIVE

Discussion of the possibilities and limitations of longitudinal cohort studies using the IMAGEN study as an example.

MATERIAL AND METHODS

The results of the European IMAGEN study are presented with a focus on addiction.

RESULTS

The longitudinal assessments of the IMAGEN cohort revealed that neuroimaging data indicating a low activation of the dopaminergic reinforcement system detected at the age of 14 years are predictive for increased drug use. In addition to genetic factors, environmental influences such as maternal smoking during pregnancy were correlated with this low activation.

CONCLUSION

Longitudinal neurobiological basic research can validate the effects of candidate genes and reveal relevant environmental factors. Relevant modifiable factors indicated by the IMAGEN study and related datasets include drug use during pregnancy, trauma and other experiences of violence, social disadvantage and exclusion.

The IMAGEN study: a decade of imaging genetics in adolescents

Imaging genetics offers the possibility of detecting associations between genotype and brain structure as well as function, with effect sizes potentially exceeding correlations between genotype and behavior. However, study results are often limited due to small sample sizes and methodological differences, thus reducing the reliability of findings. The IMAGEN cohort with 2000 young adolescents assessed from the age of 14 onwards tries to eliminate some of these limitations by offering a longitudinal approach and sufficient sample size for analyzing gene-environment interactions on brain structure and function. Here, we give a systematic review of IMAGEN publications since the start of the consortium. We then focus on the specific phenotype 'drug use' to illustrate the potential of the IMAGEN approach. We describe findings with respect to frontocortical, limbic and striatal brain volume, functional activation elicited by reward anticipation, behavioral inhibition, and affective faces, and their respective associations with drug intake. In addition to describing its strengths, we also discuss limitations of the IMAGEN study. Because of the longitudinal design and related attrition, analyses are underpowered for (epi-) genome-wide approaches due to the limited sample size. Estimating the generalizability of results requires replications in independent samples. However, such densely phenotyped longitudinal studies are still rare and alternative internal cross-validation methods (e.g., leave-one out, split-half) are also warranted. In conclusion, the IMAGEN cohort is a unique, very well characterized longitudinal sample, which helped to elucidate neurobiological mechanisms involved in complex behavior and offers the possibility to further disentangle genotype × phenotype interactions.

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.

Fetal glucocorticoid receptor (Nr3c1) deficiency alters the landscape of DNA methylation of murine placenta in a sex-dependent manner and is associated to anxiety-like behavior in adulthood

Prenatal stress defines long-term phenotypes through epigenetic programming of the offspring. These effects are potentially mediated by glucocorticoid release and by sex. We hypothesized that the glucocorticoid receptor (Gr, Nr3c1) fashions the DNA methylation profile of offspring. Consistent with this hypothesis, fetal Nr3c1 heterozygosity leads to altered DNA methylation landscape in fetal placenta in a sex-specific manner. There was a significant overlap of differentially methylated genes in fetal placenta and adult frontal cortex in Nr3c1 heterozygotes. Phenotypically, Nr3c1 heterozygotes show significantly more anxiety-like behavior than wildtype. DNA methylation status of fetal placental tissue is significantly correlated with anxiety-like behavior of the same animals in adulthood. Thus, placental DNA methylation might predict behavioral phenotypes in adulthood. Our data supports the hypothesis that Nr3c1 influences DNA methylation at birth and that DNA methylation in placenta correlates with adult frontal cortex DNA methylation and anxiety-like phenotypes.

Low-level Gestational Lead Exposure Alters Dendritic Spine Plasticity in the Hippocampus and Reduces Learning and Memory in Rats

Lead (Pb) is known to impair children's cognitive function. It has been previously shown that developmental Pb exposure alters dendritic spine formation in hippocampal pyramidal neurons. However, the underlying mechanism has not yet been defined. In this study, a low-level gestational Pb exposure (GLE) rat model was employed to investigate the impact of Pb on the spine density of the hippocampal pyramidal neurons and its regulatory mechanism. Pb exposure resulted in impaired performance of the rats in the Morris water maze tasks, and in decreased EPSC amplitudes in hippocampal CA3-CA1 regions. With a 3D reconstruction by the Imaris software, the results from Golgi staining showed that the spine density in the CA1 region was reduced in the Pb-exposed rats in a dose-dependent manner. Decreased spine density was also observed in cultured hippocampal neurons following the Pb treatment. Furthermore, the expression level of NLGN1, a postsynaptic protein that mediates synaptogenesis, was significantly decreased following the Pb exposure both in vivo and in vitro. Up-regulation of NLGN1 in cultured primary neurons partially attenuated the impact of Pb on the spine density. Taken together, our resultssuggest that Pb exposure alters spine plasticity in the developing hippocampus by down-regulating NLGN1 protein levels.

Imaging genetics of schizophrenia in the post-GWAS era

Imaging genetics is a research methodology studying the effect of genetic variation on brain structure, function, behavior, and risk for psychopathology. Since the early 2000s, imaging genetics has been increasingly used in the research of schizophrenia (SZ). SZ is a severe mental disorder with no precise knowledge of its underlying neurobiology, however, new genetic and neurobiological data generate a climate for new avenues. The accumulating data of genome wide association studies (GWAS) continuously decode SZ risk genes. Global neuroimaging consortia produce collections of brain phenotypes from tens of thousands of people. In this context, imaging genetics will be strategically important both for the validation and discovery of SZ related findings. Thus, the study of GWAS supported risk variants as candidate genes to validate by neuroimaging is one trend. The study of epigenetic differences in relation to variations of brain phenotypes and the study of large scale multivariate analysis of genome wide and brain wide associations are other trends. While these studies hold a big potential for understanding the neurobiology of SZ, the problem of reproducibility appears as a major challenge, which requires standardizations in study designs and compensations of methodological limitations such as sensitivity and specificity. On the other hand, advancements of neuroimaging, optical and electron microscopy along with the use of genetically encoded fluorescent probes and robust statistical approaches will not only catalyze integrative methodologies but also will help better design the imaging genetics studies. In this invited paper, I will discuss the current perspective of imaging genetics and emerging opportunities of SZ research.

Opportunities for an enhanced integration of neuroscience and genomics

Neuroimaging and genetics are two rapidly expanding fields of research. Thoughtful integration of these areas is critical for ongoing large-scale research into the genetic mechanisms underlying brain structure, function, and development. Neuroimaging genetics has been slow to evolve relative to psychiatric genetics research, and some may be unaware that new statistical methods allow for the genomic analysis of more modestly-sized imaging samples. We present a broad overview of the extant imaging genetics literature, provide an interpretation of the major problems surrounding the integration of neuroimaging and genetics, discuss the influence and impact of genetics consortia, and suggest statistical genetic analyses that expand the repertoire of imaging researchers amassing rich behavioral data in modestly-sized samples. Specific attention is paid to the creative use of polygenic risk scoring in imaging genetic analyses, with primers on the most current risk scoring applications.

Imaging genetics and psychiatric disorders

Imaging genetics is an integrated research method that uses neuroimaging and genetics to assess the impact of genetic variation on brain function and structure. Imaging genetics is both a tool for the discovery of risk genes for psychiatric disorders and a strategy for characterizing the neural systems affected by risk gene variants to elucidate quantitative and mechanistic aspects of brain function implicated in psychiatric disease. Early studies of imaging genetics included association analyses between brain morphology and single nucleotide polymorphisms whose function is well known, such as catechol-Omethyltransferase (COMT) and brain-derived neurotrophic factor (BDNF). GWAS of psychiatric disorders have identified genes with unknown functions, such as ZNF804A, and imaging genetics has been used to investigate clues of the biological function of these genes. The difficulty in replicating the findings of studies with small sample sizes has motivated the creation of largescale collaborative consortiums, such as ENIGMA, CHARGE and IMAGEN, to collect thousands of images. In a genome-wide association study, the ENIGMA consortium successfully identified common variants in the genome associated with hippocampal volume at 12q24, and the CHARGE consortium replicated this finding. The new era of imaging genetics has just begun, and the next challenge we face is the discovery of small effect size signals from large data sets obtained from genetics and neuroimaging. New methods and technologies for data reduction with appropriate statistical thresholds, such as polygenic analysis and parallel independent component analysis (ICA), are warranted. Future advances in imaging genetics will aid in the discovery of genes and provide mechanistic insight into psychiatric disorders.

Genetic underpinnings of left superior temporal gyrus thickness in patients with schizophrenia

OBJECTIVES

Schizophrenia is a highly disabling psychiatric disorder with a heterogeneous phenotypic appearance. We aimed to further the understanding of some of the underlying genetics of schizophrenia, using left superior temporal gyrus (STG) grey matter thickness reduction as an endophenoptype in a genome-wide association (GWA) study.

METHODS

Structural magnetic resonance imaging (MRI) and genetic data of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia were used to analyse the interaction effects between 1,067,955 single nucleotide polymorphisms (SNPs) and disease status on left STG thickness in 126 healthy controls and 113 patients with schizophrenia. We next used a pathway approach to detect underlying pathophysiological pathways that may be related to schizophrenia.

RESULTS

No SNP by diagnosis interaction effect reached genome-wide significance (5 × 10-8) in our GWA study, but 10 SNPs reached P-values less than 10-6. The most prominent pathways included those involved in insulin, calcium, PI3K-Akt and MAPK signalling.

CONCLUSIONS

Our strongest findings in the GWA study and pathway analysis point towards an involvement of glucose metabolism in left STG thickness reduction in patients with schizophrenia only. These results are in line with recently published studies, which showed an increased prevalence of psychosis among patients with metabolic syndrome-related illnesses including diabetes.

Associations between DNA methylation and schizophrenia-related intermediate phenotypes - a gene set enrichment analysis

Multiple genetic approaches have identified microRNAs as key effectors in psychiatric disorders as they post-transcriptionally regulate expression of thousands of target genes. However, their role in specific psychiatric diseases remains poorly understood. In addition, epigenetic mechanisms such as DNA methylation, which affect the expression of both microRNAs and coding genes, are critical for our understanding of molecular mechanisms in schizophrenia. Using clinical, imaging, genetic, and epigenetic data of 103 patients with schizophrenia and 111 healthy controls of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia, we conducted gene set enrichment analysis to identify markers for schizophrenia-associated intermediate phenotypes. Genes were ranked based on the correlation between DNA methylation patterns and each phenotype, and then searched for enrichment in 221 predicted microRNA target gene sets. We found the predicted hsa-miR-219a-5p target gene set to be significantly enriched for genes (EPHA4, PKNOX1, ESR1, among others) whose methylation status is correlated with hippocampal volume independent of disease status. Our results were strengthened by significant associations between hsa-miR-219a-5p target gene methylation patterns and hippocampus-related neuropsychological variables. IPA pathway analysis of the respective predicted hsa-miR-219a-5p target genes revealed associated network functions in behavior and developmental disorders. Altered methylation patterns of predicted hsa-miR-219a-5p target genes are associated with a structural aberration of the brain that has been proposed as a possible biomarker for schizophrenia. The (dys)regulation of microRNA target genes by epigenetic mechanisms may confer additional risk for developing psychiatric symptoms. Further study is needed to understand possible interactions between microRNAs and epigenetic changes and their impact on risk for brain-based disorders such as schizophrenia.

MB-COMT promoter DNA methylation is associated with working-memory processing in schizophrenia patients and healthy controls

Many genetic studies report mixed results both for the associations between COMT polymorphisms and schizophrenia and for the effects of COMT variants on common intermediate phenotypes of the disorder. Reasons for this may include small genetic effect sizes and the modulation of environmental influences. To improve our understanding of the role of COMT in the disease etiology, we investigated the effect of DNA methylation in the MB-COMT promoter on neural activity in the dorsolateral prefrontal cortex during working memory processing as measured by fMRI - an intermediate phenotype for schizophrenia. Imaging and epigenetic data were measured in 102 healthy controls and 82 schizophrenia patients of the Mind Clinical Imaging Consortium (MCIC) study of schizophrenia. Neural activity during the Sternberg Item Recognition Paradigm was acquired with either a 3T Siemens Trio or 1.5T Siemens Sonata and analyzed using the FMRIB Software Library (FSL). DNA methylation measurements were derived from cryo-conserved blood samples. We found a positive association between MB-COMT promoter methylation and neural activity in the left dorsolateral prefrontal cortex in a model using a region-of-interest approach and could confirm this finding in a whole-brain model. This effect was independent of disease status. Analyzing the effect of MB-COMT promoter DNA methylation on a neuroimaging phenotype can provide further evidence for the importance of COMT and epigenetic risk mechanisms in schizophrenia. The latter may represent trans-regulatory or environmental risk factors that can be measured using brain-based intermediate phenotypes.

Accurate and Fully Automatic Hippocampus Segmentation Using Subject-Specific 3D Optimal Local Maps Into a Hybrid Active Contour Model

Assessing the structural integrity of the hippocampus (HC) is an essential step toward prevention, diagnosis, and follow-up of various brain disorders due to the implication of the structural changes of the HC in those disorders. In this respect, the development of automatic segmentation methods that can accurately, reliably, and reproducibly segment the HC has attracted considerable attention over the past decades. This paper presents an innovative 3-D fully automatic method to be used on top of the multiatlas concept for the HC segmentation. The method is based on a subject-specific set of 3-D optimal local maps (OLMs) that locally control the influence of each energy term of a hybrid active contour model (ACM). The complete set of the OLMs for a set of training images is defined simultaneously via an optimization scheme. At the same time, the optimal ACM parameters are also calculated. Therefore, heuristic parameter fine-tuning is not required. Training OLMs are subsequently combined, by applying an extended multiatlas concept, to produce the OLMs that are anatomically more suitable to the test image. The proposed algorithm was tested on three different and publicly available data sets. Its accuracy was compared with that of state-of-the-art methods demonstrating the efficacy and robustness of the proposed method.

Neuroimaging as a potential biomarker to optimize psychiatric research and treatment

Complex, polygenic phenotypes in psychiatry hamper our understanding of the underlying molecular pathways and mechanisms of many diseases. The unknown aetiology, together with symptoms which often show a large variability both across individuals and over time and also tend to respond comparatively slowly to medication, can be a problem for patient treatment and drug development. We argue that neuroimaging has the potential to improve psychiatric treatment in two ways. First, by reducing phenotypic complexity, neuroimaging intermediate phenotypes can help to identify disease-related genes and can shed light into the biological mechanisms of known risk genes. Second, quantitative neuroimaging markers - reflecting the spectrum of impairment on a brain-based level - can be used as a more sensitive, reliable and immediate treatment response biomarker. In the end, enhancing both our understanding of the pathophysiology of psychiatric disorders and the prediction of treatment success could eventually optimise current therapy plans.