Investigation of the involvement of MIR185 and its target genes in the development of schizophrenia.

BACKGROUND: Schizophrenia is a complex neuropsychiatric disorder of unclear etiology. The strongest known genetic risk factor is the 22q11.2 microdeletion. Research has yet to confirm which genes within the deletion region are implicated in schizophrenia. The minimal 1.5 megabase deletion contains MIR185, which encodes microRNA 185. METHODS: We determined miR-185 expression in embryonic and adult mouse brains. Common and rare variants at this locus were then investigated using a human genetics approach. First, we performed gene-based analyses for MIR185 common variants and target genes using Psychiatric Genomics Consortium genome-wide association data. Second, MIR185 was resequenced in German patients (n = 1000) and controls (n = 500). We followed up promising variants by genotyping an additional European sample (patients, n = 3598; controls, n = 4082). RESULTS: In situ hybridization in mice revealed miR-185 expression in brain regions implicated in schizophrenia. Gene-based tests revealed association between common variants in 3 MIR185 target genes (ATAT1, SH3PXD2A, NTRK3) and schizophrenia. Further analyses in mice revealed overlapping expression patterns for these target genes and miR-185. Resequencing identified 2 rare patient-specific novel variants flanking MIR185. However, follow-up genotyping provided no further evidence of their involvement in schizophrenia. LIMITATIONS: Power to detect rare variant associations was limited. CONCLUSION: Human genetic analyses generated no evidence of the involvement of MIR185 in schizophrenia. However, the expression patterns of miR-185 and its target genes in mice, and the genetic association results for the 3 target genes, suggest that further research into the involvement of miR-185 and its downstream pathways in schizophrenia is warranted.

Targeted next-generation sequencing: a novel diagnostic tool for primary immunodeficiencies.

BACKGROUND: Primary immunodeficiency (PID) disorders are a heterogeneous group of inherited disorders caused by a variety of monogenetic immune defects. Thus far, mutations in more than 170 different genes causing PIDs have been described. A clear genotype-phenotype correlation is often not available, which makes a genetic diagnosis in patients with PIDs complex and laborious. OBJECTIVE: We sought to develop a robust, time-effective, and cost-effective diagnostic method to facilitate a genetic diagnosis in any of 170 known PID-related genes by using next-generation sequencing (NGS). METHODS: We used both targeted array-based and in-solution enrichment combined with a SOLiD sequencing platform and a bioinformatic pipeline developed in house to analyze genetic changes in the DNA of 41 patients with PIDs with known mutations and 26 patients with undiagnosed PIDs. RESULTS: This novel NGS-based method accurately detected point mutations (sensitivity and specificity >99% in covered regions) and exonic deletions (100% sensitivity and specificity). For the 170 genes of interest, the DNA coverage was greater than 20× in 90% to 95%. Nine PID-related genes proved not eligible for evaluation by using this NGS-based method because of inadequate coverage. The NGS method allowed us to make a genetic diagnosis in 4 of 26 patients who lacked a genetic diagnosis despite routine functional and genetic testing. Three of these patients proved to have an atypical presentation of previously described PIDs. CONCLUSION: This novel NGS tool facilitates accurate simultaneous detection of mutations in 161 of 170 known PID-related genes. In addition, these analyses will generate more insight into genotype-phenotype correlations for the different PID disorders.

Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects.

BACKGROUND: The predominant model for regulation of gene expression through DNA methylation is an inverse association in which increased methylation results in decreased gene expression levels. However, recent studies suggest that the relationship between genetic variation, DNA methylation and expression is more complex. RESULTS: Systems genetic approaches for examining relationships between gene expression and methylation array data were used to find both negative and positive associations between these levels. A weighted correlation network analysis revealed that i) both transcriptome and methylome are organized in modules, ii) co-expression modules are generally not preserved in the methylation data and vice-versa, and iii) highly significant correlations exist between co-expression and co-methylation modules, suggesting the existence of factors that affect expression and methylation of different modules (i.e., trans effects at the level of modules). We observed that methylation probes associated with expression in cis were more likely to be located outside CpG islands, whereas specificity for CpG island shores was present when methylation, associated with expression, was under local genetic control. A structural equation model based analysis found strong support in particular for a traditional causal model in which gene expression is regulated by genetic variation via DNA methylation instead of gene expression affecting DNA methylation levels. CONCLUSIONS: Our results provide new insights into the complex mechanisms between genetic markers, epigenetic mechanisms and gene expression. We find strong support for the classical model of genetic variants regulating methylation, which in turn regulates gene expression. Moreover we show that, although the methylation and expression modules differ, they are highly correlated.

Common inversion polymorphism at 17q21.31 affects expression of multiple genes in tissue-specific manner.

BACKGROUND: Chromosome 17q21.31 contains a common inversion polymorphism of approximately 900 kb in populations with European ancestry. Two divergent MAPT haplotypes, H1 and H2 are described with distinct linkage disequilibrium patterns across the region reflecting the inversion status at this locus. The MAPT H1 haplotype has been associated with progressive supranuclear palsy, corticobasal degeneration, Parkinson's disease and Alzheimer's disease, while the H2 is linked to recurrent deletion events associated with the 17q21.31 microdeletion syndrome, a disease characterized by developmental delay and learning disability. RESULTS: In this study, we investigate the effect of the inversion on the expression of genes in the 17q21.31 region. We find the expression of several genes in and at the borders of the inversion to be affected; specific either to whole blood or different regions of the human brain. The H1 haplotype was found to be associated with an increased expression of LRRC37A4, PLEKH1M and MAPT. In contrast, a decreased expression of MGC57346, LRRC37A and CRHR1 was associated with H1. CONCLUSIONS: Studies thus far have focused on the expression of MAPT in the inversion region. However, our results show that the inversion status affects expression of other genes in the 17q21.31 region as well. Given the link between the inversion status and different neurological diseases, these genes may also be involved in disease pathology, possibly in a tissue-specific manner.

A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes.

Despite large-scale genome-wide association studies (GWAS), the underlying genes for schizophrenia are largely unknown. Additional approaches are therefore required to identify the genetic background of this disorder. Here we report findings from a large gene expression study in peripheral blood of schizophrenia patients and controls. We applied a systems biology approach to genome-wide expression data from whole blood of 92 medicated and 29 antipsychotic-free schizophrenia patients and 118 healthy controls. We show that gene expression profiling in whole blood can identify twelve large gene co-expression modules associated with schizophrenia. Several of these disease related modules are likely to reflect expression changes due to antipsychotic medication. However, two of the disease modules could be replicated in an independent second data set involving antipsychotic-free patients and controls. One of these robustly defined disease modules is significantly enriched with brain-expressed genes and with genetic variants that were implicated in a GWAS study, which could imply a causal role in schizophrenia etiology. The most highly connected intramodular hub gene in this module (ABCF1), is located in, and regulated by the major histocompatibility (MHC) complex, which is intriguing in light of the fact that common allelic variants from the MHC region have been implicated in schizophrenia. This suggests that the MHC increases schizophrenia susceptibility via altered gene expression of regulatory genes in this network.

Prediction of serotonin transporter promoter polymorphism genotypes from single nucleotide polymorphism arrays using machine learning methods.

BACKGROUND: The serotonin transporter gene (SLC6A4) and its promoter (5-HTTLPR) polymorphism have been the focus of a large number of association studies of behavioral traits and psychiatric disorders. However, large-scale genotyping of the polymorphism has been very difficult. We report the development and validation of a 5-HTTLPR genotype prediction model. METHODS: The single nucleotide polymorphisms (SNPs) from the 2000 kb region surrounding 5-HTTLPR were used to construct a prediction model through a newly developed machine learning method, multicategory vertex discriminant analysis with 2147 individuals from the Northern Finnish Birth Cohort genotyped with the Illumina 370K SNP array and manually genotyped for 5-HTTLPR polymorphism. The prediction model was applied to SNP genotypes in a Dutch/German schizophrenia case-control sample of 3318 individuals to test the association of the polymorphism with schizophrenia. RESULT: The prediction model of eight SNPs achieved a 92.4% accuracy rate and a 0.98±0.01 area under the receiving operating characteristic. Evidence for an association of the polymorphism with schizophrenia was observed (P=0.05, odds ratio=1.105). CONCLUSION: This prediction model provides an effective substitute of manually genotyped 5-HTTLPR alleles, providing a new approach for large scale association studies of this polymorphism.

Expression QTL analysis of top loci from GWAS meta-analysis highlights additional schizophrenia candidate genes.

There is genetic evidence that schizophrenia is a polygenic disorder with a large number of loci of small effect on disease susceptibility. Genome-wide association studies (GWASs) of schizophrenia have had limited success, with the best finding at the MHC locus at chromosome 6p. A recent effort of the Psychiatric GWAS consortium (PGC) yielded five novel loci for schizophrenia. In this study, we aim to highlight additional schizophrenia susceptibility loci from the PGC study by combining the top association findings from the discovery stage (9394 schizophrenia cases and 12 462 controls) with expression QTLs (eQTLs) and differential gene expression in whole blood of schizophrenia patients and controls. We examined the 6192 single-nucleotide polymorphisms (SNPs) with significance threshold at P<0.001. eQTLs were calculated for these SNPs in a sample of healthy controls (n=437). The transcripts significantly regulated by the top SNPs from the GWAS meta-analysis were subsequently tested for differential expression in an independent set of schizophrenia cases and controls (n=202). After correction for multiple testing, the eQTL analysis yielded 40 significant cis-acting effects of the SNPs. Seven of these transcripts show differential expression between cases and controls. Of these, the effect of three genes (RNF5, TRIM26 and HLA-DRB3) coincided with the direction expected from meta-analysis findings and were all located within the MHC region. Our results identify new genes of interest and highlight again the involvement of the MHC region in schizophrenia susceptibility.

Season of sampling and season of birth influence serotonin metabolite levels in human cerebrospinal fluid.

BACKGROUND: Animal studies have revealed seasonal patterns in cerebrospinal fluid (CSF) monoamine (MA) turnover. In humans, no study had systematically assessed seasonal patterns in CSF MA turnover in a large set of healthy adults. METHODOLOGY/PRINCIPAL FINDINGS: Standardized amounts of CSF were prospectively collected from 223 healthy individuals undergoing spinal anesthesia for minor surgical procedures. The metabolites of serotonin (5-hydroxyindoleacetic acid, 5-HIAA), dopamine (homovanillic acid, HVA) and norepinephrine (3-methoxy-4-hydroxyphenylglycol, MPHG) were measured using high performance liquid chromatography (HPLC). Concentration measurements by sampling and birth dates were modeled using a non-linear quantile cosine function and locally weighted scatterplot smoothing (LOESS, span = 0.75). The cosine model showed a unimodal season of sampling 5-HIAA zenith in April and a nadir in October (p-value of the amplitude of the cosine = 0.00050), with predicted maximum (PC(max)) and minimum (PC(min)) concentrations of 173 and 108 nmol/L, respectively, implying a 60% increase from trough to peak. Season of birth showed a unimodal 5-HIAA zenith in May and a nadir in November (p = 0.00339; PC(max) = 172 and PC(min) = 126). The non-parametric LOESS showed a similar pattern to the cosine in both season of sampling and season of birth models, validating the cosine model. A final model including both sampling and birth months demonstrated that both sampling and birth seasons were independent predictors of 5-HIAA concentrations. CONCLUSION: In subjects without mental illness, 5-HT turnover shows circannual variation by season of sampling as well as season of birth, with peaks in spring and troughs in fall.

Imaging gene and environmental effects on cerebellum in Attention-Deficit/Hyperactivity Disorder and typical development.

This study investigates the effects of XKR4, a recently identified candidate gene for Attention-Deficit/Hyperactivity Disorder (ADHD), birth weight, and their interaction on brain volume in ADHD. XKR4 is expressed in cerebellum and low birth weight has been associated both with changes in cerebellum and with ADHD, probably due to its relation with prenatal adversity. Anatomical MRI scans were acquired in 58 children with ADHD and 64 typically developing controls and processed to obtain volumes of cerebrum, cerebellum and gray and white matter in each structure. DNA was collected from saliva. Analyses including data on birth weight were conducted in a subset of 37 children with ADHD and 51 controls where these data were retrospectively collected using questionnaires. There was an interaction between genotype and birth weight for cerebellum gray matter volume (p = .020). The combination of homozygosity for the G-allele (the allele previously found to be overtransmitted in ADHD) and higher birth weight was associated with smaller volume. Furthermore, birth weight was positively associated with cerebellar white matter volume in controls, but not ADHD (interaction: p = .021). The interaction of genotype with birth weight affecting cerebellum gray matter is consistent with models that emphasize increased influence of genetic risk-factors in an otherwise favorable prenatal environment. The absence of an association between birth weight and cerebellum white matter volume in ADHD suggests that other genetic or environmental effects may be at play, unrelated to XKR4. These results underscore the importance of considering environmental effects in imaging genetics studies.

Hippocampal gene expression analysis highlights Ly6a/Sca-1 as candidate gene for previously mapped novelty induced behaviors in mice.

In this study, we show that the covariance between behavior and gene expression in the brain can help further unravel the determinants of neurobehavioral traits. Previously, a QTL for novelty induced motor activity levels was identified on murine chromosome 15 using consomic strains. With the goal of narrowing down the linked region and possibly identifying the gene underlying the quantitative trait, gene expression data from this F(2)-population was collected and used for expression QTL analysis. While genetic variation in these mice was limited to chromosome 15, eQTL analysis of gene expression showed strong cis-effects as well as trans-effects elsewhere in the genome. Using weighted gene co-expression network analysis, we were able to identify modules of co-expressed genes related to novelty induced motor activity levels. In eQTL analyses, the expression of Ly6a (a.k.a. Sca-1) was found to be cis-regulated by chromosome 15. Ly6a also surfaced in a group of genes resulting from the network analysis that was correlated with behavior. Behavioral analysis of Ly6a knock-out mice revealed reduced novelty induced motor activity levels when compared to wild type controls, confirming functional importance of Ly6a in this behavior, possibly through regulating other genes in a pathway. This study shows that gene expression profiling can be used to narrow down a previously identified behavioral QTL in mice, providing support for Ly6a as a candidate gene for functional involvement in novelty responsiveness.

The nucleolar GTP-binding proteins Gnl2 and nucleostemin are required for retinal neurogenesis in developing zebrafish.

Nucleostemin (NS), a member of a family of nucleolar GTP-binding proteins, is highly expressed in proliferating cells such as stem and cancer cells and is involved in the control of cell cycle progression. Both depletion and overexpression of NS result in stabilization of the tumor suppressor p53 protein in vitro. Although it has been previously suggested that NS has p53-independent functions, these to date remain unknown. Here, we report two zebrafish mutants recovered from forward and reverse genetic screens that carry loss of function mutations in two members of this nucleolar protein family, Guanine nucleotide binding-protein-like 2 (Gnl2) and Gnl3/NS. We demonstrate that these proteins are required for correct timing of cell cycle exit and subsequent neural differentiation in the brain and retina. Concomitantly, we observe aberrant expression of the cell cycle regulators cyclinD1 and p57kip2. Our models demonstrate that the loss of Gnl2 or NS induces p53 stabilization and p53-mediated apoptosis. However, the retinal differentiation defects are independent of p53 activation. Furthermore, this work demonstrates that Gnl2 and NS have both non-cell autonomously and cell-autonomous function in correct timing of cell cycle exit and neural differentiation. Finally, the data suggest that Gnl2 and NS affect cell cycle exit of neural progenitors by regulating the expression of cell cycle regulators independently of p53.

Gene expression profiling in C57BL/6J and A/J mouse inbred strains reveals gene networks specific for brain regions independent of genetic background.

BACKGROUND: We performed gene expression profiling of the amygdala and hippocampus taken from inbred mouse strains C57BL/6J and A/J. The selected brain areas are implicated in neurobehavioral traits while these mouse strains are known to differ widely in behavior. Consequently, we hypothesized that comparing gene expression profiles for specific brain regions in these strains might provide insight into the molecular mechanisms of human neuropsychiatric traits. We performed a whole-genome gene expression experiment and applied a systems biology approach using weighted gene co-expression network analysis. RESULTS: We were able to identify modules of co-expressed genes that distinguish a strain or brain region. Analysis of the networks that are most informative for hippocampus and amygdala revealed enrichment in neurologically, genetically and psychologically related pathways. Close examination of the strain-specific gene expression profiles, however, revealed no functional relevance but a significant enrichment of single nucleotide polymorphisms in the probe sequences used for array hybridization. This artifact was not observed for the modules of co-expressed genes that distinguish amygdala and hippocampus. CONCLUSIONS: The brain-region specific modules were found to be independent of genetic background and are therefore likely to represent biologically relevant molecular networks that can be studied to complement our knowledge about pathways in neuropsychiatric disease.

The relationship of DNA methylation with age, gender and genotype in twins and healthy controls.

Cytosine-5 methylation within CpG dinucleotides is a potentially important mechanism of epigenetic influence on human traits and disease. In addition to influences of age and gender, genetic control of DNA methylation levels has recently been described. We used whole blood genomic DNA in a twin set (23 MZ twin-pairs and 23 DZ twin-pairs, N = 92) as well as healthy controls (N = 96) to investigate heritability and relationship with age and gender of selected DNA methylation profiles using readily commercially available GoldenGate bead array technology. Despite the inability to detect meaningful methylation differences in the majority of CpG loci due to tissue type and locus selection issues, we found replicable significant associations of DNA methylation with age and gender. We identified associations of genetically heritable single nucleotide polymorphisms with large differences in DNA methylation levels near the polymorphism (cis effects) as well as associations with much smaller differences in DNA methylation levels elsewhere in the human genome (trans effects). Our results demonstrate the feasibility of array-based approaches in studies of DNA methylation and highlight the vast differences between individual loci. The identification of CpG loci of which DNA methylation levels are under genetic control or are related to age or gender will facilitate further studies into the role of DNA methylation and disease.