Smaug regulates germ plasm assembly and primordial germ cell number in Drosophila embryos.

During Drosophila oogenesis, the Oskar (OSK) RNA binding protein (RBP) determines the amount of germ plasm that assembles at the posterior pole of the oocyte. Here, we identify mechanisms that subsequently regulate germ plasm assembly in the early embryo. We show that the Smaug (SMG) RBP is transported into the germ plasm of the early embryo where it accumulates in the germ granules. SMG binds to and represses translation of the osk messenger RNA (mRNA) as well as the bruno 1 (bru1) mRNA, which encodes an RBP that we show promotes germ plasm production. Loss of SMG or mutation of SMG's binding sites in the osk or bru1 mRNA results in excess translation of these transcripts in the germ plasm, accumulation of excess germ plasm, and budding of excess primordial germ cells (PGCs). Therefore, SMG triggers a posttranscriptional regulatory pathway that attenuates the amount of germ plasm in embryos to modulate the number of PGCs.

Dopamine signaling enriched striatal gene set predicts striatal dopamine synthesis and physiological activity in vivo.

The polygenic architecture of schizophrenia implicates several molecular pathways involved in synaptic function. However, it is unclear how polygenic risk funnels through these pathways to translate into syndromic illness. Using tensor decomposition, we analyze gene co-expression in the caudate nucleus, hippocampus, and dorsolateral prefrontal cortex of post-mortem brain samples from 358 individuals. We identify a set of genes predominantly expressed in the caudate nucleus and associated with both clinical state and genetic risk for schizophrenia that shows dopaminergic selectivity. A higher polygenic risk score for schizophrenia parsed by this set of genes predicts greater dopamine synthesis in the striatum and greater striatal activation during reward anticipation. These results translate dopamine-linked genetic risk variation into in vivo neurochemical and hemodynamic phenotypes in the striatum that have long been implicated in the pathophysiology of schizophrenia.

Dopamine and schizophrenia from bench to bedside: Discovery of a striatal co-expression risk gene set that predicts in vivo measures of striatal function.

Schizophrenia (SCZ) is characterized by a polygenic risk architecture implicating diverse molecular pathways important for synaptic function. However, how polygenic risk funnels through these pathways to translate into syndromic illness is unanswered. To evaluate biologically meaningful pathways of risk, we used tensor decomposition to characterize gene co-expression in post-mortem brain (of neurotypicals: N=154; patients with SCZ: N=84; and GTEX samples N=120) from caudate nucleus (CN), hippocampus (HP), and dorsolateral prefrontal cortex (DLPFC). We identified a CN-predominant gene set showing dopaminergic selectivity that was enriched for genes associated with clinical state and for genes associated with SCZ risk. Parsing polygenic risk score for SCZ based on this specific gene set (parsed-PRS), we found that greater pathway-specific SCZ risk predicted greater in vivo striatal dopamine synthesis capacity measured by [ 18 F]-FDOPA PET in three independent cohorts of neurotypicals and patients (total N=235) and greater fMRI striatal activation during reward anticipation in two additional independent neurotypical cohorts (total N=141). These results reveal a 'bench to bedside' translation of dopamine-linked genetic risk variation in driving in vivo striatal neurochemical and hemodynamic phenotypes that have long been implicated in the pathophysiology of SCZ.

Changes in patterns of age-related network connectivity are associated with risk for schizophrenia.

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.

Smaug regulates germ plasm synthesis and primordial germ cell number in Drosophila embryos by repressing the oskar and bruno 1 mRNAs.

During Drosophila oogenesis, the Oskar (OSK) RNA-binding protein (RBP) determines the amount of germ plasm that assembles at the posterior pole of the oocyte. Here we identify the mechanisms that regulate the osk mRNA in the early embryo. We show that the Smaug (SMG) RBP is transported into the germ plasm of the early embryo where it accumulates in the germ granules. SMG binds to and represses translation of the osk mRNA itself as well as the bruno 1 (bru1) mRNA, which encodes an RBP that we show promotes germ plasm production. Loss of SMG or mutation of SMG's binding sites in the osk or bru1 mRNAs results in ectopic translation of these transcripts in the germ plasm and excess PGCs. SMG therefore triggers a post-transcriptional regulatory pathway that attenuates germ plasm synthesis in embryos, thus modulating the number of PGCs.

Intelligence, educational attainment, and brain structure in those at familial high-risk for schizophrenia or bipolar disorder.

First-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs) show similar patterns of brain abnormalities and cognitive alterations to patients, albeit with smaller effect sizes. First-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) show divergent patterns; on average, intracranial volume is larger compared to controls, and findings on cognitive alterations in BD-FDRs are inconsistent. Here, we performed a meta-analysis of global and regional brain measures (cortical and subcortical), current IQ, and educational attainment in 5,795 individuals (1,103 SZ-FDRs, 867 BD-FDRs, 2,190 controls, 942 schizophrenia patients, 693 bipolar patients) from 36 schizophrenia and/or bipolar disorder family cohorts, with standardized methods. Compared to controls, SZ-FDRs showed a pattern of widespread thinner cortex, while BD-FDRs had widespread larger cortical surface area. IQ was lower in SZ-FDRs (d = -0.42, p = 3 × 10-5 ), with weak evidence of IQ reductions among BD-FDRs (d = -0.23, p = .045). Both relative groups had similar educational attainment compared to controls. When adjusting for IQ or educational attainment, the group-effects on brain measures changed, albeit modestly. Changes were in the expected direction, with less pronounced brain abnormalities in SZ-FDRs and more pronounced effects in BD-FDRs. To conclude, SZ-FDRs and BD-FDRs show a differential pattern of structural brain abnormalities. In contrast, both had lower IQ scores and similar school achievements compared to controls. Given that brain differences between SZ-FDRs and BD-FDRs remain after adjusting for IQ or educational attainment, we suggest that differential brain developmental processes underlying predisposition for schizophrenia or bipolar disorder are likely independent of general cognitive impairment.

A generative-discriminative framework that integrates imaging, genetic, and diagnosis into coupled low dimensional space.

We propose a novel optimization framework that integrates imaging and genetics data for simultaneous biomarker identification and disease classification. The generative component of our model uses a dictionary learning framework to project the imaging and genetic data into a shared low dimensional space. We have coupled both the data modalities by tying the linear projection coefficients to the same latent space. The discriminative component of our model uses logistic regression on the projection vectors for disease diagnosis. This prediction task implicitly guides our framework to find interpretable biomarkers that are substantially different between a healthy and disease population. We exploit the interconnectedness of different brain regions by incorporating a graph regularization penalty into the joint objective function. We also use a group sparsity penalty to find a representative set of genetic basis vectors that span a low dimensional space where subjects are easily separable between patients and controls. We have evaluated our model on a population study of schizophrenia that includes two task fMRI paradigms and single nucleotide polymorphism (SNP) data. Using ten-fold cross validation, we compare our generative-discriminative framework with canonical correlation analysis (CCA) of imaging and genetics data, parallel independent component analysis (pICA) of imaging and genetics data, random forest (RF) classification, and a linear support vector machine (SVM). We also quantify the reproducibility of the imaging and genetics biomarkers via subsampling. Our framework achieves higher class prediction accuracy and identifies robust biomarkers. Moreover, the implicated brain regions and genetic variants underlie the well documented deficits in schizophrenia.

The Association Between Familial Risk and Brain Abnormalities Is Disease Specific: An ENIGMA-Relatives Study of Schizophrenia and Bipolar Disorder.

BACKGROUND: Schizophrenia and bipolar disorder share genetic liability, and some structural brain abnormalities are common to both conditions. First-degree relatives of patients with schizophrenia (FDRs-SZ) show similar brain abnormalities to patients, albeit with smaller effect sizes. Imaging findings in first-degree relatives of patients with bipolar disorder (FDRs-BD) have been inconsistent in the past, but recent studies report regionally greater volumes compared with control subjects. METHODS: We performed a meta-analysis of global and subcortical brain measures of 6008 individuals (1228 FDRs-SZ, 852 FDRs-BD, 2246 control subjects, 1016 patients with schizophrenia, 666 patients with bipolar disorder) from 34 schizophrenia and/or bipolar disorder family cohorts with standardized methods. Analyses were repeated with a correction for intracranial volume (ICV) and for the presence of any psychopathology in the relatives and control subjects. RESULTS: FDRs-BD had significantly larger ICV (d = +0.16, q < .05 corrected), whereas FDRs-SZ showed smaller thalamic volumes than control subjects (d = -0.12, q < .05 corrected). ICV explained the enlargements in the brain measures in FDRs-BD. In FDRs-SZ, after correction for ICV, total brain, cortical gray matter, cerebral white matter, cerebellar gray and white matter, and thalamus volumes were significantly smaller; the cortex was thinner (d < -0.09, q < .05 corrected); and third ventricle was larger (d = +0.15, q < .05 corrected). The findings were not explained by psychopathology in the relatives or control subjects. CONCLUSIONS: Despite shared genetic liability, FDRs-SZ and FDRs-BD show a differential pattern of structural brain abnormalities, specifically a divergent effect in ICV. This may imply that the neurodevelopmental trajectories leading to brain anomalies in schizophrenia or bipolar disorder are distinct.

Genetic risk mechanisms of posttraumatic stress disorder in the human brain.

Posttraumatic stress disorder (PTSD) follows exposure to a traumatic event in susceptible individuals. Recently, genome-wide association studies have identified a number of genetic sequence variants that are associated with the risk of developing PTSD. To follow up on identifying the molecular mechanisms of these risk variants, we performed genotype to RNA sequencing-derived quantitative expression (whole gene, exon, and exon junction levels) analysis in the dorsolateral prefrontal cortex (DLPFC) of normal postmortem human brains. We further investigated genotype-gene expression associations within the amygdala in a smaller independent RNA sequencing (Genotype-Tissue Expression [GTEx]) dataset. Our DLPFC analyses identified significant expression quantitative trait loci (eQTL) associations for a "candidate" PTSD risk SNP rs363276 and the expression of two genes: SLC18A2 and PDZD8, where the PTSD risk/minor allele T was associated with significantly lower levels of gene expression for both genes, in the DLPFC. These eQTL associations were independently confirmed in the amygdala from the GTEx database. Rs363276 "T" carriers also showed significantly increased activity in the amygdala during an emotional face-matching task in healthy volunteers. Taken together, our preliminary findings in normal human brains represent a tractable approach to identify mechanisms by which genetic variants potentially increase an individual's risk for developing PTSD. © 2016 Wiley Periodicals, Inc.

Novel genetic loci associated with hippocampal volume.

The hippocampal formation is a brain structure integrally involved in episodic memory, spatial navigation, cognition and stress responsiveness. Structural abnormalities in hippocampal volume and shape are found in several common neuropsychiatric disorders. To identify the genetic underpinnings of hippocampal structure here we perform a genome-wide association study (GWAS) of 33,536 individuals and discover six independent loci significantly associated with hippocampal volume, four of them novel. Of the novel loci, three lie within genes (ASTN2, DPP4 and MAST4) and one is found 200 kb upstream of SHH. A hippocampal subfield analysis shows that a locus within the MSRB3 gene shows evidence of a localized effect along the dentate gyrus, subiculum, CA1 and fissure. Further, we show that genetic variants associated with decreased hippocampal volume are also associated with increased risk for Alzheimer's disease (rg=-0.155). Our findings suggest novel biological pathways through which human genetic variation influences hippocampal volume and risk for neuropsychiatric illness.

Late-Onset Alzheimer's Disease Polygenic Risk Profile Score Predicts Hippocampal Function.

BACKGROUND: We explored the cumulative effect of several late-onset Alzheimer's disease (LOAD) risk loci using a polygenic risk profile score (RPS) approach on measures of hippocampal function, cognition, and brain morphometry. METHODS: In a sample of 231 healthy control subjects (19-55 years of age), we used an RPS to study the effect of several LOAD risk loci reported in a recent meta-analysis on hippocampal function (determined by its engagement with blood oxygen level-dependent functional magnetic resonance imaging during episodic memory) and several cognitive metrics. We also studied effects on brain morphometry in an overlapping sample of 280 subjects. RESULTS: There was almost no significant association of LOAD-RPS with cognitive or morphometric measures. However, there was a significant negative relationship between LOAD-RPS and hippocampal function (familywise error [small volume correction-hippocampal region of interest] p < .05). There were also similar associations for risk score based on APOE haplotype, and for a combined LOAD-RPS + APOE haplotype risk profile score (p < .05 familywise error [small volume correction-hippocampal region of interest]). Of the 29 individual single nucleotide polymorphisms used in calculating LOAD-RPS, variants in CLU, PICALM, BCL3, PVRL2, and RELB showed strong effects (p < .05 familywise error [small volume correction-hippocampal region of interest]) on hippocampal function, though none survived further correction for the number of single nucleotide polymorphisms tested. CONCLUSIONS: There is a cumulative deleterious effect of LOAD risk genes on hippocampal function even in healthy volunteers. The effect of LOAD-RPS on hippocampal function in the relative absence of any effect on cognitive and morphometric measures is consistent with the reported temporal characteristics of LOAD biomarkers with the earlier manifestation of synaptic dysfunction before morphometric and cognitive changes.

Novel genetic loci underlying human intracranial volume identified through genome-wide association.

Intracranial volume reflects the maximally attained brain size during development, and remains stable with loss of tissue in late life. It is highly heritable, but the underlying genes remain largely undetermined. In a genome-wide association study of 32,438 adults, we discovered five previously unknown loci for intracranial volume and confirmed two known signals. Four of the loci were also associated with adult human stature, but these remained associated with intracranial volume after adjusting for height. We found a high genetic correlation with child head circumference (ρgenetic = 0.748), which indicates a similar genetic background and allowed us to identify four additional loci through meta-analysis (Ncombined = 37,345). Variants for intracranial volume were also related to childhood and adult cognitive function, and Parkinson's disease, and were enriched near genes involved in growth pathways, including PI3K-AKT signaling. These findings identify the biological underpinnings of intracranial volume and their link to physiological and pathological traits.

Automated Quality Assessment of Structural Magnetic Resonance Brain Images Based on a Supervised Machine Learning Algorithm.

High-resolution three-dimensional magnetic resonance imaging (3D-MRI) is being increasingly used to delineate morphological changes underlying neuropsychiatric disorders. Unfortunately, artifacts frequently compromise the utility of 3D-MRI yielding irreproducible results, from both type I and type II errors. It is therefore critical to screen 3D-MRIs for artifacts before use. Currently, quality assessment involves slice-wise visual inspection of 3D-MRI volumes, a procedure that is both subjective and time consuming. Automating the quality rating of 3D-MRI could improve the efficiency and reproducibility of the procedure. The present study is one of the first efforts to apply a support vector machine (SVM) algorithm in the quality assessment of structural brain images, using global and region of interest (ROI) automated image quality features developed in-house. SVM is a supervised machine-learning algorithm that can predict the category of test datasets based on the knowledge acquired from a learning dataset. The performance (accuracy) of the automated SVM approach was assessed, by comparing the SVM-predicted quality labels to investigator-determined quality labels. The accuracy for classifying 1457 3D-MRI volumes from our database using the SVM approach is around 80%. These results are promising and illustrate the possibility of using SVM as an automated quality assessment tool for 3D-MRI.

Common genetic variants influence human subcortical brain structures.

The highly complex structure of the human brain is strongly shaped by genetic influences. Subcortical brain regions form circuits with cortical areas to coordinate movement, learning, memory and motivation, and altered circuits can lead to abnormal behaviour and disease. To investigate how common genetic variants affect the structure of these brain regions, here we conduct genome-wide association studies of the volumes of seven subcortical regions and the intracranial volume derived from magnetic resonance images of 30,717 individuals from 50 cohorts. We identify five novel genetic variants influencing the volumes of the putamen and caudate nucleus. We also find stronger evidence for three loci with previously established influences on hippocampal volume and intracranial volume. These variants show specific volumetric effects on brain structures rather than global effects across structures. The strongest effects were found for the putamen, where a novel intergenic locus with replicable influence on volume (rs945270; P = 1.08 × 10(-33); 0.52% variance explained) showed evidence of altering the expression of the KTN1 gene in both brain and blood tissue. Variants influencing putamen volume clustered near developmental genes that regulate apoptosis, axon guidance and vesicle transport. Identification of these genetic variants provides insight into the causes of variability in human brain development, and may help to determine mechanisms of neuropsychiatric dysfunction.

Normal age-related brain morphometric changes: nonuniformity across cortical thickness, surface area and gray matter volume?

Normal aging is accompanied by global as well as regional structural changes. While these age-related changes in gray matter volume have been extensively studied, less has been done using newer morphological indexes, such as cortical thickness and surface area. To this end, we analyzed structural images of 216 healthy volunteers, ranging from 18 to 87 years of age, using a surface-based automated parcellation approach. Linear regressions of age revealed a concomitant global age-related reduction in cortical thickness, surface area and volume. Cortical thickness and volume collectively confirmed the vulnerability of the prefrontal cortex, whereas in other cortical regions, such as in the parietal cortex, thickness was the only measure sensitive to the pronounced age-related atrophy. No cortical regions showed more surface area reduction than the global average. The distinction between these morphological measures may provide valuable information to dissect age-related structural changes of the brain, with each of these indexes probably reflecting specific histological changes occurring during aging.

Investigation of anatomical thalamo-cortical connectivity and FMRI activation in schizophrenia.

The purpose of this study was to examine measures of anatomical connectivity between the thalamus and lateral prefrontal cortex (LPFC) in schizophrenia and to assess their functional implications. We measured thalamocortical connectivity with diffusion tensor imaging (DTI) and probabilistic tractography in 15 patients with schizophrenia and 22 age- and sex-matched controls. The relationship between thalamocortical connectivity and prefrontal cortical blood-oxygenation-level-dependent (BOLD) functional activity as well as behavioral performance during working memory was examined in a subsample of 9 patients and 18 controls. Compared with controls, schizophrenia patients showed reduced total connectivity of the thalamus to only one of six cortical regions, the LPFC. The size of the thalamic region with at least 25% of model fibers reaching the LPFC was also reduced in patients compared with controls. The total thalamocortical connectivity to the LPFC predicted working memory task performance and also correlated with LPFC BOLD activation. Notably, the correlation with BOLD activation was accentuated in patients as compared with controls in the ventral LPFC. These results suggest that thalamocortical connectivity to the LPFC is altered in schizophrenia with functional consequences on working memory processing in LPFC.

Positive and negative regulation of odor receptor gene choice in Drosophila by acj6.

Little is known about how individual olfactory receptor neurons (ORNs) select, from among many odor receptor genes, which genes to express. Abnormal chemosensory jump 6 (Acj6) is a POU domain transcription factor essential for the specification of ORN identity and odor receptor (Or) gene expression in the Drosophila maxillary palp, one of the two adult olfactory organs. However, the mechanism by which Acj6 functions in this process has not been investigated. Here, we systematically examine the role of Acj6 in the maxillary palp and in a major subset of antennal ORNs. We define an Acj6 binding site by a reiterative in vitro selection process. The site is found upstream of Or genes regulated by Acj6, and Acj6 binds to the site in Or promoters. Mutational analysis shows that the site is essential for Or regulation in vivo. Surprisingly, a novel ORN class in acj6 adults is found to arise from ectopic expression of a larval Or gene, which is repressed in wild type via an Acj6 binding site. Thus, Acj6 acts directly in the process of receptor gene choice; it plays a dual role, positive and negative, in the logic of the process, and acts in partitioning the larval and adult receptor repertoires.

Widespread reductions of cortical thickness in schizophrenia and spectrum disorders and evidence of heritability.

CONTEXT: Schizophrenia is a brain disorder with predominantly genetic risk factors, and previous research has identified heritable cortical and subcortical reductions in local brain volume. To our knowledge, cortical thickness, a measure of particular interest in schizophrenia, has not previously been evaluated in terms of its heritability in relationship to risk for schizophrenia. OBJECTIVE: To quantify the distribution and heritability of cortical thickness changes in schizophrenia. DESIGN: We analyzed a large sample of normal controls, affected patients, and unaffected siblings using a surface-based approach. Cortical thickness was compared between diagnosis groups on a surfacewide node-by-node basis. Heritability related to disease risk was assessed in regions derived from an automated cortical parcellation algorithm by calculating the Risch lambda. SETTING: Research hospital. PARTICIPANTS: One hundred ninety-six normal controls, 115 affected patients with schizophrenia, and 192 unaffected siblings. MAIN OUTCOME MEASURE: Regional cortical thickness. RESULTS: Node-by-node mapping statistics revealed widespread thickness reductions in the patient group, most pronouncedly in the frontal lobe and temporal cortex. Unaffected siblings did not significantly differ from normal controls at the chosen conservative threshold. Risch lambda analysis revealed widespread evidence for heritability for cortical thickness reductions throughout the brain. CONCLUSIONS: To our knowledge, the present study provides the first evidence of broadly distributed and heritable reductions of cortical thickness alterations in schizophrenia. However, since only trend-level reductions of thickness were observed in siblings, cortical thickness per se (at least as measured by this approach) is not a strong intermediate phenotype for schizophrenia.

Measuring mRNA stability during early Drosophila embryogenesis.

Maternal mRNAs play a major role in directing early Drosophila melanogaster development, and thus, precise posttranscriptional regulation of these messages is imperative for normal embryogenesis. Although initially abundant on egg deposition, a subset of these maternal mRNAs is targeted for destruction during the first 2 to 3 h of embryogenesis. In this chapter, we describe molecular methods to determine the kinetics and mechanisms of maternal mRNA decay in the early D. melanogaster embryo. We show how both unfertilized eggs and fertilized embryos can be used to identify maternal mRNAs destined for degradation, to explain changes in decay kinetics over time, and to uncover the molecular mechanisms of targeted maternal mRNA turnover. In the first section, we explore the methods and outcomes of measuring decay on a "gene-by-gene" basis, which involves examination of a small number of transcripts by Northern blotting, RNA dot blotting, and real-time RT-PCR. In the second section, we provide a comprehensive examination of the applications of microarray technology to study global changes in maternal mRNA decay during early development. Genome-wide surveys of maternal mRNA turnover provide a wealth of information regarding the magnitude, temporal regulation, and genetic control of maternal mRNA turnover. Methods that permit the collection and analysis of highly reproducible and statistically robust data in this developmental system are discussed.