Regulation of granin neuropeptide gene expression in human brain during development

ing up empty handed [Konopka and Geschwind, 2010]. Therefore, the contribution of potential genomic mechanisms to human brain uniqueness still remains mostly unknown. In a recent report, Zhang et al. [2011] attempted to address this feature of human brain evolution by examining the role of genes expressed in the fetal human brain. Until the recent availability of gene expression datasets from human fetal brain, all studies of genomics within the human brain have utilized adult tissue samples. Since the period of prenatal development is critical for normal cognitive functioning (as evidenced by the existence of numerous neurodevelopmental disorders like autism), analysis of fetal brain gene expression should likely provide key insights into human cognition where adult gene expression falls short. Zhang et al. [2011] used a combination of published microarray data, unpublished RNA sequencing data, and expressed sequence tag data in their analyses. They focused specifically on what they call ‘young’ genes, or those genes that have arisen in the primate lineage. By highlighting the young genes in the transcriptome data, the authors uncovered important results regarding human brain evolution. The genome revolution that included sequencing of the human and chimpanzee genomes promised to provide answers to how humans evolved their unique cognitive abilities. However, comparisons at the DNA level in protein-coding regions of the genome among primates have revealed few clues as to the molecular mechanisms underlying human brain evolution. As previously predicted [King and Wilson, 1975], we are therefore left with differential regulation of gene expression in the human lineage as a potential major evolutionary force driving human cognition. The recent advent of microarrays and next generation sequencing technologies have allowed us to compare gene expression in the brains of humans and other species at a genomewide level [Konopka and Geschwind, 2010]. However, the identification of a few hundred genes differentially expressed between human and non-human primate brain [Caceres et al., 2003] is unlikely in itself to explain the higher cognition in humans. In addition, gene expression in the human brain has been found to be the most evolutionarily constrained compared to other tissues [Wang et al., 2007; Brawand et al., 2011], consistent with investigations into evidence for accelerated evolution among human brain genes comPublished online: March 6, 2012

Gender-specific gene expression in post-mortem human brain: localization to sex chromosomes.

Brain Response to Visceral Aversive Conditioning: A Functional Magnetic Resonance Imaging Study

It has long been proposed that for creating diverse phenotypes in closely related species, such as a striking difference between human and chimpanzee brain functions, evolutionary changes in gene expression are more important than the changes in protein structure. High‐throughput detection methods make it possible to gauge the differences in gene expression at the genome‐wide level. Initial studies using deoxyribonucleic acid microarrays have shown that gene expression evolution is more conserved in the brain than in other tissues. However, those studies have also shown that the divergence rate of gene expression in the brain is higher in humans than in the chimpanzee lineage. Subsequent studies have identified detailed differences in gene expression such as alterations in network connectivity and temporal order. In addition to the expression of protein‐coding genes, the expression of noncoding RNAs may also affect the divergence of gene expression and contribute to the phenotypic differences between humans and chimpanzees. Key Concepts: Differences in gene expression patterns rather than the changes in protein structures may have contributed to the functional differences between human and chimpanzee brains. The set of genes that have different expression patterns in human and chimpanzee brains can be identified using high‐throughput methods such as DNA microarrays and RNA‐Seq. Although we can detect functional gene expression changes specific to the human brain, it is not easy to elucidate the underlying evolutionary mechanisms. Gene expression is a complex trait; understanding the molecular mechanisms of brain development is important to unveil the genetic basis of brain evolution. Several studies revealed that the pattern of gene expression evolution in human brains was highly conservative in general, but showed acceleration after the split from the chimpanzee lineage.

Neuregulin 1 (NRG1) is a multifunctional neurotrophin that mediates neurodevelopment and schizophrenia risk. The NRG1 gene undergoes extensive alternative splicing, and association of brain NRG1 type IV isoform expression with the schizophrenia-risk polymorphism rs6994992 is a potential mechanism of risk. Novel splice variants of NRG1-IV (NRG1-IVNV), with predicted unique signaling capabilities, have been cloned in fetal brain tissue. The authors investigated the temporal dynamics of transcription of NRG1-IVNV, compared with the major NRG1 isoforms, across human prenatal and postnatal prefrontal cortical development, and they examined the association of rs6994992 with NRG1-IVNV expression. NRG1 type I-IV and NRG1-IVNV isoforms were evaluated with quantitative real-time polymerase chain reaction in human postmortem prefrontal cortex tissue samples at 14 to 39 weeks gestation and postnatal ages 0-83 years. The association of rs6994992 genotype with NRG1-IVNV expression and the subcellular distribution and proteolytic processing of NRG1-IVNV isoforms were also determined. Expression of NRG1 types I, II, and III was temporally regulated during prenatal and postnatal neocortical development. NRG1-IVNV was expressed from 16 weeks gestation until age 3. Homozygosity for the schizophrenia risk allele (T) of rs6994992 conferred lower cortical NRG1-IVNV levels. Assays showed that NRG1-IVNV is a novel nuclear-enriched, truncated NRG1 protein resistant to proteolytic processing. To the authors' knowledge, this study provides the first quantitative map of NRG1 isoform expression during human neocortical development and aging. It identifies a potential mechanism of early developmental risk for schizophrenia at the NRG1 locus, involving a novel class of NRG1 proteins.

The deoxyribonucleic acid (DNA) microarray technique enables us to gauge the difference in gene expression level between human and chimpanzee brains at a genome‐wide level. Several studies have shown that (1) gene expression is more conservative in the brain than in other tissues, (2) the divergence rate of gene expression in the brain is higher in the lineage of humans than in the chimpanzees and (3) more genes are up‐regulated in human brains.

The daily timing of mammalian physiology is coordinated by circadian clocks throughout the body. Although measurements of clock gene expression indicate that these clocks in mice are normally in phase with each other, the situation in humans remains unclear. We used publicly available data from five studies, comprising over 1000 samples, to compare the phasing of circadian gene expression in human brain and human blood. Surprisingly, after controlling for age, clock gene expression in brain was phase-delayed by ~8.5 h relative to that of blood. We then examined clock gene expression in two additional human organs and in organs from nine other mammalian species, as well as in the suprachiasmatic nucleus (SCN). In most tissues outside the SCN, the expression of clock gene orthologs showed a phase difference of ~12 h between diurnal and nocturnal species. The exception to this pattern was human brain, whose phasing resembled that of the SCN. Our results highlight the value of a multi-tissue, multi-species meta-analysis, and have implications for our understanding of the human circadian system.

BackgroundThe gene encoding sorting nexin SNX19 is highly expressed in neurons. SNX19 modulates synaptic functions that have been implicated in Aβ formation and tau phosphorylation. In our recent postmortem brain transcriptomic study (n = 409), we found an association of SNX19 gene expression with increased schizophrenia risk. To date, this gene has not been studied in AD. In this project, we will define AD risk single nucleotide variants (SNVs) at this locus, linking the SNVs to SNX19 gene expression and DNA methylation sites in human postmortem brains. we will test the hypothesis that SNX19 induces neuronal dysfunction and Alzheimer’s disease (AD)‐related pathologies in hiPSCs derived cerebral organoids.MethodWe collected human postmortem bulk brain dorsolateral prefrontal cortex (DLPFC) RNA‐seq data from Religious Orders Study/Memory and Aging Project (ROSMAP N = 656). We obtained two human induced pluripotent stem cells (hiPSCs) from HipSci.ResultWe performed a multi‐trait analysis and identified single nucleotide variants (SNVs) in SNX19 that were associated with AD and DSST. To uncover the regulatory mechanism linked to the SNX19‐AD risk SNVs, we examined the possible association of these SNVs with SNX19 abundance. As expected, the SNVs are significantly associated with SNX19 gene expression in DLPFC from 656 aging brains generated by ROSMAP (p‐value < 1e‐20).Given the role of DNA methylation in influencing local gene expression and splicing in the human brain, we evaluated the association between SNV genotype and DNA methylation levels from 401 autopsy brains in our schizophrenia study. We identified cg14779329 as the top hit with schizophrenia functional SNV rs10791098. Interestingly, this CpG site was replicated to be associated with DSST‐risk SNVs in 692 ROSMAP brains.To determine whether SNX19 protein influences aspects of neurodegeneration, we have performed CRISPR gene editing in hiPSCs. We are generating cerebral organoids to assess the SNX19 impact on neuronal morphology, AD‐related pathologies and synaptic function.ConclusionOur study identified novel AD factors at SNX19 in human postmortem brains and will define its role in AD using human‐derived tissue cultures.

A goal of psychiatric research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors. An assumption underlying this practice is that gene expression in the postmortem human brain is an accurate representation of gene expression in the living human brain. This assumption – which, until now, had not been adequately tested – was tested by comparing human prefrontal cortex gene expression between 275 living samples and 243 postmortem samples. Expression levels differed significantly for nearly 80% of genes, and a systematic examination of alternative explanations for this observation determined that these differences are not explained by cell type composition, RNA quality, postmortem interval, age, medication, morbidity, symptom severity, tissue pathology, sample handling, batch effects, or computational methods utilized. Using gene expression data from two independent cohorts, the differences identified between living and postmortem samples were replicated and shown to be present in all brain cell types. Analyses integrating the data generated for this study with data from earlier studies that used tissue from postmortem brain donors showed that postmortem brain gene expression signatures of psychiatric and neurological illnesses, as well as of normal traits such as aging, may not always be accurate representations of these gene expression signatures in the living brain. By using tissue safely obtained from large cohorts of living people, future studies of the human brain have the potential to (1) determine the biomedical research questions that can be addressed using postmortem tissue as a proxy for living tissue and (2) expand the scope of medical research to include questions about the molecular basis of human brain health and illness that can only be addressed in living people (e.g., “What happens in the brain at the molecular level as a person experiences an emotion?”).

Understanding temporal characteristics of gene expression in normal human brain can help explain the neurodevelopment, working mechanism and functional diversity. Based on the gene expression dataset of developing human brains from the Allen Brain Atlas, we accurately predicted the age of human brains using support vector machine and identified 9,934 age related genes. Significant changes occur in gene expression of human brains before and after birth, thus we establish support vector machine (SVM) models for the subjects before birth and after birth, respectively. In general, the age of subjects can be well predicted by the SVM models, with the Pearson correlation coefficient of predicted age and the labeled age of all subjects is 0.9397 with P-value < 0.001 (before birth: r = 0.9465, P-value < 0.001; after birth: r = 0.9121, P-value < 0.001). For the total subjects, mean absolute error (MAE) of age prediction is 2.82 years with standard error (SE) is 0.15 years (before birth: MAE = 1.03 post-conceptual weeks (pcws), SE = 0.08 pcws; after birth: MAE = 4.70 years, SE = 0.20 years). This investigation reveal the bulk of temporal regulation occurred during prenatal development. By analyzing the functional annotations of age related genes, we found expression differences of genes before and after birth may be related to their functions. Finally, we found the prediction accuracy of each period can reflect its specificity of gene expression, which is negatively correlated to the gene expression similarity across periods. This study provides new insights into temporal dynamic pattern of gene expression in human brains and its relationship with functions.

Prodynorphin promoter SNP associated with alcohol dependence forms noncanonical AP-1 binding site that may influence gene expression in human brain

Major depression disorder (MDD) usually comes with structural and functional alterations of the brain, which are determined by altered gene expression patterns. We extracted threefunctional metrics, including Amplitude of Low Frequency Fluctuation, fractional Amplitude of Low Frequency Fluctuation, Regional Homogeneity from fMRI data, and one structural metrics, grey matter density from MRI data, to explore inter-group differences between MDD patients and healthy controls. Based on the association analysis of gene expression and brain imaging, we found 796 gene signatures whose expression was significantly correlated with functional or structural alteration of brain images in MDD. To understand how these genes affect the development of human brains, we used a measure, DPM, to quantitatively estimate the temporal and spatial specificity of MDD-related genes expressed in a region during a period, based on the gene expression dataset of developing human brains from the Allen Brain Atlas. We found MDD-related genes displayed more spatial specificity and less temporal specificity than control genes, suggesting MDD may occur at all periods, but only cause functional and structural changes in specific brain regions. Furthermore, MDD-related genes were found significantly overexpressed in ventrolateral prefrontal cortex, posterior superior temporal cortex, inferior parietal cortex, orbital frontal cortex, primary somatosensory cortex, primary motor cortex, primary auditory cortex, mediodorsal nucleus of the thalamus and cerebellar cortex mainly during adolescence and adulthood, associated with a decrease of grey matter density and a compensatory increase of functional activities affected by MDD. These findings can provide new insights into temporal-spatial expression pattern of MDD-related genes in human brains.

Gaining insight into the genetic regulation of gene expression in human brain is key to the interpretation of genome-wide association studies for major neurological and neuropsychiatric diseases. Expression quantitative trait loci (eQTL) analyses have largely been used to achieve this, providing valuable insights into the genetic regulation of steady-state RNA in human brain, but not distinguishing between molecular processes regulating transcription and stability. RNA quantification within cellular fractions can disentangle these processes in cell types and tissues which are challenging to model in vitro. We investigated the underlying molecular processes driving the genetic regulation of gene expression specific to a cellular fraction using allele-specific expression (ASE). Applying ASE analysis to genomic and transcriptomic data from paired nuclear and cytoplasmic fractions of anterior prefrontal cortex, cerebellar cortex and putamen tissues from 4 post-mortem neuropathologically-confirmed control human brains, we demonstrate that a significant proportion of genetic regulation of gene expression occurs post-transcriptionally in the cytoplasm, with genes undergoing this form of regulation more likely to be synaptic. These findings have implications for understanding the structure of gene expression regulation in human brain, and importantly the interpretation of rapidly growing single-nucleus brain RNA-sequencing and eQTL datasets, where cytoplasm-specific regulatory events could be missed.

FIGURE 1. Changes in cortical thickness provide one measure of brain maturation. A large longitudinal study found that for most areas of cortex, children with attention deficit hyperactivity disorder (ADHD) reach peak cortical thickness several years later than typically developing children, supporting presence of developmental delay. The rate of cortical thinning also differed between the group who continued to meet diagnostic criteria into adulthood (persistent ADHD) and those who did not (remitted ADHD). Areas of cortex in which the rate of thinning correlated with adult symptom level (green, more symptoms associated with more thinning) are approximated on medial and lateral simplified representations of cortex. An earlier study also identified multiple areas in which cortex was thinner in adults with persistent ADHD compared with controls (orange). In addition, this study noted some areas of thicker cortex in remitted ADHD when compared with persistent ADHD (blue).

Whiplash injury (WHI) is characterised by a forced neck flexion/extension, which frequently occurs after motor vehicle collisions. Previous studies characterising differences in brain metabolite concentrations and correlations with neuropathic pain (NP) components with chronic whiplash-associated disorders (WAD) have been demonstrated in affective pain-processing areas such as the anterior cingulate cortex (ACC). However, the detection of a difference in metabolite concentrations within these cortical areas with chronic WAD pain has been elusive. In this study, single-voxel magnetic resonance spectroscopy (MRS), following the latest MRSinMRS consensus group guidelines, was performed in the anterior cingulate cortex (ACC), left dorsolateral prefrontal cortex (DLPFC), and occipital cortex (OCC) to quantify differences in metabolite concentrations in individuals with chronic WAD with or without neuropathic pain (NP) components. Healthy individuals (n = 29) and participants with chronic WAD (n = 29) were screened with the Douleur Neuropathique 4 Questionnaire (DN4) and divided into groups without (WAD-noNP, n = 15) or with NP components (WAD-NP, n = 14). Metabolites were quantified with LCModel following a single session in a 3 T MRI scanner within the ACC, DLPFC, and OCC. Participants with WAD-NP presented moderate pain intensity and interference compared with the WAD-noNP group. Single-voxel MRS analysis demonstrated a higher glutamate concentration in the ACC and lower total choline (tCho) in the DLPFC in the WAD-NP versus WAD-noNP group, with no intergroup metabolite difference detected in the OCC. Best fit and stepwise multiple regression revealed that the normalised ACC glutamate/total creatine (tCr) (p = 0.01), DLPFC n-acetyl-aspartate (NAA)/tCr (p = 0.001), and DLPFC tCho/tCr levels (p = 0.02) predicted NP components in the WAD-NP group (ACC r 2 = 0.26, α = 0.81; DLPFC r 2 = 0.62, α = 0.98). The normalised Glu/tCr concentration was higher in the healthy than the WAD-noNP group within the ACC (p < 0.05), but not in the DLPFC or OCC. Neither sex nor age affected key normalised metabolite concentrations related to WAD-NP components when compared to the WAD-noNP group. This study demonstrates that elevated glutamate concentrations within the ACC are related to chronic WAD-NP components, while higher NAA and lower tCho metabolite levels suggest a role for increased neuronal-glial signalling and cell membrane dysfunction in individuals with chronic WAD-NP components.