Differential Functional Constraints on the Evolution of Postsynaptic Density Proteins in Neocortical Laminae

The postsynaptic density (PSD) is a protein dense complex on the postsynaptic membrane of excitatory synapses that is implicated in normal nervous system functions such as synaptic plasticity, and also contains an enrichment of proteins involved in neuropsychiatric disorders. It has recently been reported that the genes encoding PSD proteins evolved more slowly than other genes in the human brain, but the underlying evolutionary advantage for this is not clear. Here, we show that cortical gene expression levels could explain most of this effect, indicating that expression level is a primary contributor to the evolution of these genes in the brain. Furthermore, we identify a positive correlation between the expression of PSD genes and cortical layers, with PSD genes being more highly expressed in deep layers, likely as a result of layer-enriched transcription factors. As the cortical layers of the mammalian brain have distinct functions and anatomical projections, our results indicate that the emergence of the unique six-layered mammalian cortex may have provided differential functional constraints on the evolution of PSD genes. More superficial cortical layers contain PSD genes with less constraint and these layers are primarily involved in intracortical projections, connections that may be particularly important for evolved cognitive functions. Therefore, the differential expression and evolutionary constraint of PSD genes in neocortical laminae may be critical not only for neocortical architecture but the cognitive functions that are dependent on this structure

Isoform Diversity and Regulation in Peripheral and Central Neurons Revealed through RNA-Seq

To fully understand cell type identity and function in the nervous system there is a need to understand neuronal gene expression at the level of isoform diversity. Here we applied Next Generation Sequencing of the transcriptome (RNA-Seq) to purified sensory neurons and cerebellar granular neurons (CGNs) grown on an axonal growth permissive substrate. The goal of the analysis was to uncover neuronal type specific isoforms as a prelude to understanding patterns of gene expression underlying their intrinsic growth abilities. Global gene expression patterns were comparable to those found for other cell types, in that a vast majority of genes were expressed at low abundance. Nearly 18% of gene loci produced more than one transcript. More than 8000 isoforms were differentially expressed, either to different degrees in different neuronal types or uniquely expressed in one or the other. Sensory neurons expressed a larger number of genes and gene isoforms than did CGNs. To begin to understand the mechanisms responsible for the differential gene/isoform expression we identified transcription factor binding sites present specifically in the upstream genomic sequences of differentially expressed isoforms, and analyzed the 3′ untranslated regions (3′ UTRs) for microRNA (miRNA) target sites. Our analysis defines isoform diversity for two neuronal types with diverse axon growth capabilities and begins to elucidate the complex transcriptional landscape in two neuronal populations

Machine learning for genetic prediction of psychiatric disorders: a systematic review

Machine learning methods have been employed to make predictions in psychiatry from genotypes, with the potential to bring improved prediction of outcomes in psychiatric genetics; however, their current performance is unclear. We aim to systematically review machine learning methods for predicting psychiatric disorders from genetics alone and evaluate their discrimination, bias and implementation. Medline, PsycInfo, Web of Science and Scopus were searched for terms relating to genetics, psychiatric disorders and machine learning, including neural networks, random forests, support vector machines and boosting, on 10 September 2019. Following PRISMA guidelines, articles were screened for inclusion independently by two authors, extracted, and assessed for risk of bias. Overall, 63 full texts were assessed from a pool of 652 abstracts. Data were extracted for 77 models of schizophrenia, bipolar, autism or anorexia across 13 studies. Performance of machine learning methods was highly varied (0.48–0.95 AUC) and differed between schizophrenia (0.54–0.95 AUC), bipolar (0.48–0.65 AUC), autism (0.52–0.81 AUC) and anorexia (0.62–0.69 AUC). This is likely due to the high risk of bias identified in the study designs and analysis for reported results. Choices for predictor selection, hyperparameter search and validation methodology, and viewing of the test set during training were common causes of high risk of bias in analysis. Key steps in model development and validation were frequently not performed or unreported. Comparison of discrimination across studies was constrained by heterogeneity of predictors, outcome and measurement, in addition to sample overlap within and across studies. Given widespread high risk of bias and the small number of studies identified, it is important to ensure established analysis methods are adopted. We emphasise best practices in methodology and reporting for improving future studies

Laminar and Dorsoventral Molecular Organization of the Medial Entorhinal Cortex Revealed by Large-scale Anatomical Analysis of Gene Expression

Neural circuits in the medial entorhinal cortex (MEC) encode an animal's position and orientation in space. Within the MEC spatial representations, including grid and directional firing fields, have a laminar and dorsoventral organization that corresponds to a similar topography of neuronal connectivity and cellular properties. Yet, in part due to the challenges of integrating anatomical data at the resolution of cortical layers and borders, we know little about the molecular components underlying this organization. To address this we develop a new computational pipeline for high-throughput analysis and comparison of in situ hybridization (ISH) images at laminar resolution. We apply this pipeline to ISH data for over 16,000 genes in the Allen Brain Atlas and validate our analysis with RNA sequencing of MEC tissue from adult mice. We find that differential gene expression delineates the borders of the MEC with neighboring brain structures and reveals its laminar and dorsoventral organization. We propose a new molecular basis for distinguishing the deep layers of the MEC and show that their similarity to corresponding layers of neocortex is greater than that of superficial layers. Our analysis identifies ion channel-, cell adhesion- and synapse-related genes as candidates for functional differentiation of MEC layers and for encoding of spatial information at different scales along the dorsoventral axis of the MEC. We also reveal laminar organization of genes related to disease pathology and suggest that a high metabolic demand predisposes layer II to neurodegenerative pathology. In principle, our computational pipeline can be applied to high-throughput analysis of many forms of neuroanatomical data. Our results support the hypothesis that differences in gene expression contribute to functional specialization of superficial layers of the MEC and dorsoventral organization of the scale of spatial representations

Comparative neurotranscriptomics in mammals and birds

In this thesis I apply new sequencing technologies and analytical methods derived from genomics and computer science to the neuroanatomy of gene expression. The first project explores characteristics of gene expression across adult neocortical layers in a representative mammal – the mouse. Amongst the thousands of genes and transcripts differentially expressed across layers, I found common functional characteristics of genes that define certain layers, candidate cases of isoform switching, and over a thousand apparent long intergenic non-coding RNA transcripts. The second project compares patterns of gene expression in the structurally diverged adult derivatives of the pallium in mice and chickens. Overall, gene expression levels were moderately correlated between the two species. While expression patterns of ‘marker’ genes were only poorly conserved in these regions, there nevertheless was significant conservation of cross-species marker genes for homologous structures, cell types and functionally analogous regions. Many aspects of these data from both projects can now be easily browsed and searched from custom-built web interfaces. In addition to generating unprecedented genome-wide resources for the neuroscience community to explore the functional and structural dimensions of gene expression amongst different pallial regions in mammals and birds, this work also provides new insights into the widespread evolutionary shuffling of adult marker gene expression

Retooling spare parts: gene duplication and cognition.

Two new studies provide experimental evidence of how ancient genomic duplications of synaptic genes provided the substrate for diversification that ultimately expanded vertebrate cognitive complexity. © 2013 Nature America, Inc. All rights reserved

Transcriptomic perspectives on neocortical structure, development, evolution, and disease

The cerebral cortex is the source of our most complex cognitive capabilities and a vulnerable target of many neurological and neuropsychiatric disorders. Transcriptomics offers a new approach to understanding the cortex at the level of its underlying genetic code, and rapid technological advances have propelled this field to the high-throughput study of the complete set of transcribed genes at increasingly fine resolution to the level of individual cells. These tools have revealed features of the genetic architecture of adult cortical areas, layers, and cell types, as well as spatiotemporal patterning during development. This has allowed a fresh look at comparative anatomy as well, illustrating surprisingly large differences between mammals while at the same time revealing conservation of some features from avians to mammals. Finally, transcriptomics is fueling progress in understanding the causes of neurodevelopmental diseases such as autism, linking genetic association studies to specific molecular pathways and affected brain regions

Water-phase synthesis of cationic silica/polyamine nanoparticles

Silica particles are commonly functionalized with amine groups on their surface through the hydrolytic condensation of aminotrialkoxysilanes for use in bioimaging, enzyme immobilization, and other applications. Eliminating this aminotrialkoxysilane condensation step could simplify and improve the efficiency of the synthesis of amine-functionalized silica. Here, we describe a one-pot, ambient-condition, water-phase method to synthesize silica-based nanoparticles (NPs) that present surface amine groups. The formation mechanism involves the electrostatic cross-linking of cationic polyallylamine hydrochloride by citrate anions and the infusion of the resulting polymer/salt aggregates by silicic acid. The particles were unimodal with average diameters in the range of 40 to 100 nm, as determined by the size of the templating polymer-salt aggregates. Colorimetric analysis using Coomassie brilliant blue and zeta potential measurements confirmed the presence of surface amine groups on the hybrid silica/polymer NPs. The point of zero-charge value for these NPs was ∼5, between the corresponding values of unfunctionalized and aminopropyltriethoxysilane-functionalized silica particles (∼2 and ∼10, respectively). Surface charge calculations indicated the hybrid NPs had a lower amine surface density than aminopropyltriethoxysilane-functionalized silica (0.057 #/nm 2 vs 0.169 #/nm 2 at pH 7). The polymer-salt aggregate synthesis chemistry is a new approach toward controlling the amine surface density and point of zero-charge of hybrid silica/polymer NPs. © 2012 American Chemical Society