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

FUS pathology in ALS is linked to alterations in multiple ALS-associated proteins and rescued by drugs stimulating autophagy

Amyotrophic lateral sclerosis (ALS) is a lethal disease characterized by motor neuron degeneration and associated with aggregation of nuclear RNA-binding proteins (RBPs), including FUS. How FUS aggregation and neurodegeneration are prevented in healthy motor neurons remain critically unanswered questions. Here, we use a combination of ALS patient autopsy tissue and induced pluripotent stem cell-derived neurons to study the effects of FUS mutations on RBP homeostasis. We show that FUS' tendency to aggregate is normally buffered by interacting RBPs, but this buffering is lost when FUS mislocalizes to the cytoplasm due to ALS mutations. The presence of aggregation-prone FUS in the cytoplasm causes imbalances in RBP homeostasis that exacerbate neurodegeneration. However, enhancing autophagy using small molecules reduces cytoplasmic FUS, restores RBP homeostasis and rescues motor function in vivo. We conclude that disruption of RBP homeostasis plays a critical role in FUS-ALS and can be treated by stimulating autophagy

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

Expression profiling of mouse subplate reveals a dynamic gene network and disease association with autism and schizophrenia.

The subplate zone is a highly dynamic transient sector of the developing cerebral cortex that contains some of the earliest generated neurons and the first functional synapses of the cerebral cortex. Subplate cells have important functions in early establishment and maturation of thalamocortical connections, as well as in the development of inhibitory cortical circuits in sensory areas. So far no role has been identified for cells in the subplate in the mature brain and disease association of the subplate-specific genes has not been analyzed systematically. Here we present gene expression evidence for distinct roles of the mouse subplate across development as well as unique molecular markers to extend the repertoire of subplate labels. Performing systematic comparisons between different ages (embryonic days 15 and 18, postnatal day 8, and adult), we reveal the dynamic and constant features of the markers labeling subplate cells during embryonic and early postnatal development and in the adult. This can be visualized using the online database of subplate gene expression at https://molnar.dpag.ox.ac.uk/subplate/. We also identify embryonic similarities in gene expression between the ventricular zones, intermediate zone, and subplate, and distinct postnatal similarities between subplate, layer 5, and layers 2/3. The genes expressed in a subplate-specific manner at some point during development show a statistically significant enrichment for association with autism spectrum disorders and schizophrenia. Our report emphasizes the importance of the study of transient features of the developing brain to better understand neurodevelopmental disorders