Critical period plasticity-related transcriptional aberrations in schizophrenia and bipolar disorder

An EEG-Based Neuroplastic Approach to Predictive Coding in People With Schizophrenia or Traumatic Brain Injury

Despite different etiologies, people with schizophrenia (SCZ) or with traumatic brain injury (TBI) both show aberrant neuroplasticity. One neuroplastic mechanism that may be affected is prediction error coding. We used a roving mismatch negativity (rMMN) paradigm which uses different lengths of standard tone trains and is optimized to assess predictive coding. Twenty-five SCZ, 22 TBI (mild to moderate), and 25 healthy controls were assessed. We used a frequency-deviant rMMN in which the number of standards preceding the deviant was either 2, 6, or 36. We evaluated repetition positivity to the standard tone immediately preceding a deviant tone (repetition positivity [RP], to assess formation of the memory trace), deviant negativity to the deviant stimulus (deviant negativity [DN], which reflects signaling of a prediction error), and the difference wave between the 2 (the MMN). We found that SCZ showed reduced DN and MMN compared with healthy controls and with people with mild to moderate TBI. We did not detect impairments in any index (RP, DN, or MMN) in people with TBI compared to controls. Our findings suggest that prediction error coding assessed with rMMN is aberrant in SCZ but intact in TBI, though there is a suggestion that severity of head injury results in poorer prediction error coding.

A critical period plasticity framework for the sensorimotor-association axis of cortical neurodevelopment

To understand human brain development it is necessary to describe not only the spatiotemporal patterns of neurodevelopment but also the neurobiological mechanisms that underlie them. Human neuroimaging studies have provided evidence for a hierarchical sensorimotor-to-association (S-A) axis of cortical neurodevelopment. Understanding the biological mechanisms that underlie this program of development using traditional neuroimaging approaches has been challenging. Animal models have been used to identify periods of enhanced experience-dependent plasticity - 'critical periods' - that progress along cortical hierarchies and are governed by a conserved set of neurobiological mechanisms that promote and then restrict plasticity. In this review we hypothesize that the S-A axis of cortical development in humans is partly driven by the cascading maturation of critical period plasticity mechanisms. We then describe how recent advances in in vivo neuroimaging approaches provide a promising path toward testing this hypothesis by linking signals derived from non-invasive imaging to critical period mechanisms.

ERVW-1 Activates ATF6-Mediated Unfolded Protein Response by Decreasing GANAB in Recent-Onset Schizophrenia

Schizophrenia, a mental disorder, afflicts 1% of the worldwide population. The dysregulation of homeostasis in the endoplasmic reticulum (ER) has been implicated in schizophrenia. Moreover, recent studies indicate that ER stress and the unfolded protein response (UPR) are linked to this mental disorder. Our previous research has verified that endogenous retrovirus group W member 1 envelope (ERVW-1), a risk factor for schizophrenia, is elevated in individuals with schizophrenia. Nevertheless, no literature is available regarding the underlying relationship between ER stress and ERVW-1 in schizophrenia. The aim of our research was to investigate the molecular mechanism connecting ER stress and ERVW-1 in schizophrenia. Here, we employed Gene Differential Expression Analysis to predict differentially expressed genes (DEGs) in the human prefrontal cortex of schizophrenic patients and identified aberrant expression of UPR-related genes. Subsequent research indicated that the UPR gene called XBP1 had a positive correlation with ATF6, BCL-2, and ERVW-1 in individuals with schizophrenia using Spearman correlation analysis. Furthermore, results from the enzyme-linked immunosorbent assay (ELISA) suggested increased serum protein levels of ATF6 and XBP1 in schizophrenic patients compared with healthy controls, exhibiting a strong correlation with ERVW-1 using median analysis and Mann-Whitney U analysis. However, serum GANAB levels were decreased in schizophrenic patients compared with controls and showed a significant negative correlation with ERVW-1, ATF6, and XBP1 in schizophrenic patients. Interestingly, in vitro experiments verified that ERVW-1 indeed increased ATF6 and XBP1 expression while decreasing GANAB expression. Additionally, the confocal microscope experiment suggested that ERVW-1 could impact the shape of the ER, leading to ER stress. GANAB was found to participate in ER stress regulated by ERVW-1. In conclusion, ERVW-1 induced ER stress by suppressing GANAB expression, thereby upregulating the expression of ATF6 and XBP1 and ultimately contributing to the development of schizophrenia.

Psychosis spectrum illnesses as disorders of prefrontal critical period plasticity

Emerging research on neuroplasticity processes in psychosis spectrum illnesses-from the synaptic to the macrocircuit levels-fill key gaps in our models of pathophysiology and open up important treatment considerations. In this selective narrative review, we focus on three themes, emphasizing alterations in spike-timing dependent and Hebbian plasticity that occur during adolescence, the critical period for prefrontal system development: (1) Experience-dependent dysplasticity in psychosis emerges from activity decorrelation within neuronal ensembles. (2) Plasticity processes operate bidirectionally: deleterious environmental and experiential inputs shape microcircuits. (3) Dysregulated plasticity processes interact across levels of scale and time and include compensatory mechanisms that have pathogenic importance. We present evidence that-given the centrality of progressive dysplastic changes, especially in prefrontal cortex-pharmacologic or neuromodulatory interventions will need to be supplemented by corrective learning experiences for the brain if we are to help people living with these illnesses to fully thrive.

In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways

Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

Etiopathogenic Models of Psychosis Spectrum Illnesses Must Resolve Four Key Features

Etiopathogenic models for psychosis spectrum illnesses are converging on a number of key processes, such as the influence of specific genes on the synthesis of proteins important in synaptic functioning, alterations in how neurons respond to synaptic inputs and engage in synaptic pruning, and microcircuit dysfunction that leads to more global cortical information processing vulnerabilities. Disruptions in prefrontal operations then accumulate and propagate over time, interacting with environmental factors, developmental processes, and homeostatic mechanisms, eventually resulting in symptoms of psychosis and disability. However, there are 4 key features of psychosis spectrum illnesses that are of primary clinical relevance but have been difficult to assimilate into a single model and have thus far received little direct attention: 1) the bidirectionality of the causal influences for the emergence of psychosis, 2) the catastrophic clinical threshold seen in first episodes of psychosis and why it is irreversible in some individuals, 3) observed biotypes that are neurophysiologically distinct but clinically both convergent and divergent, and 4) a reconciliation of the role of striatal dopaminergic dysfunction with models of prefrontal cortical state instability. In this selective review, we briefly describe these 4 hallmark features and we argue that theoretically driven computational perspectives making use of both algorithmic and neurophysiologic models are needed to reduce this complexity and variability of psychosis spectrum illnesses in a principled manner.

Clozapine Reverses Dysfunction of Glutamatergic Neurons Derived From Clozapine-Responsive Schizophrenia Patients

The cellular pathology of schizophrenia and the potential of antipsychotics to target underlying neuronal dysfunctions are still largely unknown. We employed glutamatergic neurons derived from induced pluripotent stem cells (iPSC) obtained from schizophrenia patients with known histories of response to clozapine and healthy controls to decipher the mechanisms of action of clozapine, spanning from molecular (transcriptomic profiling) and cellular (electrophysiology) levels to observed clinical effects in living patients. Glutamatergic neurons derived from schizophrenia patients exhibited deficits in intrinsic electrophysiological properties, synaptic function and network activity. Deficits in K⁺ and Na⁺ currents, network behavior, and glutamatergic synaptic signaling were restored by clozapine treatment, but only in neurons from clozapine-responsive patients. Moreover, neurons from clozapine-responsive patients exhibited a reciprocal dysregulation of gene expression, particularly related to glutamatergic and downstream signaling, which was reversed by clozapine treatment. Only neurons from clozapine responders showed return to normal function and transcriptomic profile. Our results underscore the importance of K⁺ and Na⁺ channels and glutamatergic synaptic signaling in the pathogenesis of schizophrenia and demonstrate that clozapine might act by normalizing perturbances in this signaling pathway. To our knowledge this is the first study to demonstrate that schizophrenia iPSC-derived neurons exhibit a response phenotype correlated with clinical response to an antipsychotic. This opens a new avenue in the search for an effective treatment agent tailored to the needs of individual patients.

N-Methyl-d-Aspartate receptor and inflammation in dorsolateral prefrontal cortex in schizophrenia

Lower N-methyl-d-aspartate receptor (NMDAR) GluN1 subunit levels and heightened neuroinflammation are found in the cortex in schizophrenia. Since neuroinflammation can lead to changes in NMDAR function, it is possible that these observations are linked in schizophrenia. We aimed to extend our previous studies by measuring molecular indices of NMDARs that define key functional properties of this receptor - particularly the ratio of GluN2A and GluN2B subunits - in dorsolateral prefrontal cortex (DLPFC) from schizophrenia and control cases (37/37). We sought to test whether changes in these measures are specific to the subset of schizophrenia cases with high levels of inflammation-related mRNAs, defined as a high inflammatory subgroup. Quantitative autoradiography was used to detect 'functional' NMDARs ([³H]MK-801), GluN1-coupled-GluN2A subunits ([³H]CGP-39653), and GluN1-coupled-GluN2B subunits ([³H]Ifenprodil). Quantitative RT-PCR was used to measure NMDAR subunit transcripts (GRIN1, GRIN2A and GRIN2B). The ratios of GluN2A:GluN2B binding and GRIN2A:GRIN2B mRNAs were calculated as an index of putative NMDAR composition. We found: 1) GluN2A binding, and 2) the ratios of GluN2A:GluN2B binding and GRIN2A:GRIN2B mRNAs were lower in schizophrenia cases versus controls (p < 0.05), and 3) lower GluN2A:GluN2B binding and GRIN2A:GRIN2B mRNA ratios were exaggerated in the high inflammation/schizophrenia subgroup compared to the low inflammation/control subgroup (p < 0.05). No other NMDAR-related indices were significantly changed in the high inflammation/schizophrenia subgroup. This suggests that neuroinflammation may alter NMDAR stoichiometry rather than targeting total NMDAR levels overall, and future studies could aim to determine if anti-inflammatory treatment can alleviate this aspect of NMDAR-related pathology.

A Practical Guide to Sparse k-Means Clustering for Studying Molecular Development of the Human Brain

Studying the molecular development of the human brain presents unique challenges for selecting a data analysis approach. The rare and valuable nature of human postmortem brain tissue, especially for developmental studies, means the sample sizes are small (n), but the use of high throughput genomic and proteomic methods measure the expression levels for hundreds or thousands of variables [e.g., genes or proteins (p)] for each sample. This leads to a data structure that is high dimensional (p ≫ n) and introduces the curse of dimensionality, which poses a challenge for traditional statistical approaches. In contrast, high dimensional analyses, especially cluster analyses developed for sparse data, have worked well for analyzing genomic datasets where p ≫ n. Here we explore applying a lasso-based clustering method developed for high dimensional genomic data with small sample sizes. Using protein and gene data from the developing human visual cortex, we compared clustering methods. We identified an application of sparse k-means clustering [robust sparse k-means clustering (RSKC)] that partitioned samples into age-related clusters that reflect lifespan stages from birth to aging. RSKC adaptively selects a subset of the genes or proteins contributing to partitioning samples into age-related clusters that progress across the lifespan. This approach addresses a problem in current studies that could not identify multiple postnatal clusters. Moreover, clusters encompassed a range of ages like a series of overlapping waves illustrating that chronological- and brain-age have a complex relationship. In addition, a recently developed workflow to create plasticity phenotypes (Balsor et al., 2020) was applied to the clusters and revealed neurobiologically relevant features that identified how the human visual cortex changes across the lifespan. These methods can help address the growing demand for multimodal integration, from molecular machinery to brain imaging signals, to understand the human brain’s development.

A Primer on Constructing Plasticity Phenotypes to Classify Experience-Dependent Development of the Visual Cortex

Many neural mechanisms regulate experience-dependent plasticity in the visual cortex (V1), and new techniques for quantifying large numbers of proteins or genes are transforming how plasticity is studied into the era of big data. With those large data sets comes the challenge of extracting biologically meaningful results about visual plasticity from data-driven analytical methods designed for high-dimensional data. In other areas of neuroscience, high-information content methodologies are revealing more subtle aspects of neural development and individual variations that give rise to a richer picture of brain disorders. We have developed an approach for studying V1 plasticity that takes advantage of the known functions of many synaptic proteins for regulating visual plasticity. We use that knowledge to rebrand protein measurements into plasticity features and combine those into a plasticity phenotype. Here, we provide a primer for analyzing experience-dependent plasticity in V1 using example R code to identify high-dimensional changes in a group of proteins. We describe using PCA to classify high-dimensional plasticity features and use them to construct a plasticity phenotype. In the examples, we show how to use this analytical framework to study and compare experience-dependent development and plasticity of V1 and apply the plasticity phenotype to translational research questions. We include an R package “PlasticityPhenotypes” that aggregates the coding packages and custom code written in RStudio to construct and analyze plasticity phenotypes.

Stress during critical periods of development and risk for schizophrenia

Schizophrenia is a neurodevelopmental disorder with genetic predisposition, and stress has long been linked to its etiology. While stress affects all stages of the illness, increasing evidence suggests that stress during critical periods of development may be particularly detrimental, increasing individual's vulnerability to psychosis. To thoroughly understand the potential causative role of stress, our group has been focusing on the prenatal methylazoxymethanol acetate (MAM) rodent model, and discovered that MAM offspring display abnormal stress reactivity and heightened anxiety prepubertally, prior to the manifestation of a hyperdopaminergic state. Furthermore, pharmacologically treating anxiety during prepuberty prevented the emergence of the dopamine dysfunction in adulthood. Interestingly, sufficiently strong stressors applied to normal rats selectively during early development can recapitulate multiple schizophrenia-related phenotypes of MAM rats, whereas the same stress paradigm during adulthood only produced short-term depression-related deficits. Altogether, the evidence is thus converging: developmental disruption (genetic or environmental) might render animals more susceptible to the deleterious effects of stress during critical time windows, during which unregulated stress can lead to the emergence of psychosis later in life. As an important region regulating the midbrain dopamine system, the ventral hippocampus is particularly vulnerable to stress, and the distinct maturational profile of its fast-spiking parvalbumin interneurons may largely underlie such vulnerability. In this review, by discussing emerging evidence spanning clinical and basic science studies, we propose developmental stress vulnerability as a novel link between early predispositions and environmental triggering events in the pathophysiology of schizophrenia. This promising line of research can potentially provide not only insights into the etiology, but also a "roadmap" for disease prevention.