Protein Interaction Networks Link Schizophrenia Risk Loci to Synaptic Function

Single-cell multi-cohort dissection of the schizophrenia transcriptome

The complexity and heterogeneity of schizophrenia have hindered mechanistic elucidation and the development of more effective therapies. Here, we performed single-cell dissection of schizophrenia-associated transcriptomic changes in the human prefrontal cortex across 140 individuals in two independent cohorts. Excitatory neurons were the most affected cell group, with transcriptional changes converging on neurodevelopment and synapse-related molecular pathways. Transcriptional alterations included known genetic risk factors, suggesting convergence of rare and common genomic variants on neuronal population-specific alterations in schizophrenia. Based on the magnitude of schizophrenia-associated transcriptional change, we identified two populations of individuals with schizophrenia marked by expression of specific excitatory and inhibitory neuronal cell states. This single-cell atlas links transcriptomic changes to etiological genetic risk factors, contextualizing established knowledge within the human cortical cytoarchitecture and facilitating mechanistic understanding of schizophrenia pathophysiology and heterogeneity.

How Variation in Risk Allele Output and Gene Interactions Shape the Genetic Architecture of Schizophrenia

Schizophrenia is a highly heritable polygenic psychiatric disorder. Characterization of its genetic architecture may lead to a better understanding of the overall burden of risk variants and how they determine susceptibility to disease. A major goal of this project is to develop a modeling approach to compare and quantify the relative effects of single nucleotide polymorphisms (SNPs), copy number variants (CNVs) and other factors. We derived a mathematical model for the various genetic contributions based on the probability of expressing a combination of risk variants at a frequency that matched disease prevalence. The model included estimated risk variant allele outputs (VAOs) adjusted for population allele frequency. We hypothesized that schizophrenia risk genes would be more interactive than random genes and we confirmed this relationship. Gene–gene interactions may cause network ripple effects that spread and amplify small individual effects of risk variants. The modeling revealed that the number of risk alleles required to achieve the threshold for susceptibility will be determined by the average functional locus output (FLO) associated with a risk allele, the risk allele frequency (RAF), the number of protective variants present and the extent of gene interactions within and between risk loci. The model can account for the quantitative impact of protective variants as well as CNVs on disease susceptibility. The fact that non-affected individuals must carry a non-trivial burden of risk alleles suggests that genetic susceptibility will inevitably reach the threshold for schizophrenia at a recurring frequency in the population.

Differential gene regulation in the anterior cingulate cortex and superior temporal cortex in schizophrenia: A molecular network approach

The closely connected anterior cingulate cortex (ACC) and superior temporal cortex (STC) are important for higher cognitive functions. Both brain regions are disturbed in schizophrenia, i.e., functional and structural alterations have been reported. This postmortem investigation in brains from patients with schizophrenia and controls compared gene expression in the left ACC and left STC. Most differentially expressed genes were unique to each brain region, but some clusters of genes were equally dysregulated in both, giving rise to a more general disease-specific pattern of gene regulation. The data was used to construct a molecular network of the genes identically expressed in both regions as primary nodes and the metabolically connected genes as secondary nodes. The network analysis identified downregulated clusters of immune-associated gene products and upregulated clusters belonging to the ubiquitin-proteasome system. These findings could help to identify new potential therapeutic targets for future approaches.

Synaptic Elimination in Neurological Disorders

Synapses are well known as the main structures responsible for transmitting information through the release and recognition of neurotransmitters by pre- and post-synaptic neurons. These structures are widely formed and eliminated throughout the whole lifespan via processes termed synaptogenesis and synaptic pruning, respectively. Whilst the first pro-cess is needed for ensuring proper connectivity between brain regions and also with the periphery, the second phenomenon is important for their refinement by eliminating weaker and unnecessary synapses and, at the same time, maintaining and fa-voring the stronger ones, thus ensuring proper synaptic transmission. It is well-known that synaptic elimination is modulated by neuronal activity. However, only recently the role of the classical complement cascade in promoting this phenomenon has been demonstrated. Specifically, microglial cells recognize activated complement component 3 (C3) bound to synapses tar-geted for elimination, triggering their engulfment. As this is a highly relevant process for adequate neuronal functioning, dis-ruptions or exacerbations in synaptic pruning could lead to severe circuitry alterations that could underlie neuropathological alterations typical of neurological and neuropsychiatric disorders. In this review, we focus on discussing the possible in-volvement of excessive synaptic elimination in Alzheimer’s disease, as it has already been reported dendritic spine loss in post-synaptic neurons, increased association of complement proteins with its synapses and, hence, augmented microglia-mediated pruning in animal models of this disorder. In addition, we briefly discuss how this phenomenon could be related to other neurological disorders, including multiple sclerosis and schizophrenia.

The proteome and its dynamics: A missing piece for integrative multi-omics in schizophrenia

The complex and heterogeneous pathophysiology of schizophrenia can be deconstructed by integration of large-scale datasets encompassing genes through behavioral phenotypes. Genome-wide datasets are now available for genetic, epigenetic and transcriptomic variations in schizophrenia, which are then analyzed by newly devised systems biology algorithms. A missing piece, however, is the inclusion of information on the proteome and its dynamics in schizophrenia. Proteomics has lagged behind omics of the genome, transcriptome and epigenome since analytic platforms were relatively less robust for proteins. There has been remarkable progress, however, in the instrumentation of liquid chromatography (LC) and mass spectrometry (MS) (LCMS), experimental paradigms and bioinformatics of the proteome. Here, we present a summary of methodological innovations of recent years in MS based proteomics and the power of new generation proteomics, review proteomics studies that have been conducted in schizophrenia to date, and propose how such data can be analyzed and integrated with other omics results. The function of a protein is determined by multiple molecular properties, i.e., subcellular localization, posttranslational modification (PTMs) and protein-protein interactions (PPIs). Incorporation of these properties poses additional challenges in proteomics and their integration with other omics; yet is a critical next step to close the loop of multi-omics integration. In sum, the recent advent of high-throughput proteome characterization technologies and novel mathematical approaches enable us to incorporate functional properties of the proteome to offer a comprehensive multi-omics based understanding of schizophrenia pathophysiology.

A Longitudinal Model of Human Neuronal Differentiation for Functional Investigation of Schizophrenia Polygenic Risk

BACKGROUND

Common psychiatric disorders are characterized by complex disease architectures with many small genetic effects that contribute and complicate biological understanding of their etiology. There is therefore a pressing need for in vitro experimental systems that allow for interrogation of polygenic psychiatric disease risk to study the underlying biological mechanisms.

METHODS

We have developed an analytical framework that integrates genome-wide disease risk from genome-wide association studies with longitudinal in vitro gene expression profiles of human neuronal differentiation.

RESULTS

We demonstrate that the cumulative impact of risk loci of specific psychiatric disorders is significantly associated with genes that are differentially expressed and upregulated during differentiation. We find the strongest evidence for schizophrenia, a finding that we replicate in an independent dataset. A longitudinal gene cluster involved in synaptic function primarily drives the association with schizophrenia risk.

CONCLUSIONS

These findings reveal that in vitro human neuronal differentiation can be used to translate the polygenic architecture of schizophrenia to biologically relevant pathways that can be modeled in an experimental system. Overall, this work emphasizes the use of longitudinal in vitro transcriptomic signatures as a cellular readout and the application to the genetics of complex traits.

Male increase in brain gene expression variability is linked to genetic risk for schizophrenia

Schizophrenia shows substantial sex differences in age of onset, course, and treatment response, but the biological basis of these effects is incompletely understood. Here we show that during human development, males show a regionally specific decrease in brain expression similarity compared to females. The genes modulating this effect were significantly co-expressed with schizophrenia risk genes during prefrontal cortex brain development in the fetal period as well as during early adolescence. This suggests a genetic contribution to a mechanism through which developmental abnormalities manifest with psychosis during adolescence. It further supports sex differences in brain expression variability as a factor underlying the well-established sex differences in schizophrenia.

Identifying pathway modules of tuberculosis in children by analyzing multiple different networks

Tuberculosis (TB), which is caused by the mycobacterium TB, is the major cause of human death worldwide. The aim of this study was to identify the biomarkers involved in child TB. Gene expression data were obtained from the Array Express Archive of Functional Genomics Data. Gene expression data and protein-protein interaction (PPI) data were downloaded to construct differential gene co-expression networks (DCNs). The Benjamini-Hochberg algorithm was used to correct the P-value. In total, 3,820 edges (PPIs) and 1,359 nodes (genes) were obtained from the human-related PPIs data and gene expression data at the criteria of absolute value of Pearson's correlation coefficient >0.8. The DCNs were formed by these edges and nodes. Thirteen seed genes were obtained by ranging z-scores. Eight significant multiple different modules were identified from DCNs using the statistical significant test. In conclusion, the seed genes and significant modules constitute potential biomarkers that reveal the underlying mechanisms in child TB. The new identified biomarkers may contribute to an understanding of TB and provide a new therapeutic method for the treatment of TB.

Detection of Janus-activated kinase-1 and its interacting proteins by the method of luminescent oxygen channeling

Janus-activated kinase-1 (JAK1) plays an important role in many signaling pathways, including the JAK–STAT and SOCS pathways.