The Stanley Neuropathology Consortium Integrative Database (SNCID) for Psychiatric Disorders

Interpretation of SNP combination effects on schizophrenia etiology based on stepwise deep learning with multi-precision data

Schizophrenia genome-wide association studies (GWAS) have reported many genomic risk loci, but it is unclear how they affect schizophrenia susceptibility through interactions of multiple SNPs. We propose a stepwise deep learning technique with multi-precision data (SLEM) to explore the SNP combination effects on schizophrenia through intermediate molecular and cellular functions. The SLEM technique utilizes two levels of precision data for learning. It constructs initial backbone networks with more precise but small amount of multilevel assay data. Then, it learns strengths of intermediate interactions with the less precise but massive amount of GWAS data. The learned networks facilitate identifying effective SNP interactions from the intractably large space of all possible SNP combinations. We have shown that the extracted SNP combinations show higher accuracy than any single SNPs and preserve the accuracy in an independent dataset. The learned networks also provide interpretations of molecular and cellular interactions of SNP combinations toward schizophrenia etiology.

Overexpression of transmembrane TNFα in brain endothelial cells induces schizophrenia-relevant behaviors

Upregulation of genes and coexpression networks related to immune function and inflammation have been repeatedly reported in the brain of individuals with schizophrenia. However, a causal relationship between the abnormal immune/inflammation-related gene expression and schizophrenia has not been determined. We conducted co-expression networks using publicly available RNA-seq data from prefrontal cortex (PFC) and hippocampus (HP) of 64 individuals with schizophrenia and 64 unaffected controls from the SMRI tissue collections. We identified proinflammatory cytokine, transmembrane tumor necrosis factor-α (tmTNFα), as a potential regulator in the module of co-expressed genes that we find related to the immune/inflammation response in endothelial cells (ECs) and/or microglia of the brain of individuals with schizophrenia. The immune/inflammation-related modules associated with schizophrenia and the TNF signaling pathway that regulate the network were replicated in an independent cohort of brain samples from 68 individuals with schizophrenia and 135 unaffected controls. To investigate the association between the overexpression of tmTNFα in brain ECs and schizophrenia-like behaviors, we induced short-term overexpression of the uncleavable form of (uc)-tmTNFα in ECs of mouse brain for 7 weeks. We found schizophrenia-relevant behavioral deficits in these mice, including cognitive impairment, abnormal sensorimotor gating, and sensitization to methamphetamine (METH) induced locomotor activity and METH-induced neurotransmitter levels. These uc-tmTNFα effects were mediated by TNF receptor2 (TNFR2) and induced activation of TNFR2 signaling in astrocytes and neurons. A neuronal module including neurotransmitter signaling pathways was down-regulated in the brain of mice by the short-term overexpression of the gene, while an immune/inflammation-related module was up-regulated in the brain of mice after long-term expression of 22 weeks. Our results indicate that tmTNFα may play a direct role in regulating neurotransmitter signaling pathways that contribute to the clinical features of schizophrenia.

Increased immune cell and altered microglia and neurogenesis transcripts in an Australian schizophrenia subgroup with elevated inflammation

We previously identified a subgroup of schizophrenia cases (~40 %) with heightened inflammation in the neurogenic subependymal zone (SEZ) (North et al., 2021b). This schizophrenia subgroup had changes indicating reduced microglial activity, increased peripheral immune cells, increased stem cell dormancy/quiescence and reduced neuronal precursor cells. The present follow-up study aimed to replicate and extend those novel findings in an independent post-mortem cohort of schizophrenia cases and controls from Australia. RNA was extracted from SEZ tissue from 20 controls and 22 schizophrenia cases from the New South Wales Brain Tissue Resource Centre, and gene expression analysis was performed. Cluster analysis of inflammation markers (IL1B, IL1R1, SERPINA3 and CXCL8) revealed a high-inflammation schizophrenia subgroup comprising 52 % of cases, which was a significantly greater proportion than the 17 % of high-inflammation controls. Consistent with our previous report (North et al., 2021b), those with high-inflammation and schizophrenia had unchanged mRNA expression of markers for steady-state and activated microglia (IBA1, HEXB, CD68), decreased expression of phagocytic microglia markers (P2RY12, P2RY13), but increased expression of markers for macrophages (CD163), monocytes (CD14), natural killer cells (FCGR3A), and the adhesion molecule ICAM1. Similarly, the high-inflammation schizophrenia subgroup emulated increased quiescent stem cell marker (GFAPD) and decreased neuronal progenitor (DLX6-AS1) and immature neuron marker (DCX) mRNA expression; but also revealed a novel increase in a marker of immature astrocytes (VIM). Replicating primary results in an independent cohort demonstrates that inflammatory subgroups in the SEZ in schizophrenia are reliable, robust and enhance understanding of neuropathological heterogeneity when studying schizophrenia.

A meta-study on transcription factor networks in the suicidal brain

There is evidence supporting the presence of brain gene expression differences between suicides and non-suicides. Such differences have been implicated in suicide pathophysiology. However, regulatory factors underlying these gene expression differences have not been fully understood. Therefore, the identification of differences in regulatory mechanisms, i.e., transcriptional factors between suicides and non-suicides is crucial for the understanding of suicide neurobiology. In this study, we conducted a transcription factor network meta-study with freely available data from the prefrontal cortex of suicides and non-suicides with different mental disorders, including major depression disorder, bipolar disorder and schizophrenia, as well as healthy controls. Disorder-specific characteristics of suicides and non-suicides transcription factor networks were detected, i.e., the presence of immune response genes in both suicides and non-suicides with major depression disorder networks. Also, we found the presence of ESR1, which has been implicated to give resilience to social stress, in the non-suicides network but not in the suicides with major depression network. Suicides and non-suicides with bipolar disorder shared only three genes in common: FOS, CRY1 and PER2. In addition, we found a higher number of genes involved in immune response in the non-suicides with bipolar disorder compared to the suicides with bipolar disorder network. The suicides and non-suicides with schizophrenia networks exhibited clear differences, including the presence of circadian cycle genes in the suicides with schizophrenia network and their absence in the non-suicides with schizophrenia network. The results of this study provide insight on the regulatory mechanisms underpinning transcriptional changes in the suicidal brain.

Phosphodiesterases PDE2A and PDE10A both change mRNA expression in the human brain with age, but only PDE2A changes in a region-specific manner with psychiatric disease

Many studies implicate altered cyclic nucleotide signaling in the pathophysiology of major depressive disorder (MDD), bipolar disorder (BPD), and schizophrenia (SCZ). As such, we explored how phosphodiesterases 2A (PDE2A) and 10A (PDE10A)-enzymes that break down cyclic nucleotides-may be altered in brains of these patients. Using autoradiographic in situ hybridization on postmortem brain tissue from the Stanley Foundation Neuropathology Consortium, we measured expression of PDE2 and PDE10 mRNA in multiple brain regions implicated in psychiatric pathophysiology, including cingulate cortex, orbital frontal cortex (OFC), superior temporal gyrus, hippocampus, parahippocampal cortex, amygdala, and the striatum. We also assessed how PDE2A and PDE10A expression changes in these brain regions across development using the Allen Institute for Brain Science Brainspan database. Compared to controls, patients with SCZ, MDD and BPD all showed reduced PDE2A mRNA in the amygdala. In contrast, PDE2A expression changes in frontal cortical regions were only significant in patients with SCZ, while those in caudal entorhinal cortex, hippocampus, and the striatum were most pronounced in patients with BPD. PDE10A expression was only detected in striatum and did not differ by disease group; however, all groups showed significantly less PDE10A mRNA expression in ventral versus dorsal striatum. Across development, PDE2A mRNA increased in these brain regions; whereas, PDE10A mRNA expression decreased in all regions except striatum. Thus, PDE2A mRNA expression changes in both a disorder- and brain region-specific manner, potentially implicating PDE2A as a novel diagnostic and/or patient-selection biomarker or therapeutic target.