Meta-analysis of genomic variants and gene expression data in schizophrenia suggests the potential need for adjunctive therapeutic interventions for neuropsychiatric disorders

Critical assessment of on-premise approaches to scalable genome analysis

BACKGROUND

Plummeting DNA sequencing cost in recent years has enabled genome sequencing projects to scale up by several orders of magnitude, which is transforming genomics into a highly data-intensive field of research. This development provides the much needed statistical power required for genotype-phenotype predictions in complex diseases.

METHODS

In order to efficiently leverage the wealth of information, we here assessed several genomic data science tools. The rationale to focus on on-premise installations is to cope with situations where data confidentiality and compliance regulations etc. rule out cloud based solutions. We established a comprehensive qualitative and quantitative comparison between BCFtools, SnpSift, Hail, GEMINI, and OpenCGA. The tools were compared in terms of data storage technology, query speed, scalability, annotation, data manipulation, visualization, data output representation, and availability.

RESULTS

Tools that leverage sophisticated data structures are noted as the most suitable for large-scale projects in varying degrees of scalability in comparison to flat-file manipulation (e.g., BCFtools, and SnpSift). Remarkably, for small to mid-size projects, even lightweight relational database.

CONCLUSION

The assessment criteria provide insights into the typical questions posed in scalable genomics and serve as guidance for the development of scalable computational infrastructure in genomics.

Analysis of transcript levels of a few schizophrenia candidate genes in neurons from a transgenic mouse embryonic stem cell model overexpressing DNMT1

Overexpression of DNA Methyltransferase I (DNMT1) is considered as one of the etiological factors for schizophrenia (SZ). However, information on genes subjected to dysregulation because of DNMT1 overexpression is limited. To test whether a larger group of SZ-associated genes are affected, we selected 15 genes reported to be dysregulated in patients (Gad1, Reln, Ank3, Cacna1c, Dkk3, As3mt, Ppp1r11, Smad5, Syn1, Wnt1, Pdgfra, Gsk3b, Cxcl12, Tcf4 and Fez1). Transcript levels of these genes were compared between neurons derived from Dnmt1^tet/tet (Tet/Tet) mouse embryonic stem cells (ESCs) that overexpress DNMT1 with R1 (wild-type) neurons. Transcript levels of thirteen genes were significantly altered in Tet/Tet neurons of which, the dysregulation patterns of 11 were similar to patients. Transcript levels of eight out of these eleven were also significantly altered in Tet/Tet ESCs, but the dysregulation patterns of only five were similar to neurons. Comparative analyses among ESCs, embryoid bodies and neurons divided the 15 genes into four distinct groups with a majority showing developmental stage-specific patterns of dysregulation. Reduced Representational Bisulfite Sequencing data from neurons did not show any altered promoter DNA methylation for the dysregulated genes. Doxycycline treatment of Tet/Tet ESCs that eliminated DNMT1, reversed the direction of dysregulation of only four genes (Gad1, Dkk3, As3mt and Syn1). These results suggest that 1. Increased DNMT1 affected the levels of a majority of the transcripts studied, 2. Dysregulation appears to be independent of promoter methylation, 3. Effects of increased DNMT1 levels were reversible for only a subset of the genes studied, and 4. Increased DNMT1 levels may affect transcript levels of multiple schizophrenia-associated genes.

Potentially repurposable drugs for schizophrenia identified from its interactome

We previously presented the protein-protein interaction network of schizophrenia associated genes, and from it, the drug-protein interactome which showed the drugs that target any of the proteins in the interactome. Here, we studied these drugs further to identify whether any of them may potentially be repurposable for schizophrenia. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the drugs from the interactome using the BaseSpace Correlation Engine, and shortlisted those that had a negative correlation with differential gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes with opposite drug-induced versus schizophrenia-associated expression profiles were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had a pathophysiological overlap with schizophrenia. Drugs that targeted the same genes as the shortlisted drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis will help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development which faces a high failure rate.