Willsey AJ et al. (2018), The Psychiatric Cell Map Initiative: A Converge...

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Cell 2018 Jul 26;1743:505-520. doi: 10.1016/j.cell.2018.06.016.

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The Psychiatric Cell Map Initiative: A Convergent Systems Biological Approach to Illuminating Key Molecular Pathways in Neuropsychiatric Disorders.

Willsey AJ , Morris MT , Wang S , Willsey HR , Sun N , Teerikorpi N , Baum TB , Cagney G , Bender KJ , Desai TA , Srivastava D , Davis GW , Doudna J , Chang E , Sohal V , Lowenstein DH , Li H , Agard D , Keiser MJ , Shoichet B , von Zastrow M , Mucke L , Finkbeiner S , Gan L , Sestan N , Ward ME , Huttenhain R , Nowakowski TJ , Bellen HJ , Frank LM , Khokha MK , Lifton RP , Kampmann M , Ideker T , State MW , Krogan NJ .

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Although gene discovery in neuropsychiatric disorders, including autism spectrum disorder, intellectual disability, epilepsy, schizophrenia, and Tourette disorder, has accelerated, resulting in a large number of molecular clues, it has proven difficult to generate specific hypotheses without the corresponding datasets at the protein complex and functional pathway level. Here, we describe one path forward-an initiative aimed at mapping the physical and genetic interaction networks of these conditions and then using these maps to connect the genomic data to neurobiology and, ultimately, the clinic. These efforts will include a team of geneticists, structural biologists, neurobiologists, systems biologists, and clinicians, leveraging a wide array of experimental approaches and creating a collaborative infrastructure necessary for long-term investigation. This initiative will ultimately intersect with parallel studies that focus on other diseases, as there is a significant overlap with genes implicated in cancer, infectious disease, and congenital heart defects.

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Species referenced: Xenopus
Genes referenced: chrd pdia2 smpd1

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	Figure 1. Levels of Pathogenesis and Analysis This figure outlines the different levels on which a disorder could manifest or be investigated, starting from a genetic variant (displayed in red). It includes future parallel investigation in both in vitro and in vivo model systems across levels of complexity (.e.g. human induced pluripotent stem cells, Xenopus tropicalis tadpoles, mice, etc.). While much headway has been made in characterizing the genetic architecture of neuropsychiatric disorders, many of the other levels of analysis remain poorly understood. The âbottom-upâ approach discussed here targets the basic levels of this hierarchy, building a strong foundation for the translation of genetics to a higher level of biological understanding. âTop-downâ and âmiddle-outâ approaches will be critical as well.
	Figure 2. Conceptual Overview of a Bottom-Up Approach The objective of a â€œbottom-upâ€ approach, such as the one depicted here, is to translate genetic findings to specific insights about pathobiology of neuropsychiatric disorders. The first step is the identification of specific genes. To date, the majority of gene-level discoveries have been made through the study of high effect-size de novo variants (top). An example association graph is displayed in the second panel, with contributions from different classes of variants shown per gene (yellow versus blue; e.g., missense versus nonsense variants). G1â€“G10 represent generic genes or the proteins they encode, and T1â€“T3 transcription factors. â€œGNâ€ represents a novel gene implicated through network analyses. There are three main types of interaction networks that could be mapped: protein-protein interaction (PPI), protein-DNA interaction (PDI), and genetic interaction (GI) networks (middle panels). The PPI networks will identify protein complexes (thick edges) as well as relationships between complexes (thin edges). Similarly, PDI networks would identify connections that correspond to putative regulatory relationships between proteins and DNA regulatory regions of other genes (e.g., â€œG4-Râ€). GI networks would identify functional relationships between genes with directionality (red indicates positive interactions, blue negative interactions). These orthogonal datasets should ultimately be integrated, in the context of a cell, to generate a pathway-level understanding (bottom panel). These hypotheses should then be followed up with cellular phenotyping and validation experiments.
	Figure 3. Neuropsychiatric Disorder Genes Overlap with Other Disorders and Initiatives (A) Risk genes, identified from de novo variants, overlap across NPDs. We utilized a common pipeline to identify high confidence genes (false discovery rate [FDR] <0.1) for autism spectrum disorders (ASD), intellectual disability (ID), epileptic encephalopathies (EE), schizophrenia, (SCZ), Tourette disorder (TD), and obsessive-compulsive disorder (OCD) (Table S1). We considered de novo variants from whole exome sequencing studies only and leveraged TADA to estimate FDRs (He et al., 2013). We omitted targeted sequencing studies in order to generate a fair â€œexome-wideâ€ comparison across disorders and focused on de novo coding variants only as non-coding variants are outside of the scope of this review. NPDs with 5 or more high confidence risk genes are shown (i.e., TD and OCD are excluded), and the sizes of the ovals are proportional to the number of genes identified for each disorder (total number in parentheses). ASD and ID as well as ASD and EE strongly overlap. Each p value is corrected for multiple comparisons. Some of these studies excluded probands with co-morbid NPDs. For example, in the EE studies, probands with moderate-to-severe developmental impairment, or diagnosis of autism or pervasive developmental disorder before the onset of seizures were excluded. Therefore, the observed overlaps may underestimate the extent of shared genetic etiology. Similarly, we identified these genes solely based on de novo (rare) variants. Hence, the well-documented genetic overlap at the common variant level is not reflected here. (B) NPD genes, identified from de novo variants, overlap with other disorders and initiatives. Using the same methods as in (A), we identified risk genes for congenital heart disease (CHD). We also generated a combined list of NPD genes, corresponding to the unique set of genes from pooling (A) with the set of genes identified from analyzing the deciphering developmental disorders (DDD) studies (Table S1), which examine developmental disorders collectively. The DDD genes were omitted from (A) because many of the NPDs are represented in this cohort and therefore the relevance of overlap is unclear. Finally, we derived a list of cancer genes from the Cancer Gene Census (Futreal et al., 2004) and Host-Pathogen Interaction (HPI) genes from the HPIDB 2.0 database (Ammari et al., 2016) (Table S1; Data S1). High confidence NPD genes significantly overlap with each of the other three gene sets (Tables S2 and S3) suggesting strong synergy between NPD initiatives and the Cancer Cell Map Initiative (CCMI), the Host Pathogen Map Initiative (HPMI), and similar efforts in CHD.

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