LAUSR

The advent of open access to genomic data offers new opportunities to revisit old clinical debates while approaching them from a different angle. We examine anew the question of whether psychiatric and neurological disorders are different from each other by assessing the pool of genes associated with disorders that are understood as psychiatric or as neurological. We do so in the context of transcriptome data tracked as human embryonic stem cells differentiate and become neurons. Building upon probabilistic layers of increasing complexity, we describe the dynamics and stochastic trajectories of the full transcriptome and the embedded genes associated with psychiatric and/or neurological disorders. From marginal distributions of a gene’s expression across hundreds of cells, to joint interactions taken globally to determine degree of pairwise dependency, to networks derived from probabilistic graphs along maximal spanning trees, we have discovered two fundamentally different classes of genes underlying these disorders and differentiating them. One class of genes boasts higher variability in expression and lower dependencies (“active genes”); the other has lower variability and higher dependencies (“lazy genes”). They give rise to different network architectures and different transitional states. Active genes have large hubs and a fragile topology, whereas lazy genes show more distributed code during the maturation toward neuronal state. Lazy genes boost differentiation between psychiatric and neurological disorders also at the level of tissue across the brain, spinal cord, and glands. These genes, with their low variability and asynchronous ON/OFF states that have been treated as gross data and excluded from traditional analyses, are helping us settle this old argument at more than one level of inquiry. 2 Manuscript Contribution to the Field There is an ongoing debate on whether psychiatric disorders are fundamentally different from neurological disorders. We examine this question anew in the context of transcriptome data tracked as human embryonic stem cells differentiate and become neurons. Building upon probabilistic layers of increasing complexity, we describe the dynamics and stochastic trajectories of the full transcriptome and the embedded genes associated with psychiatric and/or neurological disorders. Two fundamentally different types of genes emerge: “lazy genes” with low, odd, and asynchronous variability patterns in expression that would have been, under traditional approaches, considered superfluous gross data, and “active genes” likely included under traditional computational techniques. They give rise to different network architectures and different transitional dynamic states. Active genes have large hubs and a fragile topology, whereas lazy genes show more distributed code during the maturation toward neuronal state. Under these new wholistic approach, the methods reveal that the lazy genes play a fundamental role in differentiating psychiatric from neurological disorders across more than one level of analysis. Including these genes in future interrogation of transcriptome data may open new lines of inquiry across brain genomics in general.