Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders
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 wit...
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| Format: | Article |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2022.884707/full |
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| author | Theodoros Bermperidis Simon Schafer Fred H. Gage Terrence Sejnowski Elizabeth B. Torres Elizabeth B. Torres Elizabeth B. Torres |
| author_facet | Theodoros Bermperidis Simon Schafer Fred H. Gage Terrence Sejnowski Elizabeth B. Torres Elizabeth B. Torres Elizabeth B. Torres |
| author_sort | Theodoros Bermperidis |
| collection | DOAJ |
| description | 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 (High Expression Variability-HEV genes); the other has lower variability and higher dependencies (Low Expression Variability-LEV genes). They give rise to different network architectures and different transitional states. HEV genes have large hubs and a fragile topology, whereas LEV genes show more distributed code during the maturation toward neuronal state. LEV 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. |
| first_indexed | 2024-12-11T18:17:26Z |
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| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-11T18:17:26Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-bfeba5ca34ba4ba7b4ca23f5ca8146f62022-12-22T00:55:23ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2022-06-011610.3389/fnins.2022.884707884707Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric DisordersTheodoros Bermperidis0Simon Schafer1Fred H. Gage2Terrence Sejnowski3Elizabeth B. Torres4Elizabeth B. Torres5Elizabeth B. Torres6Sensory Motor Integration Laboratory, Department of Psychology, Rutgers University, Piscataway, NJ, United StatesGenetics Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United StatesGenetics Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United StatesComputational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, United StatesSensory Motor Integration Laboratory, Department of Psychology, Rutgers University, Piscataway, NJ, United StatesComputational Biomedicine Imaging and Modeling Center, Department of Computer Science, Rutgers University, Piscataway, NJ, United StatesRutgers Center for Cognitive Science, Rutgers University, Piscataway, NJ, United StatesThe 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 (High Expression Variability-HEV genes); the other has lower variability and higher dependencies (Low Expression Variability-LEV genes). They give rise to different network architectures and different transitional states. HEV genes have large hubs and a fragile topology, whereas LEV genes show more distributed code during the maturation toward neuronal state. LEV 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.https://www.frontiersin.org/articles/10.3389/fnins.2022.884707/fullembryonic stem cellstranscriptomeneurologicalpsychiatrictissuesautism |
| spellingShingle | Theodoros Bermperidis Simon Schafer Fred H. Gage Terrence Sejnowski Elizabeth B. Torres Elizabeth B. Torres Elizabeth B. Torres Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders Frontiers in Neuroscience embryonic stem cells transcriptome neurological psychiatric tissues autism |
| title | Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders |
| title_full | Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders |
| title_fullStr | Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders |
| title_full_unstemmed | Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders |
| title_short | Dynamic Interrogation of Stochastic Transcriptome Trajectories Using Disease Associated Genes Reveals Distinct Origins of Neurological and Psychiatric Disorders |
| title_sort | dynamic interrogation of stochastic transcriptome trajectories using disease associated genes reveals distinct origins of neurological and psychiatric disorders |
| topic | embryonic stem cells transcriptome neurological psychiatric tissues autism |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2022.884707/full |
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