Clinical exome sequencing routinely identifies missense variants in disease-related genes, but functional characterization is rarely undertaken, leading to diagnostic uncertainty1,2. For example, mutations in PPARG cause Mendelian lipodystrophy3,4 and increase risk of type 2 diabetes (T2D)5. While approximately one in 500 people harbor missense variants in PPARG, most are of unknown consequence. To prospectively characterize PPARγ variants we used highly parallel oligonucleotide synthesis to construct a library encoding all 9,595 possible single amino acid substitutions. We developed a pooled functional assay in human macrophages, experimentally evaluated all protein variants, and used the experimental data to train a variant classifier by supervised machine learning (http://miter.broadinstitute.org). When applied to 55 novel missense variants identified in population-based and clinical sequencing, the classifier annotated six as pathogenic; these were subsequently validated by single-variant assays. Saturation mutagenesis and prospective experimental characterization can support immediate diagnostic interpretation of newly discovered missense variants in disease-related genes.

The outer segments of cones serve as light detectors for daylight color vision, and their dysfunction leads to human blindness conditions. We show that the cone-specific disruption of DGCR8 in adult mice led to the loss of miRNAs and the loss of outer segments, resulting in photoreceptors with significantly reduced light responses. However, the number of cones remained unchanged. The loss of the outer segments occurred gradually over 1 month, and during this time the genetic signature of cones decreased. Reexpression of the sensory-cell-specific miR-182 and miR-183 prevented outer segment loss. These miRNAs were also necessary and sufficient for the formation of inner segments, connecting cilia and short outer segments, as well as light responses in stem-cell-derived retinal cultures. Our results show that miR-182- and miR-183-regulated pathways are necessary for cone outer segment maintenance in vivo and functional outer segment formation in vitro.

We present the first gene regulatory network (GRN) that pertains to post-developmental gene expression. Specifically, we mapped a transcription regulatory network of Caenorhabditis elegans metabolic gene promoters using gene-centered yeast one-hybrid assays. We found that the metabolic GRN is enriched for nuclear hormone receptors (NHRs) compared with other gene-centered regulatory networks, and that these NHRs organize into functional network modules.The NHR family has greatly expanded in nematodes; C. elegans has 284 NHRs, whereas humans have only 48. We show that the NHRs in the metabolic GRN have metabolic phenotypes, suggesting that they do not simply function redundantly.The mediator subunit MDT-15 preferentially interacts with NHRs that occur in the metabolic GRN.We describe an NHR circuit that responds to nutrient availability and propose a model for the evolution and organization of NHRs in C. elegans metabolic regulatory networks.

The prefrontal cortex encodes and stores numerous, often disparate, schemas and flexibly switches between them. Recent research on artificial neural networks trained by reinforcement learning has made it possible to model fundamental processes underlying schema encoding and storage. Yet how the brain is able to create new schemas while preserving and utilizing old schemas remains unclear. Here we propose a simple neural network framework that incorporates hierarchical gating to model the prefrontal cortex’s ability to flexibly encode and use multiple disparate schemas. We show how gating naturally leads to transfer learning and robust memory savings. We then show how neuropsychological impairments observed in patients with prefrontal damage are mimicked by lesions of our network. Our architecture, which we call DynaMoE, provides a fundamental framework for how the prefrontal cortex may handle the abundance of schemas necessary to navigate the real world.