Astrocytes respond to injury and disease in the central nervous system with reactive changes that influence the outcome of the disorder^1-4. These changes include differentially expressed genes (DEGs) whose contextual diversity and regulation are poorly understood. Here we combined biological and informatic analyses, including RNA sequencing, protein detection, assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and conditional gene deletion, to predict transcriptional regulators that differentially control more than 12,000 DEGs that are potentially associated with astrocyte reactivity across diverse central nervous system disorders in mice and humans. DEGs associated with astrocyte reactivity exhibited pronounced heterogeneity across disorders. Transcriptional regulators also exhibited disorder-specific differences, but a core group of 61 transcriptional regulators was identified as common across multiple disorders in both species. We show experimentally that DEG diversity is determined by combinatorial, context-specific interactions between transcriptional regulators. Notably, the same reactivity transcriptional regulators can regulate markedly different DEG cohorts in different disorders; changes in the access of transcriptional regulators to DNA-binding motifs differ markedly across disorders; and DEG changes can crucially require multiple reactivity transcriptional regulators. We show that, by modulating reactivity, transcriptional regulators can substantially alter disorder outcome, implicating them as therapeutic targets. We provide searchable resources of disorder-related reactive astrocyte DEGs and their predicted transcriptional regulators. Our findings show that transcriptional changes associated with astrocyte reactivity are highly heterogeneous and are customized from vast numbers of potential DEGs through context-specific combinatorial transcriptional-regulator interactions.

Over the last ten years, there has been considerable progress in using digital behavioral phenotypes, captured passively and continuously from smartphones and wearable devices, to infer depressive mood. However, most digital phenotype studies suffer from poor replicability, often fail to detect clinically relevant events, and use measures of depression that are not validated or suitable for collecting large and longitudinal data. Here, we report high-quality longitudinal validated assessments of depressive mood from computerized adaptive testing paired with continuous digital assessments of behavior from smartphone sensors for up to 40 weeks on 183 individuals experiencing mild to severe symptoms of depression. We apply a combination of cubic spline interpolation and idiographic models to generate individualized predictions of future mood from the digital behavioral phenotypes, achieving high prediction accuracy of depression severity up to three weeks in advance (R² ≥ 80%) and a 65.7% reduction in the prediction error over a baseline model which predicts future mood based on past depression severity alone. Finally, our study verified the feasibility of obtaining high-quality longitudinal assessments of mood from a clinical population and predicting symptom severity weeks in advance using passively collected digital behavioral data. Our results indicate the possibility of expanding the repertoire of patient-specific behavioral measures to enable future psychiatric research.

To gain insight into how mutant huntingtin (mHtt) CAG repeat length modifies Huntington's disease (HD) pathogenesis, we profiled mRNA in over 600 brain and peripheral tissue samples from HD knock-in mice with increasing CAG repeat lengths. We found repeat length-dependent transcriptional signatures to be prominent in the striatum, less so in cortex, and minimal in the liver. Coexpression network analyses revealed 13 striatal and 5 cortical modules that correlated highly with CAG length and age, and that were preserved in HD models and sometimes in patients. Top striatal modules implicated mHtt CAG length and age in graded impairment in the expression of identity genes for striatal medium spiny neurons and in dysregulation of cyclic AMP signaling, cell death and protocadherin genes. We used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at the protein level, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo.

Introduction

The Advancing Research and Treatment for Frontotemporal Lobar Degeneration (ARTFL) and Longitudinal Evaluation of Familial Frontotemporal Dementia Subjects (LEFFTDS) consortia are two closely connected studies, involving multiple North American centers that evaluate both sporadic and familial frontotemporal dementia (FTD) participants and study longitudinal changes.

Methods

We screened the major dementia-associated genes in 302 sporadic and 390 familial (symptomatic or at-risk) participants enrolled in these studies.

Results

Among the sporadic patients, 16 (5.3%) carried chromosome 9 open reading frame 72 (C9orf72), microtubule-associated protein tau (MAPT), and progranulin (GRN) pathogenic variants, whereas in the familial series we identified 207 carriers from 146 families. Of interest, one patient was found to carry a homozygous C9orf72 expansion, while another carried both a C9orf72 expansion and a GRN pathogenic variant. We also identified likely pathogenic variants in the TAR DNA binding protein (TARDBP), presenilin 1 (PSEN1), and valosin containing protein (VCP) genes, and a subset of variants of unknown significance in other rare FTD genes.

Discussion

Our study reports the genetic characterization of a large FTD series and supports an unbiased sequencing screen, irrespective of clinical presentation or family history.