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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 mood and diagnose major depressive disorder. 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 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 novel 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%). We show that the passive behavioral phenotypes enhance prediction of future mood over and above a baseline model which predicts future mood based on past depression severity alone for 52% of individuals in our cohort. In conclusion, 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.

Astrocytes respond toinjury and disease in the central nervous system with reactive changes that influence the outcome of the disorder1-4. 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 arepotentiallyassociated 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.

Author(s): Ramos, Eliana Marisa; Dokuru, Deepika Reddy; Van Berlo, Victoria; Wojta, Kevin; Wang, Qing; Huang, Alden Y; Deverasetty, Sandeep; Qin, Yue; van Blitterswijk, Marka; Jackson, Jazmyne; Appleby, Brian; Bordelon, Yvette; Brannelly, Patrick; Brushaber, Danielle E; Dickerson, Bradford; Dickinson, Susan; Domoto-Reilly, Kimiko; Faber, Kelley; Fields, Julie; Fong, Jamie; Foroud, Tatiana; Forsberg, Leah K; Gavrilova, Ralitza; Ghoshal, Nupur; Goldman, Jill; Graff-Radford, Jonathan; Graff-Radford, Neill; Grant, Ian; Grossman, Murray; Heuer, Hilary W; Hsiung, Ging-Yuek R; Huey, Edward; Irwin, David; Kantarci, Kejal; Karydas, Anna; Kaufer, Daniel; Kerwin, Diana; Knopman, David; Kornak, John; Kramer, Joel H; Kremers, Walter; Kukull, Walter; Litvan, Irene; Ljubenkov, Peter; Lungu, Codrin; Mackenzie, Ian; Mendez, Mario F; Miller, Bruce L; Onyike, Chiadi; Pantelyat, Alexander; Pearlman, Rodney; Petrucelli, Len; Potter, Madeline; Rankin, Katherine P; Rascovsky, Katya; Roberson, Erik D; Rogalski, Emily; Shaw, Leslie; Syrjanen, Jeremy; Tartaglia, Maria Carmela; Tatton, Nadine; Taylor, Joanne; Toga, Arthur; Trojanowski, John Q; Weintraub, Sandra; Wong, Bonnie; Wszolek, Zbigniew; Rademakers, Rosa; Boeve, Brad F; Rosen, Howard J; Boxer, Adam L; ARTFL/LEFFTDS consortium; Coppola, Giovanni | Abstract: IntroductionThe 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.MethodsWe screened the major dementia-associated genes in 302 sporadic and 390 familial (symptomatic or at-risk) participants enrolled in these studies.ResultsAmong 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.DiscussionOur study reports the genetic characterization of a large FTD series and supports an unbiased sequencing screen, irrespective of clinical presentation or family history.

Genome sequencing studies of nonhuman primate (NHP) pedigree and population samples are discovering variants on a large and rapidly growing scale. These studies are increasing the utility of several NHP species as model systems for human disease. In particular, by identifying homozygous protein truncating variants (hPTVs) in genes hypothesized to play a role in causing human diseases, it may be possible to elucidate mechanisms for the phenotypic impact of such variants through investigations that are infeasible in humans. The Caribbean vervet (Chlorocebus aethiops sabaeus) is uniquely valuable for this purpose, as the dramatic expansion of its population following severe bottlenecks has enabled PTVs that passed through the bottleneck to attain a relatively high frequency. Using whole genome sequence (WGS) data from 719 monkeys of the Vervet Research Colony (VRC) extended pedigree, we found 2,802 protein-truncating alleles in 1,747 protein-coding genes present in homozygous state in at least one monkey. Polymorphic sites for 923 SNV hPTVs were also observed in natural Caribbean populations from which the VRC descends. The vervet genome browser (VGB) includes information on these PTVs, together with a catalog of phenotypes and biological samples available for monkeys who carry them. We describe initial explorations of the possible impact of vervet PTVs on early infant mortality.

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 find repeat length dependent transcriptional signatures are prominent in the striatum, less so in cortex, and minimal in the liver. Co-expression network analyses reveal 13 striatal and 5 cortical modules that are highly correlated with CAG length and age, and that are preserved in HD models and some in the patients. Top striatal modules implicate mHtt CAG length and age in graded impairment of striatal medium spiny neuron identity gene expression and in dysregulation of cAMP signaling, cell death, and protocadherin genes. Importantly, we used proteomics to confirm 790 genes and 5 striatal modules with CAG length-dependent dysregulation at both RNA and protein levels, and validated 22 striatal module genes as modifiers of mHtt toxicities in vivo.

We present a new approach for pathogen surveillance we call Geogenomics. Geogenomics examines the geographic distribution of the genomes of pathogens, with a particular emphasis on those mutations that give rise to drug resistance. We engineered a new web system called Geogenomic Mutational Atlas of Pathogens (GoMAP) that enables investigation of the global distribution of individual drug resistance mutations. As a test case we examined mutations associated with HIV resistance to FDA-approved antiretroviral drugs. GoMAP-HIV makes use of existing public drug resistance and HIV protein sequence data to examine the distribution of 872 drug resistance mutations in ∼ 502,000 sequences for many countries in the world. We also implemented a broadened classification scheme for HIV drug resistance mutations. Several patterns for geographic distributions of resistance mutations were identified by visual mining using this web tool. GoMAP-HIV is an open access web application available at http://www.bio-toolkit.com/GoMap/project/

Minimotif Miner (MnM available at http://minimotifminer.org or http://mnm.engr.uconn.edu) is an online database for identifying new minimotifs in protein queries. Minimotifs are short contiguous peptide sequences that have a known function in at least one protein. Here we report the third release of the MnM database which has now grown 60-fold to approximately 300,000 minimotifs. Since short minimotifs are by their nature not very complex we also summarize a new set of false-positive filters and linear regression scoring that vastly enhance minimotif prediction accuracy on a test data set. This online database can be used to predict new functions in proteins and causes of disease.