Background

Smartphones provide a low-cost and efficient means to collect population level data. Several small studies have shown promise in predicting mood variability from smartphone-based sensor and usage data, but have not been generalized to nationally recruited samples. This study used passive smartphone data, demographic characteristics, and baseline depressive symptoms to predict prospective daily mood.

Method

Daily phone usage data were collected passively from 271 Android phone users participating in a fully remote randomized controlled trial of depression treatment (BRIGHTEN). Participants completed daily Patient Health Questionnaire-2. A machine learning approach was used to predict daily mood for the entire sample and individual participants.

Results

Sample-wide estimates showed a marginally significant association between physical mobility and self-reported daily mood (B = -0.04, P < 0.05), but the predictive models performed poorly for the sample as a whole (median R² ∼ 0). Focusing on individuals, 13.9% of participants showed significant association (FDR < 0.10) between a passive feature and daily mood. Personalized models combining features provided better prediction performance (median area under the curve [AUC] > 0.50) for 80.6% of participants and very strong prediction in a subset (median AUC > 0.80) for 11.8% of participants.

Conclusions

Passive smartphone data with current features may not be suited for predicting daily mood at a population level because of the high degree of intra- and interindividual variation in phone usage patterns and daily mood ratings. Personalized models show encouraging early signs for predicting an individual's mood state changes, with GPS-derived mobility being the top most important feature in the present sample.

We utilized induced pluripotent stem cells (iPSCs) derived from Huntington's disease (HD) patients as a human model of HD and determined that the disease phenotypes only manifest in the differentiated neural stem cell (NSC) stage, not in iPSCs. To understand the molecular basis for the CAG repeat expansion-dependent disease phenotypes in NSCs, we performed transcriptomic analysis of HD iPSCs and HD NSCs compared to isogenic controls. Differential gene expression and pathway analysis pointed to transforming growth factor β (TGF-β) and netrin-1 as the top dysregulated pathways. Using data-driven gene coexpression network analysis, we identified seven distinct coexpression modules and focused on two that were correlated with changes in gene expression due to the CAG expansion. Our HD NSC model revealed the dysregulation of genes involved in neuronal development and the formation of the dorsal striatum. The striatal and neuronal networks disrupted could be modulated to correct HD phenotypes and provide therapeutic targets.

Efficient tools for data management and integration are essential for many aspects of high-throughput biology. In particular, annotations of genes and human genetic variants are commonly used but highly fragmented across many resources. Here, we describe MyGene.info and MyVariant.info, high-performance web services for querying gene and variant annotation information. These web services are currently accessed more than three million times permonth. They also demonstrate a generalizable cloud-based model for organizing and querying biological annotation information. MyGene.info and MyVariant.info are provided as high-performance web services, accessible at http://mygene.info and http://myvariant.info . Both are offered free of charge to the research community.

OBJECTIVE:This investigation comprehensively assessed the technology use, preferences, and capacity of diverse injured trauma survivors with posttraumatic stress disorder (PTSD) symptoms. METHOD:A total of 121 patients participating in a randomized clinical trial (RCT) of stepped collaborative care targeting PTSD symptoms were administered baseline one-, three-, and six-month interviews that assessed technology use. Longitudinal data about the instability of patient cell phone ownership and phone numbers were collected from follow-up interviews. PTSD symptoms were also assessed over the course of the six months after injury. Regression analyses explored the associations between cell phone instability and PTSD symptoms. RESULTS:At baseline, 71.9% (n = 87) of patients reported current cell phone ownership, and over half (58.2%, n = 46) of these patients possessed basic cell phones. Only 19.0% (n = 23) of patients had no change in cell phone number or physical phone over the course of the six months postinjury. In regression models that adjusted for relevant clinical and demographic characteristics, cell phone instability was associated with higher six-month postinjury PTSD symptom levels (p < 0.001). CONCLUSIONS:Diverse injured patients at risk for the development of PTSD have unique technology use patterns, including high rates of cell phone instability. These observations should be strongly considered when developing technology-supported interventions for injured patients with PTSD.

Public health newborn screening (NBS) programs provide population-scale ascertainment of rare, treatable conditions that require urgent intervention. Tandem mass spectrometry (MS/MS) is currently used to screen newborns for a panel of rare inborn errors of metabolism (IEMs)^1-4. The NBSeq project evaluated whole-exome sequencing (WES) as an innovative methodology for NBS. We obtained archived residual dried blood spots and data for nearly all IEM cases from the 4.5 million infants born in California between mid-2005 and 2013 and from some infants who screened positive by MS/MS, but were unaffected upon follow-up testing. WES had an overall sensitivity of 88% and specificity of 98.4%, compared to 99.0% and 99.8%, respectively for MS/MS, although effectiveness varied among individual IEMs. Thus, WES alone was insufficiently sensitive or specific to be a primary screen for most NBS IEMs. However, as a secondary test for infants with abnormal MS/MS screens, WES could reduce false-positive results, facilitate timely case resolution and in some instances even suggest more appropriate or specific diagnosis than that initially obtained. This study represents the largest, to date, sequencing effort of an entire population of IEM-affected cases, allowing unbiased assessment of current capabilities of WES as a tool for population screening.

Cellular senescence irreversibly arrests cell proliferation, accompanied by a multi-component senescence-associated secretory phenotype (SASP) that participates in several age-related diseases. Using stable isotope labeling with amino acids (SILACs) and cultured cells, we identify 343 SASP proteins that senescent human fibroblasts secrete at 2-fold or higher levels compared with quiescent cell counterparts. Bioinformatic analysis reveals that 44 of these proteins participate in hemostasis, a process not previously linked with cellular senescence. We validated the expression of some of these SASP factors in cultured cells and in vivo. Mice treated with the chemotherapeutic agent doxorubicin, which induces widespread cellular senescence in vivo, show increased blood clotting. Conversely, selective removal of senescent cells using transgenic p16-3MR mice showed that clearing senescent cells attenuates the increased clotting caused by doxorubicin. Our study provides an in-depth, unbiased analysis of the SASP and unveils a function for cellular senescence in hemostasis.

Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly-interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art.

The advent of next-generation sequencing has dramatically decreased the cost for whole-genome sequencing and increased the viability for its application in research and clinical care. The Personal Genome Project (PGP) provides unrestricted access to genomes of individuals and their associated phenotypes. This resource enabled the Critical Assessment of Genome Interpretation (CAGI) to create a community challenge to assess the bioinformatics community's ability to predict traits from whole genomes. In the CAGI PGP challenge, researchers were asked to predict whether an individual had a particular trait or profile based on their whole genome. Several approaches were used to assess submissions, including ROC AUC (area under receiver operating characteristic curve), probability rankings, the number of correct predictions, and statistical significance simulations. Overall, we found that prediction of individual traits is difficult, relying on a strong knowledge of trait frequency within the general population, whereas matching genomes to trait profiles relies heavily upon a small number of common traits including ancestry, blood type, and eye color. When a rare genetic disorder is present, profiles can be matched when one or more pathogenic variants are identified. Prediction accuracy has improved substantially over the last 6 years due to improved methodology and a better understanding of features.