Characterizing patient compliance over six months in remote digital trials of Parkinson's and Huntington disease.

BACKGROUND: A growing number of clinical trials use various sensors and smartphone applications to collect data outside of the clinic or hospital, raising the question to what extent patients comply with the unique requirements of remote study protocols. Compliance is particularly important in conditions where patients are motorically and cognitively impaired. Here, we sought to understand patient compliance in digital trials of two such pathologies, Parkinson's disease (PD) and Huntington disease (HD). METHODS: Patient compliance was assessed in two remote, six-month clinical trials of PD (n = 51, Clinician Input Study funded by the Michael J. Fox Foundation for Parkinson's Research) and HD (n = 17, sponsored by Teva Pharmaceuticals). We monitored four compliance metrics specific to remote studies: smartphone app-based medication reporting, app-based symptoms reporting, the duration of smartwatch data streaming except while charging, and the performance of structured motor tasks at home. RESULTS: While compliance over time differed between the PD and HD studies, both studies maintained high compliance levels for their entire six month duration. None (- 1%) to a 30% reduction in compliance rate was registered for HD patients, and a reduction of 34 to 53% was registered for the PD study. Both studies exhibited marked changes in compliance rates during the initial days of enrollment. Interestingly, daily smartwatch data streaming patterns were similar, peaking around noon, dropping sharply in the late evening hours around 8 pm, and having a mean of 8.6 daily streaming hours for the PD study and 10.5 h for the HD study. Individual patients tended to have either high or low compliance across all compliance metrics as measured by pairwise correlation. Encouragingly, predefined schedules and app-based reminders fulfilled their intended effect on the timing of medication intake reporting and performance of structured motor tasks at home. CONCLUSIONS: Our findings suggest that maintaining compliance over long durations is feasible, promote the use of predefined app-based reminders, and highlight the importance of patient selection as highly compliant patients typically have a higher adherence rate across the different aspects of the protocol. Overall, these data can serve as a reference point for the design of upcoming remote digital studies. TRIAL REGISTRATION: Trials described in this study include a sub-study of the Open PRIDE-HD Huntington's disease study (TV7820-CNS-20016), which was registered on July 7th, 2015, sponsored by Teva Pharmaceuticals Ltd., and registered on Clinicaltrials.gov as NCT02494778 and EudraCT as 2015-000904-24 .

Changes in vaginal community state types reflect major shifts in the microbiome.

Background: Recent studies of various human microbiome habitats have revealed thousands of bacterial species and the existence of large variation in communities of microorganisms in the same habitats across individual human subjects. Previous efforts to summarize this diversity, notably in the human gut and vagina, have categorized microbiome profiles by clustering them into community state types (CSTs). The functional relevance of specific CSTs has not been established. Objective: We investigate whether CSTs can be used to assess dynamics in the microbiome. Design: We conduct a re-analysis of five sequencing-based microbiome surveys derived from vaginal samples with repeated measures. Results: We observe that detection of a CST transition is largely insensitive to choices in methods for normalization or clustering. We find that healthy subjects persist in a CST for two to three weeks or more on average, while those with evidence of dysbiosis tend to change more often. Changes in CST can be gradual or occur over less than one day. Upcoming CST changes and switches to high-risk CSTs can be predicted with high accuracy in certain scenarios. Finally, we observe that presence of Gardnerella vaginalis is a strong predictor of an upcoming CST change. Conclusion: Overall, our results show that the CST concept is useful for studying microbiome dynamics.

Visualization of single mRNAs reveals temporal association of proteins with microRNA-regulated mRNA.

Although many proteins are known to function in microRNA (miRNA)-based translational repression, we lack a comprehensive understanding of temporal relationships between the mRNA, miRNA and their constituent proteins. To understand the dynamics of miRNA and protein interactions, we created a synthetic inducible miRNA system in mammalian cells. By visualizing single mRNAs and observing their co-localization with proteins over time, we produced a temporal association map of miRNA-associated factors. Argonaute2, Dcp1a, hedls and Rck co-localize with miRNA-regulated mRNA after 24 h of miRNA induction, and RNAi knockdown of any one of these proteins affected the co-localization of any of the other proteins with miRNA-regulated mRNA, demonstrating that these proteins could interact with each other in a complex. We identified Argonaute2 and hedls as proteins that co-localize and interact with miRNA-regulated mRNA, indicating that processing body components are involved in long-term storage of miRNA-regulated mRNA.

Analyzing the "CareGap": assessing gaps in adherence to clinical guidelines in adult soft tissue sarcoma.

Clinical decision support systems (CDSSs) are gaining popularity as tools that assist physicians in optimizing medical care. These systems typically comply with evidence-based medicine and are designed with input from domain experts. Nonetheless, deviations from CDSS recommendations are abundant across a broad spectrum of disorders, raising the question as to why this phenomenon exists. Here, we analyze this gap in adherence to a clinical guidelines-based CDSS by examining the physician treatment decisions for 1329 adult soft tissue sarcoma patients in northern Italy using patient-specific parameters. Dubbing this analysis "CareGap", we find that deviations correlate strongly with certain disease features such as local versus metastatic clinical presentation. We also notice that deviations from the guideline-based CDSS suggestions occur more frequently for patients with shorter survival time. Such observations can direct physicians' attention to distinct patient cohorts that are prone to higher deviation levels from clinical practice guidelines. This illustrates the value of CareGap analysis in assessing quality of care for subsets of patients within a larger pathology.

Cell-to-cell variability of alternative RNA splicing.

Heterogeneity in the expression levels of mammalian genes is large even in clonal populations and has phenotypic consequences. Alternative splicing is a fundamental aspect of gene expression, yet its contribution to heterogeneity is unknown. Here, we use single-molecule imaging to characterize the cell-to-cell variability in mRNA isoform ratios for two endogenous genes, CAPRIN1 and MKNK2. We show that isoform variability in non-transformed, diploid cells is remarkably close to the minimum possible given the stochastic nature of individual splicing events, while variability in HeLa cells is considerably higher. Analysis of the potential sources of isoform ratio heterogeneity indicates that a difference in the control over splicing factor activity is one origin of this increase. Our imaging approach also visualizes non-alternatively spliced mRNA and active transcription sites, and yields spatial information regarding the relationship between splicing and transcription. Together, our work demonstrates that mammalian cells minimize fluctuations in mRNA isoform ratios by tightly regulating the splicing machinery.

Evicase: an evidence-based case structuring approach for personalized healthcare.

The personalized medicine era stresses a growing need to combine evidence-based medicine with case based reasoning in order to improve the care process. To address this need we suggest a framework to generate multi-tiered statistical structures we call Evicases. Evicase integrates established medical evidence together with patient cases from the bedside. It then uses machine learning algorithms to produce statistical results and aggregators, weighted predictions, and appropriate recommendations. Designed as a stand-alone structure, Evicase can be used for a range of decision support applications including guideline adherence monitoring and personalized prognostic predictions.

Engineering a synthetic dual-organism system for hydrogen production.

Molecular hydrogen produced biologically from renewable biomass is an attractive replacement for fossil fuels. One potential route for biological hydrogen production is the conversion of biomass into formate, which can subsequently be processed into hydrogen by Escherichia coli. Formate is also a widely used commodity chemical, making its bioproduction even more attractive. Here we demonstrate the implementation of a formate-overproducing pathway in Saccharomyces cerevisiae, a well-established industrial organism. By expressing the anaerobic enzyme pyruvate formate lyase from E. coli, we engineered a strain of yeast that overproduced formate relative to undetectable levels in the wild type. The addition of a downstream enzyme, AdhE of E. coli, resulted in an additional 4.5-fold formate production increase as well as an increase in growth rate and biomass yield. Overall, an 18-fold formate increase was achieved in a strain background whose formate degradation pathway had been deleted. Finally, as a proof of concept, we were able to produce hydrogen from this formate-containing medium by using E. coli as a catalyst in a two-step process. With further optimizations, it may be feasible to use S. cerevisiae on a larger scale as the foundation for yeast-based biohydrogen.

Epidemiological Models Lacking Process Noise Can Be Overconfident.

Mathematic models of epidemics are the key tool for predicting future course of disease in a population and analyzing the effects of possible intervention policies. Typically, models that produce deterministic are applied for making predictions and reaching decisions. Stochastic modeling methods present an alternative. Here, we demonstrate by example why it is important that stochastic modeling be used in population health decision support systems.

Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins.

Compiling a comprehensive list of cancer driver genes is imperative for oncology diagnostics and drug development. While driver genes are typically discovered by analysis of tumor genomes, infrequently mutated driver genes often evade detection due to limited sample sizes. Here, we address sample size limitations by integrating tumor genomics data with a wide spectrum of gene-specific properties to search for rare drivers, functionally classify them, and detect features characteristic of driver genes. We show that our approach, CAnceR geNe similarity-based Annotator and Finder (CARNAF), enables detection of potentially novel drivers that eluded over a dozen pan-cancer/multi-tumor type studies. In particular, feature analysis reveals a highly concentrated pool of known and putative tumor suppressors among the <1% of genes that encode very large, chromatin-regulating proteins. Thus, our study highlights the need for deeper characterization of very large, epigenetic regulators in the context of cancer causality.

Understanding Deviations from Clinical Practice Guidelines in Adult Soft Tissue Sarcoma.

In recent years we have witnessed the increasing adoption of clinical practice guidelines (CPGs) as decision support tools that guide medical treatment. As CPGs gain popularity, it has become evident that physicians frequently deviate from CPG recommendations, both erroneously and due to sound medical rationale. In this study we developed a methodology to computationally identify these deviation cases and understand their movitation. This was achieved using an integrated approach consisting of natural language processing, data modeling, and comparison methods to characterize deviations from CPG recommendations for 1431 adult soft tissue sarcoma patients. The results show that 48.9% of patient treatment programs deviate from CPG recommendations, with the largest deviation type being overtreatment, followed by differences in drug treatments. Interestingly, we identified over a dozen potential reasons for these deviations, with those directly related to the patients' cancer status being most abundant. These findings can be used to modify CPGs, increase adherence to CPG recommendations, reduce treatment cost, and potentially impact sarcoma care. Our approach can be applied to additional diseases that are subject to high deviation levels from CPGs.

A model-driven approach to clinical practice guidelines representation and evaluation using standards.

Clinical Practice Guidelines (CPGs) contain a set of schematic plans for the treatment and management of patients who have a particular clinical condition. CPGs are increasingly being used to support physician decision making. Many groups develop tools for the representation of CPGs. These differ in their approaches to addressing particular modeling challenges. Despite this strong effort, physicians still primarily rely on free-text narrative descriptions. Thus, a core challenge is to develop a formal representation of CPGs that physicians can easily read and verify, yet a machine can process, analyze and apply directly to a patient's EHR data. Our paper proposes a solution to this fundamental problem by describing an approach to CPG formalization using the Natural Rule Language (NRL), coupled with transformation to Object Constraint Language (OCL) constraints that are applied on a patient's clinical data record, in our case an HL7 Continuity of Care Document (CCD). We illustrate our approach on a simple guideline directive for Essential Hypertension.

Systems-level engineering of nonfermentative metabolism in yeast.

We designed and experimentally validated an in silico gene deletion strategy for engineering endogenous one-carbon (C1) metabolism in yeast. We used constraint-based metabolic modeling and computer-aided gene knockout simulations to identify five genes (ALT2, FDH1, FDH2, FUM1, and ZWF1), which, when deleted in combination, predicted formic acid secretion in Saccharomyces cerevisiae under aerobic growth conditions. Once constructed, the quintuple mutant strain showed the predicted increase in formic acid secretion relative to a formate dehydrogenase mutant (fdh1 fdh2), while formic acid secretion in wild-type yeast was undetectable. Gene expression and physiological data generated post hoc identified a retrograde response to mitochondrial deficiency, which was confirmed by showing Rtg1-dependent NADH accumulation in the engineered yeast strain. Formal pathway analysis combined with gene expression data suggested specific modes of regulation that govern C1 metabolic flux in yeast. Specifically, we identified coordinated transcriptional regulation of C1 pathway enzymes and a positive flux control coefficient for the branch point enzyme 3-phosphoglycerate dehydrogenase (PGDH). Together, these results demonstrate that constraint-based models can identify seemingly unrelated mutations, which interact at a systems level across subcellular compartments to modulate flux through nonfermentative metabolic pathways.