Standardizing the CAP Score in Huntington's Disease by Predicting Age-at-Onset.

Background: Huntington's disease (HD) is an autosomal dominant, neurological disease caused by an expanded CAG repeat near the N-terminus of the huntingtin (HTT) gene. A leading theory concerning the etiology of HD is that both onset and progression are driven by cumulative exposure to the effects of mutant (or CAG expanded) huntingtin (mHTT). The CAG-Age-Product (CAP) score (i.

A Machine-Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design.

Background: Applying machine-learning algorithms to large datasets such as those available in Huntington's disease offers the opportunity to discover hidden patterns, often not discernible to clinical observation.

Objectives: To develop and validate a model of Huntington's disease progression using probabilistic machine learning methods.

Methods: Longitudinal data encompassing 2079 assessment measures from four observational studies (PREDICT-HD, REGISTRY, TRACK-HD, and Enroll-HD) were integrated and machine-learning methods (Bayesian latent-variable analysis and continuous-time hidden Markov models) were applied to develop a probabilistic model of disease progression.

Estimating the causal effects of modifiable, non-genetic factors on Huntington disease progression using propensity score weighting.

Introduction: Despite being genetically inherited, it is unclear how non-genetic factors (e.g., substance use, employment) might contribute to the progression and severity of Huntington's disease (HD).

Cognitive and Motor Norms for Huntington's Disease.

Background: The progression of Huntington's disease (HD) for gene-expanded carriers is well-studied. Natural aging effects, however, are not often considered in the evaluation of HD progression.

Objective: To examine the effects of natural aging for healthy controls and to develop normative curves by age, sex, and education from the distribution of observed scores for the Symbol Digit Modalities Test, Stroop Word Reading Test, Stroop Color Naming Test, Stroop Interference Test, Total Motor Score, and Total Functional Capacity (TFC) from the Unified Huntington's Disease Rating Scale (UHDRS) along with a composite score.

Robust Markers and Sample Sizes for Multicenter Trials of Huntington Disease.

Objective: The identification of sensitive biomarkers is essential to validate therapeutics for Huntington disease (HD). We directly compare structural imaging markers across the largest collective imaging HD dataset to identify a set of imaging markers robust to multicenter variation and to derive upper estimates on sample sizes for clinical trials in HD.

Methods: We used 1 postprocessing pipeline to retrospectively analyze T1-weighted magnetic resonance imaging (MRI) scans from 624 participants at 3 time points, from the PREDICT-HD, TRACK-HD, and IMAGE-HD studies.

A probabilistic disease progression modeling approach and its application to integrated Huntington's disease observational data.

Objective: Chronic diseases often have long durations with slow, nonlinear progression and complex, and multifaceted manifestation. Modeling the progression of chronic diseases based on observational studies is challenging. We developed a framework to address these challenges by building probabilistic disease progression models to enable better understanding of chronic diseases and provide insights that could lead to better disease management.

G-Computation and Hierarchical Models for Estimating Multiple Causal Effects From Observational Disease Registries With Irregular Visits.

Huntington's Disease (HD) is a neurodegenerative disorder with serious motor, cognitive, and behavioral symptoms. Chorea, a motor symptom of HD characterized by abrupt involuntary movements, is typically treated with tetrabenazine or certain off-label antipsychotics. Clinical trial evidence about the impact of these drugs in the HD population is scant.

An image-based model of brain volume biomarker changes in Huntington's disease.

Objective: Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine-grained model of temporal progression of Huntington's disease from premanifest through to manifest stages.

Methods: We employ a probabilistic event-based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track-HD study, as well as to estimate the uncertainty in the ordering.

An Exploration of Latent Structure in Observational Huntington's Disease Studies.

Huntington's disease (HD) is a monogenic neurodegenerative disorder characterized by the progressive decay of motor and cognitive abilities accompanied by psychiatric episodes. Tracking and modeling the progression of the multi-faceted clinical symptoms of HD is a challenging problem that has important implications for staging of HD patients and the development of improved enrollment criteria for future HD studies and trials. In this paper, we describe the first steps towards this goal.

Influence of sequence changes and environment on intrinsically disordered proteins.

Many large-scale studies on intrinsically disordered proteins are implicitly based on the structural models deposited in the Protein Data Bank. Yet, the static nature of deposited models supplies little insight into variation of protein structure and function under diverse cellular and environmental conditions. While the computational predictability of disordered regions provides practical evidence that disorder is an intrinsic property of proteins, the robustness of disordered regions to changes in sequence or environmental conditions has not been systematically studied.

Identification, analysis, and prediction of protein ubiquitination sites.

Ubiquitination plays an important role in many cellular processes and is implicated in many diseases. Experimental identification of ubiquitination sites is challenging due to rapid turnover of ubiquitinated proteins and the large size of the ubiquitin modifier. We identified 141 new ubiquitination sites using a combination of liquid chromatography, mass spectrometry, and mutant yeast strains.

Intrinsic disorder in pathogenic and non-pathogenic microbes: discovering and analyzing the unfoldomes of early-branching eukaryotes.

Parasitic protozoal infections have long been known to cause profound degrees of sickness and death in humans as well as animal populations. Despite the increase in the number of annotated genomes available for a large variety of protozoa, a great deal more has yet to be learned about them, from their fundamental physiology to mechanisms invoked during host-pathogen interactions. Most of these genomes share a common feature, namely a high prevalence of low complexity regions in their predicted proteins, which is believed to contribute to the uniqueness of the individual species within this diverse group of early-branching eukaryotes.

Characterization of molecular recognition features, MoRFs, and their binding partners.

Molecular Recognition Features (MoRFs) are short, interaction-prone segments of protein disorder that undergo disorder-to-order transitions upon specific binding, representing a specific class of intrinsically disordered regions that exhibit molecular recognition and binding functions. MoRFs are common in various proteomes and occupy a unique structural and functional niche in which function is a direct consequence of intrinsic disorder. Example MoRFs collected from the Protein Data Bank (PDB) have been divided into three subtypes according to their structures in the bound state: alpha-MoRFs form alpha-helices, beta-MoRFs form beta-strands, and iota-MoRFs form structures without a regular pattern of backbone hydrogen bonds.

Analysis of molecular recognition features (MoRFs).

Several proteomic studies in the last decade revealed that many proteins are either completely disordered or possess long structurally flexible regions. Many such regions were shown to be of functional importance, often allowing a protein to interact with a large number of diverse partners. Parallel to these findings, during the last five years structural bioinformatics has produced an explosion of results regarding protein-protein interactions and their importance for cell signaling.