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.e., the product of excess CAG length and age) is a commonly used measure of this cumulative exposure. CAP score has been widely used as a predictor of a variety of disease state variables in HD. The utility of the CAP score has been somewhat diminished, however, by a lack of agreement on its precise definition. The most commonly used forms of the CAP score are highly correlated so that, for purposes of prediction, it makes little difference which is used. However, reported values of CAP scores, based on commonly used definitions, differ substantially in magnitude when applied to the same data. This complicates the process of inter-study comparison. OBJECTIVE: In this paper, we propose a standardized definition for the CAP score which will resolve this difficulty. Our standardization is chosen so that CAP = 100 at the expected age of diagnosis. METHODS: Statistical methods include novel survival analysis methodology applied to the 13 disease landmarks taken from the Enroll-HD database (PDS 5) and comparisons with the existing, gold standard, onset model. RESULTS: Useful by-products of our work include up-to-date, age-at-onset (AO) results and a refined AO model suitable for use in other contexts, a discussion of several useful properties of the CAP score that have not previously been noted in the literature and the introduction of the concept of a toxicity onset model. CONCLUSION: We suggest that taking L = 30 and K = 6.49 provides a useful standardization of the CAP score, suitable for use in the routine modeling of clinical data in HD.

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. The model was validated using a separate Enroll-HD dataset and compared with existing clinical reference assessments (Unified Huntington's Disease Rating Scale [UHDRS] diagnostic confidence level, total functional capacity, and total motor scores) and CAG-age product. RESULTS: Nine disease states were discovered based on 44 motor, cognitive, and functional measures, which correlated with reference assessments. The validation set included 3158 participants (mean age, 48.4 years) of whom 61.5% had manifest disease. Analysis of transition times showed that "early-disease" states 1 and 2, which occur before motor diagnosis, lasted ~16 years. Increasing numbers of participants had motor onset during "transition" states 3 to 5, which collectively lasted ~10 years, and the "late-disease" states 6 to 9 also lasted ~10 years. The annual probability of conversion from one of the nine identified disease states to the next ranged from 5% to 27%. CONCLUSIONS: The natural history of Huntington's disease can be described by nine disease states of increasing severity. The ability to derive characteristics of disease states and probabilities for progression through these states will improve trial design and participant selection. © 2021 International Parkinson and Movement Disorder Society.

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). METHODS: We used propensity score (PS) weighting in a large (n = 2914) longitudinal dataset (Enroll-HD) to examine the impact of education, employment status, and use of tobacco, alcohol, and recreational and therapeutic drugs on HD progression. Each factor was investigated in isolation while controlling for 19 other factors to ensure that groups were balanced at baseline on potential confounders using PS weights. Outcomes were compared several years later using doubly robust models. RESULTS: Our results highlighted cases where modifiable (non-genetic) factors - namely light and moderate alcohol use and employment - would have been associated with HD progression in models that did not use PS weights to control for baseline imbalances. These associations did not hold once we applied PS weights to balance baseline groups. We also found potential evidence of a protective effect of substance use (primarily marijuana use), and that those who needed antidepressant treatment were likely to progress faster than non-users. CONCLUSIONS: Our study is the first to examine the effect of non-genetic factors on HD using a novel application of PS weighting. We show that previously-reported associated factors - including light and moderate alcohol use - are reduced and no longer significantly linked to HD progression after PS weighting. This indicates the potential value of PS weighting in examining non-genetic factors contributing to HD as well as in addressing the known biases that occur with observational data.

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. METHODS: After combining longitudinal REGISTRY and Enroll-HD data, we used quantile regression and natural cubic splines for age to fit models for healthy controls (N = 3,394; N observations = 8,619). Normative curves were estimated for the 0.05, 0.25, 0.50, 0.75, and 0.95 quantiles. Two types of reference curves were considered: unconditional curves were dependent on age alone, whereas conditional curves were dependent on age and other covariates, namely sex and education. RESULTS: Conditioning on education was necessary for the Symbol Digit, Stroop Word, Stroop Color, Stroop Interference, and composite UHDRS. Unconditional curves were sufficient for the Total Motor Score. TFC was unique in that the curve was constant over age with its intercept at the maximum score (TFC = 13). For all measures, sex effects were minimal, so conditioning on sex was unwarranted. CONCLUSIONS: Extreme quantile estimates for each measure can be considered as boundaries for natural aging and scores falling beyond these thresholds are likely the result of disease progression. Normative curves and tables are developed and can serve as references for clinical characterization in HD.

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. We used mixed effects models to adjust regional brain volumes for covariates, calculate effect sizes, and simulate possible treatment effects in disease-affected anatomical regions. We used our model to estimate the statistical power of possible treatment effects for anatomical regions and clinical markers. RESULTS: We identified a set of common anatomical regions that have similarly large standardized effect sizes (>0.5) between healthy control and premanifest HD (PreHD) groups. These included subcortical, white matter, and cortical regions and nonventricular cerebrospinal fluid (CSF). We also observed a consistent spatial distribution of effect size by region across the whole brain. We found that multicenter studies were necessary to capture treatment effect variance; for a 20% treatment effect, power of >80% was achieved for the caudate (n = 661), pallidum (n = 687), and nonventricular CSF (n = 939), and, crucially, these imaging markers provided greater power than standard clinical markers. INTERPRETATION: Our findings provide the first cross-study validation of structural imaging markers in HD, supporting the use of these measurements as endpoints for both observational studies and clinical trials. ANN NEUROL 2020;87:751-762.

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. However, multiple observational HD registries have recently been used with success to model HD progression1,2 and provide an opportunity to obtain effect estimates in the absence of clinical trials. We use a dataset integrated from four large-scale HD registries to generate evidence on the efficacy of chorea treatments for chorea as well as their impact on other aspects of HD progression. Clinical conclusions are meant only to illustrate our methodological approach. We employ parametric G-computation for causal inference to adjust for confounding and accommodate irregular visits and treatment patterns. We fit Bayesian hierarchical models to the results of multiple related analyses to share strength across studies and handle multiple comparisons concerns.

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. MATERIALS AND METHODS: We developed a framework to build probabilistic disease progression models using observational medical data. The framework consists of two steps. The first step determines the number of disease states. The second step builds a probabilistic disease progression model with the determined number of states. The model discovers typical states along the trajectory of the target disease, learns the characteristics of these states, and transition probabilities between the states. We applied the framework to an integrated observational HD dataset curated from four recent observational HD studies. RESULTS: The resulting HD progression model identified nine disease states. Compared to state-of-art HD staging system, the model 1) covers wider range of HD progression; 2) is able to quantitatively describe complex changes around the time of clinical diagnosis; 3) discovers multiple potential HD progression pathways; and 4) reveals expected time durations of the identified states. DISCUSSION AND CONCLUSION: The proposed framework addresses practical challenges in observational data and can help enhance the understanding of progression of chronic diseases. The framework could be applied to other chronic diseases with the help of clinical knowledge.

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. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides. RESULTS: The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross-validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow-up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers. INTERPRETATION: We used a data-driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event-based model, to provide new insight into Huntington's disease progression and to support fine-grained patient stratification for future precision medicine in Huntington's disease.

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. We begin by curating data from four recent observational HD studies, each containing a diverse collection of clinical assessments. The resulting dataset is unprecedented in size and contains data from 19,269 study participants. By analyzing this large dataset, we are able to discover hidden low dimensional structure in the data that correlates well with surrogate measures of HD progression. The discovered structures are promising candidates for future consumption by downstream statistical HD progression models.

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. Investigation of the sequence biases and structural preferences around known ubiquitination sites indicated that their properties were similar to those of intrinsically disordered protein regions. Using a combined set of new and previously known ubiquitination sites, we developed a random forest predictor of ubiquitination sites, UbPred. The class-balanced accuracy of UbPred reached 72%, with the area under the ROC curve at 80%. The application of UbPred showed that high confidence Rsp5 ubiquitin ligase substrates and proteins with very short half-lives were significantly enriched in the number of predicted ubiquitination sites. Proteome-wide prediction of ubiquitination sites in Saccharomyces cerevisiae indicated that highly ubiquitinated substrates were prevalent among transcription/enzyme regulators and proteins involved in cell cycle control. In the human proteome, cytoskeletal, cell cycle, regulatory, and cancer-associated proteins display higher extent of ubiquitination than proteins from other functional categories. We show that gain and loss of predicted ubiquitination sites may likely represent a molecular mechanism behind a number of disease-associatedmutations. UbPred is available at http://www.ubpred.org.

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. We analyzed intrinsically disordered regions in the same or similar proteins crystallized independently and studied their sensitivity to changes in protein sequence and parameters of crystallographic experiments. The observed changes in the existence, position, and length of disordered regions indicate that their appearance in X-ray structures dramatically depends on changes in amino acid sequence and peculiarities of the crystallographic experiment. Our study also raises general questions regarding protein evolution and the regulation of protein structure, dynamics, and function via variations in cellular and environmental conditions.

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. In the case of Plasmodium species, which cause malaria, such regions have also been reported to hamper the identification of homologues, thus making functional genomics exceptionally challenging. One of the better accepted theories accounting for the high number of low complexity regions is the presence of intrinsic disorder in these microbes. In this study we compare the degree of disordered proteins that are predicted to be expressed in many such ancient eukaryotic cells. Our findings indicate an unusual bias in the amino acids comprising protozoal proteomes, and show that intrinsic disorder is remarkably abundant among their predicted proteins. Additionally, the intrinsically disordered regions tend to be considerably longer in the early-branching eukaryotes. An analysis of a Plasmodium falciparum interactome indicates that protein-protein interactions may be at least one function of the intrinsic disorder. This study provides a bioinfomatics basis for the discovery and analysis of unfoldomes (the complement of intrinsically disordered proteins in a given proteome) of early-branching eukaryotes. It also provides new insights into the evolution of intrinsic disorder in the context of adapting to a parasitic lifestyle and lays the foundation for further work on the subject.

An integrated approach to inferring gene-disease associations in humans.

UNLABELLED: One of the most important tasks of modern bioinformatics is the development of computational tools that can be used to understand and treat human disease. To date, a variety of methods have been explored and algorithms for candidate gene prioritization are gaining in their usefulness. Here, we propose an algorithm for detecting gene-disease associations based on the human protein-protein interaction network, known gene-disease associations, protein sequence, and protein functional information at the molecular level. Our method, PhenoPred, is supervised: first, we mapped each gene/protein onto the spaces of disease and functional terms based on distance to all annotated proteins in the protein interaction network. We also encoded sequence, function, physicochemical, and predicted structural properties, such as secondary structure and flexibility. We then trained support vector machines to detect gene-disease associations for a number of terms in Disease Ontology and provided evidence that, despite the noise/incompleteness of experimental data and unfinished ontology of diseases, identification of candidate genes can be successful even when a large number of candidate disease terms are predicted on simultaneously. AVAILABILITY: www.phenopred.org.

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. These example MoRFs were indicated to be intrinsically disordered in the absence of their binding partners by several criteria. In this study, we used several geometric and physiochemical criteria to examine the properties of 62 alpha-, 20 beta-, and 176 iota-MoRF complex structures. Interface residues were examined by calculating differences in accessible surface area between the complex and isolated monomers. The compositions and physiochemical properties of MoRF and MoRF partner interface residues were compared to the interface residues of homodimers, heterodimers, and antigen-antibody complexes. Our analysis indicates that there are significant differences in residue composition and several geometric and physicochemical properties that can be used to discriminate, with a high degree of accuracy, between various interfaces in protein interaction data sets. Implications of these findings for the development of MoRF-partner interaction predictors are discussed. In addition, structural changes upon MoRF-to-partner complex formation were examined for several illustrative examples.

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. We studied the occurrence of relatively short (10-70 residues), loosely structured protein regions within longer, largely disordered sequences that were characterized as bound to larger proteins. We call these regions molecular recognition features (MoRFs, also known as molecular recognition elements, MoREs). Interestingly, upon binding to their partner(s), MoRFs undergo disorder-to-order transitions. Thus, in our interpretation, MoRFs represent a class of disordered region that exhibits molecular recognition and binding functions. This work extends previous research showing the importance of flexibility and disorder for molecular recognition. We describe the development of a database of MoRFs derived from the RCSB Protein Data Bank and present preliminary results of bioinformatics analyses of these sequences. Based on the structure adopted upon binding, at least three basic types of MoRFs are found: alpha-MoRFs, beta-MoRFs, and iota-MoRFs, which form alpha-helices, beta-strands, and irregular secondary structure when bound, respectively. Our data suggest that functionally significant residual structure can exist in MoRF regions prior to the actual binding event. The contribution of intrinsic protein disorder to the nature and function of MoRFs has also been addressed. The results of this study will advance the understanding of protein-protein interactions and help towards the future development of useful protein-protein binding site predictors.