RNA Expression Profile and Potential Biomarkers in Patients With Spinocerebellar Ataxia Type 3 From Mainland China

In Silico Discovery of Group II Intron RNA Splicing Inhibitors

Here, we describe the discovery of compounds that inhibit self-splicing in group II introns. Using docking calculations, we targeted the catalytic active site within the Oceanobacillus iheyensis group IIC intron and virtually screened a library of lead-like compounds. From this initial virtual screen, we identified three unique scaffolds that inhibit splicing in vitro. Additional tests revealed that an analog of the lead scaffold inhibits splicing in an intron-dependent manner. Furthermore, this analog exhibited activity against the group II intron from a different class: the yeast ai5γ IIB intron. The splicing inhibitors we identified could serve as chemical tools for developing group II intron-targeted antifungals, and, more broadly, our results highlight the potential of in silico techniques for identifying bioactive hits against structured and functionally complex RNAs.

Detection of repeat expansions in large next generation DNA and RNA sequencing data without alignment

Bioinformatic methods for detecting short tandem repeat expansions in short-read sequencing have identified new repeat expansions in humans, but require alignment information to identify repetitive motif enrichment at genomic locations. We present superSTR, an ultrafast method that does not require alignment. superSTR is used to process whole-genome and whole-exome sequencing data, and perform the first STR analysis of the UK Biobank, efficiently screening and identifying known and potential disease-associated STRs in the exomes of 49,953 biobank participants. We demonstrate the first bioinformatic screening of RNA sequencing data to detect repeat expansions in humans and mouse models of ataxia and dystrophy.

New Model for Estimation of the Age at Onset in Spinocerebellar Ataxia Type 3

OBJECTIVES

The aim of this study was to develop an appropriate parametric survival model to predict patient's age at onset (AAO) for spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD) populations from mainland China.

METHODS

We compared the efficiency and performance of 6 parametric survival analysis methods (exponential, weibull, log-gaussian, gaussian, log-logistic, and logistic) based on cytosine-adenine-guanine (CAG) repeat length at ATXN3 to predict the probability of AAO in the largest cohort of patients with SCA3/MJD. A set of evaluation criteria, including -2 log-likelihood statistic, Akaike information criterion (AIC), bayesian information criterion (BIC), Nagelkerke R-squared (Nagelkerke R^2), and Cox-Snell residual plot, were used to identify the best model.

RESULTS

Among these 6 parametric survival models, the logistic model had the lowest -2 log-likelihood (6,560.12), AIC (6,566.12), and BIC (6,566.14) and the highest value of Nagelkerke R^2 (0.54), with the closest graph to the bisector Cox-Snell residual graph. Therefore, the logistic survival model was the best fit to the studied data. Using the optimal logistic survival model, we indicated the age-specific probability distribution of AAO according to the CAG repeat size and current age.

CONCLUSIONS

We first demonstrated that the logistic survival model provided the best fit for AAO prediction in patients with SCA3/MJD from mainland China. This optimal model can be valuable in clinical and research. However, the rigorous clinical testing and practice of other independent cohorts are needed for its clinical application. A unified model across multiethnic cohorts is worth further exploration by identifying regional differences and significant modifiers in AAO determination.