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Roberts, E. G.

Publications and source records attributed to Roberts, E. G..

5 recordsLinked to original sources

Reversal of contractile defects by mediating calcium homeostasis in human mini-heart models of heart failure with preserved ejection fraction (HFpEF) leads to first-in-human gene therapy clinical trial

AimsHeart failure with preserved ejection fraction (HFpEF), is a global health problem lacking disease-modifying therapeutic options, reflecting a lack of predictive models for preclinical drug testing. Aligned with FDA Modernization Act 2.0, we aimed to create the first in vitro human-specific mini-heart models of HFpEF, and to test the efficacy of a candidate gene therapy to improve cardiac kinetics and correct the disease phenotype. Methods and ResultsHealthy human pluripotent stem cell-derived ventricular cardiomyocytes were used to bioengineer beating cardiac tissue strips and pumping cardiac chambers. When conditioned with transforming growth factor-{beta}1 and endothelin-1, these mini-heart models exhibited signature disease phenotypes of significantly elevated diastolic force and tissue stiffness, and slowed contraction and relaxation kinetics, with no significant deficit in systolic force or ejection fraction versus unconditioned controls. Bioinformatic analysis of bulk RNA sequencing data from HFpEF mini-heart models and patient ventricular samples identified downregulation of SERCA2a of the calcium signalling pathway as a key differentially expressed gene. After dosage optimization, AAV-mediated expression of SERCA2a abrogated the disease phenotype and improved the cardiac kinetics in HFpEF mini-Hearts. ConclusionsThese findings contributed to FDA approval of an ongoing first-in-human gene therapy clinical trial for HFpEF, with Fast Track designation. We conclude that such human-based disease-specific mini-heart platforms are relevant for target discovery and validation that can facilitate clinical translation of novel cardiac therapies. Translational PerspectiveHeart failure with preserved ejection fraction (HFpEF) is a significant and growing global health concern lacking disease-modifying therapeutic options, reflecting inadequate preclinical models of the disease. Aligned with FDA Modernization Act 2.0, we created the first in vitro human-specific mini-heart models of HFpEF, demonstrated phenotypic disease characteristics of elevated stiffness and slowed kinetics, showed transcriptomic consistency with HFpEF patient data, identified SERCA2a as a key downregulated gene, performed dosing titration of SERCA2a gene therapy, and showed improvement of cardiac kinetics post-treatment. The findings contributed to FDA approval of an ongoing first-in-human gene therapy clinical trial for HFpEF.

bioengineering↗

Assessing and assuring interoperability of a genomics file format

BackgroundBioinformatics software tools operate largely through the use of specialized genomics file formats. Often these formats lack formal specification, and only rarely do the creators of these tools robustly test them for correct handling of input and output. This causes problems in interoperability between different tools that, at best, wastes time and frustrates users. At worst, interoperability issues could lead to undetected errors in scientific results. MethodsWe sought (1) to assess the interoperability of a wide range of bioinformatics software using a shared genomics file format and (2) to provide a simple, reproducible method for enhancing inter-operability. As a focus, we selected the popular Browser Extensible Data (BED) file format for genomic interval data. Based on the file formats original documentation, we created a formal specification. We developed a new verification system, Acidbio (https://github.com/hoffmangroup/acidbio), which tests for correct behavior in bioinformatics software packages. We crafted tests to unify correct behavior when tools encounter various edge cases--potentially unexpected inputs that exemplify the limits of the format. To analyze the performance of existing software, we tested the input validation of 80 Bioconda packages that parsed the BED format. We also used a fuzzing approach to automatically perform additional testing. ResultsOf 80 software packages examined, 75 achieved less than 70% correctness on our test suite. We categorized multiple root causes for the poor performance of different types of software. Fuzzing detected other errors that the manually designed test suite could not. We also created a badge system that developers can use to indicate more precisely which BED variants their software accepts and to advertise the softwares performance on the test suite. DiscussionAcidbio makes it easy to assess interoperability of software using the BED format, and therefore to identify areas for improvement in individual software packages. Applying our approach to other file formats would increase the reliability of bioinformatics software and data.

bioinformatics↗

Semi-supervised segmentation and genome annotation

Segmentation and genome annotation methods automatically discover joint signal patterns in whole genome datasets. Previously, researchers trained these algorithms in a fully unsupervised way, with no prior knowledge of the functions of particular regions. Adding information provided by expert-created annotations to supervise training could improve the annotations created by these methods. We implemented semi-supervised learning using virtual evidence in the annotation method Segway. Additionally, we defined a positionally tolerant precision and recall metric for scoring genome annotations based on the proximity of each annotation feature to the truth set. We demonstrate semi-supervised Segways ability to learn patterns corresponding to provided transcription start sites on a specified supervision label, and subsequently recover other transcription start sites in unseen data on the same supervision label.

bioinformatics↗

Segway 2.0: Gaussian mixture models and minibatch training

SummarySegway performs semi-automated genome annotation, discovering joint patterns across multiple genomic signal datasets. We discuss a major new version of Segway and highlight its ability to model data with substantially greater accuracy. Major enhancements in Segway 2.0 include the ability to model data with a mixture of Gaussians, enabling capture of arbitrarily complex signal distributions, and minibatch training, leading to better learned parameters.\n\nAvailability and ImplementationSegway and its source code are freely available for download at https://segway.hoffmanlab.org. We have made available scripts (https://doi.org/10.5281/zenodo.802940) and datasets (https://doi.org/10.5281/zenodo.802907) for this papers analysis.\n\nContactmichael.hoffman@utoronto.ca

bioinformatics↗

Semi-automated genome annotation using epigenomic data and Segway

Biochemical techniques measure many individual properties of chromatin along the genome. These properties include DNA accessibility (measured by DNase-seq) and the presence of individual transcription factors and histone modifications (measured by ChIP-seq). Segway is software that transforms multiple datasets on chromatin properties into a single annotation of the genome that a biologist can more easily interpret. This protocol describes how to use Segway to annotate the genome, starting with reads from a ChIP-seq experiment. It includes pre-processing of data, training the Segway model, annotating the genome, assigning biological meanings to labels, and visualizing the annotation in a genome browser.

bioinformatics↗