Our Science
We develop therapeutics to cure diseases caused by RNA splicing errors
In all of us, proteins carry out functions necessary for life. Our cells convert DNA into functional proteins by using RNA as a set of instructions to copy genes. These RNA strands contain many instructions, but not all are needed to produce the intended protein.
RNA Splicing
The process of selecting the proper instructions at the proper time is called “RNA splicing.” If the wrong instructions are sent, a “splicing error” occurs that can result in the production of a dysfunctional protein, potentially causing disease.
At Envisagenics, we are discovering therapeutic points of intervention to fix these RNA splicing errors.
The Right Time
Recent availability of massive amounts of RNA sequencing data, advances in machine learning, and the scale of cloud computing is now making it possible to discover novel therapeutics through the analysis of RNA splicing errors.
- Traditional methods to detect, catalog and interpret the variations of RNA splicing errors in these diseases have been laborious, slow, and costly.
- The efficacy, toxicity, and mechanism of action of an RNA therapeutic can be simulated computationally, all before clinical testing in humans.
- Conventional drugs typically target enzymes and receptors, whereas an RNA therapeutic can be target a specific RNA, regardless of the function of the protein they encode for.
SPINRAZA®
In December 2016, SPINRAZA® became the first FDA approved RNA therapeutic to treat Spinal Muscular Atrophy (SMA) by correcting the disease-causing splicing error.
Since then, with further advances in RNA therapeutic delivery systems, an increasing number of RNA therapeutics have received or are close to obtaining FDA approval.
Publications
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Differentiation of mammary tumors and reduction in metastasis upon Malat1 lncRNA loss
Genes & Development 2016. 30: 34-51
Application of SpliceCore algorithms to investigate alternative splicing implications in MALAT1-dependent breast cancer.
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Differential connectivity of splicing activators and repressors to the human spliceosome
Genome Biology 2015 16:119
Our method to predict protein-protein interactions between spliceosomal proteins.
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Interactome analysis brings splicing into focus
Genome Biology 2015 16: 135
The spliceosome is a huge molecular machine that assembles dynamically onto its pre-mRNA substrates. A new study based on interactome analysis provides clues about how splicing-regulatory proteins modulate assembly of the spliceosome to either activate or repress splicing.
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SRSF1-regulated alternative splicing in breast cancer
Molecular Cell 2015 1: 105-117
Over a hundred experimental validations for SpliceCore algorithms reveal the regulatory network of the SRSF1 oncogene. The CASC4 gene is predicted and confirmed to be involved in cancer progression.
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SpliceTrap: a method to quantify alternative splicing under single cellular conditions
Bioinformatics 2011 21: 3010–3016
Here we introduce SpliceTrap, a method to quantify exon inclusion levels using paired-end RNA-seq data. Unlike other tools, which focus on full-length transcript isoforms, SpliceTrap approaches the expression-level estimation of each exon as an independent Bayesian inference problem.