Bradrick, S. | Search bioRxiv

bioRxiv2022

The RNA helicase DDX39B activates FOXP3 RNA splicing to control T regulatory cell fate

Genes associated with increased susceptibility to multiple sclerosis (MS) have been identified, but their functions are incompletely understood. One of these genes codes for the RNA helicase DExD/H-Box Polypeptide 39B (DDX39B), which shows genetic and functional epistasis with interleukin-7 receptor- gene (IL7R) in MS-risk. Based on evolutionary and functional arguments we postulated that DDX39B enhances immune tolerance decreasing MS risk. Consistent with such a role we show that DDX39B controls the expression of many MS susceptibility genes and important immune-related genes. Among these we identified ForkheadBox P3 (FOXP3), which codes for the master transcriptional factor in CD4+/CD25+ T regulatory cells. DDX39B knockdown led to loss of immune-regulatory and gain of immune-effector expression signatures. Splicing of FOXP3 introns, which belong to a previously unrecognized subclass of introns with C-rich polypyrimidine tracts, was exquisitely sensitive to DDX39B levels. Given the importance of FOXP3 in autoimmunity, this work cements DDX39B as an important guardian of immune tolerance.

molecular biology↗

bioRxiv2020

Topoisomerase III-beta is required for efficient replication of positive-sense RNA viruses

Based on genome-scale loss-of-function screens we discovered that Topoisomerase III-{beta} (TOP3B), a human topoisomerase that acts on DNA and RNA, is required for yellow fever virus and dengue virus-2 replication. Remarkably, we found that TOP3B is required for efficient replication of all positive-sense-single stranded RNA viruses tested, including SARS-CoV-2. While there are no drugs that specifically inhibit this topoisomerase, we posit that TOP3B is an attractive anti-viral target.

microbiology↗

bioRxiv2020

Vorpal: A Novel RNA Virus Feature-Extraction Algorithm Demonstrated Through Interpretable Genotype-to-Phenotype Linear Models

In the analysis of genomic sequence data, so-called "alignment free" approaches are often selected for their relative speed compared to alignment-based approaches, especially in the application of distance comparisons and taxonomic classification1,2,3,4. These methods are typically reliant on excising K-length substrings of the input sequence, called K-mers5. In the context of machine learning, K-mer based feature vectors have been used in applications ranging from amplicon sequencing classification to predictive modeling for antimicrobial resistance genes6,7,8. This can be seen as an analogy of the "bag-of-words" model successfully employed in natural language processing and computer vision for document and image classification9,10. Feature extraction techniques from natural language processing have previously been analogized to genomics data11; however, the "bag-of-words" approach is brittle in the RNA virus space due to the high intersequence variance and the exact matching requirement of K-mers. To reconcile the simplicity of "bag-of-words" methods with the complications presented by the intrinsic variance of RNA virus space, a method to resolve the fragility of extracted K-mers in a way that faithfully reflects an underlying biological phenomenon was devised. Our algorithm, Vorpal, allows the construction of interpretable linear models with clustered, representative degenerate K-mers as the input vector and, through regularization, sparse predictors of binary phenotypes as the output. Here, we demonstrate the utility of Vorpal by identifying nucleotide-level genomic motif predictors for binary phenotypes in three separate RNA virus clades; human pathogen vs. non-human pathogen in Orthocoronavirinae, hemorrhagic fever causing vs. non-hemorrhagic fever causing in Ebolavirus, and human-host vs. non-human host in Influenza A. The capacity of this approach for in silico identification of hypotheses which can be validated by direct experimentation, as well as identification of genomic targets for preemptive biosurveillance of emerging viruses, is discussed. The code is available for download at https://github.com/mriglobal/vorpal.

bioinformatics↗