Crawford, K. H. | bioRxiv Science

bioRxiv2020

APOBEC3C tandem domain proteins create super restriction factors against HIV-1

Humans encode proteins, called restriction factors, that inhibit replication of viruses like HIV-1. One family of antiviral proteins, apolipoprotein B mRNA-editing enzyme catalytic polypeptide-like 3 (APOBEC3, shortened to A3) acts by deaminating cytidines to uridines during the reverse transcription reaction of HIV-1. The A3 locus encodes seven genes, named A3A-A3H. These genes either have one or two cytidine deaminase domains and several of these A3s potently restrict HIV-1. A3C, which has only a single cytidine deaminase domain, however, inhibits HIV-1 only very weakly. We tested novel double domain protein combinations by genetically linking two A3C genes to make a synthetic tandem domain protein. This protein created a "super restriction factor" that had more potent antiviral activity than the native A3C protein, which correlated with increased packaging into virions. Furthermore, disabling one of the active sites of the synthetic tandem domain protein results in an even greater increase in the antiviral activity--recapitulating a similar evolution seen in A3F and A3G (double domain A3s that only use a single catalytically active deaminase domain). These A3C tandem domain proteins do not have an increase in mutational activity, but instead inhibit formation of reverse transcription products which correlates with their ability to form large higher order complexes in cells. Finally, the A3C-A3C super restriction factor largely escaped antagonism by the HIV-1 viral protein, Vif. ImportanceAs a part of the innate immune system, humans encode proteins that inhibit viruses like HIV-1. These broadly acting antiviral proteins do not protect humans from viral infections because viruses encode proteins that antagonize the host antiviral proteins to evade the innate immune system. One such example of a host antiviral protein is APOBEC3C (A3C), which weakly inhibits HIV-1. Here, we show that we can improve the antiviral activity of A3C by duplicating the DNA sequence to create a synthetic tandem domain, and furthermore, are relatively resistant to the viral antagonist, Vif. Together, these data give insights about how nature has evolved a defense against viral pathogens like HIV.

microbiology↗

bioRxiv2019

alignparse: A Python package for parsing complex features from high-throughput long-read sequencing

Summary & PurposeAdvances in sequencing technology have made it possible to generate large numbers of long, high-accuracy sequencing reads. For instance, the new PacBio Sequel platform can generate hundreds of thousands of high-quality circular consensus sequences in a single run (Rhoads and F. 2015; Hebert et al. 2018). Good programs exist for aligning these reads for genome assembly (Chaisson and Tesler 2012; Li 2018). However, these long reads can also be used for other purposes, such as sequencing PCR amplicons that contain various features of interest. For instance, PacBio circular consensus sequences have been used to identify the mutations in influenza viruses in single cells (Russell et al. 2019), or to link barcodes to gene mutants in deep mutational scanning (Matreyek et al. 2018). For such applications, the alignment of the sequences to the targets may be fairly trivial, but it is not trivial to then parse specific features of interest (such as mutations, unique molecular identifiers, cell barcodes, and flanking sequences) from these alignments. Here we describe alignparse, a Python package for parsing complex sets of features from long sequences that map to known targets. Specifically, it allows the user to provide complex target sequences in Genbank format that contain an arbitrary number of user-defined sub-sequence features. It then aligns the sequencing reads to these targets and filters alignments based on whether the user-specified features are present with the desired identities (which can be set to different thresholds for different features). Finally, it parses out the sequences, mutations, and/or accuracy of these features as specified by the user. The flexibility of this package therefore fulfills the need for a tool to extract and analyze complex sets of features in large numbers of long sequencing reads.

bioinformatics↗