Nelson, D. L. | Search bioRxiv

bioRxiv2026

FXR2 Promotes Autophagic Clearance of Sindbis Virus Capsid and Protects Against Viral Encephalitis

Autophagy protects neurons from viral infection by removing toxic viral components, but the host factors that recognize viral proteins and direct their clearance remain incompletely defined. Here, we identify fragile X mental retardation syndrome-related protein 2 (FXR2) as a mediator of protective autophagy during Sindbis virus (SINV) infection. Quantitative proteomic analysis of host proteins associated with SINV capsid identified FXR2, an RNA-binding protein linked to stress granules and protein homeostasis. Co-immunoprecipitation and immunofluorescence confirmed that FXR2 associates and colocalizes with viral capsid in infected cells. Loss of FXR2 increased capsid accumulation, reduced LC3 lipidation and autophagosome formation, and impaired p62 degradation following infection. Reconstitution of FXR2-deficient cells restored autophagy and promoted capsid clearance. Although FXR2 deficiency did not significantly increase viral replication, it enhanced virus-induced cell death, suggesting that FXR2 protects infected cells by limiting viral protein toxicity rather than by directly restricting virus production. Consistent with this model, both Fxr2-null and heterozygous mice exhibited increased mortality following neurotropic SINV challenge, and Fxr2-null mice developed greater neuronal degeneration, inflammation, and cell death despite viral titers comparable to those in wild-type animals. Together, these findings identify FXR2 as a previously unrecognized component of the antiviral autophagy pathway that promotes viral capsid clearance and protects neural tissue from infection-associated damage.

microbiology↗

bioRxiv2026

A computational model for quantifying instability of tandem repeats across the genome

Tandem repeats (TRs) exhibit high levels of somatic mosaicism, which is increasingly recognized as an important modifier of repeat expansion disorders. Long-read sequencing can capture full-length repeat alleles, yet robust frameworks for quantifying instability across TRs genome-wide are still needed. Here, we introduce a general-purpose model for quantifying TR instability in a given long-read sequencing dataset, without explicitly distinguishing biological mosaicism from technical noise, and which is broadly applicable to both simple and structurally complex loci. This model accurately characterizes allelic instability at each TR locus by representing the distribution of read-to-consensus deviations for each allele. Using HiFi sequencing data from 256 HPRC cell line samples, we fitted models for 617,007 TR loci, including known pathogenic repeats. We observe that instability levels are generally low, but vary substantially across individual TRs, and are driven more strongly by repeat composition than overall repeat length. Furthermore, we applied our method to targeted PureTarget long-read data from samples with known repeat expansions and identified significant mosaicism in the majority of expanded alleles. Our model offers a practical way to quantify instability of tandem repeats across the genome and to detect unusually unstable repeat alleles.

bioinformatics↗