Biology subjects

Grimes, K.

Publications and source records attributed to Grimes, K..

4 recordsLinked to original sources

bioRxiv2022

Enterovirus replication and dissemination are differentially controlled by type I and III interferons in the GI tract

Enteroviruses are amongst the most common viral infectious agents of humans and cause a broad spectrum of mild-to-severe illness. Enteroviruses are primarily transmitted by the fecal-oral route, but the events associated with their intestinal replication in vivo are poorly defined. Here, we developed a neonatal mouse model of enterovirus infection by the enteral route using echovirus 5 and used this model to define the differential roles of type I and III interferons (IFNs) in enterovirus replication in the intestinal epithelium and subsequent dissemination to secondary tissues. We show that human FcRn, the primary receptor for echoviruses, is essential for intestinal infection by the enteral route and that type I IFNs control dissemination to secondary sites, including the liver. In contrast, type III IFNs limit enterovirus infection in the intestinal epithelium and mice lacking this pathway exhibit persistent epithelial replication. Finally, we show that echovirus infection in the small intestine is cell-type specific and occurs exclusively in enterocytes. These studies define the type-specific roles of IFNs in enterovirus infection of the GI tract and the cellular tropism of echovirus intestinal replication.

microbiology↗

bioRxiv2021

Haplotype-aware single-cell multiomics uncovers functional effects of somatic structural variation

Somatic structural variants (SVs) are widespread in cancer genomes, however, their impact on tumorigenesis and intra-tumour heterogeneity is incompletely understood, since methods to functionally characterize the broad spectrum of SVs arising in cancerous single-cells are lacking. We present a computational method, scNOVA, that couples SV discovery with nucleosome occupancy analysis by haplotype-resolved single-cell sequencing, to systematically uncover SV effects on cis-regulatory elements and gene activity. Application to leukemias and cell lines uncovered SV outcomes at several loci, including dysregulated cancer-related pathways and mono-allelic oncogene expression near SV breakpoints. At the intra-patient level, we identified different yet overlapping subclonal SVs that converge on aberrant Wnt signaling. We also deconvoluted the effects of catastrophic chromosomal rearrangements resulting in oncogenic transcription factor dysregulation. scNOVA directly links SVs to their functional consequences, opening the door for single-cell multiomics of SVs in heterogeneous cell populations.

genomics↗

bioRxiv2020

From random to predictive: a context-specific interaction framework improves selection of drug protein-protein interactions for unknown drug pathways

With high drug attrition, interaction network methods are increasingly attractive as quick and inexpensive methods for prediction of drug safety and efficacy effects when a drug pathway is unknown. However, these methods suffer from high false positive rates for selecting drug phenotypic effects, their performance is often no better than random (AUROC ~0.5), and this limits the use of network methods in regulatory and industrial decision making. In contrast to many network engineering approaches that apply mathematical thresholds to discover phenotype associations, we hypothesized that interaction networks associated with true positive drug phenotypes are context specific. We tested this hypothesis on 16 designated medical event (DMEs) phenotypes which are a subset of adverse events that are of upmost concern to FDA review using a novel data set extracted from drug labels. We demonstrated that context-specific interactions (CSIs) distinguished true from false positive DMEs with an 50% improvement over non-context-specific approaches (AUROC 0.77 compared to 0.51). By reducing false positives, CSI analysis has the potential to advance network techniques to influence decision making in regulatory and industry settings. Author summaryDrugs bind proteins that interact with multiple downstream proteins and these protein networks are responsible for drug efficacy and safety. Protein interaction network methods predict drug effects aggregating information about proteins around drug-binding protein targets. However, many frameworks exist for identifying proteins relevant to a drugs effect. We consider three frameworks for selecting these proteins and show increased performance from a context-specific approach on selecting proteins relevant to severe drug side effects. The context-specific approach leverages the idea that the proteins responsible for a drug side effect are specific to each side-effect. By discovering the relevant proteins, we can better understand downstream effects of drugs and better anticipate drug side effects for new drugs in development. Further, we focus on designated medical events, a subset of the most severe drug side-effects that are high priority for regulatory review.

systems biology↗

bioRxiv2020

Human FcRn expression and Type I Interferon signaling control Echovirus 11 pathogenesis in mice

Neonatal echovirus infections are characterized by severe hepatitis and neurological complications that can be fatal. Here, we show that expression of the human homologue of the neonatal Fc receptor (hFcRn), the primary receptor for echoviruses, and ablation of type I interferon (IFN) signaling are key host determinants involved in echovirus pathogenesis. We show that expression of hFcRn alone is insufficient to confer susceptibility to echovirus infections in mice. However, expression of hFcRn in mice deficient in type I interferon (IFN) signaling, hFcRn-IFNAR-/-, recapitulate the echovirus pathogenesis observed in humans. Luminex-based multianalyte profiling from E11 infected hFcRn-IFNAR-/- mice revealed a robust systemic immune response to infection, including the induction of type I IFNs. Furthermore, similar to the severe hepatitis observed in humans, E11 infection in hFcRn-IFNAR-/- mice caused profound liver damage. Our findings define the host factors involved in echovirus pathogenesis and establish in vivo models that recapitulate echovirus disease.

microbiology↗