Catch me if you can: viral nucleic acids to host sensors

Abstract

The 2002 movie Catch Me If You Can is a cat-and-mouse story in which Frank Abagnale Jr. successfully conned his way into several high-profile jobs while evading capture by FBI agent Carl Hanratty. Similarly, after entering host cells, viruses interact with or hijack host cellular machinery to replicate their genetical materials and assemble themselves for the next round of infection. Analogous to an FBI agent, host cells have numerous molecular "detectives" that recognize viral nucleic acids (NAs). These include RIG-I, MDA5, LGP2, TLR3, TLR7, TLR8, DHX36, DICER1, PKR, OAS1, ZAP, and NLRP1/6 for viral RNA, as well as cGAS, TLR9, AIM2, IFI16, IFIX, Ku70, MRE11, RNA polymerase III, hnRNPA2B1, LRRFIP1, DAI, DHX9 and DDX41 for viral DNA. However, much like the brilliant Frank Abagnale Jr., viruses have developed various strategies to evade host cellular surveillance-for example, by sequestering or modifying viral NAs and inhibiting or degrading host sensors. In this review, we will summarize the host sensors identified so far, discuss the latest understandings of the various strategies employed by viruses, and highlight the challenges associated with drug development to target virus or host factors. Considering recent global health challenges such as the COVID-19 pandemic and undergoing measles outbreak, understanding virus-host interactions at the molecular and cellular levels remains essential for the development of novel therapeutic strategies.

Keywords: DNA virus; RNA virus; adaptive immunity; innate immunity; sensor.

Figure 1

Alignment and classification of human viral nucleic acid sensors. (A) The alignment of human DNA sensors (above dash line) and RNA sensors (below dash line). The DNA or RNA binding domains are highlighted in orange, and an individual protein’s functional domain(s) and motif(s) are indicated. IFI16 contains a pyrin domain and two HIN domains (HINa and HINb) that bind DNA; IFIX has a pyrin domain and a HIN domain that binds DNA; AIM2 has a pyrin domain and a DNA binding domain HIN-200; DDX41 contains a helicase core domain; DHX9 contains dsRNA binding domain 1 and 2, a helicase core domain, an OB-fold (oligosaccharide-binding) and a RGG (repeated arginine-glycine-glycine) domain; Ku70 has a central DNA-binding domain and a SAP (SAF-A/B, Acinus, and PIAS) domain; MRE11 has two DNA binding domains (A and B) and a GAR (Glycine-Arginine-Rich) motif; hnRNPA2B1 contains two quasi-RRM (RNA recognition motif) domains and a RGG; TLR3, 7, 8, 9 have various LRRs (leucine-rich repeats), a transmembrane domain (TM), and TIR (Toll/Interleukin-1 receptor) domain; LRRFIP1 contains a helix, coiled-coil and a LRR domain; DAI (ZBP1) contains two Z-DNA binding domains (Zα and Zβ); cGAS has three DNA binding domains (A, B and C); RIG-I, MDA5 and LGP2 contain two CARD (Caspase Activation and Recruitment Domain, except LGP2), a helicase core domain, a C-terminal domain (CTD), and a Picer domain; DHX36 has a DSM (DHX36-specific motif), a helicase core domain, a WH (winged-helix) domain, RL (ratchet-like) domain, a OB fold, and a C-terminal extension; DICER1 has a helicase core domain, a domain of unknown function (DUF283), PAZ (Piwi-Argonaute-Zwille) domain, two RNase III domains (IIIa and IIIb), and a double-stranded RNA binding domain (dsRBD); NLRP1 and 6 have a Pyrin domain, NACHT (NAIP, CIITA, HET-E and TP-1) domain, and LRR, NLRP1 has additional ZU5 (ZO-1 and UNC5), UPA (UNC5, PIDD and Ankyrin domain) and a CARD; PKR has two dsRNA-binding domains and a kinase domain; OAS1 contains two dsRNA binding domains and a catalytic domain; ZAP has five zinc finger domain and two WWE domains. NLS, nuclear localization signal; NES, nuclear export signal. (B) Phylogenetic tree of human viral nucleic acid sensors. Protein sequences were retrieved from UniProt and aligned using the AlignSeqs function from the DECIPHER package in R (15). Positions with >40% gaps were trimmed prior to tree construction. The best-fitting substitution model (LG+G[4]+I) was identified using the modelTest function in the phangorn package for more accurate estimation of evolutionary distances, branch lengths, and topology. An initial neighbor-joining tree was constructed from a maximum likelihood (ML)-based distance matrix and optimized using the optim.pml function under the selected model. Bootstrap support values were calculated from 1,000 replicates using the bootstrap.pml function with topology optimization enabled. The final tree was midpoint-rooted and visualized using the ggtree package (16), with bootstrap values displayed on internal nodes using a viridis color scale. Branch lengths were suppressed to highlight evolutionary relationships and clade structure based on amino acid sequence homology. (C) Correlation heatmap of human viral nucleic acid sensors. Semantic data for Gene Ontology (GO) Biological Process (BP) terms were obtained from the org.Hs.eg.db annotation database using the godata function in the GOSemSim package (17). Pairwise semantic similarity scores between genes were computed using the mgeneSim function, yielding a score from 0 (functionally unrelated) to 1 (identical) for each gene pair. These similarity scores were then converted into a distance matrix (1 – similarity) and visualized as a clustered heatmap using the ComplexHeatmap package (18). Hierarchical clustering was performed using average linkage, and the same clustering was applied to both rows and columns. For interpretability, protein names are displayed along the axes instead of gene symbols. (D) Cluster dendrogram of human viral nucleic acid sensors. The tree was constructed from the same distance matrix used in the heatmap (C) and visualized using the ggtree package. Six clusters were defined based on cluster stability analysis performed with the ConsensusClusterPlus package. Branches and tip labels are color-coded by cluster assignment, with protein names displayed at the tips. The scale bar represents functional distance.

Figure 2

Summary of the interplay between viruses and human viral nucleic acid sensors. For simplicity, only DNA and RNA viruses were shown as examples. Focusing was mainly on different forms of viral nucleic acids and their sensors, downstream pathways were simplified. In addition, many of these sensors can recognize various forms of nucleic acids and are involved in different signalling pathways. Images were created by BioRender.