Viral Insulin/IGF-like Peptides Inhibit IGF-1 Receptor Signaling to Enhance Viral Replication

The insulin/IGF system plays a central role in regulating metabolism and growth. We identified viral insulin/IGF1-like peptides (VILPs) in Iridoviridae and investigated their role in host-virus interactions. Using Grouper Iridovirus (GIV) on grouper and zebrafish cells, we show that VILPs are early viral genes and are secreted during infection. VILPs activate insulin receptor (IR) and IGF-1 receptor (IGF1R) phosphorylation and stimulate the PI3K pathway. Supernatants from infected cells trigger dose and time-dependent signaling with GIV-VILP selectively interacts with IGF1R. Functionally, IR inhibition suppresses GIV replication, whereas IGF1R inhibition enhances it, and IGF-1 stimulation reduces replication. During infection, GIV-VILP competes with IGF-1, attenuating IGF1R signaling and reducing proliferation. Transcriptome analysis confirms negative regulation of cell cycle pathways. Using a zebrafish infection model, we demonstrate VILP expression and IGF-1 signaling inhibition. Our findings reveal a viral mimicry mechanism that modulates host IGF-1 signaling to promote viral replication. Overall design: Total RNA were extracted using the RNeasy Plus Mini Kit (Qiagen #74134) from GK cells that were serum-starved for 12 hours, followed by 6 hours of stimulation with respective ligands (100 nM) or infection with GIV (MOI 1) for 18 hours. The RNA samples were submitted to the YCGA (Yale Center for Genome Analysis) for quality testing. Subsequently, cDNA libraries were prepared using the KAPA mRNA HyperPrep Kit (Roche) and sequenced on the NovaSeq X (Illumina) with a read depth of 20 million reads. Transcriptomic datasets generated from the sequencing were trimmed for low quality reads and adaptor contamination was removed using TrimGalore 58. Trimmed reads were assembled using Trinity de novo assembly tool (version v2.15.1)59. Coding regions for the assembly were identified using TransDecoder60.The identified 65,169 contigs were annotated to identify known genes using Blast2Go61. Counts of reads mapping to the assembled cds in each sample were estimated using the Trinity tool (align_and_estimate_abundance.pl.) These read counts were used to estimate the differential transcript expression using DESeq262.All differentially expressed transcripts included in the following analysis were subject to a false discovery rate (FDR) of = 0.01 and a fold change (FC) of = 1.5.