Predictive analysis of miRNA interactions with RPS28 and RPL31 ribosomal genes and their role in the immune response of chickens to avian influenza infection

Introduction: MicroRNAs (miRNAs) are short non-coding RNA molecules, typically 18–24 nucleotides in length, that regulate gene expression post-transcriptionally by binding to complementary sequences in target mRNAs. This interaction can lead to mRNA degradation or translational repression. miRNAs play essential roles in various biological processes, including cell growth, differentiation, apoptosis, metabolism, and immune responses. In poultry, miRNAs are particularly important in modulating immune responses to viral infections such as avian influenza. These molecules can either enhance host defense mechanisms or be exploited by viruses to facilitate infection. The discovery of miRNAs has revolutionized our understanding of gene regulatory networks and opened new avenues for research in genomics, bioinformatics, and infectious disease biology. This study aimed to identify and analyze key miRNAs involved in the immune response of chickens to avian influenza, focusing on their predicted interactions with two ribosomal genes of RPS28 and RPL31.
Materials and methods: Two ribosomal genes (RPS28 and RPL31) were selected based on prior transcriptomic analysis using microarray data (GSE96837) from the GEO/NCBI database. The reference genome Gallus gallus GRCg6a (GenBank: GCA_000002315.5) was used for bioinformatic analyses. The tools4miRs platform was employed to predict miRNA–mRNA interactions, integrating 10 algorithms including TargetSpy, miRanda, RNAhybrid, TargetScan, DIANA-microT, MicroTar, miRMap, PITA, RNA22, and Guugle. These tools assess binding potential based on parameters such as free energy, seed sequence complementarity, and evolutionary conservation. Identified miRNAs were cross-referenced with the miRBase database to confirm their annotation status.
Results and discussion: Eleven miRNAs (gga-miR-1593, gga-miR-1609, gga-miR-194, gga-miR-214, gga-miR-3532-3p, gga-miR-6567-3p, gga-miR-6579-5p, gga-miR-6612-5p, gga-miR-6642-3p, gga-miR-7477-5p, and gga-miR-708-5p) were analyzed for their interactions with RPS28 and RPL31. For RPL31, gga-miR-6642-3p and gga-miR-3532-3p emerged as the strongest candidates, supported by four independent tools and possessing 15 and 12 binding sites, respectively. For RPS28, gga-miR-6567-3p, gga-miR-6642-3p, gga-miR-7477-5p, and gga-miR-6579-5p were confirmed by five tools and had 8-10 binding sites, indicating strong regulatory potential. Comparative analysis revealed that gga-miR-6642-3p had high regulatory potential for both genes, although binding patterns varied due to gene-specific sequence and structural differences. These findings suggest specificity and overlap in miRNA function across biological pathways. Experimental validation is essential to confirm these bioinformatic predictions. Ribosomal gene expression changes have been linked to immune responses and stress tolerance in poultry. During viral outbreaks such as avian influenza, ribosomal gene expression patterns shift, influencing inflammatory and immune responses. Understanding these changes can aid in vaccine development and disease control strategies. Ribosomal genes are vital for protein synthesis and cellular function, directly impacting poultry growth, development, and health. Located in nucleolus organizer regions (NORs), these genes vary in copy number across poultry lines, correlating with phenotypic traits and breeding responses. For example, white and brown laying hens exhibit different ribosomal gene profiles, affecting protein synthesis capacity and physiological development. RPL3L has been identified as a key gene associated with muscle growth and body weight, influencing skeletal muscle proliferation and differentiation. Mutations in this gene may serve as molecular markers for breeding programs. Ribosomal genes also play roles in immune responses. Their expression changes during infections such as avian influenza, offering insights into disease mechanisms and potential therapeutic targets. Microbiota profiling using 16S rRNA sequencing has furthered our understanding of gut health and its impact on poultry nutrition and productivity. Modern technologies like RNA-Seq and bioinformatic network analysis have enhanced our ability to study ribosomal gene function across tissues and developmental stages, contributing to improved breeding and production management.
Conclusions: This comprehensive in silico analysis highlights the regulatory potential of specific miRNAs in the immune response of chickens to avian influenza. miRNAs such as gga-miR-214, gga-miR-194, gga-miR-7477-5p, and gga-miR-6642-3p demonstrate strong interactions with RPS28 and RPL31 ribosomal genes, suggesting roles in antiviral defense and cellular regulation. These findings provide a foundation for future experimental validation and may contribute to the development of miRNA-based strategies for enhancing disease resistance and improving poultry health. The integration of network analysis and bioinformatics offers a powerful model for studying miRNA roles in other diseases and species, paving the way for innovative approaches in veterinary medicine and bioelectronics. Further functional studies are recommended to evaluate the efficacy of epigenetic interventions targeting these miRNAs in managing avian influenza infections.