Viral spillover represents the transmission of pathogen viruses from one species to another that can give rise to an outbreak. It is a critical concept that has gained increasing attention, particularly after the SARS-CoV-2 pandemic. However, the term is often used inaccurately to describe events that do not meet the true definition of spillover. This review aims to clarify the proper use of the term and provides a detailed analysis of the mechanisms driving zoonotic spillover, with a focus on the genetic and environmental factors that enable viruses to adapt to new hosts. Key topics include viral genetic variability in reservoir species, biological barriers to cross-species transmission, and the factors that influence viral adaptation and spread in novel hosts. The review also examines the role of evolutionary processes such as mutation and epistasis, alongside ecological conditions that facilitate the emergence of new pathogens. Ultimately, it underscores the need for more accurate predictive models and improved surveillance to better anticipate and mitigate future spillover events.

Therapy against the Hepatitis C virus (HCV) has significantly improved with the introduction of direct-acting antiviral drugs (DAAs), achieving over 95% sustained virological response (SVR). Despite this, the development of an effective anti-HCV vaccine remains a critical challenge due to the low number of patients treated with DAAs and the occurrence of HCV reinfections in high-risk groups. Current vaccine strategies aim to stimulate either B-cell or T-cell responses. Vaccines based on E1 and E2 proteins can elicit broad cross-neutralizing antibodies against all major HCV genotypes, though with varying efficiencies and without full protection against infection. In humans, the neutralizing antibodies induced by such vaccines mainly target the AR3 region, but their levels are generally insufficient for broad neutralization. Various HCV proteins expressed through different viral vectors have been utilized to elicit T cell immune responses, showing sustained expansion of HCV-specific effector memory T cells and improved proliferation and polyfunctionality of memory T cells over time. However, despite these advancements, the frequency and effectiveness of T-cell responses remain limited.

In recent decades, microRNAs (miRNAs) have emerged as key regulators of gene expression, and the identification of viral miRNAs (v-miRNAs) within some viruses, including hepatitis B virus (HBV), has attracted significant attention. HBV infections often progress to chronic states (CHB) and may induce fibrosis/cirrhosis and hepatocellular carcinoma (HCC). The presence of HBV can dysregulate host miRNA expression, influencing several biological pathways, such as apoptosis, innate and immune response, viral replication, and pathogenesis. Consequently, miRNAs are considered a promising biomarker for diagnostic, prognostic, and treatment response. The dynamics of miRNAs during HBV infection are multifaceted, influenced by host variability and miRNA interactions. Given the ability of miRNAs to target multiple messenger RNA (mRNA), understanding the viral–host (human) interplay is complex but essential to develop novel clinical applications. Therefore, bioinformatics can help to analyze, identify, and interpret a vast amount of miRNA data. This review explores the bioinformatics tools available for viral and host miRNA research. Moreover, we introduce a brief overview focusing on the role of miRNAs during HBV infection. In this way, this review aims to help the selection of the most appropriate bioinformatics tools based on requirements and research goals.

Persistence is a strategy used by many viruses to evade eradication by the immune system, ensuring their permanence and transmission within the host and optimizing viral fitness. During persistence, viruses can trigger various phenomena, including target organ damage, mainly due to an inflammatory state induced by infection, as well as cell proliferation and/or immortalization. In addition to immune evasion and chronic inflammation, factors contributing to viral persistence include low-level viral replication, the accumulation of viral mutants, and, most importantly, maintenance of the viral genome and reliance on viral oncoprotein production. This review focuses on the process of genome integration, which may occur at different stages of infection (e.g., HBV), during the chronic phase of infection (e.g., HPV, EBV), or as an essential part of the viral life cycle, as seen in retroviruses (HIV, HTLV-1). It also explores the close relationship between integration, persistence, and oncogenesis. Several models have been proposed to describe the genome integration process, including non-homologous recombination, looping, and microhomology models. Integration can occur either randomly or at specific genomic sites, often leading to genome destabilization. In some cases, integration results in the loss of genomic regions or impairs the regulation of oncogene and/or oncosuppressor expression, contributing to tumor development.

Background: Human papillomavirus (HPV) infection is linked to several cancers, including anal and oral cancers. The incidence of anal cancer is particularly high among HIV-positive men who have sex with men (MSM). DNA methylation markers have shown promise as biomarkers for identifying precancerous lesions and cancer in HPV-infected individuals. The aim of this study was to investigate the correlation of DNA methylation with HPV infection in oral samples and the correlation of DNA methylation with lesion degree in the anal samples of HIV-positive MSM. Methods: This study investigated DNA methylation in oral and anal samples from HIV-positive MSM at the National Institute for Infectious Diseases (INMI) in Rome, Italy. Exfoliated oral epithelial cells and anal samples were collected and analyzed for 28 HPV genotypes using the Allplex 28 HPV assay. DNA methylation was assessed with the PrecursorM+ kit for oral samples and the AnoGyn kit for anal samples, focusing on the promoter regions of specific genes. Results: The study included 63 participants, with a median age of 49 and a median CD4+ count of 705 cells/µL. The oral samples showed HPV16 as the most common type, with 22% testing positive for DNA methylation. The anal samples exhibited HPV-related methylation changes linked to cytological lesions, with a 30% increase in the observed ddCt ratio. Significant differences were found in both ASCL1 and ZNF582 genes, particularly for HSILvsNILM and HSILvsLSIL lesions. Of the samples with an increased ddCt ratio, 80% were from patients over 35 years old, and multiple HPV infections were common. Conclusions: DNA methylation markers could be valuable in identifying high-risk HPV infections in oral samples and detecting potential precancerous lesions in anal samples. These markers may enhance the early detection and prevention strategies for HPV-related cancers in high-risk populations, with follow-up data indicating potential for monitoring lesion progression.

The hepatitis delta virus (HDV) exhibits high genetic and evolutionary variability and is classified into eight genotypes (HDV-1 to -8). HDV-1 is the most widespread genotype worldwide and includes several subtypes. It predominates mainly in Europe, the Middle East, North America, and Northern Africa, and is associated with both severe and mild forms of liver disease. In this study, we performed phylogenetic and phylodynamic analyses of HDV strains circulating in Regione Lazio, Italy, to understand when these strains were introduced into the Lazio region and to define their genetic variability in Italy. Fifty HDV RNA positive patient samples were amplified using a nested RT-PCR approach targeting the HDV R0 region and sequenced. A phylogenetic tree of patient-derived sequences and reference sequences representing HDV-1 to -8 was constructed using the GTRGAMMA model in RAxML v8. The results indicated that HDV-1 was the predominant genotype with HDV-1d being the most frequently inferred subtype. HDV-1 sequences clustering with subtypes 1b and 1e were also identified. A phylodynamic analysis of HDV-1 sequences employing a Bayesian birth-death model inferred a clock rate of 3.04 × 10⁻⁴ substitutions per site per million years, with a 95% Highest Posterior Density (HPD) interval of 3.45 × 10⁻⁵ to 5.72 × 10⁻⁴. A Bayesian birth-death analysis with tree calibration based on a sample dating approach indicated multiple original sources of infection (from the late 1950s to late 1980s). Overall, these results suggest that HDV sequences from the native Italian and non-Italian patients analyzed in this study represent multiple lineages introduced across a wide period. A common ancestral origin should be excluded.