Divergent Traits and Ligand-Binding Properties of the Cytomegalovirus CD48 Gene Family

Evolution of the Cytomegalovirus RL11 gene family in Old World monkeys and Great Apes

Cytomegalovirus (CMV) is a genus of herpesviruses, members of which share a long history of coevolution with their primate hosts including New World monkeys, Old World monkeys (OWMs), and Great Apes (GAs). These viruses are ubiquitous within their host populations and establish lifelong infection in most individuals. Although asymptomatic in healthy individuals, infection poses a significant risk to individuals with a weakened or underdeveloped immune system. The genome of human CMV is the largest among human-infecting viruses and comprises at least 15 separate gene families, which may have arisen by gene duplication. Within human CMV, the RL11 gene family is the largest. RL11 genes are nonessential in vitro but have immune evasion roles that are likely critical to persistence in vivo. These genes demonstrate an extreme level of inter-species and intra-strain sequence diversity, which makes it challenging to deduce the evolutionary relationships within this gene family. Understanding the evolutionary relationships of these genes, especially accurate ortholog identification, is essential for reconstructing ancestral genomes, deciphering gene repertoire and order, and enabling reliable functional analyses across the CMV species, thereby offering insights into evolutionary processes, genetic diversity, and the functional significance of genes. In this work, we combined in silico genome screening with sequence-based and structure-guided phylogenetic analysis to reconstruct the evolutionary history of the RL11 gene family. We confirmed that RL11 genes are unique to OWM and GA CMVs, showing that this gene family was formed by multiple early duplication events and later lineage-specific losses. We identified four main clades of RL11 genes and showed that their expansions were mainly lineage specific and happened independently in CMVs of GAs, African OWMs, and Asian OWMs. We also identified groups of orthologous genes across the CMV tree, showing that some human CMV-specific RL11 genes emerged before the divergence of human and chimpanzee CMVs but were subsequently lost in the latter. The extensive and dynamic species-specific evolution of this gene family suggests that their functions target elements of host immunity that have similarly coevolved during speciation.

Defining the characteristics of interferon-alpha-stimulated human genes: insight from expression data and machine learning

BACKGROUND

A virus-infected cell triggers a signalling cascade, resulting in the secretion of interferons (IFNs), which in turn induces the upregulation of the IFN-stimulated genes (ISGs) that play a role in antipathogen host defence. Here, we conducted analyses on large-scale data relating to evolutionary gene expression, sequence composition, and network properties to elucidate factors associated with the stimulation of human genes in response to IFN-α.

RESULTS

We find that ISGs are less evolutionary conserved than genes that are not significantly stimulated in IFN experiments (non-ISGs). ISGs show obvious depletion of GC content in the coding region. This influences the representation of some compositions following the translation process. IFN-repressed human genes (IRGs), downregulated genes in IFN experiments, can have similar properties to the ISGs. Additionally, we design a machine learning framework integrating the support vector machine and novel feature selection algorithm that achieves an area under the receiver operating characteristic curve (AUC) of 0.7455 for ISG prediction. Its application in other IFN systems suggests the similarity between the ISGs triggered by type I and III IFNs.

CONCLUSIONS

ISGs have some unique properties that make them different from the non-ISGs. The representation of some properties has a strong correlation with gene expression following IFN-α stimulation, which can be used as a predictive feature in machine learning. Our model predicts several genes as putative ISGs that so far have shown no significant differential expression when stimulated with IFN-α in the cell/tissue types in the available databases. A web server implementing our method is accessible at http://isgpre.cvr.gla.ac.uk/. The docker image at https://hub.docker.com/r/hchai01/isgpre can be downloaded to reproduce the prediction.