A consideration of within-host human cytomegalovirus genetic variation

Human cytomegalovirus glycoprotein variants governing viral tropism and syncytium formation in epithelial cells and macrophages

Human cytomegalovirus (HCMV) displays a broad cell tropism, and the infection of biologically relevant cells such as epithelial, endothelial, and hematopoietic cells supports viral transmission, systemic spread, and pathogenesis in the human host. HCMV strains differ in their ability to infect and replicate in these cell types, but the genetic basis of these differences has remained incompletely understood. In this study, we investigated HCMV strain VR1814, which is highly infectious for epithelial cells and macrophages and induces cell-cell fusion in both cell types. A VR1814-derived bacterial artificial chromosome (BAC) clone, FIX-BAC, was generated many years ago but has fallen out of favor because of its modest infectivity. By sequence comparison and genetic engineering of FIX, we demonstrate that the high infectivity of VR1814 and its ability to induce syncytium formation in epithelial cells and macrophages depends on VR1814-specific variants of the envelope glycoproteins gB, UL128, and UL130. We also show that UL130-neutralizing antibodies inhibit syncytium formation, and a FIX-specific mutation in UL130 is responsible for its low infectivity by reducing the amount of the pentameric glycoprotein complex in viral particles. Moreover, we found that a VR1814-specific mutation in US28 further increases viral infectivity in macrophages, possibly by promoting lytic rather than latent infection of these cells. Our findings show that variants of gB and the pentameric complex are major determinants of infectivity and syncytium formation in epithelial cells and macrophages. Furthermore, the VR1814-adjusted FIX strains can serve as valuable tools to study HCMV infection of myeloid cells.IMPORTANCEHuman cytomegalovirus (HCMV) is a major cause of morbidity and mortality in transplant patients and the leading cause of congenital infections. HCMV infects various cell types, including epithelial cells and macrophages, and some strains induce the fusion of neighboring cells, leading to the formation of large multinucleated cells called syncytia. This process may limit the exposure of the virus to host immune factors and affect pathogenicity. However, the reason why some HCMV strains exhibit a broader cell tropism and why some induce cell fusion more than others is not well understood. We compared two closely related HCMV strains and provided evidence that small differences in viral envelope glycoproteins can massively increase or decrease the virus infectivity and its ability to induce syncytium formation. The results of the study suggest that natural strain variations may influence HCMV infection and pathogenesis in humans.

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modeled by a realistic mutation rate and as part of a realistic distribution of fitness effects, as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modeled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false-positive rates are in excess of true-positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.

Evaluating power to detect recurrent selective sweeps under increasingly realistic evolutionary null models

The detection of selective sweeps from population genomic data often relies on the premise that the beneficial mutations in question have fixed very near the sampling time. As it has been previously shown that the power to detect a selective sweep is strongly dependent on the time since fixation as well as the strength of selection, it is naturally the case that strong, recent sweeps leave the strongest signatures. However, the biological reality is that beneficial mutations enter populations at a rate, one that partially determines the mean wait time between sweep events and hence their age distribution. An important question thus remains about the power to detect recurrent selective sweeps when they are modelled by a realistic mutation rate and as part of a realistic distribution of fitness effects (DFE), as opposed to a single, recent, isolated event on a purely neutral background as is more commonly modelled. Here we use forward-in-time simulations to study the performance of commonly used sweep statistics, within the context of more realistic evolutionary baseline models incorporating purifying and background selection, population size change, and mutation and recombination rate heterogeneity. Results demonstrate the important interplay of these processes, necessitating caution when interpreting selection scans; specifically, false positive rates are in excess of true positive across much of the evaluated parameter space, and selective sweeps are often undetectable unless the strength of selection is exceptionally strong.

Teaser Text

Outlier-based genomic scans have proven a popular approach for identifying loci that have potentially experienced recent positive selection. However, it has previously been shown that an evolutionarily appropriate baseline model that incorporates non-equilibrium population histories, purifying and background selection, and variation in mutation and recombination rates is necessary to reduce often extreme false positive rates when performing genomic scans. Here we evaluate the power to detect recurrent selective sweeps using common SFS-based and haplotype-based methods under these increasingly realistic models. We find that while these appropriate evolutionary baselines are essential to reduce false positive rates, the power to accurately detect recurrent selective sweeps is generally low across much of the biologically relevant parameter space.

Developing an appropriate evolutionary baseline model for the study of SARS-CoV-2 patient samples

Over the past 3 years, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread through human populations in several waves, resulting in a global health crisis. In response, genomic surveillance efforts have proliferated in the hopes of tracking and anticipating the evolution of this virus, resulting in millions of patient isolates now being available in public databases. Yet, while there is a tremendous focus on identifying newly emerging adaptive viral variants, this quantification is far from trivial. Specifically, multiple co-occurring and interacting evolutionary processes are constantly in operation and must be jointly considered and modeled in order to perform accurate inference. We here outline critical individual components of such an evolutionary baseline model-mutation rates, recombination rates, the distribution of fitness effects, infection dynamics, and compartmentalization-and describe the current state of knowledge pertaining to the related parameters of each in SARS-CoV-2. We close with a series of recommendations for future clinical sampling, model construction, and statistical analysis.

Identifying high-confidence variants in human cytomegalovirus genomes sequenced from clinical samples

Understanding the intrahost evolution of viral populations has implications in pathogenesis, diagnosis, and treatment and has recently made impressive advances from developments in high-throughput sequencing. However, the underlying analyses are very sensitive to sources of bias, error, and artefact in the data, and it is important that these are addressed adequately if robust conclusions are to be drawn. The key factors include (1) determining the number of viral strains present in the sample analysed; (2) monitoring the extent to which the data represent these strains and assessing the quality of these data; (3) dealing with the effects of cross-contamination; and (4) ensuring that the results are reproducible. We investigated these factors by generating sequence datasets, including biological and technical replicates, directly from clinical samples obtained from a small cohort of patients who had been infected congenitally with the herpesvirus human cytomegalovirus, with the aim of developing a strategy for identifying high-confidence intrahost variants. We found that such variants were few in number and typically present in low proportions and concluded that human cytomegalovirus exhibits a very low level of intrahost variability. In addition to clarifying the situation regarding human cytomegalovirus, our strategy has wider applicability to understanding the intrahost variability of other viruses.

Ongoing Recombination in SARS-CoV-2 Revealed through Genealogical Reconstruction

The evolutionary process of genetic recombination has the potential to rapidly change the properties of a viral pathogen, and its presence is a crucial factor to consider in the development of treatments and vaccines. It can also significantly affect the results of phylogenetic analyses and the inference of evolutionary rates. The detection of recombination from samples of sequencing data is a very challenging problem and is further complicated for SARS-CoV-2 by its relatively slow accumulation of genetic diversity. The extent to which recombination is ongoing for SARS-CoV-2 is not yet resolved. To address this, we use a parsimony-based method to reconstruct possible genealogical histories for samples of SARS-CoV-2 sequences, which enables us to pinpoint specific recombination events that could have generated the data. We propose a statistical framework for disentangling the effects of recurrent mutation from recombination in the history of a sample, and hence provide a way of estimating the probability that ongoing recombination is present. We apply this to samples of sequencing data collected in England and South Africa and find evidence of ongoing recombination.

Inferring the distribution of fitness effects in patient-sampled and experimental virus populations: two case studies

We here propose an analysis pipeline for inferring the distribution of fitness effects (DFE) from either patient-sampled or experimentally-evolved viral populations, that explicitly accounts for non-Wright-Fisher and non-equilibrium population dynamics inherent to pathogens. We examine the performance of this approach via extensive power and performance analyses, and highlight two illustrative applications - one from an experimentally-passaged RNA virus, and the other from a clinically-sampled DNA virus. Finally, we discuss how such DFE inference may shed light on major research questions in virus evolution, ranging from a quantification of the population genetic processes governing genome size, to the role of Hill-Robertson interference in dictating adaptive outcomes, to the potential design of novel therapeutic approaches to eradicate within-patient viral populations via induced mutational meltdown.

Genetic Variability of Human Cytomegalovirus Clinical Isolates Correlates With Altered Expression of Natural Killer Cell-Activating Ligands and IFN-γ

Human cytomegalovirus (HCMV) infection often leads to systemic disease in immunodeficient patients and congenitally infected children. Despite its clinical significance, the exact mechanisms contributing to HCMV pathogenesis and clinical outcomes have yet to be determined. One of such mechanisms involves HCMV-mediated NK cell immune response, which favors viral immune evasion by hindering NK cell-mediated cytolysis. This process appears to be dependent on the extent of HCMV genetic variation as high levels of variability in viral genes involved in immune escape have an impact on viral pathogenesis. However, the link between viral genome variations and their functional effects has so far remained elusive. Thus, here we sought to determine whether inter-host genetic variability of HCMV influences its ability to modulate NK cell responses to infection. For this purpose, five HCMV clinical isolates from a previously characterized cohort of pediatric patients with confirmed HCMV congenital infection were evaluated by next-generation sequencing (NGS) for genetic polymorphisms, phylogenetic relationships, and multiple-strain infection. We report variable levels of genetic characteristics among the selected clinical strains, with moderate variations in genome regions associated with modulation of NK cell functions. Remarkably, we show that different HCMV clinical strains differentially modulate the expression of several ligands for the NK cell-activating receptors NKG2D, DNAM-1/CD226, and NKp30. Specifically, the DNAM-1/CD226 ligand PVR/CD155 appears to be predominantly upregulated by fast-replicating (“aggressive”) HCMV isolates. On the other hand, the NGK2D ligands ULBP2/5/6 are downregulated regardless of the strain used, while other NK cell ligands (i.e., MICA, MICB, ULBP3, Nectin-2/CD112, and B7-H6) are not significantly modulated. Furthermore, we show that IFN-γ; production by NK cells co-cultured with HCMV-infected fibroblasts is directly proportional to the aggressiveness of the HCMV clinical isolates employed. Interestingly, loss of NK cell-modulating genes directed against NK cell ligands appears to be a common feature among the “aggressive” HCMV strains, which also share several gene variants across their genomes. Overall, even though further studies based on a higher number of patients would offer a more definitive scenario, our findings provide novel mechanistic insights into the impact of HCMV genetic variability on NK cell-mediated immune responses.

Where do we Stand after Decades of Studying Human Cytomegalovirus?

Human cytomegalovirus (HCMV), a linear double-stranded DNA betaherpesvirus belonging to the family of Herpesviridae, is characterized by widespread seroprevalence, ranging between 56% and 94%, strictly dependent on the socioeconomic background of the country being considered. Typically, HCMV causes asymptomatic infection in the immunocompetent population, while in immunocompromised individuals or when transmitted vertically from the mother to the fetus it leads to systemic disease with severe complications and high mortality rate. Following primary infection, HCMV establishes a state of latency primarily in myeloid cells, from which it can be reactivated by various inflammatory stimuli. Several studies have shown that HCMV, despite being a DNA virus, is highly prone to genetic variability that strongly influences its replication and dissemination rates as well as cellular tropism. In this scenario, the few currently available drugs for the treatment of HCMV infections are characterized by high toxicity, poor oral bioavailability, and emerging resistance. Here, we review past and current literature that has greatly advanced our understanding of the biology and genetics of HCMV, stressing the urgent need for innovative and safe anti-HCMV therapies and effective vaccines to treat and prevent HCMV infections, particularly in vulnerable populations.