Rapid transmission and tight bottlenecks constrain the evolution of highly transmissible SARS-CoV-2 variants

The consequences of SARS-CoV-2 within-host persistence

SARS-CoV-2 causes an acute respiratory tract infection that resolves in most people in less than a month. Yet some people with severely weakened immune systems fail to clear the virus, leading to persistent infections with high viral titres in the respiratory tract. In a subset of cases, persistent SARS-CoV-2 replication results in an accelerated accumulation of adaptive mutations that confer escape from neutralizing antibodies and enhance cellular infection. This may lead to the evolution of extensively mutated SARS-CoV-2 variants and introduce an element of chance into the timing of variant evolution, as variant formation may depend on evolution in a single person. Whether long COVID is also caused by persistence of replicating SARS-CoV-2 is controversial. One line of evidence is detection of SARS-CoV-2 RNA and proteins in different body compartments long after SARS-CoV-2 infection has cleared from the upper respiratory tract. However, thus far, no replication competent virus has been cultured from individuals with long COVID who are immunocompetent. In this Review, we consider mechanisms of viral persistence, intra-host evolution in persistent infections, the connection of persistent infections with SARS-CoV-2 variants and the possible role of SARS-CoV-2 persistence in long COVID. Understanding persistent infections may therefore resolve much of what is still unclear in COVID-19 pathophysiology, with possible implications for other emerging viruses.

SARS-CoV-2 cellular coinfection is limited by superinfection exclusion

The coinfection of individual cells is a requirement for exchange between two or more virus genomes, which is a major mechanism driving virus evolution. Coinfection is restricted by a mechanism known as superinfection exclusion (SIE), which prohibits the infection of a previously infected cell by a related virus after a period of time. SIE regulates coinfection for many different viruses, but its relevance to the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was unknown. In this study, we investigated this using a pair of SARS-CoV-2 variant viruses encoding distinct fluorescent reporter proteins. We show for the first time that SARS-CoV-2 coinfection of individual cells is limited temporally by SIE. We defined the kinetics of the onset of SIE for SARS-CoV-2 in this system, showing that the potential for coinfection starts to diminish within the first hour of primary infection and then falls exponentially as the time between the two infection events is increased. We then asked how these kinetics would affect the potential for coinfection with viruses during a spreading infection. We used plaque assays to model the localized spread of SARS-CoV-2 observed in infected tissue and showed that the kinetics of SIE restrict coinfection-and therefore sites of possible genetic exchange-to a small interface of infected cells between spreading viral infections. This indicates that SIE, by reducing the likelihood of coinfection of cells, likely reduces the opportunities for genetic exchange between different strains of SARS-CoV-2 and therefore is an underappreciated factor in shaping SARS-CoV-2 evolution.

IMPORTANCE

Since SARS-CoV-2 first emerged in 2019, it has continued to evolve, occasionally generating variants of concern. One of the ways that SARS-CoV-2 can evolve is through recombination, where genetic information is swapped between different genomes. Recombination requires the coinfection of cells; therefore, factors impacting coinfection are likely to influence SARS-CoV-2 evolution. Coinfection is restricted by SIE, a phenomenon whereby a previously infected cell becomes increasingly resistant to subsequent infection. Here we report that SIE is activated following SARS-CoV-2 infection and reduces the likelihood of coinfection exponentially following primary infection. Furthermore, we show that SIE prevents coinfection of cells at the boundary between two expanding areas of infection, the scenario most likely to lead to recombination between different SARS-CoV-2 lineages. Our work suggests that SIE reduces the likelihood of recombination between SARS-CoV-2 genomes and therefore likely shapes SARS-CoV-2 evolution.

Geospatial and demographic patterns of SARS-CoV-2 spread in Massachusetts from over 130,000 genomes

Despite intensive study, gaps remain in our understanding of SARS-CoV-2 transmission patterns during the COVID-19 pandemic, in part due to limited contextual metadata accompanying most large genomic surveillance datasets. We analyzed over 130,000 SARS-CoV-2 genomes, over 85,000 with matched epidemiological data, collected in Massachusetts from November 2021 to January 2023, to investigate viral transmission dynamics at high resolution. The data were drawn from diagnostic testing at >600 facilities representing schools, workplaces, public testing, and other sectors, and encompass the emergence of six major viral lineages, each representing a new outbreak. We found urban areas as key hubs for new lineage introduction and interurban transmission as facilitating spread throughout the state. Young adults, especially those on college campuses, served as early indicators of emerging lineage dominance. Resident-aged populations in college campuses and nursing homes exhibited a higher likelihood of being linked to within-facility transmission, while staff-aged at those facilities were more linked to their surrounding community. Individuals with recent vaccine doses, including boosters, had a lower likelihood of initiating transmission. This dataset shows the value of linking genomic and epidemiologic data at scale for higher resolution insights into viral dynamics and their implication for public health strategy.

In depth sequencing of a serially sampled household cohort reveals the within-host dynamics of Omicron SARS-CoV-2 and rare selection of novel spike variants

SARS-CoV-2 has undergone repeated and rapid evolution to circumvent host immunity. However, outside of prolonged infections in immunocompromised hosts, within-host positive selection has rarely been detected. Here we combine daily longitudinal sampling of individuals with replicate sequencing to increase the accuracy of and lower the threshold for variant calling. We sequenced 577 specimens from 105 individuals in a household cohort during the BA.1/BA.2 variant period. Individuals exhibited extremely low viral diversity, and we estimated a low within-host evolutionary rate. Within-host dynamics were dominated by genetic drift and purifying selection. Positive selection was rare but highly concentrated in spike. A Wright Fisher Approximate Bayesian Computational model identified positive selection at 14 loci with 7 in spike, including S:448 and S:339. This detectable immune-mediated selection is unusual in acute respiratory infections and may be caused by the relatively narrow antibody repertoire in individuals during the early Omicron phase of the SARS-CoV-2 pandemic.

Risk period for transmission of SARS-CoV-2 and seasonal influenza: a rapid review

BACKGROUND

Restricting infectious healthcare workers (HCWs) from the workplace is an important infection prevention strategy. The duration of viral shedding or symptoms are often used as proxies for the infectious period in adults but may not accurately estimate it.

OBJECTIVE

To determine the risk period for transmission among previously healthy adults infected with SARS-CoV-2 omicron variant (omicron) or influenza A (influenza) by examining the duration of shedding and symptoms, and day of symptom onset in secondary cases of transmission pairs.

DESIGN

Rapid review.

METHODS

This rapid review adhered to PRISMA-ScR; five databases were searched. The cumulative daily proportion of participants with an outcome of interest was calculated for each study and summarized.

RESULTS

Forty-three studies were included. Shedding resolved among ≥ 70% of participants by the end of day nine post symptom onset for omicron, and day seven for influenza; and for ≥ 90% of participants, by the end of day 10 for omicron and day nine for influenza. Two studies suggested shedding continues > 24 hours post-fever resolution for both viruses. Symptom onset occurred in ≥ 80% of secondary cases by the end of day seven post-primary case symptom onset for omicron and day six for influenza.

CONCLUSIONS

Omicron shedding is consistent with previous recommendations to exclude infected HCWs from work for 10 days; and influenza follows a similar trend. Earlier symptom onset in most secondary cases for both pathogens indicates that, despite persistent viral shedding, most transmission occurs earlier; and the cumulative serial interval might better approximate the duration of infectiousness.

Intra-host variability of SARS-CoV-2: Patterns, causes and impact on COVID-19

Intra-host viral variability is related to pathogenicity, persistence, drug resistance, and the emergence of new clades. This work reviews the large amount of data on SARS-CoV-2 intra-host variability accumulated to date, addressing known and potential implications in COVID-19 and the emergence of VOCs and lineage-defining mutations. Topics covered include the distribution of intra-host polymorphisms across the genome, the corresponding mutational signatures, their patterns of emergence and extinction throughout infection, and the processes governing their abundance, frequency, and type (synonymous, nonsynonymous, indels, nonsense). Besides, evidence is reviewed that the virus can replicate and mutate in isolation at different anatomical compartments, which may imply that what we have learned from respiratory samples could be part of a broader picture.

Barcoded SARS-CoV-2 viruses define the impact of duration and route of exposure on the transmission bottleneck in a hamster model

The transmission bottleneck, defined as the number of viruses shed from one host to infect another, is an important determinant of the rate of virus evolution and the level of immunity required to protect against virus transmission. Despite its importance, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission bottleneck remains poorly characterized. We adapted a SARS-CoV-2 reverse genetics system to generate a pool of >200 isogenic SARS-CoV-2 viruses harboring specific 6-nucleotide barcodes, infected donor hamsters with this pool, and exposed contact hamsters to paired infected donors, varying the duration and route of exposure. Following exposure, the nasal turbinates, trachea, and lungs were collected and the number of barcodes in each tissue was enumerated. We found that longer and more direct exposures increased the transmission bottleneck and that the upper airway is the primary source of transmitted virus in this model. Together, these findings highlight the utility of barcoded viruses as tools to rigorously study virus transmission.

Revealing SARS-CoV-2 Mpro mutation cold and hot spots: Dynamic residue network analysis meets machine learning

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.

In depth sequencing of a serially sampled household cohort reveals the within-host dynamics of Omicron SARS-CoV-2 and rare selection of novel spike variants

SARS-CoV-2 has undergone repeated and rapid evolution to circumvent host immunity. However, outside of prolonged infections in immunocompromised hosts, within-host positive selection has rarely been detected. The low diversity within-hosts and strong genetic linkage among genomic sites make accurately detecting positive selection difficult. Longitudinal sampling is a powerful method for detecting selection that has seldom been used for SARS-CoV-2. Here we combine longitudinal sampling with replicate sequencing to increase the accuracy of and lower the threshold for variant calling. We sequenced 577 specimens from 105 individuals from a household cohort primarily during the BA.1/BA.2 variant period. There was extremely low diversity and a low rate of divergence. Specimens had 0-12 intrahost single nucleotide variants (iSNV) at >0.5% frequency, and the majority of the iSNV were at frequencies <2%. Within-host dynamics were dominated by genetic drift and purifying selection. Positive selection was rare but highly concentrated in spike. Two individuals with BA.1 infections had S:371F, a lineage defining substitution for BA.2. A Wright Fisher Approximate Bayesian Computational model identified positive selection at 14 loci with 7 in spike, including S:448 and S:339. We also detected significant genetic hitchhiking between synonymous changes and nonsynonymous iSNV under selection. The detectable immune-mediated selection may be caused by the relatively narrow antibody repertoire in individuals during the early Omicron phase of the SARS-CoV-2 pandemic. As both the virus and population immunity evolve, understanding the corresponding shifts in SARS-CoV-2 within-host dynamics will be important.

Severe Acute Respiratory Syndrome Coronavirus 2 Household Transmission During the Omicron Era in Massachusetts: A Prospective, Case-Ascertained Study Using Genomic Epidemiology

Background

Households are a major setting for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections, but there remains a lack of knowledge regarding the dynamics of viral transmission, particularly in the setting of preexisting SARS-CoV-2 immunity and evolving variants.

Methods

We conducted a prospective, case-ascertained household transmission study in the greater Boston area in March-July 2022. Anterior nasal swabs, along with clinical and demographic data, were collected for 14 days. Nasal swabs were tested for SARS-CoV-2 by polymerase chain reaction (PCR). Whole genome sequencing was performed on high-titer samples.

Results

We enrolled 33 households in a primary analysis set, with a median participant age of 25 years (range, 2-66 years), 98% of whom had received at least 2 doses of a coronavirus disease 2019 (COVID-19) vaccine. Fifty-eight percent of households had a secondary case during follow-up and the secondary attack rate (SAR) for contacts was 39%. We further examined a strict analysis set of 21 households that had only 1 PCR-positive case at baseline, finding an SAR of 22.5%. Genomic epidemiology further determined that there were multiple sources of infection for household contacts, including the index case and outside introductions. When limiting estimates to only highly probable transmissions given epidemiologic and genomic data, the SAR was 18.4%.

Conclusions

Household contacts of a person newly diagnosed with COVID-19 are at high risk for SARS-CoV-2 infection in the following 2 weeks. This is, however, not only due to infection from the household index case, but also because the presence of an infected household member implies increased SARS-CoV-2 community transmission.

Genomic epidemiology reveals the variation and transmission properties of SARS-CoV-2 in a single-source community outbreak

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused the coronavirus disease 2019 (COVID-19) pandemic, which is still a global public health concern. During March 2022, a rapid and confined single-source outbreak of SARS-CoV-2 was identified in a community in Nanjing municipal city. Overall, 95 individuals had laboratory-confirmed SARS-CoV-2 infection. The whole genomes of 61 viral samples were obtained, which were all members of the BA.2.2 lineage and clearly demonstrated the presence of one large clade, and all the infections could be traced back to the original index case. The most distant sequence from the index case presented a difference of 4 SNPs, and 118 intrahost single-nucleotide variants (iSNVs) at 74 genomic sites were identified. Some minor iSNVs can be transmitted and subsequently rapidly fixed in the viral population. The minor iSNVs transmission resulted in at least two nucleotide substitutions among all seven SNPs identified in the outbreak, generating genetically diverse populations. We estimated the overall transmission bottleneck size to be 3 using 11 convincing donor-recipient transmission pairs. Our study provides new insights into genomic epidemiology and viral transmission, revealing how iSNVs become fixed in local clusters, followed by viral transmission across the community, which contributes to population diversity.

Chronic infections can generate SARS-CoV-2-like bursts of viral evolution without epistasis

Multiple SARS-CoV-2 variants have arisen during the first years of the pandemic, often bearing many new mutations. Several explanations have been offered for the surprisingly sudden emergence of multiple mutations that enhance viral fitness, including cryptic transmission, spillover from animal reservoirs, epistasis between mutations, and chronic infections. Here, we simulated pathogen evolution combining within-host replication and between-host transmission. We found that, under certain conditions, chronic infections can lead to SARS-CoV-2-like bursts of mutations even without epistasis. Chronic infections can also increase the global evolutionary rate of a pathogen even in the absence of clear mutational bursts. Overall, our study supports chronic infections as a plausible origin for highly mutated SARS-CoV-2 variants. More generally, we also describe how chronic infections can influence pathogen evolution under different scenarios.

Influenza A virus within-host evolution and positive selection in a densely sampled household cohort over three seasons

While influenza A virus (IAV) antigenic drift has been documented globally, in experimental animal infections, and in immunocompromised hosts, positive selection has generally not been detected in acute infections. This is likely due to challenges in distinguishing selected rare mutations from sequencing error, a reliance on cross-sectional sampling, and/or the lack of formal tests of selection for individual sites. Here, we sequenced IAV populations from 346 serial, daily nasal swabs from 143 individuals collected over three influenza seasons in a household cohort. Viruses were sequenced in duplicate, and intrahost single nucleotide variants (iSNVs) were identified at a 0.5% frequency threshold. Within-host populations exhibited low diversity, with >75% mutations present at <2% frequency. Children (0-5 years) had marginally higher within-host evolutionary rates than adolescents (6-18 years) and adults (>18 years, 4.4 × 10-6 vs. 9.42 × 10-7 and 3.45 × 10-6, P < .001). Forty-five iSNVs had evidence of parallel evolution but were not over-represented in HA and NA. Several increased from minority to consensus level, with strong linkage among iSNVs across segments. A Wright-Fisher approximate Bayesian computational model identified positive selection at 23/256 loci (9%) in A(H3N2) specimens and 19/176 loci (11%) in A(H1N1)pdm09 specimens, and these were infrequently found in circulation. Overall, we found that within-host IAV populations were subject to genetic drift and purifying selection, with only subtle differences across seasons, subtypes, and age strata. Positive selection was rare and inconsistently detected.

Modelling COVID-19 mutant dynamics: understanding the interplay between viral evolution and disease transmission dynamics

Understanding virus mutations is critical for shaping public health interventions. These mutations lead to complex multi-strain dynamics often under-represented in models. Aiming to understand the factors influencing variants' fitness and evolution, we explore several scenarios of virus spreading to gain qualitative insight into the factors dictating which variants ultimately predominate at the population level. To this end, we propose a two-strain stochastic model that accounts for asymptomatic transmission, mutations and the possibility of disease import. We find that variants with milder symptoms are likely to spread faster than those with severe symptoms. This is because severe variants can prompt affected individuals to seek medical help earlier, potentially leading to quicker identification and isolation of cases. However, milder or asymptomatic cases may spread more widely, making it harder to control the spread. Therefore, increased transmissibility of milder variants can still result in higher hospitalizations and fatalities due to widespread infection. The proposed model highlights the interplay between viral evolution and transmission dynamics. Offering a nuanced view of factors influencing variant spread, the model provides a foundation for further investigation into mitigating strategies and public health interventions.

Influenza A virus within-host evolution and positive selection in a densely sampled household cohort over three seasons

While influenza A virus (IAV) antigenic drift has been documented globally, in experimental animal infections, and in immunocompromised hosts, positive selection has generally not been detected in acute infections. This is likely due to challenges in distinguishing selected rare mutations from sequencing error, a reliance on cross-sectional sampling, and/or the lack of formal tests of selection for individual sites. Here, we sequenced IAV populations from 346 serial, daily nasal swabs from 143 individuals collected over three influenza seasons in a household cohort. Viruses were sequenced in duplicate, and intrahost single nucleotide variants (iSNV) were identified at a 0.5% frequency threshold. Within-host populations were subject to purifying selection with >75% mutations present at <2% frequency. Children (0-5 years) had marginally higher within-host evolutionary rates than adolescents (6-18 years) and adults (>18 years, 4.4×10-6 vs. 9.42×10-7 and 3.45×10-6, p <0.001). Forty-five iSNV had evidence of parallel evolution, but were not overrepresented in HA and NA. Several increased from minority to consensus level, with strong linkage among iSNV across segments. A Wright Fisher Approximate Bayesian Computational model identified positive selection at 23/256 loci (9%) in A(H3N2) specimens and 19/176 loci (11%) in A(H1N1)pdm09 specimens, and these were infrequently found in circulation. Overall, we found that within-host IAV populations were subject to purifying selection and genetic drift, with only subtle differences across seasons, subtypes, and age strata. Positive selection was rare and inconsistently detected.

The Asymptomatic Proportion of SARS-CoV-2 Omicron Variant Infections in Households: A Systematic Review

Understanding the clinical spectrum of SARS-CoV-2 infection, including the asymptomatic fraction, is important as asymptomatic individuals are still able to infect other individuals and contribute to ongoing transmission. The WHO Unity Household transmission investigation (HHTI) protocol provides a platform for the prospective and systematic collection of high-quality clinical, epidemiological, serological and virological data from SARS-CoV-2 confirmed cases and their household contacts. These data can be used to understand key severity and transmissibility parameters-including the asymptomatic proportion-in relation to local epidemic context and help inform public health response. We aimed to estimate the asymptomatic proportion of SARS-CoV-2 Omicron variant infections in Unity-aligned HHTIs. We conducted a systematic review and meta-analysis in alignment with the PRISMA 2020 guidelines and registered our systematic review on PROSPERO (CRD42022378648). We searched EMBASE, Web of Science, MEDLINE and bioRxiv and medRxiv from 1 November 2021 to 22 August 2023. We identified 8368 records, of which 98 underwent full text review. We identified only three studies for data extraction, with substantial variation in study design and corresponding estimates of the asymptomatic proportion. As a result, we did not generate a pooled estimate or I2 metric. The limited number of quality studies that we identified highlights the need for improved preparedness and response capabilities to facilitate robust HHTI implementation, analysis and reporting, to better inform national, regional and global risk assessments and policymaking.

Barcoded SARS-CoV-2 viruses define the impact of time and route of transmission on the transmission bottleneck in a Syrian hamster model

The transmission bottleneck, defined as the number of viruses that transmit from one host to infect another, is an important determinant of the rate of virus evolution and the level of immunity required to protect against virus transmission. Despite its importance, SARS-CoV-2's transmission bottleneck remains poorly characterized, in part due to a lack of quantitative measurement tools. To address this, we adapted a SARS-CoV-2 reverse genetics system to generate a pool of >200 isogenic SARS-CoV-2 viruses harboring specific 6-nucleotide barcodes inserted in ORF10, a non-translated ORF. We directly inoculated donor Syrian hamsters intranasally with this barcoded virus pool and exposed a paired naïve contact hamster to each donor. Following exposure, the nasal turbinates, trachea, and lungs were collected, viral titers were measured, and the number of barcodes in each tissue were enumerated to quantify the transmission bottleneck. The duration and route (airborne, direct contact, and fomite) of exposure were varied to assess their impact on the transmission bottleneck. In airborne-exposed hamsters, the transmission bottleneck increased with longer exposure durations. We found that direct contact exposure produced the largest transmission bottleneck (average 27 BCs), followed by airborne exposure (average 16 BCs) then fomite exposure (average 8 BCs). Interestingly, we detected unique BCs in both the upper and lower respiratory tract of contact animals from all routes of exposure, suggesting that SARS-CoV-2 can directly infect hamster lungs. Altogether, these findings highlight the utility of barcoded viruses as tools to rigorously study virus transmission. In the future, barcoded SARS-CoV-2 will strengthen studies of immune factors that influence virus transmission.

The airborne transmission of viruses causes tight transmission bottlenecks

The transmission bottleneck describes the number of viral particles that initiate an infection in a new host. Previous studies have used genome sequence data to suggest that transmission bottlenecks for influenza and SARS-CoV-2 involve few viral particles, but the general principles of virus transmission are not fully understood. Here we show that, across a broad range of circumstances, tight transmission bottlenecks are a simple consequence of the physical process of airborne viral transmission. We use mathematical modelling to describe the physical process of the emission and inhalation of infectious particles, deriving the result that that the great majority of transmission bottlenecks involve few viral particles. While exceptions to this rule exist, the circumstances needed to create these exceptions are likely very rare. We thus provide a physical explanation for previous inferences of bottleneck size, while predicting that tight transmission bottlenecks prevail more generally in respiratory virus transmission.

Estimating the reproduction number and transmission heterogeneity from the size distribution of clusters of identical pathogen sequences

Quantifying transmission intensity and heterogeneity is crucial to ascertain the threat posed by infectious diseases and inform the design of interventions. Methods that jointly estimate the reproduction number R and the dispersion parameter k have however mainly remained limited to the analysis of epidemiological clusters or contact tracing data, whose collection often proves difficult. Here, we show that clusters of identical sequences are imprinted by the pathogen offspring distribution, and we derive an analytical formula for the distribution of the size of these clusters. We develop and evaluate an inference framework to jointly estimate the reproduction number and the dispersion parameter from the size distribution of clusters of identical sequences. We then illustrate its application across a range of epidemiological situations. Finally, we develop a hypothesis testing framework relying on clusters of identical sequences to determine whether a given pathogen genetic subpopulation is associated with increased or reduced transmissibility. Our work provides tools to estimate the reproduction number and transmission heterogeneity from pathogen sequences without building a phylogenetic tree, thus making it easily scalable to large pathogen genome datasets.

Within-host evolution of SARS-CoV-2: how often are de novo mutations transmitted from symptomatic infections?

Despite a relatively low mutation rate, the large number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections has allowed for substantial genetic change, leading to a multitude of emerging variants. Using a recently determined mutation rate (per site replication), as well as within-host parameter estimates for symptomatic SARS-CoV-2 infection, we apply a stochastic transmission-bottleneck model to describe the survival probability of de novo SARS-CoV-2 mutations as a function of bottleneck size and selection coefficient. For narrow bottlenecks, we find that mutations affecting per-target-cell attachment rate (with phenotypes associated with fusogenicity and ACE2 binding) have similar transmission probabilities to mutations affecting viral load clearance (with phenotypes associated with humoral evasion). We further find that mutations affecting the eclipse rate (with phenotypes associated with reorganization of cellular metabolic processes and synthesis of viral budding precursor material) are highly favoured relative to all other traits examined. We find that mutations leading to reduced removal rates of infected cells (with phenotypes associated with innate immune evasion) have limited transmission advantage relative to mutations leading to humoral evasion. Predicted transmission probabilities, however, for mutations affecting innate immune evasion are more consistent with the range of clinically estimated household transmission probabilities for de novo mutations. This result suggests that although mutations affecting humoral evasion are more easily transmitted when they occur, mutations affecting innate immune evasion may occur more readily. We examine our predictions in the context of a number of previously characterized mutations in circulating strains of SARS-CoV-2. Our work offers both a null model for SARS-CoV-2 mutation rates and predicts which aspects of viral life history are most likely to successfully evolve, despite low mutation rates and repeated transmission bottlenecks.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Household Transmission during the Omicron Era in Massachusetts: A Prospective, Case-Ascertained Study using Genomic Epidemiology

Background

Households are a major setting for SARS-CoV-2 infections, but there remains a lack of knowledge regarding the dynamics of viral transmission, particularly in the setting of widespread pre-existing SARS-CoV-2 immunity and evolving variants.

Methods

We conducted a prospective, case-ascertained household transmission study in the greater Boston area in March-July 2022. Anterior nasal swabs, along with clinical and demographic data, were collected for 14 days. Nasal swabs were tested for SARS-CoV-2 by PCR. Whole genome sequencing was performed on high-titer samples.

Results

We enrolled 33 households in a primary analysis set, with a median age of participants of 25 years old (range 2-66); 98% of whom had received at least 2 doses of a COVID-19 vaccine. 58% of households had a secondary case during follow up and the secondary attack rate (SAR) for contacts infected was 39%. We further examined a strict analysis set of 21 households that had only 1 PCR+ case at baseline, finding an SAR of 22.5%. Genomic epidemiology further determined that there were multiple sources of infection for household contacts, including the index case and outside introductions. When limiting estimates to only highly probable transmissions given epidemiologic and genomic data, the SAR was 18.4%.

Conclusions

Household contacts of a person newly diagnosed with COVID-19 are at high risk for SARS-CoV-2 infection in the following 2 weeks. This is, however, not only due to infection from the household index case, but also because the presence of an infected household member implies increased SARS-CoV-2 community transmission. Further studies to understand and mitigate household transmission are needed.

Genetic consequences of effective and suboptimal dosing with mutagenic drugs in a hamster model of SARS-CoV-2 infection

Mutagenic antiviral drugs have shown promise against multiple viruses, but concerns have been raised about whether their use might promote the emergence of new and harmful viral variants. Recently, genetic signatures associated with molnupiravir use have been identified in the global SARS-COV-2 population. Here, we examine the consequences of using favipiravir and molnupiravir to treat SARS-CoV-2 infection in a hamster model, comparing viral genome sequence data collected from (1) untreated hamsters, and (2) from hamsters receiving effective and suboptimal doses of treatment. We identify a broadly linear relationship between drug dose and the extent of variation in treated viral populations, with a high proportion of this variation being composed of variants at frequencies of less than 1 per cent, below typical thresholds for variant calling. Treatment with an effective dose of antiviral drug was associated with a gain of between 7 and 10 variants per viral genome relative to drug-free controls: even after a short period of treatment a population founded by a transmitted virus could contain multiple sequence differences to that of the original host. Treatment with a suboptimal dose of drug showed intermediate gains of variants. No dose-dependent signal was identified in the numbers of single-nucleotide variants reaching frequencies in excess of 5 per cent. We did not find evidence to support the emergence of drug resistance or of novel immune phenotypes. Our study suggests that where onward transmission occurs, a short period of treatment with mutagenic drugs may be sufficient to generate a significant increase in the number of viral variants transmitted.

Transmission Bottleneck Size Estimation from De Novo Viral Genetic Variation

Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. They have also been used to estimate transmission bottleneck sizes from identified donor-recipient pairs. These bottleneck sizes quantify the number of viral particles that establish genetic lineages in the recipient host and are important to estimate due to their impact on viral evolution. Current approaches for estimating bottleneck sizes exclusively consider the subset of viral sites that are observed as polymorphic in the donor individual. However, these approaches have the potential to substantially underestimate true transmission bottleneck sizes. Here, we present a new statistical approach for instead estimating bottleneck sizes using patterns of viral genetic variation that arise de novo within a recipient individual. Specifically, our approach makes use of the number of clonal viral variants observed in a transmission pair, defined as the number of viral sites that are monomorphic in both the donor and the recipient but carry different alleles. We first test our approach on a simulated dataset and then apply it to both influenza A virus sequence data and SARS-CoV-2 sequence data from identified transmission pairs. Our results confirm the existence of extremely tight transmission bottlenecks for these 2 respiratory viruses.

A Simulation Framework for Modeling the Within-Patient Evolutionary Dynamics of SARS-CoV-2

The global impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to considerable interest in detecting novel beneficial mutations and other genomic changes that may signal the development of variants of concern (VOCs). The ability to accurately detect these changes within individual patient samples is important in enabling early detection of VOCs. Such genomic scans for rarely acting positive selection are best performed via comparison of empirical data with simulated data wherein commonly acting evolutionary factors, including mutation and recombination, reproductive and infection dynamics, and purifying and background selection, can be carefully accounted for and parameterized. Although there has been work to quantify these factors in SARS-CoV-2, they have yet to be integrated into a baseline model describing intrahost evolutionary dynamics. To construct such a baseline model, we develop a simulation framework that enables one to establish expectations for underlying levels and patterns of patient-level variation. By varying eight key parameters, we evaluated 12,096 different model-parameter combinations and compared them with existing empirical data. Of these, 592 models (∼5%) were plausible based on the resulting mean expected number of segregating variants. These plausible models shared several commonalities shedding light on intrahost SARS-CoV-2 evolutionary dynamics: severe infection bottlenecks, low levels of reproductive skew, and a distribution of fitness effects skewed toward strongly deleterious mutations. We also describe important areas of model uncertainty and highlight additional sequence data that may help to further refine a baseline model. This study lays the groundwork for the improved analysis of existing and future SARS-CoV-2 within-patient data.

SARS-CoV-2 shedding and evolution in immunocompromised hosts during the Omicron period: a multicenter prospective analysis

Background

Prolonged SARS-CoV-2 infections in immunocompromised hosts may predict or source the emergence of highly mutated variants. The types of immunosuppression placing patients at highest risk for prolonged infection and associated intrahost viral evolution remain unclear.

Methods

Adults aged ≥18 years were enrolled at 5 hospitals and followed from 4/11/2022 - 2/1/2023. Eligible patients were SARS-CoV-2-positive in the previous 14 days and had a moderate or severely immunocompromising condition or treatment. Nasal specimens were tested by rRT-PCR every 2-4 weeks until negative in consecutive specimens. Positive specimens underwent viral culture and whole genome sequencing. A Cox proportional hazards model was used to assess factors associated with duration of infection.

Results

We enrolled 150 patients with: B cell malignancy or anti-B cell therapy (n=18), solid organ or hematopoietic stem cell transplant (SOT/HSCT) (n=59), AIDS (n=5), non-B cell malignancy (n=23), and autoimmune/autoinflammatory conditions (n=45). Thirty-eight (25%) were rRT-PCR-positive and 12 (8%) were culture-positive ≥21 days after initial SARS-CoV-2 detection or illness onset. Patients with B cell dysfunction had longer duration of rRT-PCR-positivity compared to those with autoimmune/autoinflammatory conditions (aHR 0.32, 95% CI 0.15-0.64). Consensus (>50% frequency) spike mutations were identified in 5 individuals who were rRT-PCR-positive >56 days; 61% were in the receptor-binding domain (RBD). Mutations shared by multiple individuals were rare (<5%) in global circulation.

Conclusions

In this cohort, prolonged replication-competent Omicron SARS-CoV-2 infections were uncommon. Within-host evolutionary rates were similar across patients, but individuals with infections lasting >56 days accumulated spike mutations, which were distinct from those seen globally.

SARS-CoV-2 Co-Infections and Recombinations Identified by Long-Read Single-Molecule Real-Time Sequencing

Co-infection with at least 2 strains of virus is the prerequisite for recombination, one of the means of genetic diversification. Little is known about the prevalence of these events in SARS-CoV-2, partly because it is difficult to detect them. We used long-read PacBio single-molecule real-time (SMRT) sequencing technology to sequence whole genomes and targeted regions for haplotyping. We identified 17 co-infections with SARS-CoV-2 strains belonging to different clades in 6829 samples sequenced between January and October, 2022 (prevalence 0.25%). There were 3 Delta/Omicron co-infections and 14 Omicron/Omicron co-infections (4 cases of 21K/21L, 1 case of 21L/22A, 2 cases of 21L/22B, 4 cases of 22A/22B, 2 cases of 22B/22C and 1 case of 22B/22E). Four of these patients (24%) also harbored recombinant minor haplotypes, including one with a recombinant virus that was selected in the viral quasispecies over the course of his chronic infection. While co-infections remain rare among SARS-CoV-2-infected individuals, long-read SMRT sequencing is a useful tool for detecting them as well as recombinant events, providing the basis for assessing their clinical impact, and a precise indicator of epidemic evolution. IMPORTANCE SARS-CoV-2 variants have been responsible for the successive waves of infection over the 3 years of pandemic. While co-infection followed by recombination is one driver of virus evolution, there have been few reports of co-infections, mainly between Delta and Omicron variants or between the first 2 Omicron variants 21K_BA.1 and 21L_BA.2. The 17 co-infections we detected during 2022 included cases with the recent clades of Omicron 22A, 22B, 22C, and 22E; 24% harbored recombinant variants. This study shows that long-read SMRT sequencing is well suited to SARS-CoV-2 genomic surveillance.

Transmission bottleneck size estimation from de novo viral genetic variation

Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. They have also been used to estimate transmission bottleneck sizes from identified donor-recipient pairs. These bottleneck sizes quantify the number of viral particles that establish genetic lineages in the recipient host and are important to estimate due to their impact on viral evolution. Current approaches for estimating bottleneck sizes exclusively consider the subset of viral sites that are observed as polymorphic in the donor individual. However, allele frequencies can change dramatically over the course of an individual's infection, such that sites that are polymorphic in the donor at the time of transmission may not be polymorphic in the donor at the time of sampling and allele frequencies at donor-polymorphic sites may change dramatically over the course of a recipient's infection. Because of this, transmission bottleneck sizes estimated using allele frequencies observed at a donor's polymorphic sites may be considerable underestimates of true bottleneck sizes. Here, we present a new statistical approach for instead estimating bottleneck sizes using patterns of viral genetic variation that arose de novo within a recipient individual. Specifically, our approach makes use of the number of clonal viral variants observed in a transmission pair, defined as the number of viral sites that are monomorphic in both the donor and the recipient but carry different alleles. We first test our approach on a simulated dataset and then apply it to both influenza A virus sequence data and SARS-CoV-2 sequence data from identified transmission pairs. Our results confirm the existence of extremely tight transmission bottlenecks for these two respiratory viruses, using an approach that does not tend to underestimate transmission bottleneck sizes.

The effects of amino acid substitution of spike protein and genomic recombination on the evolution of SARS-CoV-2

Over three years' pandemic of 2019 novel coronavirus disease (COVID-19), multiple variants and novel subvariants have emerged successively, outcompeted earlier variants and become predominant. The sequential emergence of variants reflects the evolutionary process of mutation-selection-adaption of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Amino acid substitution/insertion/deletion in the spike protein causes altered viral antigenicity, transmissibility, and pathogenicity of SARS-CoV-2. Early in the pandemic, D614G mutation conferred virus with advantages over previous variants and increased transmissibility, and it also laid a conservative background for subsequent substantial mutations. The role of genomic recombination in the evolution of SARS-CoV-2 raised increasing concern with the occurrence of novel recombinants such as Deltacron, XBB.1.5, XBB.1.9.1, and XBB.1.16 in the late phase of pandemic. Co-circulation of different variants and co-infection in immunocompromised patients accelerate the emergence of recombinants. Surveillance for SARS-CoV-2 genomic variations, particularly spike protein mutation and recombination, is essential to identify ongoing changes in the viral genome and antigenic epitopes and thus leads to the development of new vaccine strategies and interventions.

A simulation framework for modeling the within-patient evolutionary dynamics of SARS-CoV-2

The global impact of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has led to considerable interest in detecting novel beneficial mutations and other genomic changes that may signal the development of variants of concern (VOCs). The ability to accurately detect these changes within individual patient samples is important in enabling early detection of VOCs. Such genomic scans for positive selection are best performed via comparison of empirical data to simulated data wherein evolutionary factors, including mutation and recombination rates, reproductive and infection dynamics, and purifying and background selection, can be carefully accounted for and parameterized. While there has been work to quantify these factors in SARS-CoV-2, they have yet to be integrated into a baseline model describing intra-host evolutionary dynamics. To construct such a baseline model, we develop a simulation framework that enables one to establish expectations for underlying levels and patterns of patient-level variation. By varying eight key parameters, we evaluated 12,096 different model-parameter combinations and compared them to existing empirical data. Of these, 592 models (~5%) were plausible based on the resulting mean expected number of segregating variants. These plausible models shared several commonalities shedding light on intra-host SARS-CoV-2 evolutionary dynamics: severe infection bottlenecks, low levels of reproductive skew, and a distribution of fitness effects skewed towards strongly deleterious mutations. We also describe important areas of model uncertainty and highlight additional sequence data that may help to further refine a baseline model. This study lays the groundwork for the improved analysis of existing and future SARS-CoV-2 within-patient data.

Rapid review and meta-analysis of serial intervals for SARS-CoV-2 Delta and Omicron variants

BACKGROUND

The serial interval is the period of time between symptom onset in the primary case and symptom onset in the secondary case. Understanding the serial interval is important for determining transmission dynamics of infectious diseases like COVID-19, including the reproduction number and secondary attack rates, which could influence control measures. Early meta-analyses of COVID-19 reported serial intervals of 5.2 days (95% CI: 4.9-5.5) for the original wild-type variant and 5.2 days (95% CI: 4.87-5.47) for Alpha variant. The serial interval has been shown to decrease over the course of an epidemic for other respiratory diseases, which may be due to accumulating viral mutations and implementation of more effective nonpharmaceutical interventions. We therefore aggregated the literature to estimate serial intervals for Delta and Omicron variants.

METHODS

This study followed Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A systematic literature search was conducted of PubMed, Scopus, Cochrane Library, ScienceDirect, and preprint server medRxiv for articles published from April 4, 2021, through May 23, 2023. Search terms were: ("serial interval" or "generation time"), ("Omicron" or "Delta"), and ("SARS-CoV-2" or "COVID-19"). Meta-analyses were done for Delta and Omicron variants using a restricted maximum-likelihood estimator model with a random effect for each study. Pooled average estimates and 95% confidence intervals (95% CI) are reported.

RESULTS

There were 46,648 primary/secondary case pairs included for the meta-analysis of Delta and 18,324 for Omicron. Mean serial interval for included studies ranged from 2.3-5.8 days for Delta and 2.1-4.8 days for Omicron. The pooled mean serial interval for Delta was 3.9 days (95% CI: 3.4-4.3) (20 studies) and Omicron was 3.2 days (95% CI: 2.9-3.5) (20 studies). Mean estimated serial interval for BA.1 was 3.3 days (95% CI: 2.8-3.7) (11 studies), BA.2 was 2.9 days (95% CI: 2.7-3.1) (six studies), and BA.5 was 2.3 days (95% CI: 1.6-3.1) (three studies).

CONCLUSIONS

Serial interval estimates for Delta and Omicron were shorter than ancestral SARS-CoV-2 variants. More recent Omicron subvariants had even shorter serial intervals suggesting serial intervals may be shortening over time. This suggests more rapid transmission from one generation of cases to the next, consistent with the observed faster growth dynamic of these variants compared to their ancestors. Additional changes to the serial interval may occur as SARS-CoV-2 continues to circulate and evolve. Changes to population immunity (due to infection and/or vaccination) may further modify it.

Traceability of SARS-CoV-2 transmission through quasispecies analysis

During COVID-19 pandemic, consensus genomic sequences were used for rapidly monitor the spread of the virus worldwide. However, less attention was paid to intrahost genetic diversity. In fact, in the infected host, SARS-CoV-2 consists in an ensemble of replicating and closely related viral variants so-called quasispecies. Here we show that intrahost single nucleotide variants (iSNVs) represent a target for contact tracing analysis. Our data indicate that in the acute phase of infection, in highly likely transmission links, the number of viral particles transmitted from one host to another (bottleneck size) is large enough to propagate iSNVs among individuals. Furthermore, we demonstrate that, during SARS-CoV-2 outbreaks when the consensus sequences are identical, it is possible to reconstruct the transmission chains by genomic investigations of iSNVs. Specifically, we found that it is possible to identify transmission chains by limiting the analysis of iSNVs to only three well-conserved genes, namely nsp2, ORF3, and ORF7.

Immunosuppression as a Hub for SARS-CoV-2 Mutational Drift

The clinical course of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is largely determined by host factors, with a wide range of outcomes. Despite an extensive vaccination campaign and high rates of infection worldwide, the pandemic persists, adapting to overcome antiviral immunity acquired through prior exposure. The source of many such major adaptations is variants of concern (VOCs), novel SARS-CoV-2 variants produced by extraordinary evolutionary leaps whose origins remain mostly unknown. In this study, we tested the influence of factors on the evolutionary course of SARS-CoV-2. Electronic health records of individuals infected with SARS-CoV-2 were paired to viral whole-genome sequences to assess the effects of host clinical parameters and immunity on the intra-host evolution of SARS-CoV-2. We found slight, albeit significant, differences in SARS-CoV-2 intra-host diversity, which depended on host parameters such as vaccination status and smoking. Only one viral genome had significant alterations as a result of host parameters; it was found in an immunocompromised, chronically infected woman in her 70s. We highlight the unusual viral genome obtained from this woman, which had an accelerated mutational rate and an excess of rare mutations, including near-complete truncating of the accessory protein ORF3a. Our findings suggest that the evolutionary capacity of SARS-CoV-2 during acute infection is limited and mostly unaffected by host characteristics. Significant viral evolution is seemingly exclusive to a small subset of COVID-19 cases, which typically prolong infections in immunocompromised patients. In these rare cases, SARS-CoV-2 genomes accumulate many impactful and potentially adaptive mutations; however, the transmissibility of such viruses remains unclear.