Fitness, growth and transmissibility of SARS-CoV-2 genetic variants

Modeling zoonotic and vector-borne viruses

The 2013-2016 Ebola virus disease epidemic and the coronavirus disease 2019 pandemic galvanized tremendous growth in models for emerging zoonotic and vector-borne viruses. Therefore, we have reviewed the main goals and methods of models to guide scientists and decision-makers. The elements of models for emerging viruses vary across spectrums: from understanding the past to forecasting the future, using data across space and time, and using statistical versus mechanistic methods. Hybrid/ensemble models and artificial intelligence offer new opportunities for modeling. Despite this progress, challenges remain in translating models into actionable decisions, particularly in areas at highest risk for viral disease outbreaks. To address this issue, we must identify gaps in models for specific viruses, strengthen validation, and involve policymakers in model development.

Phylogenetic signatures reveal multilevel selection and fitness costs in SARS-CoV-2

Background

Large-scale sequencing of SARS-CoV-2 has enabled the study of viral evolution during the COVID-19 pandemic. Some viral mutations may be advantageous to viral replication within hosts but detrimental to transmission, thus carrying a transient fitness advantage. By affecting the number of descendants, persistence times and growth rates of associated clades, these mutations generate localised imbalance in phylogenies. Quantifying these features in closely-related clades with and without recurring mutations can elucidate the tradeoffs between within-host replication and between-host transmission.

Methods

We implemented a novel phylogenetic clustering algorithm ( mlscluster, https://github.com/mrc-ide/mlscluster) to systematically explore time-scaled phylogenies for mutations under transient/multilevel selection. We applied this method to a SARS-CoV-2 time-calibrated phylogeny with >1.2 million sequences from England, and characterised these recurrent mutations that may influence transmission fitness across PANGO-lineages and genomic regions using Poisson regressions and summary statistics.

Results

We found no major differences across two epidemic stages (before and after Omicron), PANGO-lineages, and genomic regions. However, spike, nucleocapsid, and ORF3a were proportionally more enriched for transmission fitness polymorphisms (TFP)-homoplasies than other proteins. We provide a catalog of SARS-CoV-2 sites under multilevel selection, which can guide experimental investigations within and beyond the spike protein.

Conclusions

This study provides empirical evidence for the existence of important tradeoffs between within-host replication and between-host transmission shaping the fitness landscape of SARS-CoV-2. This method may be used as a fast and scalable means to shortlist large sequence databases for sites under putative multilevel selection which may warrant subsequent confirmatory analyses and experimental confirmation.

Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US

Omicron surged as a variant of concern in late 2021. Several distinct Omicron variants appeared and overtook each other. We combined variant frequencies and infection estimates from a nowcasting model for each US state to estimate variant-specific infections, attack rates, and effective reproduction numbers (Rt). BA.1 rapidly emerged, and we estimate that it infected 47.7% of the US population before it was replaced by BA.2. We estimate that BA.5 infected 35.7% of the US population, persisting in circulation for nearly 6 months. Other variants-BA.2, BA.4, and XBB-together infected 30.7% of the US population. We found a positive correlation between the state-level BA.1 attack rate and social vulnerability and a negative correlation between the BA.1 and BA.2 attack rates. Our findings illustrate the complex interplay between viral evolution, population susceptibility, and social factors during the Omicron emergence in the US.

Evidence that the SARS-CoV-2 S protein undergoes a conformational change at the Golgi complex that leads to the formation of virus neutralising antibody binding epitopes in the S1 protein subunit

Recombinant SARS-CoV-2 S protein expression was examined in Vero cells by imaging using the human monoclonal antibody panel (PD4, PD5, sc23, and sc29). The PD4 and sc29 antibodies recognised conformational specific epitopes in the S2 protein subunit at the Endoplasmic reticulum and Golgi complex. While PD5 and sc23 detected conformationally specific epitopes in the S1 protein subunit at the Golgi complex, only PD5 recognised the receptor binding domain (RBD). A comparison of the staining patterns of PD5 with non-conformationally specific antibodies that recognises the S1 subunit and RBD suggested the PD5 recognised a conformational structure within the S1 protein subunit. Our data suggests the antibody binding epitopes recognised by the human monoclonal antibodies formed at different locations in the secretory pathway during S protein transport, but a conformational change in the S1 protein subunit at the Golgi complex formed antibody binding epitopes that are recognised by virus neutralising antibodies.

Drivers of epidemic dynamics in real time from daily digital COVID-19 measurements

Understanding the drivers of respiratory pathogen spread is challenging, particularly in a timely manner during an ongoing epidemic. Here we present insights obtained using daily data from the NHS COVID-19 app for England and Wales and shared with health authorities in almost real time. Our indicator of the reproduction number R(t) was available days earlier than other estimates, with a novel capability to decompose R(t) into contact rates and probabilities of infection. When Omicron arrived, the main epidemic driver switched from contacts to transmissibility. We separate contacts and transmissions by day of exposure and setting, finding pronounced variability over days of the week and during Christmas holidays and events. As an example, during the Euro football tournament in 2021, days with England matches showed sharp spikes in exposures and transmissibility. Digital contact tracing technologies can help control epidemics not only by directly preventing transmissions but also by enabling rapid analysis at scale and with unprecedented resolution.

Estimation of SARS-CoV-2 fitness gains from genomic surveillance data without prior lineage classification

The emergence of SARS-CoV-2 variants with increased fitness has had a strong impact on the epidemiology of COVID-19, with the higher effective reproduction number of the viral variants leading to new epidemic waves. Tracking such variants and their genetic signatures, using data collected through genomic surveillance, is therefore crucial for forecasting likely surges in incidence. Current methods of estimating fitness advantages of variants rely on tracking the changing proportion of a particular lineage over time, but describing successful lineages in a rapidly evolving viral population is a difficult task. We propose a method of estimating fitness gains directly from nucleotide information generated by genomic surveillance, without a priori assigning isolates to lineages from phylogenies, based solely on the abundance of single nucleotide polymorphisms (SNPs). The method is based on mapping changes in the genetic population structure over time. Changes in the abundance of SNPs associated with periods of increasing fitness allow for the unbiased discovery of new variants, thereby obviating a deliberate lineage assignment and phylogenetic inference. We conclude that the method provides a fast and reliable way to estimate fitness advantages of variants without the need for a priori assigning isolates to lineages.

Global genomics of Aedes aegypti unveils widespread and novel infectious viruses capable of triggering a small RNA response

The mosquito Aedes aegypti is a prominent vector for arboviruses, but the breadth of mosquito viruses that infects this specie is not fully understood. In the broadest global survey to date of over 200 Ae. aegypti small RNA samples, we detected viral small interfering RNAs (siRNAs) and Piwi interacting RNAs (piRNAs) arising from mosquito viruses. We confirmed that most academic laboratory colonies of Ae. aegypti lack persisting viruses, yet two commercial strains were infected by a novel tombus-like virus. Ae. aegypti from North to South American locations were also teeming with multiple insect viruses, with Anphevirus and a bunyavirus displaying geographical boundaries from the viral small RNA patterns. Asian Ae. aegypti small RNA patterns indicate infections by similar mosquito viruses from the Americas and reveal the first wild example of dengue virus infection generating viral small RNAs. African Ae. aegypti also contained various viral small RNAs including novel viruses only found in these African substrains. Intriguingly, viral long RNA patterns can differ from small RNA patterns, indicative of viral transcripts evading the mosquitoes' RNA interference (RNAi) machinery. To determine whether the viruses we discovered via small RNA sequencing were replicating and transmissible, we infected C6/36 and Aag2 cells with Ae. aegypti homogenates. Through blind passaging, we generated cell lines stably infected by these mosquito viruses which then generated abundant viral siRNAs and piRNAs that resemble the native mosquito viral small RNA patterns. This mosquito small RNA genomics approach augments surveillance approaches for emerging infectious diseases.

A multiscale modeling framework for Scenario Modeling: Characterizing the heterogeneity of the COVID-19 epidemic in the US

The Scenario Modeling Hub (SMH) initiative provides projections of potential epidemic scenarios in the United States (US) by using a multi-model approach. Our contribution to the SMH is generated by a multiscale model that combines the global epidemic metapopulation modeling approach (GLEAM) with a local epidemic and mobility model of the US (LEAM-US), first introduced here. The LEAM-US model consists of 3142 subpopulations each representing a single county across the 50 US states and the District of Columbia, enabling us to project state and national trajectories of COVID-19 cases, hospitalizations, and deaths under different epidemic scenarios. The model is age-structured, and multi-strain. It integrates data on vaccine administration, human mobility, and non-pharmaceutical interventions. The model contributed to all 17 rounds of the SMH, and allows for the mechanistic characterization of the spatio-temporal heterogeneities observed during the COVID-19 pandemic. Here we describe the mathematical and computational structure of our model, and present the results concerning the emergence of the SARS-CoV-2 Alpha variant (lineage designation B.1.1.7) as a case study. Our findings show considerable spatial and temporal heterogeneity in the introduction and diffusion of the Alpha variant, both at the level of individual states and combined statistical areas, as it competes against the ancestral lineage. We discuss the key factors driving the time required for the Alpha variant to rise to dominance within a population, and quantify the impact that the emergence of the Alpha variant had on the effective reproduction number at the state level. Overall, we show that our multiscale modeling approach is able to capture the complexity and heterogeneity of the COVID-19 pandemic response in the US.

Phylogenomic early warning signals for SARS-CoV-2 epidemic waves

BACKGROUND

Epidemic waves of coronavirus disease 2019 (COVID-19) infections have often been associated with the emergence of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Rapid detection of growing genomic variants can therefore serve as a predictor of future waves, enabling timely implementation of countermeasures such as non-pharmaceutical interventions (social distancing), additional vaccination (booster campaigns), or healthcare capacity adjustments. The large amount of SARS-CoV-2 genomic sequence data produced during the pandemic has provided a unique opportunity to explore the utility of these data for generating early warning signals (EWS).

METHODS

We developed an analytical pipeline (Transmission Fitness Polymorphism Scanner - designated in an R package mrc-ide/tfpscanner) for systematically exploring all clades within a SARS-CoV-2 virus phylogeny to detect variants showing unusually high growth rates. We investigated the use of these cluster growth rates as the basis for a variety of statistical time series to use as leading indicators for the epidemic waves in the UK during the pandemic between August 2020 and March 2022. We also compared the performance of these phylogeny-derived leading indicators with a range of non-phylogeny-derived leading indicators. Our experiments simulated data generation and real-time analysis.

FINDINGS

Using phylogenomic analysis, we identified leading indicators that would have generated EWS ahead of significant increases in COVID-19 hospitalisations in the UK between August 2020 and March 2022. Our results also show that EWS lead time is sensitive to the threshold set for the number of false positive (FP) EWS. It is often possible to generate longer EWS lead times if more FP EWS are tolerated. On the basis of maximising lead time and minimising the number of FP EWS, the best performing leading indicators that we identified, amongst a set of 1.4 million, were the maximum logistic growth rate (LGR) amongst clusters of the dominant Pango lineage and the mean simple LGR across a broader set of clusters. In the case of the former, the time between the EWS and wave inflection points (a conservative measure of wave start dates) for the seven waves ranged between a 20-day lead time and a 7-day lag, with a mean lead time of 5.4 days. The maximum number of FP EWS generated prior to a true positive (TP) EWS was two and this only occurred for two of the seven waves in the period. The mean simple LGR amongst a broader set of clusters also performed well in terms of lead time but with slightly more FP EWS.

INTERPRETATION

As a result of the significant surveillance effort during the pandemic, early detection of SARS-CoV-2 variants of concern Alpha, Delta, and Omicron provided some of the first examples where timely detection and characterisation of pathogen variants has been used to tailor public health response. The success of our method in generating early warning signals based on phylogenomic analysis for SARS-CoV-2 in the UK may make it a worthwhile addition to existing surveillance strategies. In addition, the method may be translatable to other countries and/or regions, and to other pathogens with large-scale and rapid genomic surveillance.

FUNDING

This research was funded in whole, or in part, by the Wellcome Trust (220885_Z_20_Z). For the purpose of open access, the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. KOD, OB, VBF and EMV acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/X020258/1), jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union. RMC acknowledges funding from the Wellcome Trust Collaborators Award (206298/Z/17/Z).

Immune targets to stop future SARS-CoV-2 variants

IMPORTANCE

The emergence of SARS-CoV-2 had a major impact across the world. It is true that the collaboration of scientists from all over the world resulted in a rapid response against COVID-19, mainly with the development of vaccines against the disease. However, many viral genetic variants that threaten vaccines have emerged. Our study reveals highly conserved antigenic regions in the vaccines have emerged. Our study reveals highly conserved antigenic regions in the spike protein in all variants of concern (Alpha, Beta, Gamma, Delta, and Omicron) as well as in the wild-type virus. Such immune targets can be used to fight future SARS-CoV-2 variants.

The Ways of the Virus: Interactions of Platelets and Red Blood Cells with SARS-CoV-2, and Their Potential Pathophysiological Significance in COVID-19

The hematological effects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are important in COVID-19 pathophysiology. However, the interactions of SARS-CoV-2 with platelets and red blood cells are still poorly understood. There are conflicting data regarding the mechanisms and significance of these interactions. The aim of this review is to put together available data and discuss hypotheses, the known and suspected effects of the virus on these blood cells, their pathophysiological and diagnostic significance, and the potential role of platelets and red blood cells in the virus's transport, propagation, and clearance by the immune system. We pay particular attention to the mutual activation of platelets, the immune system, the endothelium, and blood coagulation and how this changes with the evolution of SARS-CoV-2. There is now convincing evidence that platelets, along with platelet and erythroid precursors (but not mature erythrocytes), are frequently infected by SARS-CoV-2 and functionally changed. The mechanisms of infection of these cells and their role are not yet entirely clear. Still, the changes in platelets and red blood cells in COVID-19 are significantly associated with disease severity and are likely to have prognostic and pathophysiological significance in the development of thrombotic and pulmonary complications.

Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021

Phylodynamic methods have lately played a key role in understanding the spread of infectious diseases. During the coronavirus disease (COVID-19) pandemic, large scale genomic surveillance has further increased the potential of dynamic inference from viral genomes. With the continual emergence of novel severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants, explicitly allowing transmission rate differences between simultaneously circulating variants in phylodynamic inference is crucial. In this study, we present and empirically validate an extension to the BEAST2 package birth-death skyline model (BDSKY), BDSKY[Formula: see text], which introduces a scaling factor for the transmission rate between independent, jointly inferred trees. In an extensive simulation study, we show that BDSKY[Formula: see text] robustly infers the relative transmission rates under different epidemic scenarios. Using publicly available genome data of SARS-CoV-2, we apply BDSKY[Formula: see text] to quantify the transmission advantage of the Omicron over the Delta variant in Berlin, Germany. We find the overall transmission rate of Omicron to be scaled by a factor of two with pronounced variation between the individual clusters of each variant. These results quantify the transmission advantage of Omicron over the previously circulating Delta variant, in a crucial period of pre-established non-pharmaceutical interventions. By inferring variant- as well as cluster-specific transmission rate scaling factors, we show the differences in transmission dynamics for each variant. This highlights the importance of incorporating lineage-specific transmission differences in phylodynamic inference.

Determinants of de novo B cell responses to drifted epitopes in post-vaccination SARS-CoV-2 infections

Vaccine-induced immunity may impact subsequent de novo responses to drifted epitopes in SARS-CoV-2 variants, but this has been difficult to quantify due to the challenges in recruiting unvaccinated control groups whose first exposure to SARS-CoV-2 is a primary infection. Through local, statewide, and national SARS-CoV-2 testing programs, we were able to recruit cohorts of individuals who had recovered from either primary or post-vaccination infections by either the Delta or Omicron BA.1 variants. Regardless of variant, we observed greater Spike-specific and neutralizing antibody responses in post-vaccination infections than in those who were infected without prior vaccination. Through analysis of variant-specific memory B cells as markers of de novo responses, we observed that Delta and Omicron BA.1 infections led to a marked shift in immunodominance in which some drifted epitopes elicited minimal responses, even in primary infections. Prior immunity through vaccination had a small negative impact on these de novo responses, but this did not correlate with cross-reactive memory B cells, arguing against competitive inhibition of naïve B cells. We conclude that dampened de novo B cell responses against drifted epitopes are mostly a function of altered immunodominance hierarchies that are apparent even in primary infections, with a more modest contribution from pre-existing immunity, perhaps due to accelerated antigen clearance.