Fitness effects of mutations to SARS-CoV-2 proteins

Fitness models provide accurate short-term forecasts of SARS-CoV-2 variant frequency

Genomic surveillance of pathogen evolution is essential for public health response, treatment strategies, and vaccine development. In the context of SARS-COV-2, multiple models have been developed including Multinomial Logistic Regression (MLR) describing variant frequency growth as well as Fixed Growth Advantage (FGA), Growth Advantage Random Walk (GARW) and Piantham parameterizations describing variant R t . These models provide estimates of variant fitness and can be used to forecast changes in variant frequency. We introduce a framework for evaluating real-time forecasts of variant frequencies, and apply this framework to the evolution of SARS-CoV-2 during 2022 in which multiple new viral variants emerged and rapidly spread through the population. We compare models across representative countries with different intensities of genomic surveillance. Retrospective assessment of model accuracy highlights that most models of variant frequency perform well and are able to produce reasonable forecasts. We find that the simple MLR model provides ~0.6% median absolute error and ~6% mean absolute error when forecasting 30 days out for countries with robust genomic surveillance. We investigate impacts of sequence quantity and quality across countries on forecast accuracy and conduct systematic downsampling to identify that 1000 sequences per week is fully sufficient for accurate short-term forecasts. We conclude that fitness models represent a useful prognostic tool for short-term evolutionary forecasting.

Contributions of Hyperactive Mutations in Mpro from SARS-CoV-2 to Drug Resistance

The appearance and spread of mutations that cause drug resistance in rapidly evolving diseases, including infections by the SARS-CoV-2 virus, are major concerns for human health. Many drugs target enzymes, and resistance-conferring mutations impact inhibitor binding or enzyme activity. Nirmatrelvir, the most widely used inhibitor currently used to treat SARS-CoV-2 infections, targets the main protease (Mpro) preventing it from processing the viral polyprotein into active subunits. Our previous work systematically analyzed resistance mutations in Mpro that reduce binding to inhibitors; here, we investigate mutations that affect enzyme function. Hyperactive mutations that increase Mpro activity can contribute to drug resistance but have not been thoroughly studied. To explore how hyperactive mutations contribute to resistance, we comprehensively assessed how all possible individual mutations in Mpro affect enzyme function using a mutational scanning approach with a fluorescence resonance energy transfer (FRET)-based yeast readout. We identified hundreds of mutations that significantly increased the Mpro activity. Hyperactive mutations occurred both proximal and distal to the active site, consistent with protein stability and/or dynamics impacting activity. Hyperactive mutations were observed 3 times more than mutations which reduced apparent binding to nirmatrelvir in recent studies of laboratory-grown viruses selected for drug resistance. Hyperactive mutations were also about three times more prevalent than nirmatrelvir binding mutations in sequenced isolates from circulating SARS-CoV-2. Our findings indicate that hyperactive mutations are likely to contribute to the natural evolution of drug resistance in Mpro and provide a comprehensive list for future surveillance efforts.

Modulation of Biophysical Properties of Nucleocapsid Protein in the Mutant Spectrum of SARS-CoV-2

Genetic diversity is a hallmark of RNA viruses and the basis for their evolutionary success. Taking advantage of the uniquely large genomic database of SARS-CoV-2, we examine the impact of mutations across the spectrum of viable amino acid sequences on the biophysical phenotypes of the highly expressed and multifunctional nucleocapsid protein. We find variation in the physicochemical parameters of its extended intrinsically disordered regions (IDRs) sufficient to allow local plasticity, but also exhibiting functional constraints that similarly occur in related coronaviruses. In biophysical experiments with several N-protein species carrying mutations associated with major variants, we find that point mutations in the IDRs can have nonlocal impact and modulate thermodynamic stability, secondary structure, protein oligomeric state, particle formation, and liquid-liquid phase separation. In the Omicron variant, distant mutations in different IDRs have compensatory effects in shifting a delicate balance of interactions controlling protein assembly properties, and include the creation of a new protein-protein interaction interface in the N-terminal IDR through the defining P13L mutation. A picture emerges where genetic diversity is accompanied by significant variation in biophysical characteristics of functional N-protein species, in particular in the IDRs.

A regional genomic surveillance program is implemented to monitor the occurrence and emergence of SARS-CoV-2 variants in Yubei District, China

BACKGROUND

In December 2022, Chongqing experienced a significant surge in coronavirus disease 2019 (COVID-19) epidemic after adjusting control measures in China. Given the widespread immunization of the population with the BA.5 variant, it is crucial to actively monitor severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant evolution in Chongqing's Yubei district.

METHODS

In this retrospective study based on whole genome sequencing, we collected oropharyngeal and nasal swab of native COVID-19 cases from Yubei district between January to May 2023, along with imported cases from January 2022 to January 2023. Through second-generation sequencing, we generated a total of 578 genomes.

RESULTS

Phylogenetic analyses revealed these genomes belong to 47 SARS-CoV-2 Pango lineages. BA.5.2.48 was dominant from January to April 2023, rapidly replaced by XBB* variants from April to May 2023. Bayesian Skyline Plot reconstructions indicated a higher evolutionary rate (6.973 × 10-4 subs/site/year) for the XBB.1.5* lineage compared to others. The mean time to the most recent common ancestor (tMRCA) of BA.5.2.48* closely matched BA.2.75* (May 27, 2022). Using multinomial logistic regression, we estimated growth advantages, with XBB.1.9.1 showing the highest growth advantage (1.2, 95% HPI:1.1-1.2), followed by lineage FR.1 (1.1, 95% HPI:1.1-1.2).

CONCLUSIONS

Our monitoring reveals the rapid replacement of the previously prevalent BA.5.2.48 variant by XBB and its sub-variants, underscoring the ineffectiveness of herd immunity and breakthrough BA.5 infections against XBB variants. Given the ongoing evolutionary pressure, sustaining a SARS-CoV-2 genomic surveillance program is imperative.

SARS-CoV-2 in an immunocompromised host: convalescent plasma therapy and viral evolution elucidated by whole genome sequencing

The evolution of SARS-CoV-2 within immunocompromised hosts who fail to clear the virus over many months has been proposed as a route to the development of Variants of Concern (VoCs). We present a case of an immunocompromised male patient with a prolonged SARS-CoV-2 infection. During hospitalisation, 7 weeks after first diagnosis, his condition worsened to require continuous ventilation support. Resolution of symptoms was observed after convalescent plasma therapy. Whole genome sequencing of the virus showed Pango lineage B.1.221. Between the first sample and the second from bronchoalveolar lavage fluid 7 weeks later, we identified eight mutations, including minor variants, which could be used to estimate the chronology of mutations. This suggests an elevated mutation rate, in-host accumulation of mutations and further evidence for sources of VoCs. Prolonged SARS-CoV-2 infections in immunocompromised hosts increase the likelihood of hospital stays and morbidity, and also pose an increased risk to global public health.

Sequencing of SARS-CoV-2 RNA Fragments in Wastewater Detects the Spread of New Variants during Major Events

The sequencing of SARS-CoV-2 RNA in wastewater is an unbiased method to detect the spread of emerging variants and to track regional infection dynamics, which is especially useful in case of limited testing and clinical sequencing. To test how major international events influence the spread of new variants we have sequenced SARS-CoV-2 RNA in the wastewater samples of Davos, Landquart, Lostallo, and St. Moritz in the Swiss canton of Grisons in the time around the international sports competitions in Davos and St. Moritz in December 2021, and additionally in May 2022 and January 2023 in Davos and St. Moritz during the World Economic Forum (WEF) in Davos. The prevalence of the variants identified from the wastewater sequencing data showed that the Omicron variant BA.1 had spread in Davos and St. Moritz during the international sporting events hosted there in December 2021. This spread was associated with an increase in case numbers, while it was not observed in Landquart and Lostallo. Another instance of new variant spread occurred during the WEF in January 2023, when the Omicron variant BA.2.75 arrived in Davos but not in St. Moritz. We can therefore conclude that major international events promote the spread of new variants in the respective host region, which has important implications for the protective measures that should be taken.

Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs

Deep mutational scanning (DMS) is a high-throughput experimental technique that measures the effects of thousands of mutations to a protein. These experiments can be performed on multiple homologs of a protein or on the same protein selected under multiple conditions. It is often of biological interest to identify mutations with shifted effects across homologs or conditions. However, it is challenging to determine if observed shifts arise from biological signal or experimental noise. Here, we describe a method for jointly inferring mutational effects across multiple DMS experiments while also identifying mutations that have shifted in their effects among experiments. A key aspect of our method is to regularize the inferred shifts, so that they are nonzero only when strongly supported by the data. We apply this method to DMS experiments that measure how mutations to spike proteins from SARS-CoV-2 variants (Delta, Omicron BA.1, and Omicron BA.2) affect cell entry. Most mutational effects are conserved between these spike homologs, but a fraction have markedly shifted. We experimentally validate a subset of the mutations inferred to have shifted effects, and confirm differences of > 1,000-fold in the impact of the same mutation on spike-mediated viral infection across spikes from different SARS-CoV-2 variants. Overall, our work establishes a general approach for comparing sets of DMS experiments to identify biologically important shifts in mutational effects.

Diversity of Short Linear Interaction Motifs in SARS-CoV-2 Nucleocapsid Protein

Molecular mimicry of short linear interaction motifs has emerged as a key mechanism for viral proteins binding host domains and hijacking host cell processes. Here, we examine the role of RNA-virus sequence diversity in the dynamics of the virus-host interface, by analyzing the uniquely vast sequence record of viable SARS-CoV-2 species with focus on the multi-functional nucleocapsid protein. We observe the abundant presentation of motifs encoding several essential host protein interactions, alongside a majority of possibly non-functional and randomly occurring motif sequences absent in subsets of viable virus species. A large number of motifs emerge ex nihilo through transient mutations relative to the ancestral consensus sequence. The observed mutational landscape implies an accessible motif space that spans at least 25% of known eukaryotic motifs. This reveals motif mimicry as a highly dynamic process with the capacity to broadly explore host motifs, allowing the virus to rapidly evolve the virus-host interface.

SARS-CoV-2 genomic characterization and evolution in China

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affected global health worldwide due to its high contagiousness. During the viral spread, many mutations occurred within the virus genome. China has adopted nonpharmaceutical intervention (NPI) to contain COVID-19 outbreaks. In order to understand the evolution and genomic variation of SARS-CoV-2 in China under this policy, a total of 524 sequences downloaded from Global Initiative on Sharing All Influenza Data (GISAID) between 2019 and 2022 were included in this study. The time-scaled evolutionary analysis showed that these sequences clustered in three groups (Group A-C). Group B and C accounted for the majority of the sequences whose divergence times were around 2020 and distributed in multiple regions. Group A was mainly composed of G variants, which were mainly isolated from several regions. Moreover, we found that 191 sites had mutations with no less than 3 times, including 30 amino acids that were deleted. Finally, we found that spike and nucleocapsid genes underwent positive selection evolution, indicating that the mutations within spike and nucleocapsid genes increased the SARS-CoV-2 contagiousness. Hence, this study preliminarily elucidates the evolutionary characteristics and genomic mutations of SARS-CoV-2 under the implementation of the NPI policy in China, providing scientific basis for further understanding the control effect of the NPI policy on the epidemic.

Evaluating the effect of SARS-CoV-2 spike mutations with a linear doubly robust learner

Driven by various mutations on the viral Spike protein, diverse variants of SARS-CoV-2 have emerged and prevailed repeatedly, significantly prolonging the pandemic. This phenomenon necessitates the identification of key Spike mutations for fitness enhancement. To address the need, this manuscript formulates a well-defined framework of causal inference methods for evaluating and identifying key Spike mutations to the viral fitness of SARS-CoV-2. In the context of large-scale genomes of SARS-CoV-2, it estimates the statistical contribution of mutations to viral fitness across lineages and therefore identifies important mutations. Further, identified key mutations are validated by computational methods to possess functional effects, including Spike stability, receptor-binding affinity, and potential for immune escape. Based on the effect score of each mutation, individual key fitness-enhancing mutations such as D614G and T478K are identified and studied. From individual mutations to protein domains, this paper recognizes key protein regions on the Spike protein, including the receptor-binding domain and the N-terminal domain. This research even makes further efforts to investigate viral fitness via mutational effect scores, allowing us to compute the fitness score of different SARS-CoV-2 strains and predict their transmission capacity based solely on their viral sequence. This prediction of viral fitness has been validated using BA.2.12.1, which is not used for regression training but well fits the prediction. To the best of our knowledge, this is the first research to apply causal inference models to mutational analysis on large-scale genomes of SARS-CoV-2. Our findings produce innovative and systematic insights into SARS-CoV-2 and promotes functional studies of its key mutations, serving as reliable guidance about mutations of interest.