The inherent mutational tolerance and antigenic evolvability of influenza hemagglutinin

Deep mutational scanning and CRISPR-engineered viruses: tools for evolutionary and functional genomics studies

Recent advancements in synthetic biology and sequencing technologies have revolutionized the ability to manipulate viral genomes with unparalleled precision. This review focuses on two powerful methodologies: deep mutational scanning and CRISPR-based genome editing, that enable comprehensive mutagenesis and detailed functional characterization of viral proteins. These approaches have significantly deepened our understanding of the molecular determinants driving viral evolution and adaptation. Furthermore, we discuss how these advances provide transformative insights for future vaccine development and therapeutic strategies.

Predicting pathogen evolution and immune evasion in the age of artificial intelligence

The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants after their detection, leading to delays in effective public health responses. Because of this, a long-standing yet challenging objective has been to forecast viral evolution by predicting potentially harmful viral mutations prior to their emergence. The promises of artificial intelligence (AI) coupled with the exponential growth of viral data collection infrastructures spurred by the COVID-19 pandemic, have resulted in a research ecosystem highly conducive to this objective. Due to the COVID-19 pandemic accelerating the development of pandemic mitigation and preparedness strategies, many of the methods discussed here were designed in the context of SARS-CoV-2 evolution. However, most of these pipelines were intentionally designed to be adaptable across RNA viruses, with several strategies already applied to multiple viral species. In this review, we explore recent breakthroughs that have facilitated the forecasting of viral evolution in the context of an ongoing pandemic, with particular emphasis on deep learning architectures, including the promising potential of language models (LM). The approaches discussed here employ strategies that leverage genomic, epidemiologic, immunologic and biological information.

RSV F evolution escapes some monoclonal antibodies but does not strongly erode neutralization by human polyclonal sera

Vaccines and monoclonal antibodies targeting the respiratory syncytial virus (RSV) fusion protein (F) have recently begun to be widely used to protect infants and high-risk adults. Some other viral proteins evolve to erode polyclonal antibody neutralization and escape individual monoclonal antibodies. However, little is known about how RSV F evolution affects antibodies. Here we develop an experimental system for measuring neutralization titers against RSV F using pseudotyped lentiviral particles. This system is easily adaptable to evaluate neutralization of relevant clinical strains. We apply this system to demonstrate that natural evolution of RSV F leads to escape from some monoclonal antibodies, but at most modestly affects neutralization by polyclonal serum antibodies. Overall, our work sheds light on RSV antigenic evolution and describes a tool to measure the ability of antibodies and sera to neutralize contemporary RSV strains.

Deep mutation, insertion and deletion scanning across the Enterovirus A proteome reveals constraints shaping viral evolution

Insertions and deletions (InDels) are essential to protein evolution. In RNA viruses, InDels contribute to the emergence of viruses with new phenotypes, including altered host engagement and tropism. However, the tolerance of viral proteins for InDels has not been extensively studied. Here, we conduct deep mutational scanning to map and quantify the mutational tolerance of a complete viral proteome to insertion, deletion and substitution. We engineered approximately 45,000 insertions, 6,000 deletions and 41,000 amino acid substitutions across the nearly 2,200 coding positions of the Enterovirus A71 proteome, quantifying their effects on viral fitness by population sequencing. The vast majority of InDels are lethal to the virus, tolerated at only a few hotspots. Some of these hotspots overlap with sites of host recognition and immune engagement, suggesting tolerance at these sites reflects the important role InDels have played in the past phenotypic diversification of Enterovirus A.

Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance

H5 influenza is considered a potential pandemic threat. Recently, H5 viruses belonging to clade 2.3.4.4b have caused large outbreaks in avian and multiple nonhuman mammalian species. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic potential in humans, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera. However, prior experimental work has only measured how these phenotypes are affected by a handful of the >10,000 different possible amino-acid mutations to HA. Here, we use pseudovirus deep mutational scanning to measure how all mutations to a 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind α2-6-linked sialic acids and show that some viruses already carry mutations that stabilize HA. We also measure how all HA mutations affect neutralization by sera from mice and ferrets vaccinated against or infected with 2.3.4.4b H5 viruses. These antigenic maps enable rapid assessment of when new viral strains have acquired mutations that may create mismatches with candidate vaccine virus, and we show that a mutation present in some recent H5 HAs causes a large antigenic change. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive measurements of the phenotypic effects of mutations that can inform real-time interpretation of viral variation observed during surveillance of H5 influenza.

Impact of VP2 structure on antigenicity: comparison of BTV1 and the highly virulent BTV8 serotype

Bluetongue virus (BTV) is an agriculturally and economically significant insect-borne virus that causes serious illness and death in sheep and other domestic and wild ruminants in large areas of the world. Numerous BTV serotypes exist, and distant serotypes exhibit unique neutralizing antibody profiles, which target the outermost capsid protein VP2. The predominant serotype-specific nature of the antibody response to VP2 is a barrier to the development of broad-spectrum prophylactic BTV vaccine candidates. Although VP2 is the main serotype determinant of BTV, the structural basis of serotype specificity has not been investigated. In this study, we utilized the recently available atomic structure of VP2 with a modeled tip domain to carry out in silico structural comparisons between distant serotypes BTV1 and BTV8. These analyses identified structural differences in the tip domain, positioned at the apex of VP2, and informed the design of mutant VP2 constructs. Dissection of tip domain antigenicity demonstrated that the region of structural difference between BTV1 and highly virulent BTV8 was a target of BTV neutralizing antibodies and that mutation of this region resulted in a loss of neutralizing antibody recognition. This study has for the first time provided insights into the structural differences, which underpin the serotype-specific neutralizing antibody response to BTV.IMPORTANCEThe immune system can protect against virus infection by producing antibodies, which bind and inhibit the virus from infecting the susceptible host. These antibodies are termed neutralizing antibodies and generally target the viral receptor binding protein, such as the VP2 of bluetongue virus (BTV). This pressure from the immune system can drive mutation of the viral protein resulting in escape from antibody-mediated neutralization and the evolution of serotypes, as is the case for BTV. Understanding the structural differences, which underpin the different BTV serotypes, could help guide the design of a BTV vaccine that targets multiple serotypes. In this study, we have mapped the VP2 structural differences between distant serotypes, to a region targeted by neutralizing antibodies, and have demonstrated for the first time how VP2 structure is the fundamental basis of serotype specificity.

Waning immunity drives respiratory virus evolution and reinfection

Reinfections with respiratory viruses such as influenza viruses and coronaviruses are thought to be driven by ongoing antigenic immune escape in the viral population. However, this does not explain why antigenic variation is frequently observed in these viruses relative to viruses such as measles that undergo systemic replication. Here, we suggest that the rapid rate of waning immunity in the respiratory tract is the key driver of antigenic evolution in respiratory viruses. Waning immunity results in hosts with immunity levels that protect against homologous reinfection but are insufficient to protect against infection with a heterologous, antigenically different strain. As such, when partially immune hosts are present at a high enough density, an immune escape variant can invade the viral population even though that variant cannot infect fully immune hosts. Invasion can occur even when the variant's immune escape mutation incurs a fitness cost, and we expect the expanding mutant population will evolve compensatory mutations that mitigate this cost. Thus the mutant lineage should replace the wild-type, and as immunity to it builds, the process will repeat. Our model provides a new explanation for the pattern of successive emergence and replacement of antigenic variants that has been observed in many respiratory viruses. We discuss our model relative to others for understanding the drivers of antigenic evolution in these and other respiratory viruses.

popDMS infers mutation effects from deep mutational scanning data

SUMMARY

Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.

AVAILABILITY AND IMPLEMENTATION

popDMS is implemented in Python and Julia, and is freely available on GitHub at https://github.com/bartonlab/popDMS.

Deep mutational scanning of H5 hemagglutinin to inform influenza virus surveillance

H5 influenza is a potential pandemic threat. Previous studies have identified molecular phenotypes of the viral hemagglutinin (HA) protein that contribute to pandemic risk, including cell entry, receptor preference, HA stability, and reduced neutralization by polyclonal sera. Here we use pseudovirus deep mutational scanning to measure how all mutations to a clade 2.3.4.4b H5 HA affect each phenotype. We identify mutations that allow HA to better bind a2-6-linked sialic acids, and show that some viruses already carry mutations that stabilize HA. We also identify recent viral strains with reduced neutralization to sera elicited by candidate vaccine virus. Overall, the systematic nature of deep mutational scanning combined with the safety of pseudoviruses enables comprehensive characterization of mutations to inform surveillance of H5 influenza.

Increased Positive Selection in Highly Recombining Genes Does not Necessarily Reflect an Evolutionary Advantage of Recombination

It is commonly thought that the long-term advantage of meiotic recombination is to dissipate genetic linkage, allowing natural selection to act independently on different loci. It is thus theoretically expected that genes with higher recombination rates evolve under more effective selection. On the other hand, recombination is often associated with GC-biased gene conversion (gBGC), which theoretically interferes with selection by promoting the fixation of deleterious GC alleles. To test these predictions, several studies assessed whether selection was more effective in highly recombining genes (due to dissipation of genetic linkage) or less effective (due to gBGC), assuming a fixed distribution of fitness effects (DFE) for all genes. In this study, I directly derive the DFE from a gene's evolutionary history (shaped by mutation, selection, drift, and gBGC) under empirical fitness landscapes. I show that genes that have experienced high levels of gBGC are less fit and thus have more opportunities for beneficial mutations. Only a small decrease in the genome-wide intensity of gBGC leads to the fixation of these beneficial mutations, particularly in highly recombining genes. This results in increased positive selection in highly recombining genes that is not caused by more effective selection. Additionally, I show that the death of a recombination hotspot can lead to a higher dN/dS than its birth, but with substitution patterns biased towards AT, and only at selected positions. This shows that controlling for a substitution bias towards GC is therefore not sufficient to rule out the contribution of gBGC to signatures of accelerated evolution. Finally, although gBGC does not affect the fixation probability of GC-conservative mutations, I show that by altering the DFE, gBGC can also significantly affect nonsynonymous GC-conservative substitution patterns.

Epistasis facilitates functional evolution in an ancient transcription factor

A protein's genetic architecture - the set of causal rules by which its sequence produces its functions - also determines its possible evolutionary trajectories. Prior research has proposed that the genetic architecture of proteins is very complex, with pervasive epistatic interactions that constrain evolution and make function difficult to predict from sequence. Most of this work has analyzed only the direct paths between two proteins of interest - excluding the vast majority of possible genotypes and evolutionary trajectories - and has considered only a single protein function, leaving unaddressed the genetic architecture of functional specificity and its impact on the evolution of new functions. Here, we develop a new method based on ordinal logistic regression to directly characterize the global genetic determinants of multiple protein functions from 20-state combinatorial deep mutational scanning (DMS) experiments. We use it to dissect the genetic architecture and evolution of a transcription factor's specificity for DNA, using data from a combinatorial DMS of an ancient steroid hormone receptor's capacity to activate transcription from two biologically relevant DNA elements. We show that the genetic architecture of DNA recognition consists of a dense set of main and pairwise effects that involve virtually every possible amino acid state in the protein-DNA interface, but higher-order epistasis plays only a tiny role. Pairwise interactions enlarge the set of functional sequences and are the primary determinants of specificity for different DNA elements. They also massively expand the number of opportunities for single-residue mutations to switch specificity from one DNA target to another. By bringing variants with different functions close together in sequence space, pairwise epistasis therefore facilitates rather than constrains the evolution of new functions.

Functional and antigenic characterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning

The fusion peptide of SARS-CoV-2 spike protein is functionally important for membrane fusion during virus entry and is part of a broadly neutralizing epitope. However, sequence determinants at the fusion peptide and its adjacent regions for pathogenicity and antigenicity remain elusive. In this study, we perform a series of deep mutational scanning (DMS) experiments on an S2 region spanning the fusion peptide of authentic SARS-CoV-2 in different cell lines and in the presence of broadly neutralizing antibodies. We identify mutations at residue 813 of the spike protein that reduced TMPRSS2-mediated entry with decreased virulence. In addition, we show that an F823Y mutation, present in bat betacoronavirus HKU9 spike protein, confers resistance to broadly neutralizing antibodies. Our findings provide mechanistic insights into SARS-CoV-2 pathogenicity and also highlight a potential challenge in developing broadly protective S2-based coronavirus vaccines.

popDMS infers mutation effects from deep mutational scanning data

Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.

Uncovering Structural Plasticity of Enterovirus A through Deep Insertional and Deletional Scanning

Insertions and deletions (InDels) are essential sources of novelty in protein evolution. In RNA viruses, InDels cause dramatic phenotypic changes contributing to the emergence of viruses with altered immune profiles and host engagement. This work aimed to expand our current understanding of viral evolution and explore the mutational tolerance of RNA viruses to InDels, focusing on Enterovirus A71 (EV-A71) as a prototype for Enterovirus A species (EV-A). Using newly described deep InDel scanning approaches, we engineered approximately 45,000 insertions and 6,000 deletions at every site across the viral proteome, quantifying their effects on viral fitness. As a general trend, most InDels were lethal to the virus. However, our screen reproducibly identified a set of InDel-tolerant regions, demonstrating our ability to comprehensively map tolerance to these mutations. Tolerant sites highlighted structurally flexible and mutationally plastic regions of viral proteins that avoid core structural and functional elements. Phylogenetic analysis on EV-A species infecting diverse mammalian hosts revealed that the experimentally-identified hotspots overlapped with sites of InDels across the EV-A species, suggesting structural plasticity at these sites is an important function for InDels in EV speciation. Our work reveals the fitness effects of InDels across EV-A71, identifying regions of evolutionary capacity that require further monitoring, which could guide the development of Enterovirus vaccines.

ProteinGym: Large-Scale Benchmarks for Protein Design and Fitness Prediction

Predicting the effects of mutations in proteins is critical to many applications, from understanding genetic disease to designing novel proteins that can address our most pressing challenges in climate, agriculture and healthcare. Despite a surge in machine learning-based protein models to tackle these questions, an assessment of their respective benefits is challenging due to the use of distinct, often contrived, experimental datasets, and the variable performance of models across different protein families. Addressing these challenges requires scale. To that end we introduce ProteinGym, a large-scale and holistic set of benchmarks specifically designed for protein fitness prediction and design. It encompasses both a broad collection of over 250 standardized deep mutational scanning assays, spanning millions of mutated sequences, as well as curated clinical datasets providing high-quality expert annotations about mutation effects. We devise a robust evaluation framework that combines metrics for both fitness prediction and design, factors in known limitations of the underlying experimental methods, and covers both zero-shot and supervised settings. We report the performance of a diverse set of over 70 high-performing models from various subfields (eg., alignment-based, inverse folding) into a unified benchmark suite. We open source the corresponding codebase, datasets, MSAs, structures, model predictions and develop a user-friendly website that facilitates data access and analysis.

Functional and antigenic characterization of SARS-CoV-2 spike fusion peptide by deep mutational scanning

The fusion peptide of SARS-CoV-2 spike protein is functionally important for membrane fusion during virus entry and is part of a broadly neutralizing epitope. However, sequence determinants at the fusion peptide and its adjacent regions for pathogenicity and antigenicity remain elusive. In this study, we performed a series of deep mutational scanning (DMS) experiments on an S2 region spanning the fusion peptide of authentic SARS-CoV-2 in different cell lines and in the presence of broadly neutralizing antibodies. We identified mutations at residue 813 of the spike protein that reduced TMPRSS2-mediated entry with decreased virulence. In addition, we showed that an F823Y mutation, present in bat betacoronavirus HKU9 spike protein, confers resistance to broadly neutralizing antibodies. Our findings provide mechanistic insights into SARS-CoV-2 pathogenicity and also highlight a potential challenge in developing broadly protective S2-based coronavirus vaccines.

An atlas of continuous adaptive evolution in endemic human viruses

Through antigenic evolution, viruses such as seasonal influenza evade recognition by neutralizing antibodies. This means that a person with antibodies well tuned to an initial infection will not be protected against the same virus years later and that vaccine-mediated protection will decay. To expand our understanding of which endemic human viruses evolve in this fashion, we assess adaptive evolution across the genome of 28 endemic viruses spanning a wide range of viral families and transmission modes. Surface proteins consistently show the highest rates of adaptation, and ten viruses in this panel are estimated to undergo antigenic evolution to selectively fix mutations that enable the escape of prior immunity. Thus, antibody evasion is not an uncommon evolutionary strategy among human viruses, and monitoring this evolution will inform future vaccine efforts. Additionally, by comparing overall amino acid substitution rates, we show that SARS-CoV-2 is accumulating protein-coding changes at substantially faster rates than endemic viruses.

Viral Evolution Shaped by Host Proteostasis Networks

Understanding the factors that shape viral evolution is critical for developing effective antiviral strategies, accurately predicting viral evolution, and preventing pandemics. One fundamental determinant of viral evolution is the interplay between viral protein biophysics and the host machineries that regulate protein folding and quality control. Most adaptive mutations in viruses are biophysically deleterious, resulting in a viral protein product with folding defects. In cells, protein folding is assisted by a dynamic system of chaperones and quality control processes known as the proteostasis network. Host proteostasis networks can determine the fates of viral proteins with biophysical defects, either by assisting with folding or by targeting them for degradation. In this review, we discuss and analyze new discoveries revealing that host proteostasis factors can profoundly shape the sequence space accessible to evolving viral proteins. We also discuss the many opportunities for research progress proffered by the proteostasis perspective on viral evolution and adaptation.

Forecasting influenza hemagglutinin mutations through the lens of anomaly detection

The influenza virus hemagglutinin is an important part of the virus attachment to the host cells. The hemagglutinin proteins are one of the genetic regions of the virus with a high potential for mutations. Due to the importance of predicting mutations in producing effective and low-cost vaccines, solutions that attempt to approach this problem have recently gained significant attention. A historical record of mutations has been used to train predictive models in such solutions. However, the imbalance between mutations and preserved proteins is a big challenge for the development of such models that need to be addressed. Here, we propose to tackle this challenge through anomaly detection (AD). AD is a well-established field in Machine Learning (ML) that tries to distinguish unseen anomalies from normal patterns using only normal training samples. By considering mutations as anomalous behavior, we could benefit existing rich solutions in this field that have emerged recently. Such methods also fit the problem setup of extreme imbalance between the number of unmutated vs. mutated training samples. Motivated by this formulation, our method tries to find a compact representation for unmutated samples while forcing anomalies to be separated from the normal ones. This helps the model to learn a shared unique representation between normal training samples as much as possible, which improves the discernibility and detectability of mutated samples from the unmutated ones at the test time. We conduct a large number of experiments on four publicly available datasets, consisting of three different hemagglutinin protein datasets, and one SARS-CoV-2 dataset, and show the effectiveness of our method through different standard criteria.

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.

Genotype-phenotype landscapes for immune-pathogen coevolution

Our immune systems constantly coevolve with the pathogens that challenge them, as pathogens adapt to evade our defense responses, with our immune repertoires shifting in turn. These coevolutionary dynamics take place across a vast and high-dimensional landscape of potential pathogen and immune receptor sequence variants. Mapping the relationship between these genotypes and the phenotypes that determine immune-pathogen interactions is crucial for understanding, predicting, and controlling disease. Here, we review recent developments applying high-throughput methods to create large libraries of immune receptor and pathogen protein sequence variants and measure relevant phenotypes. We describe several approaches that probe different regions of the high-dimensional sequence space and comment on how combinations of these methods may offer novel insight into immune-pathogen coevolution.

Mutational fitness landscape and drug resistance

Robust technology has been developed to systematically quantify fitness landscapes that provide valuable opportunities to improve our understanding of drug resistance and define new avenues to develop drugs with reduced resistance susceptibility. We outline the critical importance of drug resistance studies and the potential for fitness landscape approaches to contribute to this effort. We describe the major technical advancements in mutational scanning, which is the primary approach used to quantify protein fitness landscapes. There are many complex steps to consider in planning and executing mutational scanning projects including developing a selection scheme, generating mutant libraries, tracking the frequency of variants using next-generation sequencing, and processing and interpreting the data. Key experimental parameters impacting each of these steps are discussed to aid in planning fitness landscape studies. There is a strong need for improved understanding of drug resistance, and fitness landscapes provide a promising new approach.

Mutational fitness landscape of human influenza H3N2 neuraminidase

Influenza neuraminidase (NA) has received increasing attention as an effective vaccine target. However, its mutational tolerance is not well characterized. Here, the fitness effects of >6,000 mutations in human H3N2 NA are probed using deep mutational scanning. Our result shows that while its antigenic regions have high mutational tolerance, there are solvent-exposed regions with low mutational tolerance. We also find that protein stability is a major determinant of NA mutational fitness. The deep mutational scanning result correlates well with mutational fitness inferred from natural sequences using a protein language model, substantiating the relevance of our findings to the natural evolution of circulating strains. Additional analysis further suggests that human H3N2 NA is far from running out of mutations despite already evolving for >50 years. Overall, this study advances our understanding of the evolutionary potential of NA and the underlying biophysical constraints, which in turn provide insights into NA-based vaccine design.

Silencing Transcription from an Influenza Reverse Genetics Plasmid in E. coli Enhances Gene Stability

Reverse genetics (RG) systems have been instrumental for determining the molecular aspects of viral replication, pathogenesis, and for the development of therapeutics. Here, we demonstrate that genes encoding the influenza surface antigens hemagglutinin and neuraminidase have varying stability when cloned into a common RG plasmid and transformed into Escherichia coli. Using GFP as a reporter, we demonstrate that E. coli expresses the target genes in the RG plasmid at low levels. Incorporating lac operators or a transcriptional terminator into the plasmid reduced expression and stabilized the viral genes to varying degrees. Sandwiching the viral gene between two lac operators provided the largest contribution to stability and we confirmed the stabilization is Lac repressor-dependent and crucial for subsequent plasmid propagations in E. coli. Viruses rescued from the lac operator-stabilized plasmid displayed similar kinetics and titers to the original plasmid in two different viral backbones. Together, these results indicate that silencing transcription from the plasmid in E. coli helps to maintain the correct influenza gene sequence and that the lac operator addition does not impair virus production. It is envisaged that sandwiching DNA segments between lac operators can be used for reducing DNA segment instability in any plasmid that is propagated in E. coli which express the Lac repressor.

On the lookout for influenza viruses in Italy during the 2021-2022 season: Along came A(H3N2) viruses with a new phylogenetic makeup of their hemagglutinin

AIMS

To assess influenza viruses (IVs) circulation and to evaluate A(H3N2) molecular evolution during the 2021-2022 season in Italy.

MATERIALS AND METHODS

12,393 respiratory specimens (nasopharyngeal swabs or broncho-alveolar lavages) collected from in/outpatients with influenza illness in the period spanning from January 1, 2022 (week 2022-01) to May 31, 2022 (week 2022-22) were analysed to identify IV genome and were molecularly characterized by 12 laboratories throughout Italy. A(H3N2) evolution was studied by conducting an in-depth phylogenetic analysis of the hemagglutinin (HA) gene sequences. The predicted vaccine efficacy (pVE) of vaccine strain against circulating A(H3N2) viruses was estimated using the sequence-based Pepitope model.

RESULTS

The overall IV-positive rate was 7.2% (894/12,393), all were type A IVs. Almost all influenza A viruses (846/894; 94.6%) were H3N2 that circulated in Italy with a clear epidemic trend, with 10% positivity rate threshold crossed for six consecutive weeks from week 2022-11 to week 2022-16. According to the phylogenetic analysis of a subset of A(H3N2) strains (n=161), the study HA sequences were distributed into five different genetic clusters, all of them belonging to the clade 3C.2a, sub-clade 3C.2a1 and the genetic subgroup 3C.2a1b.2a.2. The selective pressure analysis of A(H3N2) sequences showed evidence of diversifying selection particularly in the amino acid position 156. The comparison between the predicted amino acid sequence of the 2021-2022 vaccine strain (A/Cambodia/e0826360/2020) and the study strains revealed 65 mutations in 59 HA amino acid positions, including the substitution H156S and Y159N in antigenic site B, within major antigenic sites adjacent to the receptor-binding site, suggesting the presence of drifted strains. According to the sequence-based Pepitope model, antigenic site B was the dominant antigenic site and the p(VE) against circulating A(H3N2) viruses was estimated to be -28.9%.

DISCUSSION AND CONCLUSION

After a long period of very low IV activity since public health control measures have been introduced to face COVID-19 pandemic, along came A(H3N2) with a new phylogenetic makeup. Although the delayed 2021-2022 influenza season in Italy was characterized by a significant reduction of the width of the epidemic curve and in the intensity of the influenza activity compared to historical data, a marked genetic diversity of the HA of circulating A(H3N2) strains was observed. The identification of the H156S and Y159N substitutions within the main antigenic sites of most HA sequences also suggested the circulation of drifted variants with respect to the 2021-2022 vaccine strain. Molecular surveillance plays a critical role in the influenza surveillance architecture and it has to be strengthened also at local level to timely assess vaccine effectiveness and detect novel strains with potential impact on public health.

Identification of Unique and Conserved Neutralizing Epitopes of Vestigial Esterase Domain in HA Protein of the H9N2 Subtype of Avian Influenza Virus

The H9N2 subtype of avian influenza virus (AIV) has been reported to infect not only birds, but also humans. The hemagglutinin (HA) protein is the main surface antigen of AIV and plays an important role in the viral infection. For treatment strategies and vaccine development, HA protein has been an important target for the development of broadly neutralizing antibodies against influenza A virus. To investigate the vital target determinant cluster in HA protein in this work, HA gene was cloned and expressed in the prokaryotic expression vector pET28a. The spleen lymphocytes from BALC/c mice immunized with the purified recombinant HA protein were fused with SP2/0 cells. After Hypoxanthine-Aminopterin-Thymidine (HAT) medium screening and indirect ELISA detection, six hybridoma cell lines producing anti-HA monoclonal antibodies were screened. The gradually truncated HA gene expression and western blotting were used to identify their major locations in epitopes specific to these monoclonal antibodies. It was found that the epitopes were located in three areas: 112NVENLEEL119, 117EELRSLFS124, and 170PIQDAQ175. Epitope 112NVENLEEL119 has a partial amino acid crossover with 117EELRSLFS124, which is located in the vestigial esterase domain "110-helix" of HA, and the monoclonal antibody recognizing these epitopes showed the neutralizing activity, suggesting that the region 112NVENLEELRSLFS124 might be a novel neutralizing epitope. The results of the homology analysis showed that these three epitopes were generally conserved in H9N2 subtype AIV, and will provide valuable insights into H9N2 vaccine design and improvement, as well as antibody-based therapies for treatment of H9N2 AIV infection.

Bioinformatics and Functional Analysis of a New Nuclear Localization Sequence of the Influenza A Virus Nucleoprotein

Influenza viruses deliver their genome into the nucleus of infected cells for replication. This process is mediated by the viral nucleoprotein (NP), which contains two nuclear localization sequences (NLSs): NLS1 at the N-terminus and a recently identified NLS2 (²¹²GRKTR²¹⁶). Through mutagenesis and functional studies, we demonstrated that NP must have both NLSs for an efficient nuclear import. As with other NLSs, there may be variations in the basic residues of NLS2 in different strains of the virus, which may affect the nuclear import of the viral genome. Although all NLS2 variants fused to the GFP mediated nuclear import of GFP, bioinformatics showed that 98.8% of reported NP sequences contained either the wild-type sequence ²¹²GRKTR²¹⁶ or ²¹²GRRTR²¹⁶. Bioinformatics analyses used to study the presence of NLS2 variants in other viral and nuclear proteins resulted in very low hits, with only 0.4% of human nuclear proteins containing putative NLS2. From these, we studied the nucleolar protein 14 (NOP14) and found that NLS2 does not play a role in the nuclear import of this protein but in its nucleolar localization. We also discovered a functional NLS at the C-terminus of NOP14. Our findings indicate that NLS2 is a highly conserved influenza A NP sequence.

Predicting Permissive Mutations That Improve the Fitness of A(H1N1)pdm09 Viruses Bearing the H275Y Neuraminidase Substitution

Oseltamivir-resistant influenza viruses arise due to amino acid mutations in key residues of the viral neuraminidase (NA). These changes often come at a fitness cost; however, it is known that permissive mutations in the viral NA can overcome this cost. This result was observed in former seasonal A(H1N1) viruses in 2007 which expressed the H275Y substitution (N1 numbering) with no apparent fitness cost and lead to widespread oseltamivir resistance. Therefore, this study aims to predict permissive mutations that may similarly enable fit H275Y variants to arise in currently circulating A(H1N1)pdm09 viruses. The first approach in this study utilized in silico analyses to predict potentially permissive mutations. The second approach involved the generation of a virus library which encompassed all possible NA mutations while keeping H275Y fixed. Fit variants were then selected by serially passaging the virus library either through ferrets by transmission or passaging once in vitro. The fitness impact of selected substitutions was further evaluated experimentally. The computational approach predicted three candidate permissive NA mutations which, in combination with each other, restored the replicative fitness of an H275Y variant. The second approach identified a stringent bottleneck during transmission between ferrets; however, three further substitutions were identified which may improve transmissibility. A comparison of fit H275Y variants in vitro and in experimentally infected animals showed a statistically significant correlation in the variants that were positively selected. Overall, this study provides valuable tools and insights into potential permissive mutations that may facilitate the emergence of a fit H275Y A(H1N1)pdm09 variant.

IMPORTANCE Oseltamivir (Tamiflu) is the most widely used antiviral for the treatment of influenza infections. Therefore, resistance to oseltamivir is a public health concern. This study is important as it explores the different evolutionary pathways available to current circulating influenza viruses that may lead to widespread oseltamivir resistance. Specifically, this study develops valuable experimental and computational tools to evaluate the fitness landscape of circulating A(H1N1)pmd09 influenza viruses bearing the H275Y mutation. The H275Y substitution is most commonly reported to confer oseltamivir resistance but also leads to loss of virus replication and transmission fitness, which limits its spread. However, it is known from previous influenza seasons that influenza viruses can evolve to overcome this loss of fitness. Therefore, this study aims to prospectively predict how contemporary A(H1N1)pmd09 influenza viruses may evolve to overcome the fitness cost of bearing the H275Y NA substitution, which could result in widespread oseltamivir resistance.

Novel H7N9 influenza immunogen design enhances mobilization of seasonal influenza T cell memory in H3N2 pre-immune mice

Strategies that improve influenza vaccine immunogenicity are critical for the development of vaccines for pandemic preparedness. Hemagglutinin (HA)-specific CD4⁺ T cell epitopes support protective B cell responses against seasonal influenza. However, in the case of avian H7N9, which poses a pandemic threat, HA elicits only weak neutralizing antibody responses in infection and vaccination without adjuvant. We hypothesized that an immune-engineered H7N9 HA incorporating a broadly reactive H3N2 HA-specific memory CD4⁺ T cell epitope that replaces a regulatory T cell-inducing epitope at the corresponding position in H7N9 HA could harness preexisting influenza T cell immunity to increase CD4⁺ T cells that are needed for protective antibody development. We designed and produced a virus-like particle (VLP) vaccine that carries the epitope augmented H7N9 HA (OPT1) and immunized HLA-DR3 transgenic mice with established H3N2 immunity. OPT1-VLPs stimulated higher stem cell, central, and effector memory CD4⁺ T cell levels over wild type VLP immunization. In addition, activated, IL-21-producing follicular helper T cell frequencies were enhanced. This novel immunogen design strategy illustrates that site-specific modifications aimed to augment T cell epitope content enhance CD4⁺ T cell responses among critical subpopulations capable of aiding protective immune responses upon antigen re-encounter and that mobilization of immune memory can be used to overcome the poor immunogenicity of avian influenza viruses.

The 2009 Pandemic H1N1 Hemagglutinin Stalk Remained Antigenically Stable after Circulating in Humans for a Decade

An H1N1 influenza virus caused a pandemic in 2009, and descendants of this virus continue to circulate seasonally in humans. Upon infection with the 2009 H1N1 pandemic strain (pH1N1), many humans produced antibodies against epitopes in the hemagglutinin (HA) stalk. HA stalk-focused antibody responses were common among pH1N1-infected individuals because HA stalk epitopes were conserved between the pH1N1 strain and previously circulating H1N1 strains. Here, we completed a series of experiments to determine if the pH1N1 HA stalk has acquired substitutions since 2009 that prevent the binding of human antibodies. We identified several amino acid substitutions that accrued in the pH1N1 HA stalk from 2009 to 2019. We completed enzyme-linked immunosorbent assays, absorption-based binding assays, and surface plasmon resonance experiments to determine if these substitutions affect antibody binding. Using sera collected from 230 humans (aged 21 to 80 years), we found that pH1N1 HA stalk substitutions that have emerged since 2009 do not affect antibody binding. Our data suggest that the HA stalk domain of pH1N1 viruses remained antigenically stable after circulating in humans for a decade. IMPORTANCE In 2009, a new pandemic H1N1 (pH1N1) virus began circulating in humans. Many individuals mounted hemagglutinin (HA) stalk-focused antibody responses upon infection with the 2009 pH1N1 strain, since the HA stalk of this virus was relatively conserved with other seasonal H1N1 strains. Here, we completed a series of studies to determine if the 2009 pH1N1 strain has undergone antigenic drift in the HA stalk domain over the past decade. We found that serum antibodies from 230 humans could not antigenically distinguish the 2009 and 2019 HA stalk. These data suggest that the HA stalk of pH1N1 has remained antigenically stable, despite the presence of high levels of HA stalk antibodies within the human population.

ARHGAP1 Transported with Influenza Viral Genome Ensures Integrity of Viral Particle Surface through Efficient Budozone Formation

Influenza viral particles are assembled at the plasma membrane concomitantly with Rab11a-mediated endocytic transport of viral ribonucleoprotein complexes (vRNPs). The mechanism of spatiotemporal regulation of viral budozone formation and its regulatory molecules on the endocytic vesicles remain unclear. Here, we performed a proximity-based proteomics approach for Rab11a and found that ARHGAP1, a Rho GTPase-activating protein, is transported through the Rab11a-mediated apical transport of vRNP. ARHGAP1 stabilized actin filaments in infected cells for the lateral clustering of hemagglutinin (HA) molecules, a viral surface membrane protein, to the budozone. Disruption of the HA clustering results in the production of virions with low HA content, and such virions were less resistant to protease and had enhanced antigenicity, presumably because reduced clustering of viral membrane proteins exposes hidden surfaces. Collectively, these results demonstrate that Rab11a-mediated endocytic transport of ARHGAP1 with vRNPs stimulates budozone formation to ensure the integrity of virion surface required for viral survival.

Evolutionary Landscapes of Host-Virus Arms Races

Vertebrate immune systems suppress viral infection using both innate restriction factors and adaptive immunity. Viruses mutate to escape these defenses, driving hosts to counterevolve to regain fitness. This cycle recurs repeatedly, resulting in an evolutionary arms race whose outcome depends on the pace and likelihood of adaptation by host and viral genes. Although viruses evolve faster than their vertebrate hosts, their proteins are subject to numerous functional constraints that impact the probability of adaptation. These constraints are globally defined by evolutionary landscapes, which describe the fitness and adaptive potential of all possible mutations. We review deep mutational scanning experiments mapping the evolutionary landscapes of both host and viral proteins engaged in arms races. For restriction factors and some broadly neutralizing antibodies, landscapes favor the host, which may help to level the evolutionary playing field against rapidly evolving viruses. We discuss the biophysical underpinnings of these landscapes and their therapeutic implications.

Evolutionary pressures rendered by animal husbandry practices for avian influenza viruses to adapt to humans

Commercial poultry operations produce and crowd billions of birds every year, which is a source of inexpensive animal protein. Commercial poultry is intensely bred for desirable production traits, and currently presents very low variability at the major histocompatibility complex. This situation dampens the advantages conferred by the MHC’s high genetic variability, and crowding generates immunosuppressive stress. We address the proteins of influenza A viruses directly and indirectly involved in host specificities. We discuss how mutants with increased virulence and/or altered host specificity may arise if few class I alleles are the sole selective pressure on avian viruses circulating in immunocompromised poultry. This hypothesis is testable with peptidomics of MHC ligands. Breeding strategies for commercial poultry can easily and inexpensively include high variability of MHC as a trait of interest, to help save billions of dollars as a disease burden caused by influenza and decrease the risk of selecting highly virulent strains.

Human Antibodies for Viral Infections

Antibodies have been used to prevent or treat viral infections since the nineteenth century, but the full potential to use passive immunization for infectious diseases has yet to be realized. The advent of efficient methods for isolating broad and potently neutralizing human monoclonal antibodies is enabling us to develop antibodies with unprecedented activities. The discovery of IgG Fc region modifications that extend antibody half-life in humans to three months or more suggests that antibodies could become the principal tool with which we manage future viral epidemics. Antibodies for members of most virus families that cause severe disease in humans have been isolated, and many of them are in clinical development, an area that has accelerated during the effort to prevent or treat COVID-19 (coronavirus disease 2019). Broad and potently neutralizing antibodies are also important research reagents for identification of protective epitopes that can be engineered into active vaccines through structure-based reverse vaccinology. Expected final online publication date for the Annual Review of Immunology, Volume 40 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Viral protein instability enhances host-range evolvability

Viruses are highly evolvable, but what traits endow this property? The high mutation rates of viruses certainly play a role, but factors that act above the genetic code, like protein thermostability, are also expected to contribute. We studied how the thermostability of a model virus, bacteriophage λ, affects its ability to evolve to use a new receptor, a key evolutionary transition that can cause host-range evolution. Using directed evolution and synthetic biology techniques we generated a library of host-recognition protein variants with altered stabilities and then tested their capacity to evolve to use a new receptor. Variants fell within three stability classes: stable, unstable, and catastrophically unstable. The most evolvable were the two unstable variants, whereas seven of eight stable variants were significantly less evolvable, and the two catastrophically unstable variants could not grow. The slowly evolving stable variants were delayed because they required an additional destabilizing mutation. These results are particularly noteworthy because they contradict a widely supported contention that thermostabilizing mutations enhance evolvability of proteins by increasing mutational robustness. Our work suggests that the relationship between thermostability and evolvability is more complex than previously thought, provides evidence for a new molecular model of host-range expansion evolution, and identifies instability as a potential predictor of viral host-range evolution.

Antigenic evolution of human influenza H3N2 neuraminidase is constrained by charge balancing

As one of the main influenza antigens, neuraminidase (NA) in H3N2 virus has evolved extensively for more than 50 years due to continuous immune pressure. While NA has recently emerged as an effective vaccine target, biophysical constraints on the antigenic evolution of NA remain largely elusive. Here, we apply combinatorial mutagenesis and next-generation sequencing to characterize the local fitness landscape in an antigenic region of NA in six different human H3N2 strains that were isolated around 10 years apart. The local fitness landscape correlates well among strains and the pairwise epistasis is highly conserved. Our analysis further demonstrates that local net charge governs the pairwise epistasis in this antigenic region. In addition, we show that residue coevolution in this antigenic region is correlated with the pairwise epistasis between charge states. Overall, this study demonstrates the importance of quantifying epistasis and the underlying biophysical constraint for building a model of influenza evolution.

Mechanical couplings of protein backbone and side chains exhibit scale-free network properties and specific hotspots for function

•

Statistical learning from protein dynamics unravels rigidities in interaction network.

•

Backbone and side-chain mechanical couplings exhibit scale-free network properties.

•

Graphical depiction of network rigidities captures sequence co-evolution patterns.

•

Functional sites at secondary structure peripheries are mechanical hotspots.

•

Our rigidity scores are compelling metrics for residue biological significance.

A backbone-side-chain elastic network model (bsENM) is devised in this contribution to decipher the network of molecular interactions during protein dynamics. The chemical details in 5 μs all-atom molecular dynamics (MD) simulation are mapped onto the bsENM spring constants by self-consistent iterations. The elastic parameters obtained by this structure-mechanics statistical learning are then used to construct inter-residue rigidity graphs for the chemical components in protein amino acids. A key discovery is that the mechanical coupling strengths of both backbone and side chains exhibit heavy-tailed distributions and scale-free network properties. In both rat trypsin and PDZ3 proteins, the statistically prominent modes of rigidity graphs uncover the sequence-specific coupling patterns and mechanical hotspots. Based on the contributions to graphical modes, our residue rigidity scores in backbone and side chains are found to be very useful metrics for the biological significance. Most functional sites have high residue rigidity scores in side chains while the biologically important glycines are generally next to mechanical hotspots. Furthermore, prominent modes in the rigidity graphs involving side chains oftentimes coincide with the co-evolution patterns due to evolutionary restraints. The bsENM specifically devised to resolve the protein chemical character thus provides useful means for extracting functional information from all-atom MD.

Evaluation of Multivalent Sialylated Polyglycerols for Resistance Induction in and Broad Antiviral Activity against Influenza A Viruses

The development of multivalent sialic acid-based inhibitors active against a variety of influenza A virus (IAV) strains has been hampered by high genetic and structural variability of the targeted viral hemagglutinin (HA). Here, we addressed this challenge by employing sialylated polyglycerols (PGs). Efficacy of prototypic PGs was restricted to a narrow spectrum of IAV strains. To understand this restriction, we selected IAV mutants resistant to a prototypic multivalent sialylated PG by serial passaging. Resistance mutations mapped to the receptor binding site of HA, which was accompanied by altered receptor binding profiles of mutant viruses as detected by glycan array analysis. Specifying the inhibitor functionalization to 2,6-α-sialyllactose (SL) and adjusting the linker yielded a rationally designed inhibitor covering an extended spectrum of inhibited IAV strains. These results highlight the importance of integrating virological data with chemical synthesis and structural data for the development of sialylated PGs toward broad anti-influenza compounds.

In Vivo Therapy with M2e-Specific IgG Selects for an Influenza A Virus Mutant with Delayed Matrix Protein 2 Expression

The ectodomain of matrix protein 2 (M2e) of influenza A viruses is a universal influenza A vaccine candidate. Here, we report potential evasion strategies of influenza A viruses under in vivo passive anti-M2e IgG immune selection pressure in severe combined immune-deficient (SCID) mice. A/Puerto Rico/8/34-infected SCID mice were treated with the M2e-specific mouse IgG monoclonal antibodies (MAbs) MAb 65 (IgG2a) or MAb 37 (IgG1), which recognize amino acids 5 to 15 in M2e, or with MAb 148 (IgG1), which binds to the invariant N terminus of M2e. Treatment of challenged SCID mice with any of these MAbs significantly prolonged survival compared to isotype control IgG treatment. Furthermore, M2e-specific IgG2a protected significantly better than IgG1, and even resulted in virus clearance in some of the SCID mice. Deep sequencing analysis of viral RNA isolated at different time points after treatment revealed that the sequence variation in M2e was limited to P10H/L and/or I11T in anti-M2e MAb-treated mice. Remarkably, in half of the samples isolated from moribund MAb 37-treated mice and in all MAb 148-treated mice, virus was isolated with a wild-type M2 sequence but with nonsynonymous mutations in the polymerases and/or the hemagglutinin genes. Some of these mutations were associated with delayed M2 and other viral gene expression and with increased resistance to anti-M2e MAb treatment of SCID mice. Treatment with M2e-specific MAbs thus selects for viruses with limited variation in M2e. Importantly, influenza A viruses may also undergo an alternative escape route by acquiring mutations that result in delayed wild-type M2 expression. IMPORTANCE Broadly protective influenza vaccine candidates may have a higher barrier to immune evasion compared to conventional influenza vaccines. We used Illumina MiSeq deep sequence analysis to study the mutational patterns in A/Puerto Rico/8/34 viruses that evolve in chronically infected SCID mice that were treated with different M2e-specific MAbs. We show that under these circumstances, viruses emerged in vivo with mutations in M2e that were limited to positions 10 and 11. Moreover, we discovered an alternative route for anti-M2e antibody immune escape, in which a virus is selected with wild-type M2e but with mutations in other gene segments that result in delayed M2 and other viral protein expression. Delayed expression of the viral antigen that is targeted by a protective antibody thus represents an influenza virus immune escape mechanism that does not involve epitope alterations.

Protective function and durability of mouse lymph node-resident memory CD8+ T cells

Protective lung tissue-resident memory CD8+T cells (Trm) form after influenza A virus (IAV) infection. We show that IAV infection of mice generates CD69+CD103+and other memory CD8+T cell populations in lung-draining mediastinal lymph nodes (mLNs) from circulating naive or memory CD8+T cells. Repeated antigen exposure, mimicking seasonal IAV infections, generates quaternary memory (4M) CD8+T cells that protect mLN from viral infection better than 1M CD8+T cells. Better protection by 4M CD8+T cells associates with enhanced granzyme A/B expression and stable maintenance of mLN CD69+CD103+4M CD8+T cells, vs the steady decline of CD69+CD103+1M CD8+T cells, paralleling the durability of protective CD69+CD103+4M vs 1M in the lung after IAV infection. Coordinated upregulation in canonical Trm-associated genes occurs in circulating 4M vs 1M populations without the enrichment of canonical downregulated Trm genes. Thus, repeated antigen exposure arms circulating memory CD8+T cells with enhanced capacity to form long-lived populations of Trm that enhance control of viral infections of the mLN.

Applications of Deep Mutational Scanning in Virology

Several recently developed high-throughput techniques have changed the field of molecular virology. For example, proteomics studies reveal complete interactomes of a viral protein, genome-wide CRISPR knockout and activation screens probe the importance of every single human gene in aiding or fighting a virus, and ChIP-seq experiments reveal genome-wide epigenetic changes in response to infection. Deep mutational scanning is a relatively novel form of protein science which allows the in-depth functional analysis of every nucleotide within a viral gene or genome, revealing regions of importance, flexibility, and mutational potential. In this review, we discuss the application of this technique to RNA viruses including members of the Flaviviridae family, Influenza A Virus and Severe Acute Respiratory Syndrome Coronavirus 2. We also briefly discuss the reverse genetics systems which allow for analysis of viral replication cycles, next-generation sequencing technologies and the bioinformatics tools that facilitate this research.

Evolution of A(H1N1) pdm09 influenza virus masking by glycosylation

Introduction: As the pathogen that caused the first influenza virus pandemic in this century, the swine-origin A(H1N1) pdm09 influenza virus has caused continuous harm to human public health. The evolution of hemagglutinin protein glycosylation sites, including the increase in number and positional changes, is an important way for influenza viruses to escape host immune pressure. Based on the traditional influenza virus molecular monitoring, special attention should be paid to the influence of glycosylation evolution on the biological characteristics of virus antigenicity, transmission and pathogenicity. The epidemiological significance of glycosylation mutants should be analyzed as a predictive tool for early warning of new outbreaks and pandemics, as well as the design of vaccines and drug targets.Areas covered: We review on the evolutionary characteristics of glycosylation on the HA protein of the A(H1N1)pdm09 influenza virus in the last ten years.Expert opinion: We discuss the crucial impact of evolutionary glycosylation on the biological characteristics of the virus and the host immune responses, summarize studies revealing different roles of glycosylation play during host adaptation. Although these studies show the significance of glycosylation evolution in host-virus interaction, much remains to be discovered about the mechanism.

Risk of rapid evolutionary escape from biomedical interventions targeting SARS-CoV-2 spike protein

The spike protein receptor-binding domain (RBD) of SARS-CoV-2 is the molecular target for many vaccines and antibody-based prophylactics aimed at bringing COVID-19 under control. Such a narrow molecular focus raises the specter of viral immune evasion as a potential failure mode for these biomedical interventions. With the emergence of new strains of SARS-CoV-2 with altered transmissibility and immune evasion potential, a critical question is this: how easily can the virus escape neutralizing antibodies (nAbs) targeting the spike RBD? To answer this question, we combined an analysis of the RBD structure-function with an evolutionary modeling framework. Our structure-function analysis revealed that epitopes for RBD-targeting nAbs overlap one another substantially and can be evaded by escape mutants with ACE2 affinities comparable to the wild type, that are observed in sequence surveillance data and infect cells in vitro. This suggests that the fitness cost of nAb-evading mutations is low. We then used evolutionary modeling to predict the frequency of immune escape before and after the widespread presence of nAbs due to vaccines, passive immunization or natural immunity. Our modeling suggests that SARS-CoV-2 mutants with one or two mildly deleterious mutations are expected to exist in high numbers due to neutral genetic variation, and consequently resistance to vaccines or other prophylactics that rely on one or two antibodies for protection can develop quickly -and repeatedly- under positive selection. Predicted resistance timelines are comparable to those of the decay kinetics of nAbs raised against vaccinal or natural antigens, raising a second potential mechanism for loss of immunity in the population. Strategies for viral elimination should therefore be diversified across molecular targets and therapeutic modalities.

Quantifying the Evolutionary Constraints and Potential of Hepatitis C Virus NS5A Protein

RNA viruses, such as hepatitis C virus (HCV), influenza virus, and SARS-CoV-2, are notorious for their ability to evolve rapidly under selection in novel environments. It is known that the high mutation rate of RNA viruses can generate huge genetic diversity to facilitate viral adaptation. However, less attention has been paid to the underlying fitness landscape that represents the selection forces on viral genomes, especially under different selection conditions. Here, we systematically quantified the distribution of fitness effects of about 1,600 single amino acid substitutions in the drug-targeted region of NS5A protein of HCV. We found that the majority of nonsynonymous substitutions incur large fitness costs, suggesting that NS5A protein is highly optimized. The replication fitness of viruses is correlated with the pattern of sequence conservation in nature, and viral evolution is constrained by the need to maintain protein stability. We characterized the adaptive potential of HCV by subjecting the mutant viruses to selection by the antiviral drug daclatasvir at multiple concentrations. Both the relative fitness values and the number of beneficial mutations were found to increase with the increasing concentrations of daclatasvir. The changes in the spectrum of beneficial mutations in NS5A protein can be explained by a pharmacodynamics model describing viral fitness as a function of drug concentration. Overall, our results show that the distribution of fitness effects of mutations is modulated by both the constraints on the biophysical properties of proteins (i.e., selection pressure for protein stability) and the level of environmental stress (i.e., selection pressure for drug resistance).

IMPORTANCE Many viruses adapt rapidly to novel selection pressures, such as antiviral drugs. Understanding how pathogens evolve under drug selection is critical for the success of antiviral therapy against human pathogens. By combining deep sequencing with selection experiments in cell culture, we have quantified the distribution of fitness effects of mutations in hepatitis C virus (HCV) NS5A protein. Our results indicate that the majority of single amino acid substitutions in NS5A protein incur large fitness costs. Simulation of protein stability suggests viral evolution is constrained by the need to maintain protein stability. By subjecting the mutant viruses to selection under an antiviral drug, we find that the adaptive potential of viral proteins in a novel environment is modulated by the level of environmental stress, which can be explained by a pharmacodynamics model. Our comprehensive characterization of the fitness landscapes of NS5A can potentially guide the design of effective strategies to limit viral evolution.

Deep Mutational Scanning of Viral Glycoproteins and Their Host Receptors

Deep mutational scanning or deep mutagenesis is a powerful tool for understanding the sequence diversity available to viruses for adaptation in a laboratory setting. It generally involves tracking an in vitro selection of protein sequence variants with deep sequencing to map mutational effects based on changes in sequence abundance. Coupled with any of a number of selection strategies, deep mutagenesis can explore the mutational diversity available to viral glycoproteins, which mediate critical roles in cell entry and are exposed to the humoral arm of the host immune response. Mutational landscapes of viral glycoproteins for host cell attachment and membrane fusion reveal extensive epistasis and potential escape mutations to neutralizing antibodies or other therapeutics, as well as aiding in the design of optimized immunogens for eliciting broadly protective immunity. While less explored, deep mutational scans of host receptors further assist in understanding virus-host protein interactions. Critical residues on the host receptors for engaging with viral spikes are readily identified and may help with structural modeling. Furthermore, mutations may be found for engineering soluble decoy receptors as neutralizing agents that specifically bind viral targets with tight affinity and limited potential for viral escape. By untangling the complexities of how sequence contributes to viral glycoprotein and host receptor interactions, deep mutational scanning is impacting ideas and strategies at multiple levels for combatting circulating and emergent virus strains.

Relative Model Fit Does Not Predict Topological Accuracy in Single-Gene Protein Phylogenetics

It is regarded as best practice in phylogenetic reconstruction to perform relative model selection to determine an appropriate evolutionary model for the data. This procedure ranks a set of candidate models according to their goodness of fit to the data, commonly using an information theoretic criterion. Users then specify the best-ranking model for inference. Although it is often assumed that better-fitting models translate to increase accuracy, recent studies have shown that the specific model employed may not substantially affect inferences. We examine whether there is a systematic relationship between relative model fit and topological inference accuracy in protein phylogenetics, using simulations and real sequences. Simulations employed site-heterogeneous mechanistic codon models that are distinct from protein-level phylogenetic inference models, allowing us to investigate how protein models performs when they are misspecified to the data, as will be the case for any real sequence analysis. We broadly find that phylogenies inferred across models with vastly different fits to the data produce highly consistent topologies. We additionally find that all models infer similar proportions of false-positive splits, raising the possibility that all available models of protein evolution are similarly misspecified. Moreover, we find that the parameter-rich GTR (general time reversible) model, whose amino acid exchangeabilities are free parameters, performs similarly to models with fixed exchangeabilities, although the inference precision associated with GTR models was not examined. We conclude that, although relative model selection may not hinder phylogenetic analysis on protein data, it may not offer specific predictable improvements and is not a reliable proxy for accuracy.

A human coronavirus evolves antigenically to escape antibody immunity

There is intense interest in antibody immunity to coronaviruses. However, it is unknown if coronaviruses evolve to escape such immunity, and if so, how rapidly. Here we address this question by characterizing the historical evolution of human coronavirus 229E. We identify human sera from the 1980s and 1990s that have neutralizing titers against contemporaneous 229E that are comparable to the anti-SARS-CoV-2 titers induced by SARS-CoV-2 infection or vaccination. We test these sera against 229E strains isolated after sera collection, and find that neutralizing titers are lower against these "future" viruses. In some cases, sera that neutralize contemporaneous 229E viral strains with titers >1:100 do not detectably neutralize strains isolated 8-17 years later. The decreased neutralization of "future" viruses is due to antigenic evolution of the viral spike, especially in the receptor-binding domain. If these results extrapolate to other coronaviruses, then it may be advisable to periodically update SARS-CoV-2 vaccines.

Mutations in the Hemagglutinin Stalk Domain Do Not Permit Escape from a Protective, Stalk-Based Vaccine-Induced Immune Response in the Mouse Model

Current seasonal influenza virus vaccines target regions of the hemagglutinin (HA) head domain that undergo constant antigenic change, forcing the painstaking annual reformulation of vaccines. The development of broadly protective or universal influenza virus vaccines that induce cross-reactive, protective immune responses could circumvent the need to reformulate current seasonal vaccines. Many of these vaccine candidates target the HA stalk domain, which displays epitopes conserved within and across influenza virus subtypes, including those with pandemic potential. While HA head-mediated antigenic drift is well understood, the potential for antigenic drift in the stalk domain is understudied. Using a panel of HA stalk-specific monoclonal antibodies (MAbs), we applied selection pressure to the stalk domain of A/Netherlands/602/2009 (pdmH1N1) to determine fitness and phenotypes of escape mutant viruses (EMVs). We found that HA stalk MAbs with lower cross-reactivity caused single HA stalk escape mutations, whereas MAbs with broader cross-reactivity forced multiple mutations in the HA. Each escape mutant virus greatly decreased mAb neutralizing activity, but escape mutations did not always ablate MAb binding or Fc-Fc receptor-based effector functions. Escape mutant viruses were not attenuated in vitro but showed attenuation in an in vivo mouse model. Importantly, mice vaccinated with a chimeric HA universal vaccine candidate were protected from lethal challenge with EMVs despite these challenge viruses containing escape mutations in the stalk domain. Our study indicates that while the HA stalk domain can mutate under strong MAb selection pressure, mutant viruses may have attenuated phenotypes and do not evade a polyclonal, stalk-based vaccine-induced response.

Plasticity of the Influenza Virus H5 HA Protein

The HA protein of influenza A viruses is the major viral antigen. In this study, we simultaneously introduced mutations at 17 amino acid positions of an H5 HA expected to affect antigenicity. Viruses with ≥13 amino acid changes in HA were viable, and some had altered antigenic properties. H5 HA can therefore accommodate many mutations in regions that affect antigenicity. The substantial plasticity of H5 HA may facilitate the emergence of novel antigenic variants.

Since the emergence of highly pathogenic avian influenza viruses of the H5 subtype, the major viral antigen, hemagglutinin (HA), has undergone constant evolution, resulting in numerous genetic and antigenic (sub)clades. To explore the consequences of amino acid changes at sites that may affect the antigenicity of H5 viruses, we simultaneously mutated 17 amino acid positions of an H5 HA by using a synthetic gene library that, theoretically, encodes all combinations of the 20 amino acids at the 17 positions. All 251 mutant viruses sequenced possessed ≥13 amino acid substitutions in HA, demonstrating that the targeted sites can accommodate a substantial number of mutations. Selection with ferret sera raised against H5 viruses of different clades resulted in the isolation of 39 genotypes. Further analysis of seven variants demonstrated that they were antigenically different from the parental virus and replicated efficiently in mammalian cells. Our data demonstrate the substantial plasticity of the influenza virus H5 HA protein, which may lead to novel antigenic variants.