Deep mutational scanning of hemagglutinin helps predict evolutionary fates of human H3N2 influenza variants

Conserved sites on the influenza H1 and H3 hemagglutinin recognized by human antibodies

Monoclonal antibodies (mAbs) targeting the influenza hemagglutinin (HA) can be used as prophylactics or templates for next-generation vaccines. Here, we isolated broad, subtype-neutralizing mAbs from human B cells recognizing the H1 or H3 HA "head" and a mAb engaging the conserved stem. The H1 mAbs bind the lateral patch epitope on HAs from 1933 to 2021 and a prepandemic swine H1N1 virus. We improved neutralization potency using directed evolution toward a contemporary H1 HA. Deep mutational scanning of four antigenically distinct H1N1 viruses identified potential viral escape pathways. For the H3 mAbs, we used cryo-electron microscopy to define their epitopes: One mAb binds the side of the HA head, accommodating the N133 glycan and a pocket underneath the receptor binding site; the other mAb recognizes an HA stem epitope that partially overlaps with previously characterized mAbs but with distinct antibody variable genes. Collectively, these mAbs identify conserved sites recognized by broadly-reactive mAbs that may be elicited by next-generation vaccines.

IFIT3 RNA-binding activity promotes influenza A virus infection and translation efficiency

Host cells produce a vast network of antiviral factors in response to viral infection. The interferon-induced proteins with tetratricopeptide repeats (IFITs) are important effectors of a broad-spectrum antiviral response. In contrast to their canonical roles, we previously identified IFIT2 and IFIT3 as pro-viral host factors during influenza A virus (IAV) infection. During IAV infection, IFIT2 binds and enhances translation of AU-rich cellular mRNAs, including many IFN-simulated gene products, establishing a model for its broad antiviral activity. But, IFIT2 also bound viral mRNAs and enhanced their translation resulting in increased viral replication. The ability of IFIT3 to bind RNA and whether this is important for its function was not known. Here we validate direct interactions between IFIT3 and RNA using electromobility shift assays (EMSAs). RNA-binding site identification (RBS-ID) experiments then identified an RNA-binding surface composed of residues conserved in IFIT3 orthologs and IFIT2 paralogs. Mutation of the RNA-binding site reduced the ability IFIT3 to promote IAV gene expression and translation efficiency when compared to wild type IFIT3. The functional units of IFIT2 and IFIT3 are homo- and heterodimers, however the RNA-binding surfaces are located near the dimerization interface. Using co-immunoprecipitation, we showed that mutations to these sites do not affect dimerization. Together, these data establish the link between IFIT3 RNA-binding and its ability to modulate translation of host and viral mRNAs during IAV infection.

Robustness of Ancestral Sequence Reconstruction to Among-site and Among-lineage Evolutionary Heterogeneity

Ancestral sequence reconstruction is typically performed using homogeneous evolutionary models, which assume that the same substitution propensities affect all sites and lineages. These assumptions are routinely violated: heterogeneous structural and functional constraints favor different amino acids at different sites, and these constraints often change among lineages as epistatic substitutions accrue at other sites. To evaluate how violations of the homogeneity assumption affect ancestral sequence reconstruction under realistic conditions, we developed site-specific substitution models and parameterized them using data from deep mutational scanning experiments on three protein families; we then used these models to perform ancestral sequence reconstruction on the empirical alignments and on alignments simulated under heterogeneous conditions derived from the experiments. Extensive among-site and -lineage heterogeneity is present in these datasets, but the sequences reconstructed from empirical alignments are almost identical when heterogeneous or homogeneous models are used for ancestral sequence reconstruction. Using models fit to deep mutational scanning data from distantly related proteins in which mutational effects are very different also has a minimal impact on ancestral sequence reconstruction. The rare differences occur primarily where phylogenetic signal is weak-at fast-evolving sites and nodes connected by long branches. When ancestral sequence reconstruction is performed on simulated data, errors in the reconstructed sequences become more likely as branch lengths increase, but incorporating heterogeneity into the model does not improve accuracy. These data establish that ancestral sequence reconstruction is robust to unincorporated realistic forms of evolutionary heterogeneity, because the primary determinant of ancestral sequence reconstruction is phylogenetic signal, not the substitution model. The best way to improve accuracy is therefore not to develop more elaborate models but to apply ancestral sequence reconstruction to densely sampled alignments that maximize phylogenetic signal at the nodes of interest.

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.

PA and PA-X: two key proteins from segment 3 of the influenza viruses

In recent years, the influenza viruses have posed an increasingly severe threat to public health. It is essential to analyze the virulence and pathogenesis of influenza viruses to prevent and control them, as well as create antiviral drugs. Previous studies have revealed that influenza virus segment 3 codes for not only the PA protein but also a novel protein, PA-X. PA protein is one subunit of the polymerase of influenza viruses and plays a critical role in its life cycle. PA presented endonuclease activity, the transcription and replication of the viral genome, viral virulence, protein degradation, and host immune response by interacting with viral proteins, including PB2, PB1, and host factors, including ANP32A, CHD6, HAX1, hCLE, HDAC6, MCM complex. PA mutations were involved in the viral replication, pathogenicity, and transmission of influenza viruses in poultry, mammals, and humans. PA-X is an open reading frame generated by +1 ribosomal code shift at the N-terminal amino acids of segment 3 and possesses the shutoff activity of host gene expression, regulating the host immune response, viral virulence and transmission. Therefore, PA is one ideal target for the development of antiviral drugs against influenza viruses. Baloxavir marboxil (BXM) and Favipiravir are two very effective anti-influenza virus drugs targeting the PA endonuclease domain of influenza A viruses. In this review, we summarized the structures, viral replication, virulent determinants and transmission, host factors, innate immunity, and antiviral drugs involved in PA and PA-X. The information is of great value for underlying the mechanism of viral replication and developing novel effective strategies to prevent and control influenza infection and the pandemic.

Immune history shapes human antibody responses to H5N1 influenza viruses

Avian H5N1 influenza viruses are circulating widely in cattle and other mammals and pose a risk for a human pandemic. Previous studies suggest that older humans are more resistant to H5N1 infections due to childhood imprinting with other group 1 viruses (H1N1 and H2N2); however, the immunological basis for this is incompletely understood. Here we measured H5N1 antibody responses in sera from 157 individuals born between 1927 and 2016. We show that antibody titers to historical and recent H5N1 strains are highest in older individuals and correlate more strongly with birth year than with age, consistent with immune imprinting. Young children, who were likely not yet exposed to seasonal influenza viruses, had low levels of H5-specific antibodies. We also measured H5N1 antibody responses in sera from 100 individuals before and after receiving an A/Vietnam/1203/2004 H5N1 vaccine. We found that both younger and older humans produced H5-reactive antibodies to the A/Vietnam/1203/2004 vaccine strain and to a contemporary clade 2.3.4.4b strain, with higher seroconversion rates in young children who had lower levels of antibodies before vaccination. These studies suggest that younger individuals might benefit more from vaccination than older individuals in the event of an H5N1 pandemic.

High-throughput neutralization measurements correlate strongly with evolutionary success of human influenza strains

Human influenza viruses rapidly acquire mutations in their hemagglutinin (HA) protein that erode neutralization by antibodies from prior exposures. Here, we use a sequencing-based assay to measure neutralization titers for 78 recent H3N2 HA strains against a large set of children and adult sera, measuring ~10,000 total titers. There is substantial person-to-person heterogeneity in the titers against different viral strains, both within and across age cohorts. The growth rates of H3N2 strains in the human population in 2023 are highly correlated with the fraction of sera with low titers against each strain. Notably, strain growth rates are less correlated with neutralization titers against pools of human sera, demonstrating the importance of population heterogeneity in shaping viral evolution. Overall, these results suggest that high-throughput neutralization measurements of human sera against many different viral strains can help explain the evolution of human influenza.

Characterization of spike S1/S2 processing and entry pathways of lentiviral pseudoviruses bearing seasonal human coronaviruses NL63, 229E, and HKU1 spikes

Although much has been learned about the entry mechanism of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), many details of the entry mechanisms of seasonal human coronaviruses (HCoVs) remain less well understood. In the present study, we used 293T cell lines stably expressing angiotensin converting enzyme (ACE2), aminopeptidase N (APN), or transmembrane serine protease 2 (TMPRSS2), which support high-level transduction of lentiviral pseudoviruses bearing spike proteins of seasonal HCoVs, HCoV-NL63, -229E, or -HKU1, respectively, to compare spike processing and virus entry pathways among these viruses. Our results showed that the entry of HCoV-NL63, -229E, and -HKU1 pseudoviruses into cells is sensitive to endosomal acidification inhibitors (chloroquine and NH4Cl), indicating entry via the endocytosis route. Although TMPRSS2 expression on target cell surface was required for HCoV-HKU1 spike-mediated entry and cell-cell fusion, we found that only the serine protease domain of TMPRSS2 and not the serine protease activity of TMPRSS2 was required for viral entry via endocytic route. However, the serine protease activity of TMPRSS2 and a furin processing site (RKRR) at the S1/S2 junction were essential for efficient HCoV-HKU1 spike-mediated cell-cell fusion. Additionally, we show that dibasic and monobasic arginine residues at the S1/S2 junctions of spike proteins of HCoV-NL63 and -229E are essential for virus entry, but multi-basic furin processing site at the S1/S2 junction was dispensable for HCoV-HKU1 viral entry. Our findings highlight features of the entry mechanisms of seasonal HCoVs that may support the development of novel treatment strategies.IMPORTANCEDetails of the entry mechanisms of seasonal human coronaviruses (HCoVs) remain to be fully explored. To investigate spike-mediated virus entry of HCoV-NL63, -229E, and -HKU1 CoVs, we employed 293T cells that stably express angiotensin converting enzyme (ACE2), aminopeptidase N (APN), or transmembrane serine protease 2 (TMPRSS2) to study entry mechanisms of pseudoviruses bearing spike proteins of HCoV-NL63, -229E, and -HKU1, respectively. We found that HCoV-NL63, -229E, and -HKU1 pseudoviruses entered cells via the endocytic route independently of cellular serine protease activity and therefore likely depended on endosomal cathepsin activity. Furthermore, we showed that arginine amino acids in S1/S2 junctions of HCoV-NL63 and -229E spikes were essential for entry but not essential for HCoV-HKU1 entry. Our results provide new insights into the S1/S2 junctional residues, cellular receptors, and protease requirements for seasonal HCoV pseudovirus entry into cells that may support the development of novel inhibitors.

Rapid in silico directed evolution by a protein language model with EVOLVEpro

Directed protein evolution is central to biomedical applications but faces challenges such as experimental complexity, inefficient multiproperty optimization, and local maxima traps. Although in silico methods that use protein language models (PLMs) can provide modeled fitness landscape guidance, they struggle to generalize across diverse protein families and map to protein activity. We present EVOLVEpro, a few-shot active learning framework that combines PLMs and regression models to rapidly improve protein activity. EVOLVEpro surpasses current methods, yielding up to 100-fold improvements in desired properties. We demonstrate its effectiveness across six proteins in RNA production, genome editing, and antibody binding applications. These results highlight the advantages of few-shot active learning with minimal experimental data over zero-shot predictions. EVOLVEpro opens new possibilities for artificial intelligence-guided protein engineering in biology and medicine.

Concepts and Methods for Predicting Viral Evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein hemagglutinin targeted by human antibodies. Here, we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to 1 year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available at https://previr.app .

Robustness of ancestral sequence reconstruction to among-site evolutionary heterogeneity and epistasis

Ancestral sequence reconstruction (ASR) is typically performed using homogeneous evolutionary models, which assume that the same substitution propensities affect all sites and lineages. These assumptions are routinely violated: heterogeneous structural and functional constraints favor different amino acid states at different sites, and these constraints often change among lineages as epistatic substitutions accrue at other sites. To evaluate how realistic violations of the homogeneity assumption affect ASR, we developed site-specific substitution models and parameterized them using data from deep mutational scanning experiments on three protein families; we then used these models to perform ASR on the empirical alignments and on alignments simulated under heterogeneous conditions derived from the experiments. Extensive among-site and -lineage heterogeneity is present in these datasets, but the sequences reconstructed from empirical alignments are almost identical, irrespective of whether heterogeneous or homogeneous models are used for ASR. The rare differences occur primarily when phylogenetic signal is weak - at fast-evolving sites and nodes connected by long branches. When ASR is performed on simulated data, errors in the reconstructed sequences become more likely as branch lengths increase, but incorporating heterogeneity into the model does not improve accuracy. These data establish that ASR is robust to unincorporated realistic forms of evolutionary heterogeneity, because the primary determinant of ASR is phylogenetic signal, not the substitution model. The best way to improve accuracy is therefore not to develop more elaborate models but to apply ASR to densely sampled alignments that maximize phylogenetic signal at the nodes of interest.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

Dominant-negative TP53 mutations potentiated by the HSF1-regulated proteostasis network

Protein mutational landscapes are sculpted by the impacts of the resulting amino acid substitutions on the protein's stability and folding or aggregation kinetics. These properties can, in turn, be modulated by the composition and activities of the cellular proteostasis network. Heat shock factor 1 (HSF1) is the master regulator of the cytosolic and nuclear proteostasis networks, dynamically tuning the expression of cytosolic and nuclear chaperones and quality control factors to meet demand. Chronic increases in HSF1 levels and activity are prominent hallmarks of cancer cells. One plausible explanation for this observation is that the consequent upregulation of proteostasis factors could biophysically facilitate the acquisition of oncogenic mutations. Here, we experimentally evaluate the impacts of chronic HSF1 activation on the mutational landscape accessible to the quintessential oncoprotein p53. Specifically, we apply quantitative deep mutational scanning of p53 to assess how HSF1 activation shapes the mutational pathways by which p53 can escape cytotoxic pressure conferred by the small molecule nutlin-3, which is a potent antagonist of the p53 negative regulator MDM2. We find that activation of HSF1 broadly increases the fitness of dominant-negative substitutions within p53. This effect of HSF1 activation was particularly notable for non-conservative, biophysically unfavorable amino acid substitutions within buried regions of the p53 DNA-binding domain. These results indicate that chronic HSF1 activation profoundly shapes the oncogenic mutational landscape, preferentially supporting the acquisition of cancer-associated substitutions that are biophysically destabilizing. Along with providing the first experimental and quantitative insights into how HSF1 influences oncoprotein mutational spectra, these findings also implicate HSF1 inhibition as a strategy to reduce the accessibility of mutations that drive chemotherapeutic resistance and metastasis.

Conserved sites on the influenza H1 and H3 hemagglutinin recognized by human antibodies

Monoclonal antibodies (mAbs) targeting the influenza hemagglutinin (HA) have the potential to be used as prophylactics or templates for next-generation vaccines that provide broad protection. Here, we isolated broad, subtype-neutralizing mAbs from human B cells targeting the H1 or H3 HA head as well as a unique mAb targeting the stem. The H1 mAbs target the previously defined lateral patch epitope on H1 HAs and recognize HAs from 1933 to 2021 in addition to a swine H1N1 virus with pandemic potential. Using directed evolution, we improved the neutralization potency of these H1 mAbs towards a contemporary H1 strain. Using deep mutational scanning of four antigenically distinct H1N1 viruses, we identified potential viral escape pathways. For the H3 mAbs we used cryo-EM to define the targeted epitopes: one mAb recognizes the side of the H3 head, accommodating the N133 glycan and a pocket underneath the receptor binding site. The other H3 mAb recognizes an epitope in the HA stem that overlaps with previously characterized mAbs, but with distinct antibody variable genes and mode of recognition. Collectively, these mAbs identify common sites recognized by broad, subtype-specific mAbs that may be elicited by next-generation vaccines.

Evolution of evolvability in rapidly adapting populations

Mutations can alter the short-term fitness of an organism, as well as the rates and benefits of future mutations. While numerous examples of these evolvability modifiers have been observed in rapidly adapting microbial populations, existing theory struggles to predict when they will be favoured by natural selection. Here we develop a mathematical framework for predicting the fates of genetic variants that modify the rates and benefits of future mutations in linked genomic regions. We derive analytical expressions showing how the fixation probabilities of these variants depend on the size of the population and the diversity of competing mutations. We find that competition between linked mutations can dramatically enhance selection for modifiers that increase the benefits of future mutations, even when they impose a strong direct cost on fitness. However, we also find that modest direct benefits can be sufficient to drive evolutionary dead ends to fixation. Our results suggest that subtle differences in evolvability could play an important role in shaping the long-term success of genetic variants in rapidly evolving microbial populations.

High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains

The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes using a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify the infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately 1 month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers 6 months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. We provide an experimental protocol (dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.

IMPORTANCE

We describe a new approach that can rapidly measure how the antibodies in human serum inhibit infection by many different influenza strains. This new approach is useful for understanding how viral evolution affects antibody immunity. We apply the approach to study the effect of repeated influenza vaccination.

Understanding epistatic networks in the B1 β-lactamases through coevolutionary statistical modeling and deep mutational scanning

Throughout evolution, protein families undergo substantial sequence divergence while preserving structure and function. Although most mutations are deleterious, evolution can explore sequence space via epistatic networks of intramolecular interactions that alleviate the harmful mutations. However, comprehensive analysis of such epistatic networks across protein families remains limited. Thus, we conduct a family wide analysis of the B1 metallo-β-lactamases, combining experiments (deep mutational scanning, DMS) on two distant homologs (NDM-1 and VIM-2) and computational analyses (in silico DMS based on Direct Coupling Analysis, DCA) of 100 homologs. The methods jointly reveal and quantify prevalent epistasis, as ~1/3rd of equivalent mutations are epistatic in DMS. From DCA, half of the positions have a >6.5 fold difference in effective number of tolerated mutations across the entire family. Notably, both methods locate residues with the strongest epistasis in regions of intermediate residue burial, suggesting a balance of residue packing and mutational freedom in forming epistatic networks. We identify entrenched WT residues between NDM-1 and VIM-2 in DMS, which display statistically distinct behaviors in DCA from non-entrenched residues. Entrenched residues are not easily compensated by changes in single nearby interactions, reinforcing existing findings where a complex epistatic network compounds smaller effects from many interacting residues.

High throughput profiling identified PA-L106R amino acid substitution in A(H1N1)pdm09 influenza virus that confers reduced susceptibility to baloxavir in vitro

Baloxavir acid (BXA) is a pan-influenza antiviral that targets the cap-dependent endonuclease of the polymerase acidic (PA) protein required for viral mRNA synthesis. To gain a comprehensive understanding on the molecular changes associated with reduced susceptibility to BXA and their fitness profile, we performed a deep mutational scanning at the PA endonuclease domain of an A (H1N1)pdm09 virus. The recombinant virus libraries were serially passaged in vitro under increasing concentrations of BXA followed by next-generation sequencing to monitor PA amino acid substitutions with increased detection frequencies. Enriched PA amino acid changes were each introduced into a recombinant A (H1N1)pdm09 virus to validate their effect on BXA susceptibility and viral replication fitness in vitro. The I38 T/M substitutions known to confer reduced susceptibility to BXA were invariably detected from recombinant virus libraries within 5 serial passages. In addition, we identified a novel L106R substitution that emerged in the third passage and conferred greater than 10-fold reduced susceptibility to BXA. PA-L106 is highly conserved among seasonal influenza A and B viruses. Compared to the wild-type virus, the L106R substitution resulted in reduced polymerase activity and a minor reduction of the peak viral load, suggesting the amino acid change may result in moderate fitness loss. Our results support the use of deep mutational scanning as a practical tool to elucidate genotype-phenotype relationships, including mapping amino acid substitutions with reduced susceptibility to antivirals.

Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin

Human influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population and how this heterogeneity affects virus evolution. Here, we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of two H3N2 strains, A/Hong Kong/45/2019 and A/Perth/16/2009, affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that were fixed in influenza variants after 2020 cause greater escape from sera from younger individuals compared with adults. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups and suggest approaches to understand how this heterogeneous selection shapes viral evolution.

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.

Sequence-based detection of emerging antigenically novel influenza A viruses

The detection of evolutionary transitions in influenza A (H3N2) viruses' antigenicity is a major obstacle to effective vaccine design and development. In this study, we describe Novel Influenza Virus A Detector (NIAViD), an unsupervised machine learning tool, adept at identifying these transitions, using the HA1 sequence and associated physico-chemical properties. NIAViD performed with 88.9% (95% CI, 56.5-98.0%) and 72.7% (95% CI, 43.4-90.3%) sensitivity in training and validation, respectively, outperforming the uncalibrated null model-33.3% (95% CI, 12.1-64.6%) and does not require potentially biased, time-consuming and costly laboratory assays. The pivotal role of the Boman's index, indicative of the virus's cell surface binding potential, is underscored, enhancing the precision of detecting antigenic transitions. NIAViD's efficacy is not only in identifying influenza isolates that belong to novel antigenic clusters, but also in pinpointing potential sites driving significant antigenic changes, without the reliance on explicit modelling of haemagglutinin inhibition titres. We believe this approach holds promise to augment existing surveillance networks, offering timely insights for the development of updated, effective influenza vaccines. Consequently, NIAViD, in conjunction with other resources, could be used to support surveillance efforts and inform the development of updated influenza vaccines.

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.

Rapid protein evolution by few-shot learning with a protein language model

Directed evolution of proteins is critical for applications in basic biological research, therapeutics, diagnostics, and sustainability. However, directed evolution methods are labor intensive, cannot efficiently optimize over multiple protein properties, and are often trapped by local maxima. In silico-directed evolution methods incorporating protein language models (PLMs) have the potential to accelerate this engineering process, but current approaches fail to generalize across diverse protein families. We introduce EVOLVEpro, a few-shot active learning framework to rapidly improve protein activity using a combination of PLMs and protein activity predictors, achieving improved activity with as few as four rounds of evolution. EVOLVEpro substantially enhances the efficiency and effectiveness of in silico protein evolution, surpassing current state-of-the-art methods and yielding proteins with up to 100-fold improvement of desired properties. We showcase EVOLVEpro for five proteins across three applications: T7 RNA polymerase for RNA production, a miniature CRISPR nuclease, a prime editor, and an integrase for genome editing, and a monoclonal antibody for epitope binding. These results demonstrate the advantages of few-shot active learning with small amounts of experimental data over zero-shot predictions. EVOLVEpro paves the way for broader applications of AI-guided protein engineering in biology and medicine.

Flattening the curve-How to get better results with small deep-mutational-scanning datasets

Proteins are used in various biotechnological applications, often requiring the optimization of protein properties by introducing specific amino-acid exchanges. Deep mutational scanning (DMS) is an effective high-throughput method for evaluating the effects of these exchanges on protein function. DMS data can then inform the training of a neural network to predict the impact of mutations. Most approaches use some representation of the protein sequence for training and prediction. As proteins are characterized by complex structures and intricate residue interaction networks, directly providing structural information as input reduces the need to learn these features from the data. We introduce a method for encoding protein structures as stacked 2D contact maps, which capture residue interactions, their evolutionary conservation, and mutation-induced interaction changes. Furthermore, we explored techniques to augment neural network training performance on smaller DMS datasets. To validate our approach, we trained three neural network architectures originally used for image analysis on three DMS datasets, and we compared their performances with networks trained solely on protein sequences. The results confirm the effectiveness of the protein structure encoding in machine learning efforts on DMS data. Using structural representations as direct input to the networks, along with data augmentation and pretraining, significantly reduced demands on training data size and improved prediction performance, especially on smaller datasets, while performance on large datasets was on par with state-of-the-art sequence convolutional neural networks. The methods presented here have the potential to provide the same workflow as DMS without the experimental and financial burden of testing thousands of mutants. Additionally, we present an open-source, user-friendly software tool to make these data analysis techniques accessible, particularly to biotechnology and protein engineering researchers who wish to apply them to their mutagenesis data.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of nine different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Cryptic proteins translated from deletion-containing viral genomes dramatically expand the influenza virus proteome

Productive infections by RNA viruses require faithful replication of the entire genome. Yet many RNA viruses also produce deletion-containing viral genomes (DelVGs), aberrant replication products with large internal deletions. DelVGs interfere with the replication of wild-type virus and their presence in patients is associated with better clinical outcomes. The DelVG RNA itself is hypothesized to confer this interfering activity. DelVGs antagonize replication by out-competing the full-length genome and triggering innate immune responses. Here, we identify an additionally inhibitory mechanism mediated by a new class of viral proteins encoded by DelVGs. We identified hundreds of cryptic viral proteins translated from DelVGs. These DelVG-encoded proteins (DPRs) include canonical viral proteins with large internal deletions, as well as proteins with novel C-termini translated from alternative reading frames. Many DPRs retain functional domains shared with their full-length counterparts, suggesting they may have activity during infection. Mechanistic studies of DPRs derived from the influenza virus protein PB2 showed that they poison replication of wild-type virus by acting as dominant-negative inhibitors of the viral polymerase. These findings reveal that DelVGs have a dual inhibitory mechanism, acting at both the RNA and protein level. They further show that DPRs have the potential to dramatically expand the functional proteomes of diverse RNA viruses.

The evolution, pathogenicity and transmissibility of quadruple reassortant H1N2 swine influenza virus in China: A potential threat to public health

Swine are regarded as "intermediate hosts" or "mixing vessels" of influenza viruses, capable of generating strains with pandemic potential. From 2020 to 2021, we conducted surveillance on swine H1N2 influenza (swH1N2) viruses in swine farms located in Guangdong, Yunnan, and Guizhou provinces in southern China, as well as Henan and Shandong provinces in northern China. We systematically analyzed the evolution and pathogenicity of swH1N2 isolates, and characterized their replication and transmission abilities. The isolated viruses are quadruple reassortant H1N2 viruses containing genes from pdm/09 H1N1 (PB2, PB1, PA and NP genes), triple-reassortant swine (NS gene), Eurasian Avian-like (HA and M genes), and recent human H3N2 (NA gene) lineages. The NA, PB2, and NP of SW/188/20 and SW/198/20 show high gene similarities to A/Guangdong/Yue Fang277/2017 (H3N2). The HA gene of swH1N2 exhibits a high evolutionary rate. The five swH1N2 isolates replicate efficiently in human, canine, and swine cells, as well as in the turbinate, trachea, and lungs of mice. A/swine/Shandong/198/2020 strain efficiently replicates in the respiratory tract of pigs and effectively transmitted among them. Collectively, these current swH1N2 viruses possess zoonotic potential, highlighting the need for strengthened surveillance of swH1N2 viruses.

High-throughput sequencing-based neutralization assay reveals how repeated vaccinations impact titers to recent human H1N1 influenza strains

The high genetic diversity of influenza viruses means that traditional serological assays have too low throughput to measure serum antibody neutralization titers against all relevant strains. To overcome this challenge, we have developed a sequencing-based neutralization assay that simultaneously measures titers against many viral strains using small serum volumes via a workflow similar to traditional neutralization assays. The key innovation is to incorporate unique nucleotide barcodes into the hemagglutinin (HA) genomic segment, and then pool viruses with numerous different barcoded HA variants and quantify infectivity of all of them simultaneously using next-generation sequencing. With this approach, a single researcher performed the equivalent of 2,880 traditional neutralization assays (80 serum samples against 36 viral strains) in approximately one month. We applied the sequencing-based assay to quantify the impact of influenza vaccination on neutralization titers against recent human H1N1 strains for individuals who had or had not also received a vaccine in the previous year. We found that the viral strain specificities of the neutralizing antibodies elicited by vaccination vary among individuals, and that vaccination induced a smaller increase in titers for individuals who had also received a vaccine the previous year-although the titers six months after vaccination were similar in individuals with and without the previous-year vaccination. We also identified a subset of individuals with low titers to a subclade of recent H1N1 even after vaccination. This study demonstrates the utility of high-throughput sequencing-based neutralization assays that enable titers to be simultaneously measured against many different viral strains. We provide a detailed experimental protocol (DOI: https://dx.doi.org/10.17504/protocols.io.kqdg3xdmpg25/v1) and a computational pipeline (https://github.com/jbloomlab/seqneut-pipeline) for the sequencing-based neutralization assays to facilitate the use of this method by others.

Cryptic proteins translated from deletion-containing viral genomes dramatically expand the influenza virus proteome

Productive infections by RNA viruses require faithful replication of the entire genome. Yet many RNA viruses also produce deletion-containing viral genomes (DelVGs), aberrant replication products with large internal deletions. DelVGs interfere with the replication of wild-type virus and their presence in patients is associated with better clinical outcomes as they. The DelVG RNA itself is hypothesized to confer this interfering activity. DelVGs antagonize replication by out-competing the full-length genome and triggering innate immune responses. Here, we identify an additionally inhibitory mechanism mediated by a new class of viral proteins encoded by DelVGs. We identified hundreds of cryptic viral proteins translated from DelVGs. These DelVG-encoded proteins (DPRs) include canonical viral proteins with large internal deletions, as well as proteins with novel C-termini translated from alternative reading frames. Many DPRs retain functional domains shared with their full-length counterparts, suggesting they may have activity during infection. Mechanistic studies of DPRs derived from the influenza virus protein PB2 showed that they poison replication of wild-type virus by acting as dominant-negative inhibitors of the viral polymerase. These findings reveal that DelVGs have a dual inhibitory mechanism, acting at both the RNA and protein level. They further show that DPRs have the potential to dramatically expand the functional proteomes of diverse RNA viruses.

Efficient evolution of human antibodies from general protein language models

Natural evolution must explore a vast landscape of possible sequences for desirable yet rare mutations, suggesting that learning from natural evolutionary strategies could guide artificial evolution. Here we report that general protein language models can efficiently evolve human antibodies by suggesting mutations that are evolutionarily plausible, despite providing the model with no information about the target antigen, binding specificity or protein structure. We performed language-model-guided affinity maturation of seven antibodies, screening 20 or fewer variants of each antibody across only two rounds of laboratory evolution, and improved the binding affinities of four clinically relevant, highly mature antibodies up to sevenfold and three unmatured antibodies up to 160-fold, with many designs also demonstrating favorable thermostability and viral neutralization activity against Ebola and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pseudoviruses. The same models that improve antibody binding also guide efficient evolution across diverse protein families and selection pressures, including antibiotic resistance and enzyme activity, suggesting that these results generalize to many settings.

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.

An integrated technology for quantitative wide mutational scanning of human antibody Fab libraries

Antibodies are engineerable quantities in medicine. Learning antibody molecular recognition would enable the in silico design of high affinity binders against nearly any proteinaceous surface. Yet, publicly available experiment antibody sequence-binding datasets may not contain the mutagenic, antigenic, or antibody sequence diversity necessary for deep learning approaches to capture molecular recognition. In part, this is because limited experimental platforms exist for assessing quantitative and simultaneous sequence-function relationships for multiple antibodies. Here we present MAGMA-seq, an integrated technology that combines multiple antigens and multiple antibodies and determines quantitative biophysical parameters using deep sequencing. We demonstrate MAGMA-seq on two pooled libraries comprising mutants of ten different human antibodies spanning light chain gene usage, CDR H3 length, and antigenic targets. We demonstrate the comprehensive mapping of potential antibody development pathways, sequence-binding relationships for multiple antibodies simultaneously, and identification of paratope sequence determinants for binding recognition for broadly neutralizing antibodies (bnAbs). MAGMA-seq enables rapid and scalable antibody engineering of multiple lead candidates because it can measure binding for mutants of many given parental antibodies in a single experiment.

Advancing Antibody Engineering through Synthetic Evolution and Machine Learning

Abs are versatile molecules with the potential to achieve exceptional binding to target Ags, while also possessing biophysical properties suitable for therapeutic drug development. Protein display and directed evolution systems have transformed synthetic Ab discovery, engineering, and optimization, vastly expanding the number of Ab clones able to be experimentally screened for binding. Moreover, the burgeoning integration of high-throughput screening, deep sequencing, and machine learning has further augmented in vitro Ab optimization, promising to accelerate the design process and massively expand the Ab sequence space interrogated. In this Brief Review, we discuss the experimental and computational tools employed in synthetic Ab engineering and optimization. We also explore the therapeutic challenges posed by developing Abs for infectious diseases, and the prospects for leveraging machine learning-guided protein engineering to prospectively design Abs resistant to viral escape.

Age-dependent heterogeneity in the antigenic effects of mutations to influenza hemagglutinin

Human influenza virus evolves to escape neutralization by polyclonal antibodies. However, we have a limited understanding of how the antigenic effects of viral mutations vary across the human population, and how this heterogeneity affects virus evolution. Here we use deep mutational scanning to map how mutations to the hemagglutinin (HA) proteins of the A/Hong Kong/45/2019 (H3N2) and A/Perth/16/2009 (H3N2) strains affect neutralization by serum from individuals of a variety of ages. The effects of HA mutations on serum neutralization differ across age groups in ways that can be partially rationalized in terms of exposure histories. Mutations that fixed in influenza variants after 2020 cause the greatest escape from sera from younger individuals. Overall, these results demonstrate that influenza faces distinct antigenic selection regimes from different age groups, and suggest approaches to understand how this heterogeneous selection shapes viral evolution.

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.

Co-evolution of immunity and seasonal influenza viruses

Seasonal influenza viruses cause recurring global epidemics by continually evolving to escape host immunity. The viral constraints and host immune responses that limit and drive the evolution of these viruses are increasingly well understood. However, it remains unclear how most of these advances improve the capacity to reduce the impact of seasonal influenza viruses on human health. In this Review, we synthesize recent progress made in understanding the interplay between the evolution of immunity induced by previous infections or vaccination and the evolution of seasonal influenza viruses driven by the heterogeneous accumulation of antibody-mediated immunity in humans. We discuss the functional constraints that limit the evolution of the viruses, the within-host evolutionary processes that drive the emergence of new virus variants, as well as current and prospective options for influenza virus control, including the viral and immunological barriers that must be overcome to improve the effectiveness of vaccines and antiviral drugs.

Steering and controlling evolution - from bioengineering to fighting pathogens

Control interventions steer the evolution of molecules, viruses, microorganisms or other cells towards a desired outcome. Applications range from engineering biomolecules and synthetic organisms to drug, therapy and vaccine design against pathogens and cancer. In all these instances, a control system alters the eco-evolutionary trajectory of a target system, inducing new functions or suppressing escape evolution. Here, we synthesize the objectives, mechanisms and dynamics of eco-evolutionary control in different biological systems. We discuss how the control system learns and processes information about the target system by sensing or measuring, through adaptive evolution or computational prediction of future trajectories. This information flow distinguishes pre-emptive control strategies by humans from feedback control in biotic systems. We establish a cost-benefit calculus to gauge and optimize control protocols, highlighting the fundamental link between predictability of evolution and efficacy of pre-emptive control.

Deep mutational scanning reveals the functional constraints and evolutionary potential of the influenza A virus PB1 protein

IMPORTANCE

The influenza virus polymerase is important for adaptation to new hosts and, as a determinant of mutation rate, for the process of adaptation itself. We performed a deep mutational scan of the polymerase basic 1 (PB1) protein to gain insights into the structural and functional constraints on the influenza RNA-dependent RNA polymerase. We find that PB1 is highly constrained at specific sites that are only moderately predicted by the global structure or larger domain. We identified a number of beneficial mutations, many of which have been shown to be functionally important or observed in influenza virus' natural evolution. Overall, our atlas of PB1 mutations and their fitness impacts serves as an important resource for future studies of influenza replication and evolution.

Deep mutational scanning of proteins in mammalian cells

Protein mutagenesis is essential for unveiling the molecular mechanisms underlying protein function in health, disease, and evolution. In the past decade, deep mutational scanning methods have evolved to support the functional analysis of nearly all possible single-amino acid changes in a protein of interest. While historically these methods were developed in lower organisms such as E. coli and yeast, recent technological advancements have resulted in the increased use of mammalian cells, particularly for studying proteins involved in human disease. These advancements will aid significantly in the classification and interpretation of variants of unknown significance, which are being discovered at large scale due to the current surge in the use of whole-genome sequencing in clinical contexts. Here, we explore the experimental aspects of deep mutational scanning studies in mammalian cells and report the different methods used in each step of the workflow, ultimately providing a useful guide toward the design of such studies.

Key Amino Acid Residues That Determine the Antigenic Properties of Highly Pathogenic H5 Influenza Viruses Bearing the Clade 2.3.4.4 Hemagglutinin Gene

The H5 subtype highly pathogenic avian influenza viruses bearing the clade 2.3.4.4 HA gene have been pervasive among domestic poultry and wild birds worldwide since 2014, presenting substantial risks to human and animal health. Continued circulation of clade 2.3.4.4 viruses has resulted in the emergence of eight subclades (2.3.4.4a-h) and multiple distinct antigenic groups. However, the key antigenic substitutions responsible for the antigenic change of these viruses remain unknown. In this study, we analyzed the HA gene sequences of 5713 clade 2.3.4.4 viruses obtained from a public database and found that 23 amino acid residues were highly variable among these strains. We then generated a series of single-amino-acid mutants based on the H5-Re8 (a vaccine seed virus) background and tested their reactivity with a panel of eight monoclonal antibodies (mAbs). Six mutants bearing amino acid substitutions at positions 120, 126, 141, 156, 185, or 189 (H5 numbering) led to reduced or lost reactivity to these mAbs. Further antigenic cartography analysis revealed that the amino acid residues at positions 126, 156, and 189 acted as immunodominant epitopes of H5 viruses. Collectively, our findings offer valuable guidance for the surveillance and early detection of emerging antigenic variants.

Deep mutational scanning of influenza A virus neuraminidase facilitates the identification of drug resistance mutations in vivo

IMPORTANCE

NA is a crucial surface antigen and drug target of influenza A virus. A comprehensive understanding of NA's mutational effect and drug resistance profiles in vivo is essential for comprehending the evolutionary constraints and making informed choices regarding drug selection to combat resistance in clinical settings. In the current study, we established an efficient deep mutational screening system in mouse lung tissues and systematically evaluated the fitness effect and drug resistance to three neuraminidase inhibitors of NA single-nucleotide mutations. The fitness of NA mutants is generally correlated with a natural mutation in the database. The fitness of NA mutants is influenced by biophysical factors such as protein stability, complex formation, and the immune response triggered by viral infection. In addition to confirming previously reported drug-resistant mutations, novel mutations were identified. Interestingly, we identified an allosteric drug-resistance mutation that is not located within the drug-binding pocket but potentially affects drug binding by interfering with NA tetramerization. The dual assessments performed in this study provide a more accurate assessment of the evolutionary potential of drug-resistant mutations and offer guidance for the rational selection of antiviral drugs.

Fitness effects of mutations to SARS-CoV-2 proteins

Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here, we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.

Pathogen-driven CRISPR screens identify TREX1 as a regulator of DNA self-sensing during influenza virus infection

Host:pathogen interactions dictate the outcome of infection, yet the limitations of current approaches leave large regions of this interface unexplored. Here, we develop a novel fitness-based screen that queries factors important during the middle to late stages of infection. This is achieved by engineering influenza virus to direct the screen by programming dCas9 to modulate host gene expression. Our genome-wide screen for pro-viral factors identifies the cytoplasmic DNA exonuclease TREX1. TREX1 degrades cytoplasmic DNA to prevent inappropriate innate immune activation by self-DNA. We reveal that this same process aids influenza virus replication. Infection triggers release of mitochondrial DNA into the cytoplasm, activating antiviral signaling via cGAS and STING. TREX1 metabolizes the DNA, preventing its sensing. Collectively, these data show that self-DNA is deployed to amplify innate immunity, a process tempered by TREX1. Moreover, they demonstrate the power and generality of pathogen-driven fitness-based screens to pinpoint key host regulators of infection.

Influenza virus transcription and progeny production are poorly correlated in single cells

The ultimate success of a viral infection at the cellular level is determined by the number of progeny virions produced. However, most single-cell studies of infection quantify the expression of viral transcripts and proteins, rather than the amount of progeny virions released from infected cells. Here, we overcome this limitation by simultaneously measuring transcription and progeny production from single influenza virus-infected cells by embedding nucleotide barcodes in the viral genome. We find that viral transcription and progeny production are poorly correlated in single cells. The cells that transcribe the most viral mRNA do not produce the most viral progeny and often represent aberrant infections that fail to express the influenza NS gene. However, only some of the discrepancy between transcription and progeny production can be explained by viral gene absence or mutations: there is also a wide range of progeny production among cells infected by complete unmutated virions. Overall, our results show that viral transcription is a relatively poor predictor of an infected cell's contribution to the progeny population.

IgG3 subclass antibodies recognize antigenically drifted influenza viruses and SARS-CoV-2 variants through efficient bivalent binding

The constant domains of antibodies are important for effector functions, but less is known about how they can affect binding and neutralization of viruses. Here, we evaluated a panel of human influenza virus monoclonal antibodies (mAbs) expressed as IgG1, IgG2, or IgG3. We found that many influenza virus-specific mAbs have altered binding and neutralization capacity depending on the IgG subclass encoded and that these differences result from unique bivalency capacities of the subclasses. Importantly, subclass differences in antibody binding and neutralization were greatest when the affinity for the target antigen was reduced through antigenic mismatch. We found that antibodies expressed as IgG3 bound and neutralized antigenically drifted influenza viruses more effectively. We obtained similar results using a panel of SARS-CoV-2-specific mAbs and the antigenically advanced B.1.351 and BA.1 strains of SARS-CoV-2. We found that a licensed therapeutic mAb retained neutralization breadth against SARS-CoV-2 variants when expressed as IgG3, but not IgG1. These data highlight that IgG subclasses are not only important for fine-tuning effector functionality but also for binding and neutralization of antigenically drifted viruses.

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.

An Atlas of Variant Effects to understand the genome at nucleotide resolution

Sequencing has revealed hundreds of millions of human genetic variants, and continued efforts will only add to this variant avalanche. Insufficient information exists to interpret the effects of most variants, limiting opportunities for precision medicine and comprehension of genome function. A solution lies in experimental assessment of the functional effect of variants, which can reveal their biological and clinical impact. However, variant effect assays have generally been undertaken reactively for individual variants only after and, in most cases long after, their first observation. Now, multiplexed assays of variant effect can characterise massive numbers of variants simultaneously, yielding variant effect maps that reveal the function of every possible single nucleotide change in a gene or regulatory element. Generating maps for every protein encoding gene and regulatory element in the human genome would create an 'Atlas' of variant effect maps and transform our understanding of genetics and usher in a new era of nucleotide-resolution functional knowledge of the genome. An Atlas would reveal the fundamental biology of the human genome, inform human evolution, empower the development and use of therapeutics and maximize the utility of genomics for diagnosing and treating disease. The Atlas of Variant Effects Alliance is an international collaborative group comprising hundreds of researchers, technologists and clinicians dedicated to realising an Atlas of Variant Effects to help deliver on the promise of genomics.

Outsmarting Pathogens with Antibody Engineering

There is growing interest in identifying antibodies that protect against infectious diseases, especially for high-risk individuals and pathogens for which no vaccine is yet available. However, pathogens that manifest as opportunistic or latent infections express complex arrays of virulence-associated proteins and are adept at avoiding immune responses. Some pathogens have developed strategies to selectively destroy antibodies, whereas others create decoy epitopes that trick the host immune system into generating antibodies that are at best nonprotective and at worst enhance pathogenesis. Antibody engineering strategies can thwart these efforts by accessing conserved neutralizing epitopes, generating Fc domains that resist capture or degradation and even accessing pathogens hidden inside cells. Design of pathogen-resistant antibodies can enhance protection and guide development of vaccine immunogens against these complex pathogens. Here, we discuss general strategies for design of antibodies resistant to specific pathogen defense mechanisms.

Fitness effects of mutations to SARS-CoV-2 proteins

Knowledge of the fitness effects of mutations to SARS-CoV-2 can inform assessment of new variants, design of therapeutics resistant to escape, and understanding of the functions of viral proteins. However, experimentally measuring effects of mutations is challenging: we lack tractable lab assays for many SARS-CoV-2 proteins, and comprehensive deep mutational scanning has been applied to only two SARS-CoV-2 proteins. Here we develop an approach that leverages millions of publicly available SARS-CoV-2 sequences to estimate effects of mutations. We first calculate how many independent occurrences of each mutation are expected to be observed along the SARS-CoV-2 phylogeny in the absence of selection. We then compare these expected observations to the actual observations to estimate the effect of each mutation. These estimates correlate well with deep mutational scanning measurements. For most genes, synonymous mutations are nearly neutral, stop-codon mutations are deleterious, and amino-acid mutations have a range of effects. However, some viral accessory proteins are under little to no selection. We provide interactive visualizations of effects of mutations to all SARS-CoV-2 proteins (https://jbloomlab.github.io/SARS2-mut-fitness/). The framework we describe is applicable to any virus for which the number of available sequences is sufficiently large that many independent occurrences of each neutral mutation are observed.