Evolution Of Pathogens Research Articles

Crispr and plant pathology: Revolutionizing disease resistance in crops

CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) technology has revolutionized plant pathology by providing a precise and efficient tool for enhancing disease resistance in crops. This gene-editing technique enables scientists to modify specific DNA sequences in plants, allowing for the targeted improvement of traits related to pathogen defense. By altering genes responsible for susceptibility to diseases, CRISPR facilitates the development of crops that are more resistant to bacterial, fungal, and viral pathogens, thereby reducing the reliance on chemical pesticides. Additionally, CRISPR can be used to boost plant immunity and improve resilience against emerging plant diseases that threaten global food security. The technology's accuracy and speed have accelerated breeding programs, enabling researchers to respond rapidly to agricultural challenges posed by climate change and the evolution of new pathogens. This article explores the breakthroughs achieved through CRISPR in plant pathology, highlighting case studies of its application in major crops such as rice, wheat, and tomatoes. Furthermore, it discusses the ethical, regulatory, and environmental considerations of using CRISPR for crop improvement, alongside its potential to revolutionize sustainable agriculture by reducing disease-induced yield losses and promoting food security.

Distinct impacts of each anti-anti-sigma factor ortholog of the chlamydial Rsb partner switching mechanism on development in Chlamydia trachomatis.

Partner switching mechanisms (PSMs) are signal transduction systems comprised of a sensor phosphatase (RsbU), an anti-sigma factor (RsbW, kinase), an anti-anti-sigma factor (RsbV, the RsbW substrate), and a target sigma factor. Chlamydia spp. are obligate intracellular bacterial pathogens of animals that undergo a developmental cycle transitioning between the infectious elementary body (EB) and replicative reticulate body (RB) within a host cell-derived vacuole (inclusion). Secondary differentiation events (RB to EB) are transcriptionally regulated, in part, by the housekeeping sigma factor (σ66) and two late-gene sigma factors (σ54 and σ28). Prior research supports that the PSM in Chlamydia trachomatis regulates availability of σ66. Pan-genome analysis revealed that PSM components are conserved across the phylum Chlamydiota, with Chlamydia spp. possessing an atypical arrangement of two anti-anti-sigma factors, RsbV1 and RsbV2. Bioinformatic analyses support RsbV2 as the homolog to the pan-genome-conserved RsbV with RsbV1 as an outlier. This, combined with in vitro data, indicates that RsbV1 and RsbV2 are structurally and biochemically distinct. Reduced levels or overexpression of RsbV1/RsbV2 did not significantly impact C. trachomatis growth or development. In contrast, overexpression of a non-phosphorylatable RsbV2 S55A mutant, but not overexpression of an RsbV1 S56A mutant, resulted in a 3 log reduction in infectious EB production without reduction in genomic DNA (total bacteria) or inclusion size, suggesting a block in secondary differentiation. The block was corroborated by reduced production of σ54/28-regulated late proteins and via transmission electron microscopy.IMPORTANCEChlamydia trachomatis is the leading cause of reportable bacterial sexually transmitted infections (STIs) and causes the eye infection trachoma, a neglected tropical disease. Broad-spectrum antibiotics used for treatment can lead to microbiome dysbiosis and increased antibiotic resistance development in other bacteria, and treatment failure for chlamydial STIs is a recognized clinical problem. Here, we show that disruption of a partner switching mechanism (PSM) significantly reduces infectious progeny production via blockage of reticulate body to elementary body differentiation. We also reveal a novel PSM expansion largely restricted to the species infecting animals, suggesting a role in pathogen evolution. Collectively, our results highlight the chlamydial PSM as a key regulator of development that could be a potential target for novel therapeutics.

Pangenome graph analysis reveals extensive effector copy-number variation in spinach downy mildew.

Plant pathogens adapt at speeds that challenge contemporary disease management strategies like the deployment of disease resistance genes. The strong evolutionary pressure to adapt, shapes pathogens' genomes, and comparative genomics has been instrumental in characterizing this process. With the aim to capture genomic variation at high resolution and study the processes contributing to adaptation, we here leverage an innovative, multi-genome method to construct and annotate the first pangenome graph of an oomycete plant pathogen. We expand on this approach by analysing the graph and creating synteny based single-copy orthogroups for all genes. We generated telomere-to-telomere genome assemblies of six genetically diverse isolates of the oomycete pathogen Peronospora effusa, the economically most important disease in cultivated spinach worldwide. The pangenome graph demonstrates that P. effusa genomes are highly conserved, both in chromosomal structure and gene content, and revealed the continued activity of transposable elements which are directly responsible for 80% of the observed variation between the isolates. While most genes are generally conserved, virulence related genes are highly variable between the isolates. Most of the variation is found in large gene clusters resulting from extensive copy-number expansion. Pangenome graph-based discovery can thus be effectively used to capture genomic variation at exceptional resolution, thereby providing a framework to study the biology and evolution of plant pathogens.

Genome resource announcement for emerging pathogen Bipolaris gigantea, isolated from Microstegium vimineum

Bipolaris gigantea is a pathogen of the invasive grass Microstegium vimineum and an emerging pathogen of other hosts such as hemp and barley, causing characteristic eyespot foliar lesions. The fungus is characterized by long pale hyaline conidia measuring about 300 to 350 μm. Previously, this pathogen was classified in the Drechslera genus, and recently reassigned as Bipolaris based on molecular identification. Here, we generated a high-quality draft genome sequence of B. gigantea isolate BGF using long and short read DNA sequencing. The assembled genome, 30.23 Mb in size with a BUSCO completeness score of 98.4%, exhibited synteny with the common grass pathogen B. sorokiniana isolate 10943. Average nucleotide identity (ANI) and single nucleotide polymorphisms (SNP) analyses revealed high sequence identity with B. gigantea isolates from Cannabis sativa fields in Kentucky. This research provides a genomic resource that will contribute to future studies to understand the host range evolution and pathogenicity of this novel pathogen.

Pathogens from salmon aquaculture in relation to conservation of wild Pacific salmon in Canada.

The spread of pathogens from farmed salmon is a conservation concern for wild Pacific salmon in British Columbia (BC), Canada. Three pathogens are prevalent in farmed Atlantic salmon in BC, spill over to wild Pacific salmon, and are linked to negative impacts on wild salmon: Piscine orthoreovirus, Tenacibaculum spp., and sea lice (Lepeophtheirus salmonis). Molecular screening of infectious agents in farmed and wild salmon and environmental DNA highlights a further 4 agents that are likely elevated near salmon farms and 37 that co-occur in wild and farmed salmon. Pathogens likely affect wild salmon indirectly by mediating migration, competition, and predation. Current net-pen aquaculture practices pose these risks to numerous populations of all species of wild salmon in BC, most of which are not covered in Government of Canada science and advisory reports. Climate change, pathogen evolution, and changes to disease management and aquaculture regulations will influence future risks.

Genomics and Bioinformatics in One Health: Transdisciplinary Approaches for Health Promotion and Disease Prevention.

The One Health concept underscores the interconnectedness of human, animal, and environmental health, necessitating an integrated, transdisciplinary approach to tackle contemporary health challenges. This perspective paper explores the pivotal role of genomics and bioinformatics in advancing One Health initiatives. By leveraging genomic technologies and bioinformatics tools, researchers can decode complex biological data, enabling comprehensive insights into pathogen evolution, transmission dynamics, and host-pathogen interactions across species and environments (or ecosystems). These insights are crucial for predicting and mitigating zoonotic disease outbreaks, understanding antimicrobial resistance patterns, and developing targeted interventions for health promotion and disease prevention. Furthermore, integrating genomic data with environmental and epidemiological information enhances the precision of public health responses. Here we discuss case studies demonstrating successful applications of genomics and bioinformatics in One Health contexts, such as including data integration, standardization, and ethical considerations in genomic research. By fostering collaboration among geneticists, bioinformaticians, epidemiologists, zoologists, and data scientists, the One Health approach can harness the full potential of genomics and bioinformatics to safeguard global health. This perspective underscores the necessity of continued investment in interdisciplinary education, research infrastructure, and policy frameworks to effectively employ these technologies in the service of a healthier planet.

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

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

Antibiotic-producing plant-associated bacteria, anti-virulence therapy and microbiome engineering: Integrated approaches in sustainable agriculture.

Plant health is crucial for maintaining the well-being of humans, animals and the environment. Plant pathogens pose significant challenges to agricultural production, global food security and ecosystem biodiversity. This problem is exacerbated by the impact of climate change, which is expected to alter the emergence and evolution of plant pathogens and their interaction with their plant hosts. Traditional approaches to managing phytopathogens involved the use of chemical pesticides, but alternative strategies are needed to address their ongoing decline in performance as well as their negative impact on the environment and public health. Here, we highlight the advancement and effectiveness of biocontrol strategies based on the use of antimicrobial-producing plant-associated bacteria, anti-virulence therapy (e.g. quorum quenching) and microbiome engineering as sustainable biotechnological approaches to promote plant health and foster sustainable agriculture. Notably, Enterobacterales are emerging as important biocontrol agents and as a source of new antimicrobials for potential agricultural use. We analysed here the genomes of over 250 plant-associated enterobacteria to examine their potential to synthesize secondary metabolites. Exploration of the plant microbiome is of major interest in the search for eco-friendly alternatives for reducing the use of chemical pesticides.

Artificial intelligence-based prediction of pathogen emergence and evolution in the world of synthetic biology.

The emergence of new techniques in both microbial biotechnology and artificial intelligence (AI) is opening up a completely new field for monitoring and sometimes even controlling the evolution of pathogens. However, the now famous generative AI extracts and reorganizes prior knowledge from large datasets, making it poorly suited to making predictions in an unreliable future. In contrast, an unfamiliar perspective can help us identify key issues related to the emergence of new technologies, such as those arising from synthetic biology, whilst revisiting old views of AI or including generative AI as a generator of abduction as a resource. This could enable us to identify dangerous situations that are bound to emerge in the not-too-distant future, and prepare ourselves to anticipate when and where they will occur. Here, we emphasize the fact that amongst the many causes of pathogen outbreaks, often driven by the explosion of the human population, laboratory accidents are a major cause of epidemics. This review, limited to animal pathogens, concludes with a discussion of potential epidemic origins based on unusual organisms or associations of organisms that have rarely been highlighted or studied.

Revealing SARS-CoV-2 Mpro Mutation Cold and Hot Spots: Dynamic Residue Network Analysis Meets Machine Learning

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

Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease

Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.

Changes in DNA methylation contribute to rapid adaptation in bacterial plant pathogen evolution.

Adaptation is usually explained by beneficial genetic mutations that are transmitted from parents to offspring and become fixed in the adapted population. However, genetic mutation analysis alone is not sufficient to fully explain the adaptive processes, and several studies report the existence of nongenetic (or epigenetic) inheritance that can enable adaptation to new environments. In the present work, we tested the hypothesis of the role of DNA methylation, a form of epigenetic modification, in adaptation of the plant pathogen Ralstonia pseudosolanacearum to the host during experimental evolution. Using SMRT-seq technology, we analyzed the methylomes of 31 experimentally evolved clones obtained after serial passages on 5 different plant species during 300 generations. Comparison with the methylome of the ancestral clone revealed a list of 50 differential methylated sites (DMSs) at the GTWWAC motif. Gene expression analysis of the 39 genes targeted by these DMSs revealed limited correlation between differential methylation and differential expression of the corresponding genes. Only 1 gene showed a correlation, the RSp0338 gene encoding the EpsR regulator protein. The MSRE-qPCR technology, used as an alternative approach for DNA methylation analysis, also found the 2 DMSs upstream RSp0338. Using site-directed mutagenesis, we demonstrated the contribution of these 2 DMSs in host adaptation. As these DMSs appeared very early in the experimental evolution, we hypothesize that such fast epigenetic changes can allow rapid adaptation to the plant stem environment. In addition, we found that the change in DNA methylation upstream RSp0338 remains stable at least for 100 generations outside the host and thus can contribute to long-term adaptation to the host plant. To our knowledge, this is the first study showing a direct link between bacterial epigenetic variation and adaptation to a new environment.

The evolutionary features and roles of single nucleotide variants and charged amino acid mutations in influenza outbreaks during NPI period

The epidemic and outbreaks of influenza B Victoria lineage (Bv) during 2019–2022 led to an analysis of genetic, epitopes, charged amino acids and Bv outbreaks. Based on the National Influenza Surveillance Network (NISN), the Bv 72 strains isolated during 2019–2022 were selected by spatio-temporal sampling, then were sequenced. Using the Compare Means, Correlate and Cluster, the outbreak data were analyzed, including the single nucleotide variant (SNV), amino acid (AA), epitope, evolutionary rate (ER), Shannon entropy value (SV), charged amino acid and outbreak. With the emergence of COVID-19, the non-pharmaceutical interventions (NPIs) made Less distant transmission and only Bv outbreak. The 2021–2022 strains in the HA genes were located in the same subset, but were distinct from the 2019–2020 strains (P < 0.001). The codon G → A transition in nucleotide was in the highest ratio but the transversion of C → A and T → A made the most significant contribution to the outbreaks, while the increase in amino acid mutations characterized by polar, acidic and basic signatures played a key role in the Bv epidemic in 2021–2022. Both ER and SV were positively correlated in HA genes (R = 0.690) and NA genes (R = 0.711), respectively, however, the number of mutations in the HA genes was 1.59 times higher than that of the NA gene (2.15/1.36) from the beginning of 2020 to 2022. The positively selective sites 174, 199, 214 and 563 in HA genes and the sites 73 and 384 in NA genes were evolutionarily selected in the 2021–2022 influenza outbreaks. Overall, the prevalent factors related to 2021–2022 influenza outbreaks included epidemic timing, Tv, Ts, Tv/Ts, P137 (B → P), P148 (B → P), P199 (P → A), P212 (P → A), P214 (H → P) and P563 (B → P). The preference of amino acid mutations for charge/pH could influence the epidemic/outbreak trends of infectious diseases. Here was a good model of the evolution of infectious disease pathogens. This study, on account of further exploration of virology, genetics, bioinformatics and outbreak information, might facilitate further understanding of their deep interaction mechanisms in the spread of infectious diseases.

Factors Contributing to Emergence of infectious viral diseases in India: Issues and Challenges after COVID-19 Pandemic

Emerging and re-emerging viral infections are major cause of morbidity and mortality in humans and animals. India had experienced numerous infectious disease outbreaks and epidemics in past years. However, tremendous strides have been achieved in the past in the fight against serious epidemic diseases. It has been found that a wide range of complex factors, such as pathogen evolution, human invasion, commerce in wild animals, microbial adaptation, changes in the environment and dynamic interactions between humans and animals influence the emergence and transmission of infectious viral diseases. As the COVID-19 pandemic demonstrated, India's secondary and tertiary health-care system is quite advanced. The study of infectious diseases, in particular their pathogenesis, genetic profile and diagnosis has fundamentally changed with the advent of genome sequencing and nucleic acid detection technology. Additionally, advance therapy and vaccination led to best possible patient management and care. Further scientific investigations and research are needed to discover neglected diseases that have the potential to cause epidemics as well as to develop updated vaccines, modern treatment strategy and diagnostics at molecular level. This evaluation will aid for policymakers, academics and researchers to redesign the course of action to combat the deadly emerging pathogenic viruses and also preserve the ecosystem.

Dynamic evolution of peste des petits ruminants virus in sheep and goat hosts across India reveals the swift surge of F gene.

Peste des petits ruminants (PPR), an acute febrile viral disease impacting goats and sheep flocks, manifests with pyrexia, mucopurulent nasal and ocular discharges, necrotizing and erosive stomatitis, pneumonia, and enteritis. The disease-instigating agent, PPR virus, pertains to the Morbillivirus caprinae genus in the Paramyxoviridae family. The endemic presence of PPR in India results in notable economic losses due to heightened mortality and morbidity in infected animals. Understanding viral pathogen evolution is pivotal for delineating their emergence in diverse environments. This study explores the molecular evolutionary patterns of PPRV, concentrating on the N and F structural genes isolated from Indian sheep and goats. Analyzing evolutionary rate, phylogenetics, selection pressure, and codon usage bias, we determined the time to the most recent common ancestor (tMRCA) as 1984, 1973, 2000, and 2004 for goat and sheep's N and F genes, respectively, with evolutionary rates ranging from 2.859x103 to 4.995x104. The F-gene is found to exhibit a faster evolution than the N-gene, indicating apparent virus transmission across the regions of India, as supported by phylogenetic analysis. Codon usage bias examination, incorporating nucleotide composition and various plots (effective number of codon plot, parity plot, neutrality plot), suggests the evolution in India influenced by both natural selection and mutational pressure, resulting in alterations in the virus's codon bias. The integrated analysis underscores the significant role of selection pressures, implying PPRV's co-evolution and adaptations influenced by various genes. Insights from this study can guide effective disease control and vaccine development, aiding in managing PPR outbreaks in India and beyond.

The Role of Genomics in Infectious Disease Control

The integration of genomics into infectious disease control has revolutionized pathogen detection, surveillance, and treatment strategies. With advancements in next-generation sequencing (NGS), genomic technologies have enabled the identification of infectious agents, tracing their transmission, and monitoring of antimicrobial resistance. Genomic epidemiology offers a powerful tool for outbreak investigations by identifying transmission chains and understanding pathogen evolution. Furthermore, genomics supports precision medicine, allowing for tailored treatments based on both pathogen and host genomes. This paper reviews the applications of genomics in disease detection, outbreak management, antimicrobial resistance surveillance, and precision medicine, underscoring its pivotal role in global public health efforts. The future of infectious disease control will be shaped by genomics, with an emphasis on enhanced diagnostics, personalized treatment, and real-time pathogen tracking. Keywords: Genomics, Infectious Diseases, Next-Generation Sequencing, Outbreak Investigation, Genomic Epidemiology.

Multispecies interactions and the community context of the evolution of virulence.

Pairwise host-parasite relationships are typically embedded in broader networks of ecological interactions, which have the potential to shape parasite evolutionary trajectories. Understanding this 'community context' of pathogen evolution is vital for wildlife, agricultural and human systems alike, as pathogens typically infect more than one host-and these hosts may have independent ecological relationships. Here, we introduce an eco-evolutionary model examining ecological feedback across a range of host-host interactions. Specifically, we analyse a model of the evolution of virulence of a parasite infecting two hosts exhibiting competitive, mutualistic or exploitative relationships. We first find that parasite specialism is necessary for inter-host interactions to impact parasite evolution. Furthermore, we find generally that increasing competition between hosts leads to higher shared parasite virulence while increasing mutualism leads to lower virulence. In exploitative host-host interactions, the particular form of parasite specialization is critical-for instance, specialization in terms of onward transmission, host tolerance or intra-host pathogen growth rate lead to distinct evolutionary outcomes under the same host-host interactions. Our work provides testable hypotheses for multi-host disease systems, predicts how changing interaction networks may impact virulence evolution and broadly demonstrates the importance of looking beyond pairwise relationships to understand evolution in realistic community contexts.

Host and antibiotic jointly select for greater virulence in Staphylococcus aureus.

Widespread antibiotic usage has resulted in the rapid evolution of drug-resistant bacterial pathogens and poses significant threats to public health. Resolving how pathogens respond to antibiotics under different contexts is critical for understanding disease emergence and evolution going forward. The impact of antibiotics has been demonstrated most directly through in vitro pathogen passaging experiments. Independent from antibiotic selection, interactions with hosts have also altered the evolutionary trajectories and fitness landscapes of pathogens, shaping infectious disease outcomes. However, it is unclear how interactions between hosts and antibiotics impact the evolution of pathogen virulence. Here, we evolved and re-sequenced Staphylococcus aureus , a major bacterial pathogen, varying exposure to host and antibiotics to tease apart the contributions of these selective pressures on pathogen adaptation. After 12 passages, S. aureus evolving in Caenorhabditis elegans nematodes exposed to a sub-minimum inhibitory concentration of antibiotic (oxacillin) became highly virulent, regardless of whether the ancestral pathogen was methicillin-resistant (MRSA) or methicillin-sensitive (MSSA). Host and antibiotic exposure selected for reduced drug susceptibility in MSSA lineages while increasing MRSA total growth outside hosts. We identified mutations in genes involved in complex regulatory networks linking virulence and metabolism, including codY , agr , and gdpP , suggesting that rapid adaptation to infect hosts may have pleiotropic effects. In particular, MSSA populations under selection from host and antibiotic accumulated mutations in the global regulator gene codY , which controls biofilm formation in S. aureus. These populations had indeed evolved more robust biofilms-a trait linked to both virulence and antibiotic resistance-suggesting evolution of one trait can confer multiple adaptive benefits. Despite evolving in similar environments, MRSA and MSSA populations proceeded on divergent evolutionary paths, with MSSA populations exhibiting more similarities across replicate populations. Our results underscore the importance of considering multiple and concurrent selective pressures as drivers of pervasive pathogen traits.

MSphere of Influence: Predicting the evolution of pathogen populations.

Tatum D. Mortimer works in the field of pathogen population genomics and evolution. In this mSphere of Influence article, she reflects on how "Frequency-dependent selection can forecast evolution in Streptococcus pneumoniae" by Azarian et al. and "Contingency, repeatability, and predictability in the evolution of a prokaryotic pangenome" by Beavan et al. made an impact on her by highlighting the ways in which genomic data can be used to predict pathogen evolution.

Real-time identification of epistatic interactions in SARS-CoV-2 from large genome collections

BackgroundThe emergence of the SARS-CoV-2 virus has highlighted the importance of genomic epidemiology in understanding the evolution of pathogens and guiding public health interventions. The Omicron variant in particular has underscored the role of epistasis in the evolution of lineages with both higher infectivity and immune escape, and therefore the necessity to update surveillance pipelines to detect them early on.ResultsIn this study, we apply a method based on mutual information between positions in a multiple sequence alignment, which is capable of scaling up to millions of samples. We show how it can reliably predict known experimentally validated epistatic interactions, even when using as little as 10,000 sequences, which opens the possibility of making it a near real-time prediction system. We test this possibility by modifying the method to account for the sample collection date and apply it retrospectively to multiple sequence alignments for each month between March 2020 and March 2023. We detected a cornerstone epistatic interaction in the Spike protein between codons 498 and 501 as soon as seven samples with a double mutation were present in the dataset, thus demonstrating the method’s sensitivity. We test the ability of the method to make inferences about emerging interactions by testing candidates predicted after March 2023, which we validate experimentally.ConclusionsWe show how known epistatic interaction in SARS-CoV-2 can be detected with high sensitivity, and how emerging ones can be quickly prioritized for experimental validation, an approach that could be implemented downstream of pandemic genome sequencing efforts.