Epistatic Interactions Research Articles

Systematic discovery of gene-environment interactions underlying the human plasma proteome in UK Biobank

Understanding how gene-environment interactions (GEIs) influence the circulating proteome could aid in biomarker discovery and validation. The presence of GEIs can be inferred from single nucleotide polymorphisms that associate with phenotypic variability - termed variance quantitative trait loci (vQTLs). Here, vQTL association studies are performed on plasma levels of 1463 proteins in 52,363 UK Biobank participants. A set of 677 independent vQTLs are identified across 568 proteins. They include 67 variants that lack conventional additive main effects on protein levels. Over 1100 GEIs are identified between 101 proteins and 153 environmental exposures. GEI analyses uncover possible mechanisms that explain why 13/67 vQTL-only sites lack corresponding main effects. Additional analyses also highlight how age, sex, epistatic interactions and statistical artefacts may underscore associations between genetic variation and variance heterogeneity. This study establishes the most comprehensive database yet of vQTLs and GEIs for the human proteome.

Network medicine-based epistasis detection in complex diseases: ready for quantum computing.

Most heritable diseases are polygenic. To comprehend the underlying genetic architecture, it is crucial to discover the clinically relevant epistatic interactions (EIs) between genomic single nucleotide polymorphisms (SNPs)(1-3). Existing statistical computational methods for EI detection are mostly limited to pairs of SNPs due to the combinatorial explosion of higher-order EIs. With NeEDL (network-based epistasis detection via local search), we leverage network medicine to inform the selection of EIs that are an order of magnitude more statistically significant compared to existing tools and consist, on average, of five SNPs. We further show that this computationally demanding task can be substantially accelerated once quantum computing hardware becomes available. We apply NeEDL to eight different diseases and discover genes (affected by EIs of SNPs) that are partly known to affect the disease, additionally, these results are reproducible across independent cohorts. EIs for these eight diseases can be interactively explored in the Epistasis Disease Atlas (https://epistasis-disease-atlas.com). In summary, NeEDL demonstrates the potential of seamlessly integrated quantum computing techniques to accelerate biomedical research. Our network medicine approach detects higher-order EIs with unprecedented statistical and biological evidence, yielding unique insights into polygenic diseases and providing a basis for the development of improved risk scores and combination therapies.

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.

Protein stability models fail to capture epistatic interactions of double point mutations.

There is strong interest in accurate methods for predicting changes in protein stability resulting from amino acid mutations to the protein sequence. Recombinant proteins must often be stabilized to be used as therapeutics or reagents, and destabilizing mutations are implicated in a variety of diseases. Due to increased data availability and improved modeling techniques, recent studies have shown advancements in predicting changes in protein stability when a single point mutation is made. Less focus has been directed toward predicting changes in protein stability when there are two or more mutations, despite the significance of mutation clusters for disease pathways and protein design studies. Here, we analyze the largest available dataset of double point mutation stability and benchmark several widely used protein stability models on this and other datasets. We identify a blind spot in how predictors are typically evaluated on multiple mutations, finding that, contrary to assumptions in the field, current stability models are unable to consistently capture epistatic interactions between double mutations. We observe one notable deviation from this trend, which is that epistasis-aware models provide marginally better predictions on stabilizing double point mutations. We develop an extension of the ThermoMPNN framework for double mutant modeling as well as a novel data augmentation scheme which mitigates some of the limitations in available datasets. Collectively, our findings indicate that current protein stability models fail to capture the nuanced epistatic interactions between concurrent mutations due to several factors, including training dataset limitations and insufficient model sensitivity.

Protein-truncating variant in APOL3 increases chronic kidney disease risk in epistasis with APOL1 risk alleles.

BACKGROUNDTwo coding alleles within the APOL1 gene, G1 and G2, found almost exclusively in individuals genetically similar to West African populations, contribute substantially to the pathogenesis of chronic kidney disease (CKD). The APOL gene cluster on chromosome 22 contains a total of 6 APOL genes that have arisen as a result of gene duplication.METHODSUsing a genome-first approach in the Penn Medicine BioBank, we identified 62 protein-altering variants in the 6 APOL genes with a minor allele frequency of >0.1% in a population of participants genetically similar to African reference populations and performed population-specific phenome-wide association studies.RESULTSWe identified rs1108978, a stop-gain variant in APOL3 (p.Q58*), to be significantly associated with increased CKD risk, even after conditioning on APOL1 G1/G2 carrier status. These findings were replicated in the Veterans Affairs Million Veteran Program and the All of Us Research Program. APOL3 p.Q58* was also significantly associated with a number of quantitative traits linked to CKD, including decreased kidney volume. This truncating variant contributed the most risk for CKD in patients monoallelic for APOL1 G1/G2, suggesting an epistatic interaction and a potential protective effect of wild-type APOL3 against APOL1-induced kidney disease.CONCLUSIONThis study demonstrates the utility of targeting population-specific variants in a genome-first approach, even in the context of well-studied gene-disease relationships.FUNDINGNational Heart, Lung, and Blood Institute (F30HL172382, R01HL169378, R01HL169458), Doris Duke Foundation (grant 2023-2024), National Institute of Biomedical Imaging and Bioengineering (P41EB029460), and National Center for Advancing Translational Sciences (UL1-TR-001878).

Genome scans for selection signatures identify candidate virulence genes for adaptation of the soybean cyst nematode to host resistance.

Plant pathogens are constantly under selection pressure for host resistance adaptation. Soybean cyst nematode (SCN, Heterodera glycines) is a major pest of soybean primarily managed through resistant cultivars; however, SCN populations have evolved virulence in response to selection pressures driven by repeated monoculture of the same genetic resistance. Resistance to SCN is mediated by multiple epistatic interactions between Rhg (for resistance to H. glycines) genes. However, the identity of SCN virulence genes that confer the ability to overcome resistance remains unknown. To identify candidate genomic regions showing signatures of selection for increased virulence, we conducted whole genome resequencing of pooled individuals (Pool-Seq) from two pairs of SCN populations adapted on soybeans with Peking-type (rhg1-a, rhg2, and Rhg4) resistance. Population differentiation and principal component analysis-based approaches identified approximately 0.72-0.79 million SNPs, the frequency of which showed potential selection signatures across multiple genomic regions. Chromosomes 3 and 6 between population pairs showed the greatest density of outlier SNPs with high population differentiation. Conducting multiple outlier detection tests to identify overlapping SNPs resulted in a total of 966 significantly differentiated SNPs, of which 285 exon SNPs were mapped to 97 genes. Of these, six genes encoded members of known stylet-secreted effector protein families potentially involved in host defence modulation including venom-allergen-like, annexin, glutathione synthetase, SPRYSEC, chitinase, and CLE effector proteins. Further functional analysis of identified candidate genes will provide new insights into the genetic mechanisms by which SCN overcomes soybean resistance and inform the development of molecular markers for rapidly screening the virulence profile of an SCN-infested field.

Assessing the role of mitonuclear interactions on mitochondrial function and organismal fitness in natural Drosophila populations

Abstract Mitochondrial function depends on the effective interactions between proteins and RNA encoded by the mitochondrial and nuclear genomes. Evidence suggests that both genomes respond to thermal selection and promote adaptation. However, the contribution of their epistatic interactions to life history phenotypes in the wild remains elusive. We investigated the evolutionary implications of mitonuclear interactions in a real-world scenario that sees populations adapted to different environments, altering their geographical distribution while experiencing flow and admixture. We created a Drosophila melanogaster panel with replicate native populations from the ends of the Australian east-coast cline, into which we substituted the mtDNA haplotypes that were either predominant or rare at each cline-end, thus creating putatively mitonuclear matched and mismatched populations. Our results suggest that mismatching may impact phenotype, with populations harboring the rarer mtDNA haplotype suffering a trade-off between aerobic capacity and key fitness aspects such as reproduction, growth, and survival. We discuss the significance of mitonuclear interactions as modulators of life history phenotypes in the context of future adaptation and population persistence.

Epistatic interaction has the reverse effects with its constitutive quantitative trait loci

Epistasis is one of important genetic components for a quantitative trait in plant. Eshed and Zamir found negative epistatic interactions of quantitative trait loci in Tomato first. We detected that positive (negative) QTLs generated mostly negative (positive) epistatic interactions on heading date in rice, and then proposed the hypothese that QTL epistasis plays a role of homeostasis in one of our recent papers. In order to further provide additional evidence, the effects of QTLs and their epistatic effects on two quantitative traits of plant height (ph) and thousand kernel weight (tkw) were analyzed in this study. The same regularity was verified again. We detected that positive ph QTLs and negative tkw QTLs always generated reverse epistatic effects, respectively. Moreover, high-order epistatic effects were estimated on these two traits. The sum of all epistatic effects would partially neutralize the additive of constitutive QTL effects. This feature of epistsis would be the mechanism for bionts to maintain homeostasis while the obstacle for human to achieve the pyramiding breeding objectives. More evidences are still being collected to support our assumption.

Epistatic interactions for seed yield and component traits in Ethiopian mustard (Brassica carinata)

Epistatic interactions for seed yield and component traits in Ethiopian mustard (Brassica carinata)

Divergent folding-mediated epistasis among unstable membrane protein variants.

Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally.

Divergent folding-mediated epistasis among unstable membrane protein variants

Many membrane proteins are prone to misfolding, which compromises their functional expression at the plasma membrane. This is particularly true for the mammalian gonadotropin-releasing hormone receptor GPCRs (GnRHR). We recently demonstrated that evolutionary GnRHR modifications appear to have coincided with adaptive changes in cotranslational folding efficiency. Though protein stability is known to shape evolution, it is unclear how cotranslational folding constraints modulate the synergistic, epistatic interactions between mutations. We therefore compared the pairwise interactions formed by mutations that disrupt the membrane topology (V276T) or tertiary structure (W107A) of GnRHR. Using deep mutational scanning, we evaluated how the plasma membrane expression of these variants is modified by hundreds of secondary mutations. An analysis of 251 mutants in three genetic backgrounds reveals that V276T and W107A form distinct epistatic interactions that depend on both the severity and the mechanism of destabilization. V276T forms predominantly negative epistatic interactions with destabilizing mutations in soluble loops. In contrast, W107A forms positive interactions with mutations in both loops and transmembrane domains that reflect the diminishing impacts of the destabilizing mutations in variants that are already unstable. These findings reveal how epistasis is remodeled by conformational defects in membrane proteins and in unstable proteins more generally.

Linking genetic markers and crop model parameters using neural networks to enhance genomic prediction of integrative traits.

Predicting the performance (yield or other integrative traits) of cultivated plants is complex because it involves not only estimating the genetic value of the candidates to selection, the interactions between the genotype and the environment (GxE) but also the epistatic interactions between genomic regions for a given trait, and the interactions between the traits contributing to the integrative trait. Classical Genomic Prediction (GP) models mostly account for additive effects and are not suitable to estimate non-additive effects such as epistasis. Therefore, the use of machine learning and deep learning methods has been previously proposed to model those non-linear effects. In this study, we propose a type of Artificial Neural Network (ANN) called Convolutional Neural Network (CNN) and compare it to two classical GP regression methods for their ability to predict an integrative trait of sorghum: aboveground fresh weight accumulation. We also suggest that the use of a crop growth model (CGM) can enhance predictions of integrative traits by decomposing them into more heritable intermediate traits. The results show that CNN outperformed both LASSO and Bayes C methods in accuracy, suggesting that CNN are better suited to predict integrative traits. Furthermore, the predictive ability of the combined CGM-GP approach surpassed that of GP without the CGM integration, irrespective of the regression method used. These results are consistent with recent works aiming to develop Genome-to-Phenotype models and advocate for the use of non-linear prediction methods, and the use of combined CGM-GP to enhance the prediction of crop performances.

Analysis of Genetic Architecture through Generation Mean Analysis for Yield and Yield Contributing Traits in Crosses of Indian Mustard (Brassica juncea)

Generation mean analysis was deployed to study the inheritance of 12 yield and its component traits in two crosses viz. PR-2009-6 × ALBELI (Family A) and PR-2009-6 × RGN-73 (Family B) of Indian mustard [Brassica juncea (L.) Czern& Coss] for parents P1 and P2, F1 , F2 , BC1 and BC2 generations. Analysis of variance showed presence of significant variability among families and also within families (among progenies) for various traits under study except for days to maturity, seeds per siliquae and siliquae length. Family A showed considerable interaction effects for siliquae on main raceme, number of seeds per siliquae and oil content. Family B displayed epistatic interactions for characters such as plant height, length of main raceme, siliquae on main raceme, siliquae length, oil content seed yield per plant and glucosinolate content. Fixable effects [d] and [i] were found important in the inheritance of plant height, seed yield per plant and glucosinolate content. Among non fixable effects, dominance [h] gene action was significantly important for determining length of main raceme, number of siliquae on main raceme, siliquae length, oil content, seed yield per plant and glucosinolate content. Interaction gene effects were found significant in controlling plant height, length of main raceme, number of secondary branches per plant, siliquae on main raceme, siliqua length,1000-seed weight, oil content, seed yield per plant and glucosinolate content. Thus, both main effects and epistatic effects were found important in controlling the various traits under study.

(Epi)mutation Rates and the Evolution of Composite Trait Architectures.

AbstractMutation rates vary widely along genomes and across inheritance systems. This suggests that complex traits-resulting from the contributions of multiple determinants-might be composite in terms of the underlying mutation rates. Here we investigate through mathematical modeling whether such a heterogeneity may drive changes in a trait's architecture, especially in fluctuating environments, where phenotypic instability can be beneficial. We first identify a convexity principle related to the shape of the trait's fitness function, setting conditions under which composite architectures should be adaptive or, conversely and more commonly, should be selected against. Simulations reveal, however, that applying this principle to realistic evolving populations requires taking into account pervasive epistatic interactions that take place in the system. Indeed, the fate of a mutation affecting the architecture depends on the (epi)genetic background, which itself depends on the current architecture in the population. We tackle this problem by borrowing the adaptive dynamics framework from evolutionary ecology-where it is routinely used to deal with such resident/mutant dependencies-and find that the principle excluding composite architectures generally prevails. Yet the predicted evolutionary trajectories will typically depend on the initial architecture, possibly resulting in historical contingencies. Finally, by relaxing the large population size assumption, we unexpectedly find that not only the strength of selection on a trait's architecture but also its direction depend on population size, revealing a new occurrence of the recently identified phenomenon coined "sign inversion."

Quantifying higher-order epistasis: beware the chimera.

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Identification of epistatic SNP combinations in rheumatoid arthritis using LAMPLINK and Japanese cohorts.

Genome-wide association studies have enabled the identification of important genetic factors in many trait studies. However, only a fraction of the heritability can be explained by known genetic factors, even in the most common diseases. Genetic loci combinations, or epistatic contributions expressed by combinations of single nucleotide polymorphisms (SNPs), have been argued to be one of the critical factors explaining some of the missing heritability, especially in oligogenic/polygenic diseases. Rheumatoid arthritis (RA) is a complex disease with more than 100 reported SNP associations, as well as various HLA haplotypes and amino acids; however, many associations between RA and inter-chromosomal SNP combinations are unknown. To discover novel associations of epistatic interactions with high odds ratios in RA, we applied the LAMPLINK method, a systematic enumerative procedure for identifying high-order SNP combinations, to a Japanese RA cohort (discovery cohort; 4024 patients with RA and 7731 controls). We validated the identified associations in a different Japanese cohort (validation cohort; 810 RA patients and 6303 controls). In this study, we identified 90 significant genetic associations in the discovery cohort. Among these, 74 (82.2%) associations were replicated in the validation cohort, and eight combinations were inter-chromosomal, all of which comprised rs7765379 or rs35265698 located in the HLA region. These two SNPs exhibited strong correlations with valine at amino acid position 11 in HLA-DRB1 (HLA-DRB1-11-Val). Finally, we discovered that rs9624 showed an association with RA through an epistatic interaction with HLA-DRB1-11-Val. Overall, LAMPLINK showed high reliability for identifying epistatic genetic contributions hidden in complex traits.

Mapping the genetic landscape establishing a tumor immune microenvironment favorable for anti-PD-1 response in mice and humans.

Identifying host genetic factors modulating immune checkpoint inhibitor (ICI) efficacy has been experimentally challenging because of variations in both host and tumor genomes, differences in the microbiome, and patient life exposures. Utilizing the Collaborative Cross (CC) multi-parent mouse genetic resource population, we developed an approach that fixes the tumor genomic configuration while varying host genetics. With this approach, we discovered that response to anti-PD-1 (aPD1) immunotherapy was significantly heritable in four distinct murine tumor models (H2 between 0.18-0.40). For the MC38 colorectal carcinoma system (H2 = 0.40), we mapped four significant ICI response quantitative trait loci (QTL) localized to mouse chromosomes (mChr) 5, 9, 15 and 17, and identified significant epistatic interactions between specific QTL pairs. Differentially expressed genes within these QTL were highly enriched for immune genes and pathways mediating allograft rejection and graft vs host disease. Using a cross species analytical approach, we found a core network of 48 genes within the four QTLs that showed significant prognostic value for overall survival in aPD1 treated human cohorts that outperformed all other existing validated immunotherapy biomarkers, especially in human tumors of the previously defined immune subtype 4. Functional blockade of two top candidate immune targets within the 48 gene network, GM-CSF and high affinity IL-2/IL-15 signaling, completely abrogated the MC38 tumor transcriptional response to aPD1 therapy in vivo. Thus, we have established a powerful cross species in vivo platform capable of uncovering host genetic factors that establish the tumor immune microenvironment configuration propitious for ICI response.

Exploring intra-allelic and inter-allelic gene interactions influencing seed yield and its components in inter-varietal crosses of Mungbean (Vigna radiata (L.) Wilczek)

Mungbean (Vigna radiata (L.) Wilczek) is a versatile legume widely cultivated for its nutritional value and adaptability. Meeting the increasing global demand for nutritious food requires the development of high-yielding varieties. Therefore, understanding the inheritance of yield and component traits is crucial for defining effective breeding strategies. In the present study, we aimed to investigate the genetic effects and interactions governing inheritance through generation mean analysis. The four crosses viz., IPM409-4×VGG18-002, IPM409-4×WGG42, COGG13-39×VGG16-058 and COGG13-39×VGG18-002 and their five generations (P1, P2, F1, F2, and F3) were evaluated for nine yield and yield component traits during 2023 summer season. The significance of additive, dominance, and epistatic components viz.,additive×additive [i] and dominance×dominance [l] of each trait was found to be different among all the crosses. Mungbean is a self-pollinated crop, so only fixable gene effects can be exploited for trait improvement. In the IPM409-4×VGG18-002 cross, all the traits exhibited additive or additive × additive gene action except for plant height and seed yield per plant (dominance). The scaling test was significant in IPM409-4×WGG42 cross for all the traits, except for the number of pods per cluster. Except for the number of branches per plant in which the dominance effect was evident, additive or additive×additive gene effects were observed for the other traits. In COGG13-39×VGG16-058 and COGG13-39×VGG18-002 crosses, all the yield traits recorded fixable (additive and additive×additive) gene effects except for number of pods per plant in COGG13-39×VGG18-002. Considering the results of all four crosses, gene actions that exhibit consistency across crosses revealed that epistatic interaction (additive×additive) significantly influenced the expression of various mung bean traits. Therefore, the later generation selection of short-duration segregants with high yield, bold seeds, and resistance to yellow mosaic disease from the above populations can be carried out to develop commercially valuable mung bean varieties.

Preliminary investigation of potential links between pigmentation variants and opioid analgesic effectiveness in horses during cerebrospinal fluid centesis

BackgroundThe pleiotropic effects of the melanocortin system show promise in overcoming limitations associated with large variations in opioid analgesic effectiveness observed in equine practice. Of particular interest is variation in the melanocortin-1-receptor (MC1R) gene, which dictates pigment type expression through its epistatic interaction with the agouti signalling protein (ASIP) gene. MC1R has previously been implicated in opioid efficacy in other species; however, this relationship is yet to be explored in horses. In this study, analgesic effectiveness was scored (1-3) based on noted response to dura penetration during the performance of cerebrospinal fluid centisis after sedation and tested for association with known genetic regions responsible for pigmentation variation in horses.ResultsThe chestnut phenotype was statistically significant (P < 0.05) in lowering analgesic effectiveness when compared to the bay base coat colour. The 11bp indel in ASIP known to cause the black base coat colour was not significant (P>0.05); however, six single nucleotide polymorphisms (SNPs) within the genomic region encoding the ASIP gene and one within MC1R were identified as being nominally significant (P<0.05) in association with opioid analgesic effectiveness. This included the location of the known e MC1R variant resulting in the chestnut coat colour.ConclusionsThe current study provides promising evidence for important links between pigmentation genes and opioid effectiveness in horses. The application of an easily identifiable phenotype indicating variable sensitivity presents a promising opportunity for accessible precision medicine in the use of analgesics and warrants further investigation.

Atomistic Prediction of Structures, Conformational Ensembles and Binding Energetics for the SARS-CoV-2 Spike JN.1, KP.2 and KP.3 Variants Using AlphaFold2 and Molecular Dynamics Simulations: Mutational Profiling and Binding Free Energy Analysis Reveal Epistatic Hotspots of the ACE2 Affinity and Immune Escape.

The most recent wave of SARS-CoV-2 Omicron variants descending from BA.2 and BA.2.86 exhibited improved viral growth and fitness due to convergent evolution of functional hotspots. These hotspots operate in tandem to optimize both receptor binding for effective infection and immune evasion efficiency, thereby maintaining overall viral fitness. The lack of molecular details on structure, dynamics and binding energetics of the latest FLiRT and FLuQE variants with the ACE2 receptor and antibodies provides a considerable challenge that is explored in this study. We combined AlphaFold2-based atomistic predictions of structures and conformational ensembles of the SARS-CoV-2 Spike complexes with the host receptor ACE2 for the most dominant Omicron variants JN.1, KP.1, KP.2 and KP.3 to examine the mechanisms underlying the role of convergent evolution hotspots in balancing ACE2 binding and antibody evasion. Using the ensemble-based mutational scanning of the spike protein residues and computations of binding affinities, we identified binding energy hotspots and characterized molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested that the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that enable to protect and restore ACE2 binding affinity while conferring beneficial immune escape. To examine immune escape mechanisms, we performed structure-based mutational profiling of the spike protein binding with several classes of antibodies that displayed impaired neutralization against BA.2.86, JN.1, KP.2 and KP.3. The results confirmed the experimental data that JN.1, KP.2 and KP.3 harboring the L455S and F456L mutations can significantly impair the neutralizing activity of class-1 monoclonal antibodies, while the epistatic effects mediated by F456L can facilitate the subsequent convergence of Q493E changes to rescue ACE2 binding. Structural and energetic analysis provided a rationale to the experimental results showing that BD55-5840 and BD55-5514 antibodies that bind to different binding epitopes can retain neutralizing efficacy against all examined variants BA.2.86, JN.1, KP.2 and KP.3. The results support the notion that evolution of Omicron variants may favor emergence of lineages with beneficial combinations of mutations involving mediators of epistatic couplings that control balance of high ACE2 affinity and immune evasion.