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Tobacco is an economically significant industrial crop and model plant for genetic research, yet little is known about its genetic architecture. Quantitative trait loci (QTL) analysis was performed for six agronomic traits on an F_7 population of 341 genotypes, parents, and F 1 plants using 1974 SSR markers across two environments. 31 QTLs contributing single-locus additive effects on 13 linkage groups (LGs) and 6 QTL pairs contributing epistatic effects on 6 LGs, were detected by the QTLNetwork 2.0 which was developed for the mixed-linear-model-based composite interval mapping (MCIM). Notably, 5 QTLs and 1 epistatic QTL pair were found to have pleiotropic effects on some genetically related traits. Moreover, the Broad sense heritability of the detected QTLs ranged from 1.05% to 43.33%, while genotype-by-environment interaction heritability spanned from 27.09% to 56.25%. Based on the results of QTL mapping, the potential superior lines for all or specific environments were designed and evaluated. Five major QTLs were finely dissected based on the tobacco reference genome of K326, and 31 candidate genes were predicted. This study offered new insights into the complicated genetic architecture and QTL resources for efficient breeding design for genetic improvement of agronomic traits in tobacco., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Double-strand breaks (DSBs) are genotoxic DNA lesions that pose significant threats to genomic stability, necessitating precise and efficient repair mechanisms to prevent cell death or mutations. DSBs are repaired through nonhomologous end-joining (NHEJ) or homology-directed repair (HDR), which includes homologous recombination (HR) and single-strand annealing (SSA). CtIP and Rif1 are conserved proteins implicated in DSB repair pathway choice, possibly through redundant roles in promoting DNA end-resection required for HDR. Although the roles of these proteins have been well-established in other organisms, the role of Rif1 and its potential redundancies with CtIP in Drosophila melanogaster remain elusive. To examine the roles of DmCtIP and DmRif1 in DSB repair, this study employed the direct repeat of white (DR-white) assay, tracking across indels by decomposition (TIDE) analysis, and P{wIw_2 kb 3'} assay to track repair outcomes in HR, NHEJ, and SSA, respectively. These experiments were performed in DmCtIPΔ/Δ single mutants, DmRif1Δ/Δ single mutants, and DmRif1Δ/Δ; DmCtIPΔ/Δ double mutants. This work demonstrates significant defects in both HR and SSA repair in DmCtIPΔ/Δ and DmRif1Δ/Δ single mutants. However, experiments in DmRif1Δ/Δ; DmCtIPΔ/Δ double mutants reveal that DmCtIP is epistatic to DmRif1 in promoting HDR. Overall, this study concludes that DmRif1 and DmCtIP do not perform their activities in a redundant pathway, but rather DmCtIP is the main driver in promoting HR and SSA, most likely through its role in end resection., Competing Interests: Conflicts of interest No authors have any conflicts of interest to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Genetics Society of America.)

Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified a large number of susceptibility genes, but most of AD heritability remains unexplained, implying the existence of additional genes. Furthermore, the majority of the GWAS have been conducted in people of European descent, and the genes important for AD susceptibility in people of African descent have been underexplored. In this hypothesis-generating prospective cohort study, we genotyped 191 African Americans (AAs) from three longitudinal cohorts on aging for the IgG3 allotype GM6, which is expressed exclusively in people of African descent, and assessed its interaction with IGHG, FCGRIIB, and HLA-DRB1 genes. Cox proportional hazards modeling showed that GM6 by itself was not significantly associated with AD development. However, there was evidence of epistatic interaction: The risk of developing AD associated with GM6 positivity was significantly different (p = 0.0098) in non-GM17/GM17 participants compared with GM 17/GM17 participants. Specifically, in non-GM17/GM17 participants, the risk of AD was over fourfold higher in GM6-positive participants compared with GM 6-negative participants (HR = 4.63). Similarly, risk of developing AD associated with GM6 positivity was marginally different in non-FCGRIIB TT participants compared with FCGRIIB TT participants. In non-FCGRIIB TT participants, the risk of developing AD was over twofold higher in GM6-positive participants compared with GM6-negative participants (HR = 2.44). This is the first report suggesting that immunoglobulin GM allotypes might play a role in AD etiology among AAs; however, since this was largely a hypothesis-generating study, replication in larger cohorts would be required to confirm this finding., (© 2024. The Author(s).)

Genetic epistasis is a fundamental concept in genetics that describes how interactions between genes determine phenotypic traits. To enhance students' understanding and practical application of genetic epistasis, this experiment is designed and conducted using gene mutations in the adenine biosynthesis pathway of Saccharomyces cerevisiae (baker's yeast). S. cerevisiae is a classic model organism for genetic teaching experiments. In its adenine biosynthesis pathway, a mutation in the ADE2 gene leads to the accumulation of the intermediate 5'-phosphoribosylaminoimidazole (AIR), causing the cells to appear red. However, if a gene upstream of ADE2 in the adenine biosynthesis pathway (such as ADE8 ) is defective, the red phenotype of yeast will disappear. Conversely, a defect in a gene downstream of ADE2 (such as ADE1 ) does not alter the red phenotype. Therefore, ADE8 is epistatic to ADE2. In this experiment, the CRISPR-Cas9 genome editing technology is employed, allowing students to perform single knockout of ade2 Δ, as well as double knockouts of ade2 Δ ade8 Δ and ade2 Δ ade1 Δ in S. cerevisiae . By observing the phenotypic changes in yeast mutants from white to red and back to white, students gain a profound understanding of the basic genetic theory of how genes determine phenotypes and the concept of epistasis in gene interactions. This experiment also enables students to master fundamental yeast genetic techniques, significantly enhancing their ability to design and conduct experiments in real research environments. This is of great significance for their future research work and academic development.

The interconnected processes of protein folding, mutations, epistasis, and evolution have all been the subject of extensive analysis throughout the years due to their significance for structural and evolutionary biology. The origin (molecular basis) of epistasis-the non-additive interactions between mutations-is still, nonetheless, unknown. The existence of a new perspective on protein folding, a problem that needs to be conceived as an 'analytic whole', will enable us to shed light on the origin of mutational epistasis at the simplest level-within proteins-while also uncovering the reasons why the genetic background in which they occur, a key component of molecular evolution, could foster changes in epistasis effects. Additionally, because mutations are the source of epistasis, more research is needed to determine the impact of post-translational modifications, which can potentially increase the proteome's diversity by several orders of magnitude, on mutational epistasis and protein evolvability. Finally, a protein evolution thermodynamic-based analysis that does not consider specific mutational steps or epistasis effects will be briefly discussed. Our study explores the complex processes behind the evolution of proteins upon mutations, clearing up some previously unresolved issues, and providing direction for further research., Competing Interests: Declarations Conflict of interest The author declares that there is no competing interest. Ethical approval Not applicable. Consent to participate Not applicable. Consent to publish Not applicable., (© 2024. European Biophysical Societies' Association.)

Genome-wide association studies typically considers epistatic interactions as a crucial factor in exploring complex diseases. However, the current methods primarily concentrate on the detection of two-order epistatic interactions, with flaws in accuracy. In this work, we introduce a novel method called Epi-SSA, which can be better utilized to detect high-order epistatic interactions. Epi-SSA draws inspiration from the sparrow search algorithm and optimizes the population based on multiple objective functions in each iteration, in order to be able to more precisely identify epistatic interactions. To evaluate its performance, we conducted a comprehensive comparison between Epi-SSA and seven other methods using five simulation datasets: DME 100, DNME 100, DME 1000, DNME 1000 and DNME3 100. The DME 100 dataset encompasses eight second-order epistasis disease models with marginal effects, each comprising 100 simulated data instances, featuring 100 SNPs per instance, alongside 800 case and 800 control samples. The DNME 100 encompasses eight second-order epistasis disease models without marginal effects and retains other properties consistent with DME 100. Experiments on the DME 100 and DNME 100 datasets were designed to evaluate the algorithms' capacity to detect epistasis across varying disease models. The DME 1000 and DNME 1000 datasets extend the complexity with 1000 SNPs per simulated data instance, while retaining other properties consistent with DME 100 and DNME 100. These experiments aimed to gauge the algorithms' adaptability in detecting epistasis as the number of SNPs in the data increases. The DNME3 100 dataset introduces a higher level of complexity with six third-order epistasis disease models, otherwise paralleling the structure of DNME 100, serving to test the algorithms' proficiency in identifying higher-order epistasis. The highest average F-measures achieved by the seven other existing methods on the five datasets are 0.86, 0.86, 0.41, 0.56, and 0.79 respectively, while the average F-measures of Epi-SSA on the five datasets are 0.92, 0.97, 0.79, 0.86, and 0.97 respectively. The experimental results demonstrate that the Epi-SSA algorithm outperforms other methods in a variety of epistasis detection tasks. As the number of SNPs in the data set increases and the order of epistasis rises, the advantages of the Epi-SSA algorithm become increasingly pronounced. In addition, we applied Epi-SSA to the analysis of the WTCCC dataset, uncovering numerous genes and gene pairs that might play a significant role in the pathogenesis of seven complex diseases. It is worthy of note that some of these genes have been relatedly reported in the Comparative Toxicogenomics Database (CTD). Epi-SSA is a potent tool for detecting epistatic interactions, which aids us in further comprehending the pathogenesis of common and complex diseases. The source code of Epi-SSA can be obtained at https://osf.io/6sqwj/., Competing Interests: NO authors have competing interests., (Copyright: © 2024 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The loss of a single chromosome in a diploid organism halves the dosage of many genes and is usually accompanied by a substantial decrease in fitness. We asked whether this decrease simply reflects the joint damage caused by individual gene dosage deficiencies. We measured the fitness effects of single heterozygous gene deletions in yeast and combined them for each chromosome. This predicted a negative growth rate, that is, lethality, for multiple monosomies. However, monosomic strains remained alive and grew as if much (often most) of the damage caused by single mutations had disappeared, revealing an exceptionally large and positive epistatic component of fitness. We looked for functional explanations by analyzing the transcriptomes. There was no evidence of increased (compensatory) gene expression on the monosomic chromosomes. Nor were there signs of the cellular stress response that would be expected if monosomy led to protein destabilization and thus cytotoxicity. Instead, all monosomic strains showed extensive upregulation of genes encoding ribosomal proteins, but in an indiscriminate manner that did not correspond to their altered dosage. This response did not restore the stoichiometry required for efficient biosynthesis, which probably became growth limiting, making all other mutation-induced metabolic defects much less important. In general, the modular structure of the cell leads to an effective fragmentation of the total mutational load. Defects outside the module(s) currently defining fitness lose at least some of their relevance, producing the epiphenomenon of positive interactions between individually negative effects., Competing Interests: HT, KT, AP, MM, RK No competing interests declared, (© 2023, Tutaj et al.)

Our knowledge of the contribution of genetic interactions ( epistasis ) to variation in human complex traits remains limited, partly due to the lack of efficient, powerful, and interpretable algorithms to detect interactions. Recently proposed approaches for set-based association tests show promise in improving the power to detect epistasis by examining the aggregated effects of multiple variants. Nevertheless, these methods either do not scale to large Biobank data sets or lack interpretability. We propose QuadKAST, a scalable algorithm focused on testing pairwise interaction effects ( quadratic effects ) within small to medium-sized sets of genetic variants (window size ≤100) on a trait and provide quantified interpretation of these effects. Comprehensive simulations show that QuadKAST is well-calibrated. Additionally, QuadKAST is highly sensitive in detecting loci with epistatic signals and accurate in its estimation of quadratic effects. We applied QuadKAST to 52 quantitative phenotypes measured in ≈300,000 unrelated white British individuals in the UK Biobank to test for quadratic effects within each of 9515 protein-coding genes. We detect 32 trait-gene pairs across 17 traits and 29 genes that demonstrate statistically significant signals of quadratic effects (accounting for the number of genes and traits tested). Across these trait-gene pairs, the proportion of trait variance explained by quadratic effects is comparable to additive effects, with five pairs having a ratio >1. Our method enables the detailed investigation of epistasis on a large scale, offering new insights into its role and importance., (© 2024 Fu et al.; Published by Cold Spring Harbor Laboratory Press.)

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).

Predicting how new mutations alter phenotypes is difficult because mutational effects vary across genotypes and environments. Recently discovered global epistasis, in which the fitness effects of mutations scale with the fitness of the background genotype, can improve predictions, but how the environment modulates this scaling is unknown. We measured the fitness effects of ~100 insertion mutations in 42 strains of Saccharomyces cerevisiae in six laboratory environments and found that the global epistasis scaling is nearly invariant across environments. Instead, the environment tunes one global parameter, the background fitness at which most mutations switch sign. As a consequence, the distribution of mutational effects is predictable across genotypes and environments. Our results suggest that the effective dimensionality of genotype-to-phenotype maps across environments is surprisingly low.

Sonic hedgehog (Shh) signaling regulates embryonic morphogenesis utilizing the primary cilium, the cell's antenna, which acts as a signaling hub. Fuz, an effector of planar cell polarity signaling, regulates Shh signaling by facilitating cilia formation, and the G protein-coupled receptor 161 (Gpr161) is a negative regulator of Shh signaling. The range of phenotypic malformations observed in mice bearing mutations in either of the genes encoding these proteins is similar; however, their functional relationship has not been previously explored. This study identified the genetic and biochemical linkage between Fuz and Gpr161 in mouse neural tube development. Fuz was found to be genetically epistatic to Gpr161 with respect to regulation of Shh signaling in mouse neural tube development. The Fuz protein biochemically interacts with Gpr161, and Fuz regulates Gpr161-mediated ciliary localization, a process that might utilize β-arrestin 2. Our study characterizes a previously unappreciated Gpr161-Fuz axis that regulates Shh signaling during mouse neural tube development., Competing Interests: Competing interests R.H.F. and B.J.W. participated in TeratOmic Consulting LLC, a now-defunct consulting company. R.H.F. serves on the editorial board for the journal Reproductive and Developmental Medicine and receives travel funds to attend editorial board meetings. All other authors have no conflict of interest to declare., (© 2024. Published by The Company of Biologists Ltd.)

We introduce a data-driven epistatic model of protein evolution, capable of generating evolutionary trajectories spanning very different time scales reaching from individual mutations to diverged homologs. Our in silico evolution encompasses random nucleotide mutations, insertions and deletions, and models selection using a fitness landscape, which is inferred via a generative probabilistic model for protein families. We show that the proposed framework accurately reproduces the sequence statistics of both short-time (experimental) and long-time (natural) protein evolution, suggesting applicability also to relatively data-poor intermediate evolutionary time scales, which are currently inaccessible to evolution experiments. Our model uncovers a highly collective nature of epistasis, gradually changing the fitness effect of mutations in a diverging sequence context, rather than acting via strong interactions between individual mutations. This collective nature triggers the emergence of a long evolutionary time scale, separating fast mutational processes inside a given sequence context, from the slow evolution of the context itself. The model quantitatively reproduces epistatic phenomena such as contingency and entrenchment, as well as the loss of predictability in protein evolution observed in deep mutational scanning experiments of distant homologs. It thereby deepens our understanding of the interplay between mutation and selection in shaping protein diversity and functions, allows one to statistically forecast evolution, and challenges the prevailing independent-site models of protein evolution, which are unable to capture the fundamental importance of epistasis., Competing Interests: Competing interests statement:The authors declare no competing interest.

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., (© 2024. The Author(s), under exclusive licence to The Japan Society of Human Genetics.)

Epistasis refers to nonallelic interaction between genes that cause bias in estimates of genetic parameters for a phenotype with interactions of two or more genes affecting the same trait. Partitioning of epistatic effects allows true estimation of the genetic parameters affecting phenotypes. Multigenic variation plays a central role in the evolution of complex characteristics, among which pleiotropy, where a single gene affects several phenotypic characters, has a large influence. While pleiotropic interactions provide functional specificity, they increase the challenge of gene discovery and functional analysis. Overcoming pleiotropy-based phenotypic trade-offs offers potential for assisting breeding for complex traits. Modelling higher order nonallelic epistatic interaction, pleiotropy and non-pleiotropy-induced variation, and genotype × environment interaction in genomic selection may provide new paths to increase the productivity and stress tolerance for next generation of crop cultivars. Advances in statistical models, software and algorithm developments, and genomic research have facilitated dissecting the nature and extent of pleiotropy and epistasis. We overview emerging approaches to exploit positive (and avoid negative) epistatic and pleiotropic interactions in a plant breeding context, including developing avenues of artificial intelligence, novel exploitation of large-scale genomics and phenomics data, and involvement of genes with minor effects to analyse epistatic interactions and pleiotropic quantitative trait loci, including missing heritability., (© 2024 The Author(s). Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Recent work from Nguyen et al. unveils massively parallel measurements of epistatic interactions between two enzymes, dihydrofolate reductase and thymidylate synthase, in their natural cellular context. Almost 3000 mutations of DHFR in three TYMS backgrounds reveal a complex interaction network. The authors capture much of this complexity using a simple model., Competing Interests: Declaration of interests The author declares no competing interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., (© 2024. The Author(s).)

Long-lived PFKFB3-expressing β-cells are dysfunctional partly because of prevailing glycolysis that compromises metabolic coupling of insulin secretion. Their accumulation in type 2 diabetes (T2D) appears to be related to the loss of apoptotic competency of cell fitness competition that maintains islet function by favoring constant selection of healthy "winner" cells. To investigate how PFKFB3 can disguise the competitive traits of dysfunctional "loser" β-cells, we analyzed the overlap between human β-cells with bona fide "loser signature" across diabetes pathologies using the HPAP scRNA-seq and spatial transcriptomics of PFKFB3-positive β-cells from nPOD T2D pancreata. The overlapping transcriptional profile of "loser" β-cells was represented by down-regulated ribosomal biosynthesis and genes encoding for mitochondrial respiration. PFKFB3-positive "loser" β-cells had the reduced expression of HLA class I and II genes. Gene-gene interaction analysis revealed that PFKFB3 rs1983890 can interact with the anti-apoptotic gene MAIP1 implicating positive epistasis as a mechanism for prolonged survival of "loser" β-cells in T2D. Inhibition of PFKFB3 resulted in the clearance of dysfunctional "loser" β-cells leading to restored glucose tolerance in the mouse model of T2D., (© 2024 Raval et al.)

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., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Trust is an essential human trait. Although research suggests that the interplay between oxytocinergic and dopaminergic systems affects trust formation, little research has focused on epistatic (i.e., gene by gene) interaction effects of oxytocin- and dopamine-related genes on trust. Using a sample of 348 adults (114 men), we aimed to investigate the associations between genetic variants in oxytocin- and dopamine-related genes and the general, neighborhood, and institutional trust with consideration of sex differences. Three-way interaction between oxytocin-related gene genotypes, dopamine-related genotypes, and sex was found for the oxytocin receptor gene (OXTR)rs1042778 and the Catechol-O-Methyltransferase gene (COMT) rs4680 genotypes (p = 0.02) and for OXTR rs2254298 and the dopamine D2 receptor gene (DRD2) rs1800497 genotypes (p = 0.01). Further sex-stratified analyses revealed that the interaction between OXTR rs1042778 and COMT rs4680 genotypes was associated with neighborhood trust among men (p = 0.0007). Also, the interaction between OXTR rs2254298 and DRD2 rs1800497 genotypes was associated with institutional trust among men (p = 0.005). Post-hoc analyses found that men with OXTR rs1042778 TG/TT and COMT rs4680 GG genotypes reported higher neighborhood trust than those with GG + AG/AA (B = 13.49, SE = 4.68, p = 0.02), TG/TT + AG/AA (B = 23.00, SE = 5.99, p = 0.001), and GG + GG (B = 18.53, SE = 5.25, p = 0.003). Similarly, men with OXTR rs2254298 AG/AA and DRD2 rs1800497 CC genotypes showed higher institutional trust than those with AG/AA + TT/TC (B = 15.67, SE = 5.30, p = 0.02). We could not find any interacting associations among women. While we note that our sample size and candidate gene approach have a potential risk of chance findings, our study provides an important foundation toward further exploration of sex-specific epistatic interaction effects of oxytocin- and dopamine-related genes on trust, indicating the importance of both systems in trust formation., Competing Interests: There are no conflicts of interest to declare., (Copyright: © 2024 Koyama et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data., Methods: The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions., Results: The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes., Conclusions: Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary., Competing Interests: Declaration of competing interest The authors report no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

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 the molecular basis underlying epistatic couplings between convergent mutational hotspots. The results suggested the existence of epistatic interactions between convergent mutational sites at L455, F456, Q493 positions that 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.

How complex are the rules by which a protein's sequence determines its function? High-order epistatic interactions among residues are thought to be pervasive, suggesting an idiosyncratic and unpredictable sequence-function relationship. But many prior studies may have overestimated epistasis, because they analyzed sequence-function relationships relative to a single reference sequence-which causes measurement noise and local idiosyncrasies to snowball into high-order epistasis-or they did not fully account for global nonlinearities. Here we present a reference-free method that jointly infers specific epistatic interactions and global nonlinearity using a bird's-eye view of sequence space. This technique yields the simplest explanation of sequence-function relationships and is more robust than existing methods to measurement noise, missing data, and model misspecification. We reanalyze 20 experimental datasets and find that context-independent amino acid effects and pairwise interactions, along with a simple nonlinearity to account for limited dynamic range, explain a median of 96% of phenotypic variance and over 92% in every case. Only a tiny fraction of genotypes are strongly affected by higher-order epistasis. Sequence-function relationships are also sparse: a miniscule fraction of amino acids and interactions account for 90% of phenotypic variance. Sequence-function causality across these datasets is therefore simple, opening the way for tractable approaches to characterize proteins' genetic architecture., (© 2024. The Author(s).)

Epistasis is caused by genetic interactions among mutations that affect fitness. To characterize properties and potential mechanisms of epistasis, we engineered eight double mutants that combined mutations from the rho and rpoB genes of Escherichia coli. The two genes encode essential functions for transcription, and the mutations in each gene were chosen because they were beneficial for adaptation to thermal stress (42.2 °C). The double mutants exhibited patterns of fitness epistasis that included diminishing returns epistasis at 42.2 °C, stronger diminishing returns between mutations with larger beneficial effects and both negative and positive (sign) epistasis across environments (20.0 °C and 37.0 °C). By assessing gene expression between single and double mutants, we detected hundreds of genes with gene expression epistasis. Previous work postulated that highly connected hub genes in coexpression networks have low epistasis, but we found the opposite: hub genes had high epistasis values in both coexpression and protein-protein interaction networks. We hypothesized that elevated epistasis in hub genes reflected that they were enriched for targets of Rho termination but that was not the case. Altogether, gene expression and coexpression analyses revealed that thermal adaptation occurred in modules, through modulation of ribonucleotide biosynthetic processes and ribosome assembly, the attenuation of expression in genes related to heat shock and stress responses, and with an overall trend toward restoring gene expression toward the unstressed state., (© The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution.)

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Collesano et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This study offers insights into the genetic and biological connections between nine common metabolic diseases using data from genome-wide association studies. Our goal is to unravel the genetic interactions and biological pathways of these complex diseases, enhancing our understanding of their genetic architecture. We employed a range of advanced analytical techniques to explore the genetic correlations and shared genetic variants of these diseases. These methods include Linked Disequilibrium Score Regression, High-Definition Likelihood (HDL), genetic analysis combining multiplicity and annotation (GPA), two-sample Mendelian randomization analyses, analysis under the multiplicity-complex null hypothesis (PLACO), and Functional mapping and annotation of genetic associations (FUMA). Additionally, Bayesian co-localization analyses were used to examine associations of specific loci across traits. Our study discovered significant genomic correlations and shared loci, indicating complex genetic interactions among these metabolic diseases. We found several shared single nucleotide variants and risk loci, notably highlighting the role of the immune system and endocrine pathways in these diseases. Particularly, rs2476601 and its associated gene PTPN22 appear to play a crucial role in the connection between type 2 diabetes mellitus, hypothyroidism/mucous oedema and hypoglycaemia. These findings enhance our understanding of the genetic underpinnings of these diseases and open new potential avenues for targeted therapeutic and preventive strategies. The results underscore the importance of considering pleiotropic effects in deciphering the genetic architecture of complex diseases, especially metabolic ones., (© 2024 The Author(s). Journal of Cellular and Molecular Medicine published by Foundation for Cellular and Molecular Medicine and John Wiley & Sons Ltd.)

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."

Cognitive dysfunctions are core-enduring symptoms of schizophrenia, with important sex-related differences. Genetic variants of the DTBPN1 gene associated with reduced dysbindin-1 protein (Dys) expression negatively impact cognitive functions in schizophrenia through a functional epistatic interaction with Catechol-O-methyltransferase (COMT). Dys is involved in the trafficking of dopaminergic receptors, crucial for prefrontal cortex (PFC) signaling regulation. Moreover, dopamine signaling is modulated by estrogens via inhibition of COMT expression. We hypothesized a sex dimorphism in Dys-related cognitive functions dependent on COMT and estrogen levels. Our multidisciplinary approach combined behavioral-molecular findings on genetically modified mice, human postmortem Dys expression data, and in vivo fMRI during a working memory task performance. We found cognitive impairments in male mice related to genetic variants characterized by reduced Dys protein expression (p Bonferroni = 0.0001), as well as in male humans through a COMT/Dys functional epistatic interaction involving PFC brain activity during working memory (t(23) = -3.21; p FDR  = 0.004). Dorsolateral PFC activity was associated with lower working memory performance in males only (p = 0.04). Also, male humans showed decreased Dys expression in dorsolateral PFC during adulthood (p FDR = 0.05). Female Dys mice showed preserved cognitive performances with deficits only with a lack of estrogen tested in an ovariectomy model (p Bonferroni = 0.0001), suggesting that genetic variants reducing Dys protein expression could probably become functional in females when the protective effect of estrogens is attenuated, i.e., during menopause. Overall, our results show the differential impact of functional variants of the DTBPN1 gene interacting with COMT on cognitive functions across sexes in mice and humans, underlying the importance of considering sex as a target for patient stratification and precision medicine in schizophrenia., (© 2024. The Author(s).)

The crucial molecular factors that shape the interfaces of lipid-binding proteins with their target ligands and surfaces remain unknown due to the complex makeup of biological membranes. Cholesterol, the major modulator of bilayer structure in mammalian cell membranes, is recognized by various proteins, including the well-studied cholesterol-dependent cytolysins. Here, we use in vitro evolution to investigate the molecular adaptations that preserve the cholesterol specificity of perfringolysin O, the prototypical cholesterol-dependent cytolysin from Clostridium perfringens. We identify variants with altered membrane-binding interfaces whose cholesterol-specific activity exceeds that of the wild-type perfringolysin O. These novel variants represent alternative evolutionary outcomes and have mutations at conserved positions that can only accumulate when epistatic constraints are alleviated. Our results improve the current understanding of the biochemical malleability of the surface of a lipid-binding protein., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans., (© 2024. Springer Nature Limited.)

Vascular bundles transport water and photosynthate to all organs, and increased bundle number contributes to crop lodging resistance. However, the regulation of vascular bundle formation is poorly understood in the Arabidopsis stem. We report a novel semi-dominant mutant with high vascular activity, hva-d, showing increased vascular bundle number and enhanced cambium proliferation in the stem. The activation of a C2H2 zinc finger transcription factor, AT5G27880/HVA, is responsible for the hva-d phenotype. Genetic, biochemical, and fluorescent microscopic analyses were used to dissect the functions of HVA. HVA functions as a repressor and interacts with TOPLESS via the conserved Ethylene-responsive element binding factor-associated Amphiphilic Repression motif. In contrast to the HVA activation line, knockout of HVA function with a CRISPR-Cas9 approach or expression of HVA fused with an activation domain VP16 (HVA-VP16) resulted in fewer vascular bundles. Further, HVA directly regulates the expression of the auxin transport efflux facilitator PIN1, as a result affecting auxin accumulation. Genetics analysis demonstrated that PIN1 is epistatic to HVA in controlling bundle number. This research identifies HVA as a positive regulator of vascular initiation through negatively modulating auxin transport and sheds new light on the mechanism of bundle formation in the stem., (© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.)

Chromosome substitution lines (CSLs) are tentatively supreme resources to investigate non-allelic genetic interactions. However, the difficulty of generating such lines in most species largely yielded imperfect CSL panels, prohibiting a systematic dissection of epistasis. Here, we present the development and use of a unique and complete panel of CSLs in Arabidopsis thaliana, allowing the full factorial analysis of epistatic interactions. A first comparison of reciprocal single chromosome substitutions revealed a dependency of QTL detection on different genetic backgrounds. The subsequent analysis of the complete panel of CSLs enabled the mapping of the genetic interactors and identified multiple two- and three-way interactions for different traits. Some of the detected epistatic effects were as large as any observed main effect, illustrating the impact of epistasis on quantitative trait variation. We, therefore, have demonstrated the high power of detection and mapping of genome-wide epistasis, confirming the assumed supremacy of comprehensive CSL sets., (© 2024. The Author(s).)

To elucidate the genetic basis of complex diseases, it is crucial to discover the single-nucleotide polymorphisms (SNPs) contributing to disease susceptibility. This is particularly challenging for high-order SNP epistatic interactions (HEIs), which exhibit small individual effects but potentially large joint effects. These interactions are difficult to detect due to the vast search space, encompassing billions of possible combinations, and the computational complexity of evaluating them. This study proposes a novel explicit-encoding-based multitasking harmony search algorithm (MTHS-EE-DHEI) specifically designed to address this challenge. The algorithm operates in three stages. First, a harmony search algorithm is employed, utilizing four lightweight evaluation functions, such as Bayesian network and entropy, to efficiently explore potential SNP combinations related to disease status. Second, a G-test statistical method is applied to filter out insignificant SNP combinations. Finally, two machine learning-based methods, multifactor dimensionality reduction (MDR) as well as random forest (RF), are employed to validate the classification performance of the remaining significant SNP combinations. This research aims to demonstrate the effectiveness of MTHS-EE-DHEI in identifying HEIs compared to existing methods, potentially providing valuable insights into the genetic architecture of complex diseases. The performance of MTHS-EE-DHEI was evaluated on twenty simulated disease datasets and three real-world datasets encompassing age-related macular degeneration (AMD), rheumatoid arthritis (RA), and breast cancer (BC). The results demonstrably indicate that MTHS-EE-DHEI outperforms four state-of-the-art algorithms in terms of both detection power and computational efficiency. The source code is available at https://github.com/shouhengtuo/MTHS-EE-DHEI.git ., (© 2024. International Association of Scientists in the Interdisciplinary Areas.)

Background Genetic interactions are known to play an important role in the missing heritability problem for type 2 diabetes mellitus (T2DM). Interactions between enhancers and their target genes play important roles in gene regulation and disease pathogenesis. In the present study, we aimed to identify genetic interactions between enhancers and their target genes associated with T2DM. Methods We performed genetic interaction analyses of enhancers and protein-coding genes for T2DM in 2,696 T2DM patients and 3,548 controls of European ancestry. A linear regression model was used to identify single nucleotide polymorphism (SNP) pairs that could affect the expression of the protein-coding genes. Differential expression analyses were used to identify differentially expressed susceptibility genes in diabetic and nondiabetic subjects. Results We identified one SNP pair, rs4947941×rs7785013, significantly associated with T2DM (combined P=4.84×10−10). The SNP rs4947941 was annotated as an enhancer, and rs7785013 was located in the epidermal growth factor receptor (EGFR) gene. This SNP pair was significantly associated with EGFR expression in the pancreas (P=0.033), and the minor allele “A” of rs7785013 decreased EGFR gene expression and the risk of T2DM with an increase in the dosage of “T” of rs4947941. EGFR expression was significantly upregulated in T2DM patients, which was consistent with the effect of rs4947941×rs7785013 on T2DM and EGFR expression. A functional validation study using the Mouse Genome Informatics (MGI) database showed that EGFR was associated with diabetes-relevant phenotypes. Conclusion Genetic interaction analyses of enhancers and protein-coding genes suggested that EGFR may be a novel susceptibility gene for T2DM.

Ucc1, an F-box motif-containing protein of Saccharomyces cerevisiae encoded by UCC1 regulates energy metabolism through proteasomal degradation of citrate synthase Cit2 and inactivation of the glyoxylate cycle when glucose is present as the main carbon source in the growth medium. Rrm3, a Pif1 family DNA helicase, encoded by RRM3 regulates the movement of the replication forks during the DNA replication process. Here in this study, we present evidence of binary genetic interaction between both the genes, UCC1 and RRM3, that determine the growth rate, cell morphology, cell size, apoptosis, and stress response. The absence of both genes UCC1 and RRM3 leads to altered cell morphology, increased growth rate, utilization of alternate carbon sources, resistance to hydrogen peroxide, and susceptibility to acetic acid-induced apoptosis. Further, the genetic interaction network analysis shows both the genes UCC1 and RRM3 interaction through the SGS1 and cross-link among metabolic, glyoxylate, DNA replication, and retrograde signaling pathways., (© 2024 Wiley Periodicals LLC.)

Polycystic ovary syndrome (PCOS) is an endocrine and metabolic disorder with high prevalence in women around the world. The identification of single-nucleotide polymorphisms (SNPs) through genome-wide association studies has classified it as a polygenic disease. Most studies have independently evaluated the contribution of each SNP to the risk of PCOS. Few studies have assessed the effect of epistasis among the identified SNPs. Therefore, this exploratory study aimed to evaluate the interaction of 27 SNPs identified as risk candidates and their contribution to the pathogenesis of PCOS. The study population included 49 control women and 49 women with PCOS with a normal BMI. Genotyping was carried out through the MassARRAY iPLEX single-nucleotide polymorphism typing platform. Using the multifactor dimensionality reduction (MDR) method, the interaction between SNPs was evaluated. The analysis showed that the best interaction model ( p < 0.0001) was composed of three loci (rs11692782- FSHR , rs2268361- FSHR , and rs4784165- TOX3 ). Furthermore, a tendency towards synergy was evident between rs2268361 and the SNPs rs7371084-rs11692782-rs4784165, as well as a redundancy in rs7371084-rs11692782-rs4784165. This pilot study suggests that epistasis may influence PCOS pathophysiology. Large-scale analysis is needed to deepen our understanding of its impact on this complex syndrome affecting thousands of women.

Background: The 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., Results: In 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., Conclusions: We 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., (© 2024. The Author(s).)

Mutations in protein active sites can dramatically improve function. The active site, however, is densely packed and extremely sensitive to mutations. Therefore, some mutations may only be tolerated in combination with others in a phenomenon known as epistasis. Epistasis reduces the likelihood of obtaining improved functional variants and dramatically slows natural and lab evolutionary processes. Research has shed light on the molecular origins of epistasis and its role in shaping evolutionary trajectories and outcomes. In addition, sequence- and AI-based strategies that infer epistatic relationships from mutational patterns in natural or experimental evolution data have been used to design functional protein variants. In recent years, combinations of such approaches and atomistic design calculations have successfully predicted highly functional combinatorial mutations in active sites. These were used to design thousands of functional active-site variants, demonstrating that, while our understanding of epistasis remains incomplete, some of the determinants that are critical for accurate design are now sufficiently understood. We conclude that the space of active-site variants that has been explored by evolution may be expanded dramatically to enhance natural activities or discover new ones. Furthermore, design opens the way to systematically exploring sequence and structure space and mutational impacts on function, deepening our understanding and control over protein activity., Competing Interests: Competing interests statement:R.L.-S. and S.J.F. are named inventors on patents relating to methods and designs described in the manuscript, and S.J.F. consults on the application of protein design methods.

Studies of bacterial adaptation and evolution are hampered by the difficulty of measuring traits such as virulence, drug resistance, and transmissibility in large populations. In contrast, it is now feasible to obtain high-quality complete assemblies of many bacterial genomes thanks to scalable high-accuracy long-read sequencing technologies. To exploit this opportunity, we introduce a phenotype- and alignment-free method for discovering coselected and epistatically interacting genomic variation from genome assemblies covering both core and accessory parts of genomes. Our approach uses a compact colored de Bruijn graph to approximate the intragenome distances between pairs of loci for a collection of bacterial genomes to account for the impacts of linkage disequilibrium (LD). We demonstrate the versatility of our approach to efficiently identify associations between loci linked with drug resistance and adaptation to the hospital niche in the major human bacterial pathogens Streptococcus pneumoniae and Enterococcus faecalis ., (© 2024 Kuronen et al.; Published by Cold Spring Harbor Laboratory Press.)

Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP-SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP-SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP-SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP-SNP interaction detection accuracy., (© 2024. The Author(s).)

Coxsackievirus A24 (CV-A24) is a human enterovirus that causes acute flaccid paralysis. However, a Coxsackievirus A24 variant (CV-A24v) is the most common cause of eye infections. The causes of these variable pathogenicity and tissue tropism remain unclear. To elucidate the phylodynamics of CV-A24 and CV-A24v, we analyzed a dataset of 66 strains using Bayesian phylodynamic approach, along with detailed sequence variation and epistatic analyses. Six CV-A24 strains available in GenBank and 60 CV-A24v strains, including 11 Taiwanese strains, were included in this study. The results revealed striking differences between CV-A24 and CV-A24v exhibiting long terminal branches in the phylogenetic tree, respectively. CV-A24v presented distinct ladder-like clustering, indicating immune escape mechanisms. Notably, 10 genetic recombination events in the 3D regions were identified. Furthermore, 11 missense mutation signatures were detected to differentiate CV-A24 and CV-A24v; among these mutations, the F810Y substitution may significantly affect the secondary structure of the GH loop of VP1 and subsequently affect the epitopes of the capsid proteins. In conclusion, this study provides critical insights into the evolutionary dynamics and epidemiological characteristics of CV-A24 and CV-A24v, and highlights the differences in viral evolution and tissue tropism.

Although previous studies have reported that pre-mRNA splicing factors (SFs) are involved in the repair of DNA double-strand breaks (DSBs) via homologous recombination (HR), their exact role in promoting HR remains poorly understood. Here, we showed that SART1, an SF upregulated in several types of cancer, promotes DSB end resection, an essential first step of HR. The resection-promoting function of SART1 requires phosphorylation at threonine 430 and 695 by ATM/ATR. SART1 is recruited to DSB sites in a manner dependent on transcription and its RS domain. SART1 is epistatic with BRCA1, a major HR factor, in the promotion of resection, especially transcription-associated resection in the G2 phase. SART1 and BRCA1 accumulate at DSB sites in an interdependent manner, and epistatically counteract the resection blockade posed by 53BP1 and RIF1. Furthermore, chromosome analysis demonstrated that SART1 and BRCA1 epistatically suppressed genomic alterations caused by DSB misrepair in the G2 phase. Collectively, these results indicate that SART1 and BRCA1 cooperatively facilitate resection of DSBs arising in transcriptionally active genomic regions in the G2 phase, thereby promoting faithful repair by HR, and suppressing genome instability., (© 2024. The Author(s).)

Protein engineering often targets binding pockets or active sites which are enriched in epistasis-nonadditive interactions between amino acid substitutions-and where the combined effects of multiple single substitutions are difficult to predict. Few existing sequence-fitness datasets capture epistasis at large scale, especially for enzyme catalysis, limiting the development and assessment of model-guided enzyme engineering approaches. We present here a combinatorially complete, 160,000-variant fitness landscape across four residues in the active site of an enzyme. Assaying the native reaction of a thermostable β-subunit of tryptophan synthase (TrpB) in a nonnative environment yielded a landscape characterized by significant epistasis and many local optima. These effects prevent simulated directed evolution approaches from efficiently reaching the global optimum. There is nonetheless wide variability in the effectiveness of different directed evolution approaches, which together provide experimental benchmarks for computational and machine learning workflows. The most-fit TrpB variants contain a substitution that is nearly absent in natural TrpB sequences-a result that conservation-based predictions would not capture. Thus, although fitness prediction using evolutionary data can enrich in more-active variants, these approaches struggle to identify and differentiate among the most-active variants, even for this near-native function. Overall, this work presents a large-scale testing ground for model-guided enzyme engineering and suggests that efficient navigation of epistatic fitness landscapes can be improved by advances in both machine learning and physical modeling., Competing Interests: Competing interests statement:The authors declare no competing interest.

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., (© 2024. The Author(s).)

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., (© The Author(s) 2024. Published by Oxford University Press.)

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine., (© 2024. The Author(s).)

Carotenoids are indispensable to plants and critical components of the human diet. The carotenoid metabolic pathway is conserved across plant species, but our understanding of the genetic basis of carotenoid variation remains limited for the seeds of most cereal crops. To address this issue, we systematically performed linkage and association mapping for eight carotenoid traits using six recombinant inbred line (RIL) populations. Single linkage mapping (SLM) and joint linkage mapping (JLM) identified 77 unique additive QTLs and 104 pairs of epistatic QTLs. Among these QTLs, we identified 22 overlapping hotspots of additive and epistatic loci, highlighting the important contributions of some QTLs to carotenoid levels through additive or epistatic mechanisms. A genome-wide association study based on all RILs detected 244 candidate genes significantly associated with carotenoid traits, 23 of which were annotated as carotenoid pathway genes. Effect comparisons suggested that a small number of loci linked to pathway genes have substantial effects on carotenoid variation in our tested populations, but many loci not associated with pathway genes also make important contributions to carotenoid variation. We identified ZmPTOX as the causal gene for a QTL hotspot (Q10/JLM10/GWAS019); this gene encodes a putative plastid terminal oxidase that produces plastoquinone-9 used by two enzymes in the carotenoid pathway. Natural variants in the promoter and second exon of ZmPTOX were found to alter carotenoid levels. This comprehensive assessment of the genetic mechanisms underlying carotenoid variation establishes a foundation for rewiring carotenoid metabolism and accumulation for efficient carotenoid biofortification., (© 2024 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Objective: Our primary objective was to investigate the variability of oxytocin (OT) and the GAMEN binding motif within the LNPEP oxytocinase in primates., Materials and Methods: We sequenced the LNPEP segment encompassing the GAMEN motif in 34 Platyrrhini species, with 21 of them also sequenced for the OT gene. Our dataset was supplemented with primate sequences of LNPEP, OT, and the oxytocin receptor (OTR) sourced from public databases. Evolutionary analysis and coevolution predictions were made followed by the macroevolution analysis of relevant amino acids associated with phenotypic traits, such as mating systems, parental care, and litter size. To account for phylogenetic structure, we utilized two distinct statistical tests. Additionally, we calculated binding energies focusing on the interaction between Callithtrix jacchus VAMEN and Pro 8 OT., Results: We identified two novel motifs (AAMEN and VAMEN), challenging the current knowledge of motif conservation in placental mammals. Coevolution analysis demonstrated a correlation between GAMEN, AAMEN, and VAMEN and their corresponding OTs and OTRs. Callithrix jacchus exhibited a higher binding energy between VAMEN and Pro 8 OT than orthologous molecules found in humans (GAMEN and Leu 8 OT)., Discussion: The coevolution of AAMEN and VAMEN with their corresponding OTs and OTRs suggests a functional relationship that could have contributed to specific reproductive and adaptive behaviors, including paternal care, social monogamy, and twin births, prominent traits in Cebidae species, such as marmosets and tamarins. Our findings underscore the coevolution of taxon-specific amino acids among the three studied molecules, shedding light on the oxytocinergic system as an adaptive epistatic repertoire in primates., (© 2024 Wiley Periodicals LLC.)

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., Competing Interests: LC, CK, AM, WP, CH, DR, HW, D, JM, JS No competing interests declared, (© 2023, Chamness et al.)

The process by which adaptive evolution preserves a population threatened with extinction due to environmental changes is known as evolutionary rescue. Several factors determine the fate of those populations, including demography and genetic factors, such as standing genetic variation, gene flow, availability of de novo mutations, and so on. Despite the extensive debate about evolutionary rescue in the current literature, a study about the role of epistasis and the topography of the fitness landscape on the fate of dwindling populations is missing. In the current work, we aim to fill this gap and study the influence of epistasis on the probability of extinction of populations. We present simulation results, and analytical approximations are derived. Counterintuitively, we show that the likelihood of extinction is smaller when the degree of epistasis is higher. The reason underneath is twofold: first, higher epistasis can promote mutations of more significant phenotypic effects, but also, the incongruence between the maps genotype-phenotype and phenotype-fitness turns the fitness landscape at low epistasis more rugged, thus curbing some of its advantages., (© 2024. The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature.)

The contribution of non-additive genetic effects to the genetic architecture of fitness and to the evolutionary potential of populations has been a topic of theoretical and empirical interest for a long time. Yet, the empirical study of these effects in natural populations remains scarce, perhaps because measuring dominance and epistasis relies heavily on experimental line crosses. In this study, we explored the contribution of dominance and epistasis in natural alpine populations of Arabidopsis thaliana for 2 fitness traits, the dry biomass and the estimated number of siliques, measured in a greenhouse. We found that, on average, crosses between inbred lines of A. thaliana led to mid-parent heterosis for dry biomass but outbreeding depression for an estimated number of siliques. While heterosis for dry biomass was due to dominance, we found that outbreeding depression for an estimated number of siliques could be attributed to the breakdown of beneficial epistatic interactions. We simulated and discussed the implication of these results for the adaptive potential of the studied populations, as well as the use of line-cross analyses to detect non-additive genetic effects., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Evolutionary Biology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)