The nature of 2-locus epistatic interactions in animals: evidence from Sewall Wright's guinea pig data

Summary The nature of epistatic interactions affects covariance between relatives and the expression of heterosis in various crossbred genotypes. The investigation of these interactions for metric traits requires large data sets of a suitable type. Data from Sewall Wright's early work with guinea pigs are used to compare the goodness-of-fit of seven biological models of 2-locus interaction for the six out of eleven traits in which epistatic effects are apparent. The model equivalent to additive x additive epistasis gives the best general fit over traits, with an average transformed R² value significantly greater than that of the next best fitting model (P<0.05). This result is compatible with results from the one other study in this area, using data from mice. It is concluded that, based on results available to date, the additive x additive 2-locus model of epistatic interaction appears most suitable for reduced genetic models.     

Inference on the strength of balancing selection for epistatically interacting loci

Existing inference methods for estimating the strength of balancing selection in multi-locus genotypes rely on the assumption that there are no epistatic interactions between loci. Complex systems in which balancing selection is prevalent, such as sets of human immune system genes, are known to contain components that interact epistatically. Therefore, current methods may not produce reliable inference on the strength of selection at these loci. In this paper, we address this problem by presenting statistical methods that can account for epistatic interactions in making inference about balancing selection. A theoretical result due to Fearnhead (2006) is used to build a multi-locus Wright-Fisher model of balancing selection, allowing for epistatic interactions among loci. Antagonistic and synergistic types of interactions are examined. The joint posterior distribution of the selection and mutation parameters is sampled by Markov chain Monte Carlo methods, and the plausibility of models is assessed via Bayes factors. As a component of the inference process, an algorithm to generate multi-locus allele frequencies under balancing selection models with epistasis is also presented. Recent evidence on interactions among a set of human immune system genes is introduced as a motivating biological system for the epistatic model, and data on these genes are used to demonstrate the methods.     

Translational regulation of ribosomal protein S15 drives characteristic patterns of protein‐mRNA epistasis

Do coding and regulatory segments of a gene co‐evolve with each‐other? Seeking answers to this question, here we analyze the case of Escherichia coli ribosomal protein S15, that represses its own translation by specifically binding its messenger RNA (rpsO mRNA) and stabilizing a pseudoknot structure at the upstream untranslated region, thus trapping the ribosome into an incomplete translation initiation complex. In the absence of S15, ribosomal protein S1 recognizes rpsO and promotes translation by melting this very pseudoknot. We employ a robust statistical method to detect signatures of positive epistasis between residue site pairs and find that biophysical constraints of translational regulation (S15‐rpsO and S1‐rpsO recognition, S15‐mediated rpsO structural rearrangement, and S1‐mediated melting) are strong predictors of positive epistasis. Transforming the epistatic pairs into a network, we find that signatures of two different, but interconnected regulatory cascades are imprinted in the sequence‐space and can be captured in terms of two dense network modules that are sparsely connected to each other. This network topology further reflects a general principle of how functionally coupled components of biological networks are interconnected. These results depict a model case, where translational regulation drives characteristic residue‐level epistasis—not only between a protein and its own mRNA but also between a protein and the mRNA of an entirely different protein.     

Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice

To comprehensively investigate the genetic architecture of growth and obesity, we performed Bayesian analyses of multiple epistatic quantitative trait locus (QTL) models for body weights at five ages (12 days, 3, 6, 9 and 12 weeks) and body composition traits (weights of two fat pads and five organs) in mice produced from a cross of the F1 between M16i (selected for rapid growth rate) and CAST/Ei (wild-derived strain of small and lean mice) back to M16i. Bayesian model selection revealed a temporally regulated network of multiple QTL for body weight, involving both strong main effects and epistatic effects. No QTL had strong support for both early and late growth, although overlapping combinations of main and epistatic effects were observed at adjacent ages. Most main effects and epistatic interactions had an opposite effect on early and late growth. The contribution of epistasis was more pronounced for body weights at older ages. Body composition traits were also influenced by an interacting network of multiple QTLs. Several main and epistatic effects were shared by the body composition and body weight traits, suggesting that pleiotropy plays an important role in growth and obesity.     

Epistatic models and pre-selection of markers improve prediction of performance in corn

In our previous papers, we demonstrated that the inclusion of epistatic interactions in marker models improved prediction for corn (Zea mays L.) grain quality traits. The utility of pre-selecting markers for epistatic models was not reported. In papers by other researchers, including epistatic effects in a model did not improve prediction efficacy for whole genome selection. The objectives of this study were therefore to evaluate the value of: (1) pre-selecting markers and interactions at different type 1 error levels to predict performance; (2) adding epistatic interactions to models including all markers, and (3) using marker-based models to predict performance of kernel weight (KWT), flowering date (FDT), and plant height (PHT). Data for KWT, FDT, PHT, and oil and protein concentrations were obtained for 500 S₂ lines and their testcrosses from the crosses of Illinois high oil × Illinois low oil and Illinois high protein × Illinois low protein corn strains. Pre-selection using an epistatic model including both single-locus and two-locus interaction effects significant at the P = 0.05 level significantly increased prediction efficacy over selection including all markers and epistatic interactions. Adding all epistatic interactions to a model including all markers did not improve prediction. For most traits, prediction based on the P = 0.05 epistatic pre-selection model was nearly as effective as prediction based on phenotype, suggesting subsequent marker-based selection would be effective.     

Competitive Exclusion Principle in Ecology and Absolute Asymmetric Synthesis in Chemistry

The key concepts underlying the Frank model (1953) for spontaneous asymmetric synthesis in chemistry are traced back to the pioneering works of Volterra (1926) and Lotka (1932) on biological species competition. The Lotka‐Volterra (L‐V) two‐species exclusive competition model reduces to the Frank model for the special case of distinguishable but degenerate species (i.e., the enantiomers). The important ecological principle of competitive exclusion, originally derived from the L‐V two‐competitors model, is a consequence of sufficiently antagonistic interactions between the species competing for limited common resources, or mutual inhibition, as the term is known in the chemical literature on absolute asymmetric synthesis. The L‐V and Frank models are described by the same general differential equations, nevertheless a crucial thermodynamic distinction between these models is necessary to correlate ecological selection and chemical selectivity arising from 1) the absence of reversibility in biological transformations, in marked contrast to chemical reactions, and 2) the constraints in chemical scenarios on the reaction rate constants required to fulfill the principle of micro‐reversibility. Chirality 27:722–727, 2015. © 2015 Wiley Periodicals, Inc.     

Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein?ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein?protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.     

Increase in eukaryotic initiation factor 2B activity following fertilization reflects changes in redox potential.

One of the factors involved in the postfertilization activation of protein synthesis in the sea urchin, Strongylocentrotus purpuratus, is the activation of eIF-2B, the initiation factor responsible for guanine nucleotide exchange on eIF-2. Cell-free translation systems from unfertilized eggs are stimulated by added eIF-2B, although this dependency is rapidly lost in translation systems prepared at various times following fertilization. Cell-free translation systems prepared from unfertilized eggs show significantly lower eIF-2B activities than those prepared from 2-h embryos. However, the provision of an NADPH regeneration system significantly stimulates eIF-2B activity in egg extracts and, in addition, stimulates both binding of initiator tRNA to the small ribosomal subunit and protein synthetic activity. These data suggest that the activation of eIF-2B following fertilization reflects the fertilization-induced increase in NADPH levels.     

Genome‐wide association and epistasis studies unravel the genetic architecture of sudden death syndrome resistance in soybean

Soybean [Glycine max (L.) Merr.] is an economically important crop that is grown worldwide. Sudden death syndrome (SDS), caused by Fusarium virguliforme, is one of the top yield‐limiting diseases in soybean. However, the genetic basis of SDS resistance, especially with respect to epistatic interactions, is still unclear. To better understand the genetic architecture of soybean SDS resistance, genome‐wide association and epistasis studies were performed using a population of 214 germplasm accessions and 31 914 SNPs from the SoySNP50K Illumina Infinium BeadChip. Twelve loci and 12 SNP–SNP interactions associated with SDS resistance were identified at various time points after inoculation. These additive and epistatic loci together explained 24–52% of the phenotypic variance. Disease‐resistant, pathogenesis‐related and chitin‐ and wound‐responsive genes were identified in the proximity of peak SNPs, including stress‐induced receptor‐like kinase gene 1 (SIK1), which is pinpointed by a trait‐associated SNP and encodes a leucine‐rich repeat‐containing protein. We report that the proportion of phenotypic variance explained by identified loci may be considerably improved by taking epistatic effects into account. This study shows the necessity of considering epistatic effects in soybean SDS resistance breeding using marker‐assisted and genomic selection approaches. Based on our findings, we propose a model for soybean root defense against the SDS pathogen. Our results facilitate identification of the molecular mechanism underlying SDS resistance in soybean, and provide a genetic basis for improvement of soybean SDS resistance through breeding strategies based on additive and epistatic effects.     

Dynamic map of protein interactions in the Escherichia coli chemotaxis pathway

Protein–protein interactions play key roles in virtually all cellular processes, often forming complex regulatory networks. A powerful tool to study interactions in vivo is fluorescence resonance energy transfer (FRET), which is based on the distance‐dependent energy transfer from an excited donor to an acceptor fluorophore. Here, we used FRET to systematically map all protein interactions in the chemotaxis signaling pathway in Escherichia coli, one of the most studied models of signal transduction, and to determine stimulation‐induced changes in the pathway. Our FRET analysis identified 19 positive FRET pairs out of the 28 possible protein combinations, with 9 pairs being responsive to chemotactic stimulation. Six stimulation‐dependent and five stimulation‐independent interactions were direct, whereas other interactions were apparently mediated by scaffolding proteins. Characterization of stimulation‐induced responses revealed an additional regulation through activity dependence of interactions involving the adaptation enzyme CheB, and showed complex rearrangement of chemosensory receptors. Our study illustrates how FRET can be efficiently employed to study dynamic protein networks in vivo.     

Protein-protein interactions in the secretory pathway,a growing demand for experimental approaches in vivo

The function of the secretory pathway is dependent on multiple protein-protein interactions at various stages. Currently, such interactions are mainly studied using physical methods that document direct contact or affinity in vitro. The development of vital fluorescence imaging as well as quantitative protein transport assays opens up the implementation of in vivo approaches which can be used to verify models based on in vitro work. The purpose of this review is to provide an overview of the various approaches involving living cells to resolve interactions between proteins that control complex mechanisms. In particular, it is illustrated how combinations of several methods can establish whether postulated interactions are of biological relevance or due to artefacts inherent to the experimental set-up.     

MicA sRNA links the PhoP regulon to cell envelope stress

Numerous small RNAs regulators of gene expression exist in bacteria. A large class of them binds to the RNA chaperone Hfq and act by base pairing interactions with their target mRNA, thereby affecting their translation and/or stability. They often have multiple direct targets, some of which may be regulators themselves, and production of a single sRNA can therefore affect the expression of dozens of genes. We show in this study that the synthesis of the Escherichia coli pleiotropic PhoPQ two‐component system is repressed by MicA, a σ^E‐dependent sRNA regulator of porin biogenesis. MicA directly pairs with phoPQ mRNA in the translation initiation region of phoP and presumably inhibits translation by competing with ribosome binding. Consequently, MicA downregulates several members of the PhoPQ regulon. By linking PhoPQ to σ^E, our findings suggest that major cellular processes such as Mg²⁺ transport, virulence, LPS modification or resistance to antimicrobial peptides are modulated in response to envelope stress. In addition, we found that Hfq strongly affects the expression of phoP independently of MicA, raising the possibility that even more sRNAs, which remain to be identified, could regulate PhoPQ synthesis.     

Suppression and synthetic‐lethal genetic relationships of ΔgpsB mutations indicate that GpsB mediates protein phosphorylation and penicillin‐binding protein interactions in Streptococcus pneumoniae D39

GpsB regulatory protein and StkP protein kinase have been proposed as molecular switches that balance septal and peripheral (side‐wall like) peptidoglycan (PG) synthesis in Streptococcus pneumoniae (pneumococcus); yet, mechanisms of this switching remain unknown. We report that ΔdivIVA mutations are not epistatic to ΔgpsB division‐protein mutations in progenitor D39 and related genetic backgrounds; nor is GpsB required for StkP localization or FDAA labeling at septal division rings. However, we confirm that reduction of GpsB amount leads to decreased protein phosphorylation by StkP and report that the essentiality of ΔgpsB mutations is suppressed by inactivation of PhpP protein phosphatase, which concomitantly restores protein phosphorylation levels. ΔgpsB mutations are also suppressed by other classes of mutations, including one that eliminates protein phosphorylation and may alter division. Moreover, ΔgpsB mutations are synthetically lethal with Δpbp1a, but not Δpbp2a or Δpbp1b mutations, suggesting GpsB activation of PBP2a activity. Consistent with this result, co‐IP experiments showed that GpsB complexes with EzrA, StkP, PBP2a, PBP2b and MreC in pneumococcal cells. Furthermore, depletion of GpsB prevents PBP2x migration to septal centers. These results support a model in which GpsB negatively regulates peripheral PG synthesis by PBP2b and positively regulates septal ring closure through its interactions with StkP‐PBP2x.     

High throughput analyses of epistasis for swine body dimensions and organ weights

High throughput analyses were performed to detect epistatic QTL in 17 body dimension and organ weight traits from a large F₂ pig population derived from a White Duroc and Erhualian intercross. The analyses used a nested test framework to handle multiple tests and a combined search algorithm to map epistatic QTL with empirical genome‐wide thresholds derived via prior permutation. Alternative statistical models (e.g. including vs. excluding carcass weight as a covariate) were tested to develop an in‐depth understanding of the role of epistasis in these kinds of traits. Epistasis signals were detected in only two or three traits under each statistical model studied. The interaction component of each pair of epistatic QTL explained a small proportion (0.7 to 2.1%) of the phenotypic variance in general. About half of the detected epistatic QTL pairs involved one of the two major QTL on porcine chromosomes 7 and 4. In those traits, the Erhualian allele consistently increased the phenotypes for the chromosome 7 QTL but decreased them for the chromosome 4 QTL. Models including carcass weight as covariate detected epistasis in body dimension traits whereas those excluding carcass weight found epistasis in organ weight traits. In addition, the epistasis results suggested that a QTL on chromosome 14 could be important for a number of organ weight traits. Using the high‐throughput analysis tool to examine different statistical models was essential for the generation of a complete picture of epistasis in a whole category of traits.     

Reassociation of cytoplasmic ribosomal subunits of barley and its virescens mutant to form active hybrids

Polyphenylalamine synthesis by cytoplasmic ribosomes of Gateway barley (Hordeum vulgare) and its virescens single gene nuclear mutant was compared. The cytoplasmic 80S ribosomes were isolated from unimbibed embryo material and the ribosomes were dissociated into their component 60S and 40S subunits by centrifugation through sucrose gradients containing high KCl-to-MgCl₂ buffer. These separated subunits could be reassociated by resuspension in buffer having about equimolar concentrations of MgCl₂ and KCl. Both homologous and heterologous combinations of the subunits reassociated to give monomeric 80S ribosomes, and the derived monomers as well as various combinations of the individual subunits showed equivalent activity in an in vitro system for poly (U)-directed polyphenylalanine synthesis.     

Eukaryotic elongation factor-2 (eEF2): its regulation and peptide chain elongation

Regulation at the level of translation in eukaryotes is feasible because of the longer lifetime of eukaryotic mRNAs in the cell. The elongation stage of mRNA translation requires a substantial amount of energy and also eukaryotic elongation factors (eEFs). The important component of eEFs, i.e. eEF2 promotes the GTP-dependent translocation of the nascent protein chain from the A-site to the P-site of the ribosome. Mostly the eEF2 is regulated by phosphorylation and dephosphorylation by a specific kinase known as eEF2 kinase, which itself is up-regulated by various mechanisms in the eukaryotic cell. The activity of this kinase is dependent on calcium ions and calmodulin. Recently it has been shown that the activity of eEF2 kinase is regulated by MAP kinase signalling and mTOR signalling pathway. There are also various stimuli that control the peptide chain elongation in eukaryotic cell; some stimuli inhibit and some activate eEF2. These reports provide the mechanisms by which cells likely serve to slow down protein synthesis and conserve energy under nutrient deprived conditions via regulation of eEF2. The regulation via eEF2 has also been seen in mammary tissue of lactating cows, suggesting that eEF2 may be a limiting factor in milk protein synthesis. Regulation at this level provides the molecular understanding about the control of protein translocation reactions in eukaryotes, which is critical for numerous biological phenomenons. Further the elongation factors could be potential targets for regulation of protein synthesis like milk protein synthesis and hence probably its foreseeable application to synthetic biology.     

tmRNA of Streptomyces collinus and Streptomyces griseus during the growth and in the presence of antibiotics

Streptomycetes are soil microorganisms with the potential to produce a broad spectrum of secondary metabolities. The production of antibiotics is accompanied by a decrease in protein synthesis, which raises the question of how these bacteria survived the transition from the primary to the secondary metabolism. Translating ribosomes incapable to properly elongate or terminate polypeptide chain activate bacterial trans‐translation system. Abundance and stability of the tmRNA during growth of Streptomyces collinus and Streptomyces griseus producing kirromycin and streptomycin, respectively, was analysed. The level of tmRNA is mostly proportional to the activity of the translational system. We demonstrate that the addition of sub‐inhibitory concentrations of produced antibiotics to the cultures from the beginning of the exponential phase of growth leads to an increase in tmRNA levels and to an incorporation of amino acids into the tag‐peptides at trans‐translation of stalled ribosomes. These findings suggest that produced antibiotics induce tmRNA that facilitate reactivation of stalled complex of ribosomes and maintain viability. The effect of antibiotics that inhibit the cell‐wall turnover, DNA, RNA or protein synthesis on the level of tmRNA was examined. Antibiotics interfering with ribosomal target sites are more effective at stimulation of the tmRNA level in streptomycetes examined than those affecting the synthesis of DNA, RNA or the cell wall.     

Systematic discovery of drug interaction mechanisms

Drug combinations are increasingly important in disease treatments, for combating drug resistance, and for elucidating fundamental relationships in cell physiology. When drugs are combined, their individual effects on cells may be amplified or weakened. Such drug interactions are crucial for treatment efficacy, but their underlying mechanisms remain largely unknown. To uncover the causes of drug interactions, we developed a systematic approach based on precise quantification of the individual and joint effects of antibiotics on growth of genome‐wide Escherichia coli gene deletion strains. We found that drug interactions between antibiotics representing the main modes of action are highly robust to genetic perturbation. This robustness is encapsulated in a general principle of bacterial growth, which enables the quantitative prediction of mutant growth rates under drug combinations. Rare violations of this principle exposed recurring cellular functions controlling drug interactions. In particular, we found that polysaccharide and ATP synthesis control multiple drug interactions with previously unexplained mechanisms, and small molecule adjuvants targeting these functions synthetically reshape drug interactions in predictable ways. These results provide a new conceptual framework for the design of multidrug combinations and suggest that there are universal mechanisms at the heart of most drug interactions.