Epistasis among Deleterious Mutations in the HIV-1 Protease

A central goal in molecular evolution is to understand how genetic interactions between protein mutations shape protein function and fitness. While intergenic epistasis has been extensively explored in eukaryotes, bacteria, and viruses, intragenic epistatic interactions have been insufficiently studied. Here, we employ a model system in which lambda phage fitness correlates with the enzymatic activity of human immunodeficiency virus type 1 (HIV-1) protease to systematically determine the epistatic interactions between intragenic pairs of deleterious protein substitutions. We generated 114 genotypes of the HIV-1 protease, each carrying pairs of nucleotide substitution mutations whose separated and combined deleterious effects on fitness were then determined. A high proportion (39%) of pairs displayed lethality. Several pairs exhibited significant interactions for fitness, including positive and negative epistasis. Significant negative epistatic interactions predominated (15%) over positive interactions (2%). However, the average ± SD epistatic effect, ē = 0.0025 ± 0.1334, was not significantly different from zero (p = 0.8368). Notably, epistatic interactions, regardless of epistatic direction, tend to be more frequent in the context of less deleterious mutations. In the present study, the high frequencies of lethality and negative epistasis indicate that the HIV-1 protease is highly sensitive to the effects of deleterious mutations. Therefore, proteins may not be as robust to mutational change as is usually expected.     

Epistasis causes outbreeding depression in eucalypt hybrids

The genetic architecture underlying species differentiation is essential for understanding the mechanisms of speciation and post-zygotic reproductive barriers which exist between species. We undertook line-cross analysis of multiple hybrid (F₁, F₂ and backcrosses) and pure-species populations of two diploid eucalypt species from different subseries, Eucalyptus globulus and Eucalyptus nitens, to unravel the genetic architecture of their differentiation. The populations were replicated on two sites and monitored for growth and survival over a 14-year period. The hybrids exhibited severe outbreeding depression which increased with age. Of the composite additive, dominance and epistatic effects estimated, the additive × additive epistatic component was the most important in determining population divergence in both growth and survival. Significant dominance × dominance epistasis was also detected for survival at several ages. While favourable dominance and, in the case of survival, dominance × dominance epistasis could produce novel gene combinations which enhance hybrid fitness, at the population level, these effects were clearly overridden by adverse additive × additive epistasis which appears to be a major driver of overall outbreeding depression in the hybrid populations. The lack of model fit at older ages suggested that even high-order epistatic interactions may potentially have a significant contribution to outbreeding depression in survival. The estimated composite genetic parameters were generally stable across sites. Our results argue that the development of favourable epistasis is a key mechanism underlying the genetic divergence of eucalypt species, and epistasis is an important mechanism underlying the evolution of post-zygotic reproductive barriers.     

Gene action for cold tolerance in chickpea

Summary Six crosses were investigated using combining ability and generation mean analyses for reaction to cold tolerance in chickpea (Cicer arietinum L.). The combining ability variances revealed the significance of both additive and nonadditive gene effects, with preponderance of additive gene effects. The generation mean analysis revealed the presence of genie interactions in addition to additive and dominance gene effects. Among the interactions, additive×additive and dominance×dominance with duplicate epistasis were present. Cold tolerance was dominant over susceptibility to cold. Selection for cold tolerance would be more effective if dominance and epistatic effects were reduced after a few generations of selfing.Joint contribution from ICARDA and ICRISAT (International Crops Research Institute for the Semi-Arid Tropics), Patancheru P.O., A.P. 502 324, India. ICRISAT JA No. 1239.     

An entropy-based approach for testing genetic epistasis underlying complex diseases

The genetic basis of complex diseases is expected to be highly heterogeneous, with complex interactions among multiple disease loci and environment factors. Due to the multi-dimensional property of interactions among large number of genetic loci, efficient statistical approach has not been well developed to handle the high-order epistatic complexity. In this article, we introduce a new approach for testing genetic epistasis in multiple loci using an entropy-based statistic for a case-only design. The entropy-based statistic asymptotically follows a χ² distribution. Computer simulations show that the entropy-based approach has better control of type I error and higher power compared to the standard χ² test. Motivated by a schizophrenia data set, we propose a method for measuring and testing the relative entropy of a clinical phenotype, through which one can test the contribution or interaction of multiple disease loci to a clinical phenotype. A sequential forward selection procedure is proposed to construct a genetic interaction network which is illustrated through a tree-based diagram. The network information clearly shows the relative importance of a set of genetic loci on a clinical phenotype. To show the utility of the new entropy-based approach, it is applied to analyze two real data sets, a schizophrenia data set and a published malaria data set. Our approach provides a fast and testable framework for genetic epistasis study in a case-only design.     

Genomic prediction of hybrid crops allows disentangling dominance and epistasis

We revisited, in a genomic context, the theory of hybrid genetic evaluation models of hybrid crosses of pure lines, as the current practice is largely based on infinitesimal model assumptions. Expressions for covariances between hybrids due to additive substitution effects and dominance and epistatic deviations were analytically derived. Using dense markers in a GBLUP analysis, it is possible to split specific combining ability into dominance and across-groups epistatic deviations, and to split general combining ability (GCA) into within-line additive effects and within-line additive by additive (and higher order) epistatic deviations. We analyzed a publicly available maize data set of Dent × Flint hybrids using our new model (called GCA-model) up to additive by additive epistasis. To model higher order interactions within GCAs, we also fitted “residual genetic” line effects. Our new GCA-model was compared with another genomic model which assumes a uniquely defined effect of genes across origins. Most variation in hybrids is accounted by GCA. Variances due to dominance and epistasis have similar magnitudes. Models based on defining effects either differently or identically across heterotic groups resulted in similar predictive abilities for hybrids. The currently used model inflates the estimated additive genetic variance. This is not important for hybrid predictions but has consequences for the breeding scheme—e.g. overestimation of the genetic gain within heterotic group. Therefore, we recommend using GCA-model, which is appropriate for genomic prediction and variance component estimation in hybrid crops using genomic data, and whose results can be practically interpreted and used for breeding purposes.     

A novel evolution-based method for detecting gene-gene interactions

Background

The rapid advance in large-scale SNP-chip technologies offers us great opportunities in elucidating the genetic basis of complex diseases. Methods for large-scale interactions analysis have been under development from several sources. Due to several difficult issues (e.g., sparseness of data in high dimensions and low replication or validation rate), development of fast, powerful and robust methods for detecting various forms of gene-gene interactions continues to be a challenging task.

Methodology/Principal Findings

In this article, we have developed an evolution-based method to search for genome-wide epistasis in a case-control design. From an evolutionary perspective, we view that human diseases originate from ancient mutations and consider that the underlying genetic variants play a role in differentiating human population into the healthy and the diseased. Based on this concept, traditional evolutionary measure, fixation index (Fst) for two unlinked loci, which measures the genetic distance between populations, should be able to reveal the responsible genetic interplays for disease traits. To validate our proposal, we first investigated the theoretical distribution of Fst by using extensive simulations. Then, we explored its power for detecting gene-gene interactions via SNP markers, and compared it with the conventional Pearson Chi-square test, mutual information based test and linkage disequilibrium based test under several disease models. The proposed evolution-based method outperformed these compared methods in dominant and additive models, no matter what the disease allele frequencies were. However, its performance was relatively poor in a recessive model. Finally, we applied the proposed evolution-based method to analysis of a published dataset. Our results showed that the P value of the Fst -based statistic is smaller than those obtained by the LD-based statistic or Poisson regression models.

Conclusions/Significance

With rapidly growing large-scale genetic association studies, the proposed evolution-based method can be a promising tool in the identification of epistatic effects.     

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.     

Effects of Bacillus thuringiensis toxin Cry1Ac and cytoplasmic polyhedrosis virus of Helicoverpa armigera (Hübner) (HaCPV) on cotton bollworm (Lepidoptera: Noctuidae)

In this study, interactions on the mortality and debilitating effects between Cry1Ac, a toxic protein produced by Bacillus thuringiensis (Berliner) and HaCPV (Chinese strain) on first and third instars larvae of Helicoverpa armigera were evaluated in laboratory. When first instar was exposed to combination of Bt cotton leaf discs containing HaCPV (6 × 10⁶, 1 × 10⁷, and 3 × 10⁷ PIB ml⁻¹) the effect on mortality was additive, when such instar larvae exposed to combination of Cry1Ac (0.9, 2.7, or 8.1 μg g⁻¹) and the same concentrations of HaCPV the effect on mortality was additive except for the combination of Cry1Ac (0.3 μg g⁻¹) and HaCPV concentrations that showed synergism. When third instars of H. armigera were infected using a suspension containing both HaCPV and Cry1Ac, most combinations of them showed additive effect except for the combination of Cry1Ac (0.3 μg g⁻¹) and HaCPV (3 × 10⁷ PIB ml⁻¹) that showed synergism. However, when they exposed to Bt cotton leaf discs and HaCPV the effect on mortality was synergism except combination of Bt cotton leaf discs and HaCPV (6 × 10⁶ PIB ml⁻¹) that showed additive. Most of the combinations are showed additive effect in the toxicity and in combinations of Cry1Ac at lowest and HaCPV at highest concentrations synergism is observed. Not only were larval growth and development delayed, but pupation and pupal weight also decreased when larvae were fed on artificial diet containing Cry1Ac and HaCPV or transgenic Bt cotton leaf discs specially in first instar.     

A General Model for Multilocus Epistatic Interactions in Case-Control Studies

Background

Epistasis, i.e., the interaction of alleles at different loci, is thought to play a central role in the formation and progression of complex diseases. The complexity of disease expression should arise from a complex network of epistatic interactions involving multiple genes.

Methodology

We develop a general model for testing high-order epistatic interactions for a complex disease in a case-control study. We incorporate the quantitative genetic theory of high-order epistasis into the setting of cases and controls sampled from a natural population. The new model allows the identification and testing of epistasis and its various genetic components.

Conclusions

Simulation studies were used to examine the power and false positive rates of the model under different sampling strategies. The model was used to detect epistasis in a case-control study of inflammatory bowel disease, in which five SNPs at a candidate gene were typed, leading to the identification of a significant three-locus epistasis.     

Experimental verification of the behavioral foundation of bacterial transport parameters using microfluidics

We present novel microfluidic experiments to quantify population-scale transport parameters (chemotactic sensitivity χ₀ and random motility μ) of a population of bacteria. Previously, transport parameters have been derived theoretically from single-cell swimming behavior using probabilistic models, yet the mechanistic foundations of this upscaling process have not been verified experimentally. We designed a microfluidic capillary assay to generate and accurately measure gradients of chemoattractant (α-methylaspartate) while simultaneously capturing the swimming trajectories of individual Escherichia coli bacteria using videomicroscopy and cell tracking. By measuring swimming speed and bias in the swimming direction of single cells for a range of chemoattractant concentrations and concentration gradients, we directly computed the chemotactic velocity V_C and the associated chemotactic sensitivity χ₀. We then show how μ can also be readily determined using microfluidics but that a population-scale microfluidic approach is experimentally more convenient than a single-cell analysis in this case. Measured values of both χ₀ [(12.4 ± 2.0) × 10⁻⁴ cm² s⁻¹] and μ [(3.3 ± 0.8) × 10⁻⁶ cm² s⁻¹] are comparable to literature results. This microscale approach to bacterial chemotaxis lends experimental support to theoretical derivations of population-scale transport parameters from single-cell behavior. Furthermore, this study shows that microfluidic platforms can go beyond traditional chemotaxis assays and enable the quantification of bacterial transport parameters.     

QTL Analysis of Na+ and K+ Concentrations in Roots and Shoots under Different Levels of NaCl Stress in Rice (Oryza sativa L.)

The key to plant survival under NaCl salt stress is maintaining a low Na⁺ level or Na⁺/K⁺ ratio in the cells. A population of recombinant inbred lines (RILs, F_2∶9) derived from a cross between the salt-tolerant japonica rice variety Jiucaiqing and the salt-sensitive indica variety IR26, was used to determine Na⁺ and K⁺ concentrations in the roots and shoots under three different NaCl stress conditions (0, 100 and 120 mM NaCl). A total of nine additive QTLs were identified by QTL Cartographer program using single-environment phenotypic values, whereas eight additive QTLs were identified by QTL IciMapping program. Among these additive QTLs, five were identified by both programs. Epistatic QTLs and QTL-by-environment interactions were detected by QTLNetwork program in the joint analyses of multi-environment phenotypic values, and one additive QTL and nine epistatic QTLs were identified. There were three epistatic QTLs identified for Na⁺ in roots (RNC), three additive QTLs and two epistatic QTLs identified for Na⁺ in shoots (SNC), four additive QTLs identified for K⁺ in roots (RKC), four additive QTLs and three epistatic QTLs identified for K⁺ in shoots (SKC) and one additive QTL and one epistatic QTL for salt tolerance rating (STR). The phenotypic variation explained by each additive, epistatic QTL and QTL×environment interaction ranged from 8.5 to 18.9%, 0.5 to 5.3% and 0.7 to 7.5%, respectively. By comparing the chromosomal positions of these additive QTLs with those previously identified, five additive QTLs, qSNC9, qSKC1, qSKC9, qRKC4 and qSTR7, might represent novel salt tolerance loci. The identification of salt tolerance in selected RILs showed that a major QTL qSNC11 played a significant role in rice salt tolerance, and could be used to improve salt tolerance of commercial rice varieties with marker-assisted selection (MAS) approach.     

Effects of Bacillus thuringiensis toxin Cry1Ac and Beauveria bassiana on Asiatic corn borer (Lepidoptera: Crambidae)

In this study, interactions between Cry1Ac, a toxic crystal protein produced by Bacillus thuringiensis (Berliner), and Beauveria bassiana on the mortality and survival of Ostrinia furnacalis was evaluated in the laboratory. The results showed that Cry1Ac is toxic to O. furnacalis. Not only were larval growth and development delayed, but pupation, pupal weight and adult emergency also decreased when larvae were fed on artificial diet containing purified Cry1Ac toxin. When third instars O. furnacalis were exposed to combination of B. bassiana (1.8 × 10⁵, 1.8 × 10⁶ or 1.8 × 10⁷ conidia ml⁻¹) and Cry1Ac, (0.2 or 0.8 μg g⁻¹), the effect on mortality was additive, however, the combinations of sublethal concentrations showed antagonism between Cry1Ac (3.2 or 13 μg g⁻¹) and B. bassiana (1.8 × 10⁵ or 1.8 × 10⁶ conidia ml⁻¹). When neonates were reared on sublethal concentrations of Cry1AC until the third instar, and survivors exposed B. bassiana conidial suspension, such treatments showed additive effect on mortality of O. furnacalis except for the combination of Cry1Ac (0.2 μg g⁻¹) and B. bassiana (1.8 × 10⁶ conidia ml⁻¹) that showed antagonism.     

Dissecting Genetic Architecture Underlying Seed Traits in Multiple Environments

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits.     

Simulation of quantitative characters from qualitatively acting genes

Summary The phenotypes associated with the nine genotypes in a quantitative genetic system consisting of two loci, each having two alleles can be described in terms of nine parameters, giving a system of nine linear equations. Populations with desired magnitudes and known nature of intra- and interlocus interactions are obtained by the use of this linear combination model. The total sums of squares for genotypes in these populations are partitioned into orthogonal components denoting additive and dominance effects of the two loci and the four types of nonallelic interactions between them. In most cases, the relative magnitudes of dominance and epistatic variances are found to be considerably smaller than the actual proportions of these genetic effects. Duplicate interaction produces larger epistatic variance than complementary type of gene interaction. At the higher levels of epistasis, dominant epistasis yields much larger epistatic variance than recessive epistasis. No epistatic variance is produced in the absence of epistatic effects. But, appreciable contributions of additive and dominance gene actions to the total genotypic variability are obtained even in the complete absence of these effects, if additive × dominance and dominance × dominance epistatic effects, respectively, are present. It is concluded that in elucidating the nature of gene action in simplified genetic systems, the estimates of first degree parameters obtained from the linear combination model are more useful than the orthogonal components of genotypic sum of squares.

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Zusammenfassung Die in einem quantitativ-genetischen System mit je 2 Allelen an 2 Loci möglichen 9 Phänotypen, die mit den entsprechenden Genotypen assoziiert sind, können durch einen Satz von 9 linearen Gleichungen beschrieben werden. Mit Hilfe dieses Modells der linearen Kombination wurden Populationen mit willkürlich gewählter Dimension und Art der Interaktion innerhalb der und zwischen den Loci konstruiert. Die Gesamtsummen der Abweichungsquadrate für die Genotypen derartiger Populationen werden in orthogonale Komponenten zerlegt, die den additiven und den Dominanz-Effekten bzw. den vier Arten der nichtallelen Interaktion der beiden Loci zugeschrieben werden können. In der Mehrzahl der Fälle sind die relativen Größenordnungen der Dominanz- und Epistasie-Varianzen wesentlich kleiner als die tatsächlichen Anteile dieser Effekte. Eine gegenseitige Vertretbarkeit nichtalleler Gene (duplicate gene action, 15:1-Spaltung) führt zu einer größeren Epistasievarianz als komplementäre Genwirkung (9:7-Spaltung). Bei stark ausgeprägter Epistasie führt die sog. dominante Epistasie (12:3:1-Spaltung) zu einer wesentlich größeren Epistasievarianz als die rezessive Epistasie (9:3:4-Spaltung). In Abwesenheit epistatischer Effekte wird keine Epistasievarianz beobachtet. Jedoch werden bemerkenswerte Beiträge additiver und dominanter Genwirkungen zur genotypischen Gesamtvariabilität auch bei völliger Abwesenheit derartiger Wirkungen beobachtet, wenn Interaktionen des Typs additiv × dominant bzw. dominant × dominant vorliegen. Hieraus wird geschlossen, daß die Aufklärung der Art der Genwirkung in einfachen genetischen Systemen gezeigt hat, daß die Schätzwerte der Parameter 1. Grades, die aus dem zitierten Modell mit linearer Kombination erhalten werden können, brauchbarer sind als die orthogonalen Kombinationen der genotypischen Summe der Abweichungsquadrate.

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The investigation was supported by the grant number A6221 of the National Research Council of Canada.     

Using Linkage Analysis to Detect Gene-Gene Interactions. 2. Improved Reliability and Extension to More-Complex Models

Detecting gene-gene interaction in complex diseases has become an important priority for common disease genetics, but most current approaches to detecting interaction start with disease-marker associations. These approaches are based on population allele frequency correlations, not genetic inheritance, and therefore cannot exploit the rich information about inheritance contained within families. They are also hampered by issues of rigorous phenotype definition, multiple test correction, and allelic and locus heterogeneity. We recently developed, tested, and published a powerful gene-gene interaction detection strategy based on conditioning family data on a known disease-causing allele or a disease-associated marker allele⁴. We successfully applied the method to disease data and used computer simulation to exhaustively test the method for some epistatic models. We knew that the statistic we developed to indicate interaction was less reliable when applied to more-complex interaction models. Here, we improve the statistic and expand the testing procedure. We computer-simulated multipoint linkage data for a disease caused by two interacting loci. We examined epistatic as well as additive models and compared them with heterogeneity models. In all our models, the at-risk genotypes are “major” in the sense that among affected individuals, a substantial proportion has a disease-related genotype. One of the loci (A) has a known disease-related allele (as would have been determined from a previous analysis). We removed (pruned) family members who did not carry this allele; the resultant dataset is referred to as “stratified.” This elimination step has the effect of raising the “penetrance” and detectability at the second locus (B). We used the lod scores for the stratified and unstratified data sets to calculate a statistic that either indicated the presence of interaction or indicated that no interaction was detectable. We show that the new method is robust and reliable for a wide range of parameters. Our statistic performs well both with the epistatic models (false negative rates, i.e., failing to detect interaction, ranging from 0 to 2.5%) and with the heterogeneity models (false positive rates, i.e., falsely detecting interaction, ≤1%). It works well with the additive model except when allele frequencies at the two loci differ widely. We explore those features of the additive model that make detecting interaction more difficult. All testing of this method suggests that it provides a reliable approach to detecting gene-gene interaction.     

Detecting epistatic genetic variance with a clonally replicated design: models for lowvs high-order nonallelic interaction

A quantitative genetic model, that uses known family structure with clonal replicates to separate genetic variance into its additive, dominance and epistatic components, is available in the current literature. Making use of offspring testing, this model is based on the theory that components of variance from the linear model of an experimental design may be expressed in terms of expected covariances among relatives. However, if interactions between a pair of quantitative trait loci (QTLs) explain a large proportion of the total epistasis, it will seriously overestimate the additive and dominance variances but underestimate the epistatic variance. In the present paper, a new model is developed to manipulate this problem by combining parental and offspring material into the same test. Under the condition described above, the new model can provide an accurate estimate for additive x additive variances. Also, its accuracy in estimating dominance and total epistatic variances is much greater than the accuracy of the previous model. However, if there is obvious evidence showing the major contribution of high-order interactions, especially among 4QTLs, to the total epistasis, the previous model is more appropriate to partition the genetic variance for a quantitative trait. The re-analysis of an example from a factorial mating design in poplar shows large differences in estimating variance components between the new and previous models when two different assumptions (lowvs high-order epistatic interactions) are used. The new model will be an alternative to estimating the mode of quantitative inheritance for species, especially for longlived, predominantly outcrossing forest trees, that can be clonally replicated.     

Probability of Obtaning Negative Estimates of Heritability from Full-Sib Analysis under a General Model of Gene Action

Formulae are derived for the probability of obtaining negative estimates of heritability (h²) in full-sib analysis under the additive-dominance-epistasis model of gene action. Evaluation of the probabilities was undertaken for several combinations of sire/dam number, h² and proportions of dominance and additive X additive epistatic variances, assuming two full-sibs per mating and that the dominance related epistasis is absent. In the light of the results, minimum sample sizes for obtaining admissible estimates from sire, dam components and their combination have been prescribed.     

Epistasis in the multiple locus symmetric viability model

The n-locus two-allele symmetric viability model is considered in terms of the parameters measuring the additive epistasis in fitness. The dynamics is analysed using a simple linear transformation of the gametic frequencies, and then the recurrence equations depend on the epistatic parameters and Geiringer's recombination distribution only. The model exhibits an equilibrium, the central equilibrium, where the 2 ⁿ gametes are equally frequent. The transformation simplifies the stability analysis of the central point, and provides the stability conditions in terms of the existence conditions of other equilibria. For total negative epistasis (all epistatic parameters are negative) the central point is stable for all recombination distributions. For free recombination either a central point (segregating one, two, ... or n loci) or the n-locus fixation states are stable. For no recombination and some epistatic parameters positive the central point is unstable and several boundary equilibria may be locally stable. The sign structure of the additive epistasis is therefore an important determinant of the dynamics of the n-locus symmetric viability model. The non-symmetric multiple locus models previously analysed are dynamically related, and they all have an epistatic sign structure that resembles that of the multiplicative viability model. A non-symmetric model with total negative epistasis which share dynamical properties with the similar symmetric model is suggested.Supported in part by NIH grant GM 28016, and by grant 81-5458 from the Danish Natural Science Research Council     

Measurement of two-dimensional binding constants between cell-bound major histocompatibility complex and immobilized antibodies with an acoustic biosensor

Gaining insights into the dynamic processes of molecular interactions that mediate cell-substrate and cell-cell adhesion is of great significance in the understanding of numerous physiological processes driven by intercellular communication. Here, an acoustic-wave biosensor is used to study and characterize specific interactions between cell-bound membrane proteins and surface-immobilized ligands, using as a model system the binding of major histocompatibility complex class I HLA-A2 proteins to anti-HLA-A2 monoclonal antibodies. The energy of the acoustic signal, measured as amplitude change, was found to depend directly on the number of HLA-A2/antibody complexes formed on the device surface. Real-time acoustic data were used to monitor the surface binding of cell suspensions at a range of 6.0 × 10⁴ to 6.0 × 10⁵ cells mL⁻¹. Membrane interactions are governed by two-dimensional chemistry because of the molecules’ confinement to the lipid bilayer. The two-dimensional kinetics and affinity constant of the HLA-A2/antibody interaction were calculated (k_a = 1.15 × 10^−5 μm² s⁻¹ per molecule, k_d = 2.07 × 10⁻⁵ s⁻¹, and K_A = 0.556 μm² per molecule, at 25°C), based on a detailed acoustic data analysis. Results indicate that acoustic biosensors can emerge as a significant tool for probing and characterizing cell-membrane interactions in the immune system, and for fast and label-free screening of membrane molecules using whole cells.