From Genotype �� Environment Interaction to Gene �� Environment Interaction

Historically in plant breeding a large number of statistical models has been developed and used for studying genotype × environment interaction. These models have helped plant breeders to assess the stability of economically important traits and to predict the performance of newly developed genotypes evaluated under varying environmental conditions. In the last decade, the use of relatively low numbers of markers has facilitated the mapping of chromosome regions associated with phenotypic variability (e.g., QTL mapping) and, to a lesser extent, revealed the differetial response of these chromosome regions across environments (i.e., QTL × environment interaction). QTL technology has been useful for marker-assisted selection of simple traits; however, it has not been efficient for predicting complex traits affected by a large number of loci. Recently the appearance of cheap, abundant markers has made it possible to saturate the genome with high density markers and use marker information to predict genomic breeding values, thus increasing the precision of genetic value prediction over that achieved with the traditional use of pedigree information. Genomic data also allow assessing chromosome regions through marker effects and studying the pattern of covariablity of marker effects across differential environmental conditions. In this review, we outline the most important models for assessing genotype × environment interaction, QTL × environment interaction, and marker effect (gene) × environment interaction. Since analyzing genetic and genomic data is one of the most challenging statistical problems researchers currently face, different models from different areas of statistical research must be attempted in order to make significant progress in understanding genetic effects and their interaction with environment.     

Interaction of α2-macroglobulin with L-asparaginase

Summary Obvious protection of the catalytic activity of Esch. coli L-asparaginase by ₂-macroglobulin (₂M) was observed under conditions otherwise propitious to the dissociation of the tetrameric molecule into inactive subunits, i.e. very diluted enzyme solutions or the presence of either SDS or urea. The degree of protection depended on enzyme and ₂M concentrations respectively, and on the preincubation time of the ₂M-enzyme mixture prior to substrate addition. The formation of a catalytically active complex between ₂M and L-asparaginase was confirmed by gel filtration on a Sephadex-G column and by polyacrylamide gel electrophoresis. The fact that the migration distance of the active complex corresponded to the migration of ₂M and the absence in that case of a migration band corresponding to the intact molecule suggest that complexing of the enzyme with ₂M prevented its dissociation into subunits and thus its inactivation. Addition of ₂M to the already dissociated enzyme molecule did not restore its catalytic activity.Alpha₂-macroglobulin was shown to have an inhibiting effect on the proteolytic activity of almost all proteases and no effect on their esterolytic activity. Furthermore, it prevents the inhibition of esterolytic activity by some natural compounds^1–5. The effect of ₂M on other types of catalytic activity has not been investigated enough to afford a generalization of the possible role of this macroglobulin in the control of enzyme activity in the body.This paper reports the results of an in vitro study of the effect of ₂M on the catalytic activity of an important amidase, i.e. L-asparaginase (L-asparagine amidohydrolase 3.5.1.1), which in recent years has been used in the treatment of acute lymphocytic leukemia in children^6,7.Abbreviations ₂M ₂-macroglobulin - E enzyme - SDS sodium dodecylsulfate Part of the results were reported at the 10th International Congress of Biochemistry, Hamburg 1976, Abst. p. 377.     

Nonverbal Communication and Human–Dog Interaction

Human–dog interaction relies to a large extent on nonverbal communication, and it is therefore plausible that human sensitivity to nonverbal signals affects interactions between human and dog. Experience with dogs is also likely to influence human–dog interactions, and it has been suggested that it influences human social skills. The present study investigated possible links between human nonverbal sensitivity, experience with dogs, and the quality of human–dog interactions. Two studies are reported. In study 1, 97 veterinary students took a psychometric test assessing human nonverbal sensitivity and answered a questionnaire on their experience with dogs. The data obtained were then used to investigate the relationship between experience with dogs and sensitivity to human nonverbal communication. The results did not indicate that experience with dogs improves human nonverbal sensitivity. In study 2, 16 students with high, and 15 students with low, levels of human nonverbal sensitivity were selected. Each of the 31 students interacted once with an unknown dog in a greeting situation, and these human–dog interactions were videoed. We found that a combined score of dog behaviors relating to insecurity was associated with the students' level of nonverbal sensitivity and experience with dogs: the dog showed more of the insecure behavior when interacting with students with a low level of nonverbal sensitivity and no experience with dogs than it did when interacting with students with a high level of nonverbal sensitivity (irrespective of experience with dogs).     

Host–Pathogen Interaction in Invasive Salmonellosis

Salmonella enterica infections result in diverse clinical manifestations. Typhoid fever, caused by S. enterica serovar Typhi (S. Typhi) and S. Paratyphi A, is a bacteremic illness but whose clinical features differ from other Gram-negative bacteremias. Non-typhoidal Salmonella (NTS) serovars cause self-limiting diarrhea with occasional secondary bacteremia. Primary NTS bacteremia can occur in the immunocompromised host and infants in sub-Saharan Africa. Recent studies on host–pathogen interactions in Salmonellosis using genome sequencing, murine models, and patient studies have provided new insights. The full genome sequences of numerous S. enterica serovars have been determined. The S. Typhi genome, compared to that of S. Typhimurium, harbors many inactivated or disrupted genes. This can partly explain the different immune responses both serovars induce upon entering their host. Similar genome degradation is also observed in the ST313 S. Typhimurium strain implicated in invasive infection in sub-Saharan Africa. Virulence factors, most notably, type III secretion systems, Vi antigen, lipopolysaccharide and other surface polysaccharides, flagella, and various factors essential for the intracellular life cycle of S. enterica have been characterized. Genes for these factors are commonly carried on Salmonella Pathogenicity Islands (SPIs). Plasmids also carry putative virulence-associated genes as well as those responsible for antimicrobial resistance. The interaction of Salmonella pathogen-associated molecular patterns (PAMPs) with Toll-like receptors (TLRs) and NOD-like receptors (NLRs) leads to inflammasome formation, activation, and recruitment of neutrophils and macrophages and the production of pro-inflammatory cytokines, most notably interleukin (IL)-6, IL-1β, tumor necrosis factor (TNF)-α, and interferon-gamma (IFN)-γ. The gut microbiome may be an important modulator of this immune response. S. Typhimurium usually causes a local intestinal immune response, whereas S. Typhi, by preventing neutrophil attraction resulting from activation of TLRs, evades the local response and causes systemic infection. Potential new therapeutic strategies may lead from an increased understanding of infection pathogenesis.     

Interaction of α-Chymotrypsin with Dimethyl Sulfoxide: A Change of Substrate Could “Change” the Interaction Mechanism

The kinetic behavior of -chymotrypsin was studied in water–DMSO mixtures at concentrations of the organic solvent that do not cause irreversible denaturation of the enzyme. Various substrates (N-substituted derivatives of L-tyrosine) were found to display substantially different kinetic patterns of interaction with -chymotrypsin, which can be described by totally different kinetic schemes. The differences were ascribed to competition between the N-acyl group of the substrate and the DMSO molecule at the S ₂ site of substrate binding to the active site of the enzyme.     

Interaction of Peristaltic Flow with Pulsatile Couple-Stress Fluid

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Interaction between Duodenase and α1-Proteinase Inhibitor

The interaction between duodenase, a newly recognized serine proteinase belonging to the small group of Janusfaced proteinases, and ₁-proteinase inhibitor (₁-PI) from human serum was investigated. The stoichiometry of the inhibition was 1.2 mol/mol. The presence of a stable enzyme–inhibitor complex was shown by SDS-PAGE. The mechanism of interaction between duodenase and ₁-PI was shown to be of the suicide type. The equilibrium and inhibition constants are 13 ± 3 nM and (1.9 ± 0.3)·10⁵ M^–1·sec^–1, respectively. Based on the association rate constant of the enzyme–inhibitor complex and localization of duodenase and ₁-PI in identical compartments, ₁-PI is suggested to be a duodenase inhibitor in vivo.     

Drug–DNA Interaction Studies of Acridone-Based Derivatives

N¹⁰-alkylated 2-bromoacridones are a novel series of potent antitumor compounds. DNA binding studies of these compounds were carried out using spectrophotometric titrations, Circular dichroism (CD) measurements using Calf Thymus DNA (CT DNA). The binding constants were identified at a range of K = 0.3 to 3.9 × 10⁵ M^?1 and the percentage of hypochromism from the spectral titrations at 28–54%. This study has identified a compound 9 with the good binding affinity of K = 0.39768 × 10⁵ M^?1 with CT DNA. Molecular dynamics (MD) simulations have investigated the changes in structural and dynamic features of native DNA on binding to the active compound 9. All the synthesized compounds have increased the uptake of Vinblastine in MDR KBCh^R-8-5 cells to an extent of 1.25- to1.9-fold than standard modulator Verapamil of similar concentration. These findings allowed us to draw preliminary conclusions about the structural features of 2-bromoacridones and further chemical enhancement will improve the binding affinity of the acridone derivatives to CT-DNA for better drug–DNA interaction. The molecular modeling studies have shown mechanism of action and the binding modes of the acridones to DNA.     

Protein Interaction Networks—More Than Mere Modules

It is widely believed that the modular organization of cellular function is reflected in a modular structure of molecular networks. A common view is that a “module” in a network is a cohesively linked group of nodes, densely connected internally and sparsely interacting with the rest of the network. Many algorithms try to identify functional modules in protein-interaction networks (PIN) by searching for such cohesive groups of proteins. Here, we present an alternative approach independent of any prior definition of what actually constitutes a “module”. In a self-consistent manner, proteins are grouped into “functional roles” if they interact in similar ways with other proteins according to their functional roles. Such grouping may well result in cohesive modules again, but only if the network structure actually supports this. We applied our method to the PIN from the Human Protein Reference Database (HPRD) and found that a representation of the network in terms of cohesive modules, at least on a global scale, does not optimally represent the network''s structure because it focuses on finding independent groups of proteins. In contrast, a decomposition into functional roles is able to depict the structure much better as it also takes into account the interdependencies between roles and even allows groupings based on the absence of interactions between proteins in the same functional role. This, for example, is the case for transmembrane proteins, which could never be recognized as a cohesive group of nodes in a PIN. When mapping experimental methods onto the groups, we identified profound differences in the coverage suggesting that our method is able to capture experimental bias in the data, too. For example yeast-two-hybrid data were highly overrepresented in one particular group. Thus, there is more structure in protein-interaction networks than cohesive modules alone and we believe this finding can significantly improve automated function prediction algorithms.     

Interaction between DNA Polymerase β and BRCA1

The breast cancer 1 (BRCA1) protein is a tumor suppressor playing roles in DNA repair and cell cycle regulation. Studies of DNA repair functions of BRCA1 have focused on double-strand break (DSB) repair pathways and have recently included base excision repair (BER). However, the function of BRCA1 in BER is not well defined. Here, we examined a BRCA1 role in BER, first in relation to alkylating agent (MMS) treatment of cells and the BER enzyme DNA polymerase β (pol β). MMS treatment of BRCA1 negative human ovarian and chicken DT40 cells revealed hypersensitivity, and the combined gene deletion of BRCA1 and pol β in DT40 cells was consistent with these factors acting in the same repair pathway, possibly BER. Using cell extracts and purified proteins, BRCA1 and pol β were found to interact in immunoprecipitation assays, yet in vivo and in vitro assays for a BER role of BRCA1 were negative. An alternate approach with the human cells of immunofluorescence imaging and laser-induced DNA damage revealed negligible BRCA1 recruitment during the first 60 s after irradiation, the period typical of recruitment of pol β and other BER factors. Instead, 15 min after irradiation, BRCA1 recruitment was strong and there was γ-H2AX co-localization, consistent with DSBs and repair. The rapid recruitment of pol β was similar in BRCA1 positive and negative cells. However, a fraction of pol β initially recruited remained associated with damage sites much longer in BRCA1 positive than negative cells. Interestingly, pol β expression was required for BRCA1 recruitment, suggesting a partnership between these repair factors in DSB repair.     

Interaction of α-N-acetyl-β-endorphin and calmodulin

Acetylation at the α-amino terminal is a common post-translational modification of many peptides and proteins. In the case of the potent opiate peptide β-endorphin, α-N-acetylation is a known physiological modification that abolishes opiate activity. Since there are no known receptors for α-N-acetyl-β-endorphin, we have studied the association of this peptide with calmodulin, a calcium-dependent protein that binds a variety of peptides, phenothiazines, and enzymes, as a model system for studying acetylated endorphin-protein interactions. Association of the acetylated peptide with calmodulin was demonstrated by cross-linking with bis(sulfosuccinimidyl)suberate; like β-endorphin, adducts containing 1 mol and 2 mol of acetylated peptide per mole calmodulin were formed. Some of the bound peptides are evidently in relatively close proximity to each other since, in the presence of amidated (i.e., lysine-blocked) calmodulin, cross-linking yielded peptide dimers. The acetylated peptide exhibited no appreciable helicity in aqueous solution, but in trifluoroethanol (TFE) considerable helicity was formed. Also, a mixture of acetylated peptide and calmodulin was characterized by a circular dichroic spectrum indicative of induced helicity. Empirical prediction rules, applied earlier to β-endorphin, suggest that residues 14–24 exhibit α-helix potential. This segment has the potential of forming an amphipathic helix; this structural unit is believed to be important in calmodulin binding. The acetylated peptide was capable of inhibiting the calmodulin-mediated stimulation of cyclic nucleotide phosphodiesterase (EC 3.1.4.17) activity with an effective dose for 50% inhibition of about 3 µM; this inhibitory effect was demonstrated using both an enzyme-enriched preparation as well as highly purified enzyme. Thus, acetylation at the α-amino terminal of β-endorphin, although abolishing opiate activity, does not interfere with the binding to calmodulin. Indeed, β-endorphin and the α-N-acetylated peptide behave very similarly with respect to calmodulin association.     

Efficiency of the miRNA–mRNA Interaction Prediction Programs

miRNAs play a key role in regulation of gene expression. Nowadays it is known more than 2500 human miRNAs, while a majority of miRNA–mRNA interactions remains unidentified. The recent development of a high-throughput CLASH (crosslinking, ligation and sequencing of hybrids) technique for discerning miRNA–mRNA interactions allowed an experimental analysis of the human miRNA–mRNA interactome. Therefore, it allowed us, for the first time, make an experimental analysis of the human miRNA–mRNA interactome as a whole and an evaluation of the quality of most commonly used miRNA prediction tools (TargetScan, PicTar, PITA, RNA22 and miRanda). To estimate efficiency of the miRNA–mRNA prediction tools, we used next parameters: sensitivity, positive predicted value, predictions in different mRNA regions (3' UTR, CDS, 5' UTR), predictions for different types of interactions (5 classes), predictions of “canonical” and “nocanonical” interactions, similarity with the random generated data. The analysis revealed low efficiency of all prediction programs in comparison with the CLASH data in terms of the all examined parameters.     

Interaction between DNA Polymerase λ and Anticancer Nucleoside Analogs

The anticancer activity of cytarabine (AraC) and gemcitabine (dFdC) is thought to result from chain termination after incorporation into DNA. To investigate their incorporation into DNA at atomic level resolution, we present crystal structures of human DNA polymerase λ (Pol λ) bound to gapped DNA and containing either AraC or dFdC paired opposite template dG. These structures reveal that AraC and dFdC can bind within the nascent base pair binding pocket of Pol λ. Although the conformation of the ribose of AraCTP is similar to that of normal dCTP, the conformation of dFdCTP is significantly different. Consistent with these structures, Pol λ efficiently incorporates AraCTP but not dFdCTP. The data are consistent with the possibility that Pol λ could modulate the cytotoxic effect of AraC.     

Uni-directional Interaction and Plant–Pollinator–Robber Coexistence

A mathematical model for the plant-pollinator-robber interaction is studied to understand the factors leading to the widespread occurrence and stability of such interactions. In the interaction, a flowering plant provides resource for its pollinator and the pollinator has both positive and negative effects on the plant. A nectar robber acts as a plant predator, consuming a common resource with the pollinator, but with a different functional response. Using dynamical systems theory, mechanisms of species coexistence are investigated to show how a robber could invade the plant-pollinator system and persist stably with the pollinator. In addition, circumstances are demonstrated in which the pollinator's positive and negative effects on the plant could determine the robber's invasibility and the three-species coexistence.     

Allele Interaction – Single Locus Genetics Meets Regulatory Biology

Background

Since the dawn of genetics, additive and dominant gene action in diploids have been defined by comparison of heterozygote and homozygote phenotypes. However, these definitions provide little insight into the underlying intralocus allelic functional dependency and thus cannot serve directly as a mediator between genetics theory and regulatory biology, a link that is sorely needed.

Methodology/Principal Findings

We provide such a link by distinguishing between positive, negative and zero allele interaction at the genotype level. First, these distinctions disclose that a biallelic locus can display 18 qualitatively different allele interaction sign motifs (triplets of +, – and 0). Second, we show that for a single locus, Mendelian dominance is not related to heterozygote allele interaction alone, but is actually a function of the degrees of allele interaction in all the three genotypes. Third, we demonstrate how the allele interaction in each genotype is directly quantifiable in gene regulatory models, and that there is a unique, one-to-one correspondence between the sign of autoregulatory feedback loops and the sign of the allele interactions.

Conclusion/Significance

The concept of allele interaction refines single locus genetics substantially, and it provides a direct link between classical models of gene action and gene regulatory biology. Together with available empirical data, our results indicate that allele interaction can be exploited experimentally to identify and explain intricate intra- and inter-locus feedback relationships in eukaryotes.     

Fluorescence Quenching Studies of Trp Repressor–Operator Interaction

Steady-state quenching and time-resolved fluorescence measurements of L-tryptophan binding to the tryptophan-free mutant W19/99F of the tryptophan repressor of Escherichia coli have been used to observe the coreperessor microenvirnment changes upon ligand binding. Using iodide and acrylamide as quenchers, we have resolved the emission spectra of the corepressor into two components. The bluer component of L-tryptophan buried in the holorepressor exhibits a maximum of the fluorescence emission at 336 nm and can be characterized by a Stern–Volmer quenching constant equal to about 2.0–2.3 M^?1. The second, redder component is exposed to the solvent and possesses the fluorescence emission and Stern–Volmer quenching constant characteristic of L-tryptophan in the solvent. When the Trp holorepressor is bound to the DNA operator, further alterations in the corepressor fluorescence are observed. Acrylamide quenching experiments indicate that the Stern–Volmer quenching constant of the buried component of the corepressor decreases drastically to a value of 0.56 M^?1. The fluorescence lifetimes of L-tryptophan in a complex with Trp repressor decrease substantially upon binding to DNA, which indicates a dynamic mechanism of the quenching process.