Identification of Rice Transcription Factors Associated with Drought Tolerance Using the Ecotilling Method

The drought tolerance (DT) of plants is a complex quantitative trait. Under natural and artificial selection, drought tolerance represents the crop survival ability and production capacity under drought conditions (Luo, 2010). To understand the regulation mechanism of varied drought tolerance among rice genotypes, 95 diverse rice landraces or varieties were evaluated within a field screen facility based on the ‘line–source soil moisture gradient’, and their resistance varied from extremely resistant to sensitive. The method of Ecotype Targeting Induced Local Lesions in Genomes (Ecotilling) was used to analyze the diversity in the promoters of 24 transcription factor families. The bands separated by electrophoresis using Ecotilling were converted into molecular markers. STRUCTURE analysis revealed a value of K = 2, namely, the population with two subgroups (i.e., indica and japonica), which coincided very well with the UPGMA clusters (NTSYS-pc software) using distance-based analysis and InDel markers. Then the association analysis between the promoter diversity of these transcription factors and the DT index/level of each variety was performed. The results showed that three genes were associated with the DT index and that five genes were associated with the DT level. The sequences of these associated genes are complex and variable, especially at approximately 1000 bp upstream of the transcription initiation sites. The study illuminated that association analysis aimed at Ecotilling diversity of natural groups could facilitate the isolation of rice genes related to complex quantitative traits

Adaptive Laboratory Evolution of Antibiotic Resistance Using Different Selection Regimes Lead to Similar Phenotypes and Genotypes

Antibiotic resistance is a global threat to human health, wherefore it is crucial to study the mechanisms of antibiotic resistance as well as its emergence and dissemination. One way to analyze the acquisition of de novo mutations conferring antibiotic resistance is adaptive laboratory evolution. However, various evolution methods exist that utilize different population sizes, selection strengths, and bottlenecks. While evolution in increasing drug gradients guarantees high-level antibiotic resistance promising to identify the most potent resistance conferring mutations, other selection regimes are simpler to implement and therefore allow higher throughput. The specific regimen of adaptive evolution may have a profound impact on the adapted cell state. Indeed, substantial effects of the selection regime on the resulting geno- and phenotypes have been reported in the literature. In this study we compare the geno- and phenotypes of Escherichia coli after evolution to Amikacin, Piperacillin, and Tetracycline under four different selection regimes. Interestingly, key mutations that confer antibiotic resistance as well as phenotypic changes like collateral sensitivity and cross-resistance emerge independently of the selection regime. Yet, lineages that underwent evolution under mild selection displayed a growth advantage independently of the acquired level of antibiotic resistance compared to lineages adapted under maximal selection in a drug gradient. Our data suggests that even though different selection regimens result in subtle genotypic and phenotypic differences key adaptations appear independently of the selection regime

Chlorpromazine and amitriptyline are substrates and inhibitors of the acrb multidrug efflux pump

Efflux is an important mechanism in Gram-negative bacteria conferring multidrug resistance. Inhibition of efflux is an encouraging strategy to restore the antibacterial activity of antibiotics. Chlorpromazine and amitriptyline have been shown to behave as efflux inhibitors. However, their mode of action is poorly under-stood. Exposure of Salmonella enterica serovar Typhimurium and Escherichia coli to chlorpromazine selected for mutations within genes encoding RamR and MarR, regu-lators of the multidrug tripartite efflux pump AcrAB-TolC. Further experiments with S. Typhimurium containing AcrB D408A (a nonfunctional efflux pump) and chlor-promazine or amitriptyline resulted in the reversion of the mutant acrB allele to the wild type. Together, this suggests these drugs are AcrB efflux substrates. Subsequent docking studies with AcrB from S. Typhimurium and E. coli, followed by molecular dynamics simulations and free energy calculations showed that chlorpromazine and amitriptyline bind at the hydrophobic trap, a preferred binding site for substrates and inhibitors within the distal binding pocket of AcrB. Based on these simulations, we suggest that chlorpromazine and amitriptyline inhibit AcrB-mediated efflux by in-terfering with substrate binding. Our findings provide evidence that these drugs are substrates and inhibitors of AcrB, yielding molecular details of their mechanism of action and informing drug discovery of new efflux inhibitors. IMPORTANCE Efflux pumps of the resistance nodulation-cell division (RND) super-family are major contributors to multidrug resistance for most of the Gram-negative ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acineto-bacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) pathogens. The development of inhibitors of these pumps would be highly desirable; how-ever, several issues have thus far hindered all efforts at designing new efflux in-hibitory compounds devoid of adverse effects. An alternative route to de novo design relies on the use of marketed drugs, for which side effects on human health have been already assessed. In this work, we provide experimental evidence that the antipsychotic drugs chlorpromazine and amitriptyline are inhibi-tors of the AcrB transporter, the engine of the major RND efflux pumps in Escherichia coli and Salmonella enterica serovar Typhimurium. Furthermore, in silico calculations have provided a molecular-level picture of the inhibition mechanism, allowing rationalization of experimental data and paving the way for similar studies with other classes of marketed compounds

Towards a Model-Centric Software Testing Life Cycle for Early and Consistent Testing Activities

The constant improvement of the available computing power nowadays enables the accomplishment of more and more complex tasks. The resulting implicit increase in the complexity of hardware and software solutions for realizing the desired functionality requires a constant improvement of the development methods used. On the one hand over the last decades the percentage of agile development practices, as well as testdriven development increases. On the other hand, this trend results in the need to reduce the complexity with suitable methods. At this point, the concept of abstraction comes into play, which manifests itself in model-based approaches such as MDSD or MBT. The thesis is motivated by the fact that the earliest possible detection and elimination of faults has a significant influence on product costs. Therefore, a holistic approach is developed in the context of model-driven development, which allows applying testing already in early phases and especially on the model artifacts, i.e. it provides a shift left of the testing activities. To comprehensively address the complexity problem, a modelcentric software testing life cycle is developed that maps the process steps and artifacts of classical testing to the model-level. Therefore, the conceptual basis is first created by putting the available model artifacts of all domains into context. In particular, structural mappings are specified across the included domain-specific model artifacts to establish a sufficient basis for all the process steps of the life cycle. Besides, a flexible metamodel including operational semantics is developed, which enables experts to carry out an abstract test execution on the modellevel. Based on this, approaches for test case management, automated test case generation, evaluation of test cases, and quality verification of test cases are developed. In the context of test case management, a mechanism is realized that enables the selection, prioritization, and reduction of Test Model artifacts usable for test case generation. I.e. a targeted set of test cases is generated satisfying quality criteria like coverage at the model-level. These quality requirements are accomplished by using a mutation-based analysis of the identified test cases, which builds on the model basis. As the last step of the model-centered software testing life cycle two approaches are presented, allowing an abstract execution of the test cases in the model context through structural analysis and a form of model interpretation concerning data flow information. All the approaches for accomplishing the problem are placed in the context of related work, as well as examined for their feasibility by of a prototypical implementation within the Architecture And Analysis Framework. Subsequently, the described approaches and their concepts are evaluated by qualitative as well as quantitative evaluation. Moreover, case studies show the practical applicability of the approach