Quantitative analysis of bacterial gene expression by using the gusA reporter gene system

The glpD gene is a novel reporter gene for E. coli that is superior to established reporter genes like lacZ and gusA

Reporter genes facilitate the characterization of promoter activities, transcript stabilities, translational efficiencies, or intracellular localization. Various reporter genes for Escherichia coli have been established, however, most of them have drawbacks like transcript instability or the inability to be used in genetic selections. Therefore, the glpD gene encoding glycerol-3-phosphate dehydrogenase was introduced as a novel reporter gene for E. coli. The enzymatic assay was optimized, and it was verified that growth on glycerol strictly depends on the presence of GlpD. The 5'-UTRs of three E. coli genes were chosen and cloned upstream of the new reporter gene glpD as well as the established reporter genes lacZ and gusA. Protein and transcript levels were quantified and translational efficiencies were calculated. The lacZ transcript was very unstable and its level highly depended on its translation, compromising its use as a reporter. The results obtained with gusA and glpD were similar, however, only glpD can be used for genetic selections. Therefore, glpD was found to be a superior novel reporter gene compared to the established reporter genes lacZ and gusA.

Azospirillum-plant relationships: physiological, molecular, agricultural, and environmental advances (1997-2003)

This review presents a critical and comprehensive documentation and analysis of the developments in agricultural, environmental, molecular, and physiological studies related to Azospirillum cells, and to Azospirillum interactions with plants, based solely on information published between 1997 and 2003. It was designed as an update of previous reviews (Bashan and Levanony 1990; Bashan and Holguin 1997a), with a similar scope of interest. Apart from an update and critical analysis of the current knowledge, this review focuses on the central issues of Azospirillum research today, such as, (i) physiological and molecular studies as a general model for rhizosphere bacteria; (ii) co-inoculation with other microorganisms; (iii) hormonal studies and re-consideration of the nitrogen contribution by the bacteria under specific environmental conditions; (iv) proposed Azospirillum as a non-specific plant-growth-promoting bacterium; (v) re-introduction of the "Additive Hypothesis," which suggests involvement of multiple mechanisms employed by the bacteria to affect plant growth; (vi) comment on the less researched areas, such as inoculant and pesticide research; and (vii) proposes possible avenues for the exploitation of this bacterium in environmental areas other than agriculture.Key words: Azospirillum, plant–bacteria interaction, plant-growth-promoting bacteria, PGPB, PGPR, rhizosphere bacteria.

Sensitivity function-based model reduction: A bacterial gene expression case study

Mathematical models used to predict the behavior of genetically modified organisms require 1). a (rather) large number of state variables, and 2). complicated kinetic expressions containing a large number of parameters. Since these models are hardly identifiable and of limited use in model-based optimization and control strategies, a generic methodology based on sensitivity function analysis is presented to reduce the model complexity at the level of the kinetics, while maintaining high prediction power. As a case study to illustrate the method and results obtained, the influence of the dissolved oxygen concentration on the cytN gene expression in the bacterium Azospirillum brasilense Sp7 is modeled. As a first modeling approach, available mechanistic knowledge is incorporated into a mass balance equation model with 3 states and 14 parameters. The large differences in order of magnitude of the model parameters identified on the available experimental data indicate 1). possible structural problems in the kinetic model and, associated with this, 2). a possibly too high number of model parameters. A careful sensitivity function analysis reveals that a reduced model with only seven parameters is almost as accurate as the original model.