Gene Expression Profiling of Intrinsic Thermotolerance in Escherichia coli

Whole-genome transcriptional analysis of Escherichia coli during heat inactivation processes related to industrial cooking

Escherichia coli K-12 was grown to the stationary phase, for maximum physiological resistance, in brain heart infusion (BHI) broth at 37°C. Cells were then heated at 58°C or 60°C to reach a process lethality value \[\mathbf{\left(}{{\mathit{F}}^{\mathit{o}}}_{\mathbf{70}}^{\mathbf{10}}\mathbf{\right)} \] of 2 or 3 or to a core temperature of 71°C (control industrial cooking temperature). Growth recovery and cell membrane integrity were evaluated immediately after heating, and a global transcription analysis was performed using gene expression microarrays. Only cells heated at 58°C with F(o) = 2 were still able to grow on liquid or solid BHI broth after heat treatment. However, their transcriptome did not differ from that of bacteria heated at 58°C with F(o) = 3 (P value for the false discovery rate [P-FDR] > 0.01), where no growth recovery was observed posttreatment. Genome-wide transcriptomic data obtained at 71°C were distinct from those of the other treatments without growth recovery. Quantification of heat shock gene expression by real-time PCR revealed that dnaK and groEL mRNA levels decreased significantly above 60°C to reach levels similar to those of control cells at 37°C (P < 0.0001). Furthermore, despite similar levels of cell inactivation measured by growth on BHI media after heating, 132 and 8 genes were differentially expressed at 71°C compared to 58°C and 60°C at F(o) = 3, respectively (P-FDR < 0.01). Among them, genes such as aroA, citE, glyS, oppB, and asd, whose expression was upregulated at 71°C, may be worth investigating as good biomarkers for accurately determining the efficiency of heat treatments, especially when cells are too injured to be enumerated using growth media.

Phenotypic engineering by reprogramming gene transcription using novel artificial transcription factors in Escherichia coli

Now that many genomes have been sequenced and the products of newly identified genes have been annotated, the next goal is to engineer the desired phenotypes in organisms of interest. For the phenotypic engineering of microorganisms, we have developed novel artificial transcription factors (ATFs) capable of reprogramming innate gene expression circuits in Escherichia coli. These ATFs are composed of zinc finger (ZF) DNA-binding proteins, with distinct specificities, fused to an E. coli cyclic AMP receptor protein (CRP). By randomly assembling 40 different types of ZFs, we have constructed more than 6.4 × 10⁴ ATFs that consist of 3 ZF DNA-binding domains and a CRP effector domain. Using these ATFs, we induced various phenotypic changes in E. coli and selected for industrially important traits, such as resistance to heat shock, osmotic pressure and cold shock. Genes associated with the heat-shock resistance phenotype were then characterized. These results and the general applicability of this platform clearly indicate that novel ATFs are powerful tools for the phenotypic engineering of microorganisms and can facilitate microbial functional genomic studies.

High-sensitivity detection methods for low-abundance RNA species: applications for functional genomics research

Gene expression analysis has facilitated a more complete understanding of the molecular biology of cellular processes and how variations of RNA expression are useful for the classification of various diseases. Furthermore, recent analysis of a variety of noncoding RNAs, such as microRNAs, has demonstrated that these RNAs play an important role in many cellular events, including cell differentiation and death, and may also serve as biological markers for disease. Besides helping in the understanding of diseases, RNA analysis is used in drug discovery, patient prognosis and treatment evaluation. One obstacle left to overcome is the amount of material required for the analysis, particularly when trying to extract information from precious, limited, clinical samples. Here we review the many approaches scientists take to either amplify the amount of RNA or amplify the signal generated from small amounts of RNA.