Interactive visualization of clusters in microarray data: an efficient tool for improved metabolic analysis of E. coli

A comparative analysis of industrial Escherichia coli K-12 and B strains in high-glucose batch cultivations on process-, transcriptome- and proteome level

Escherichia coli K-12 and B strains are among the most frequently used bacterial hosts for production of recombinant proteins on an industrial scale. To improve existing processes and to accelerate bioprocess development, we performed a detailed host analysis. We investigated the different behaviors of the E. coli production strains BL21, RV308, and HMS174 in response to high-glucose concentrations. Tightly controlled cultivations were conducted under defined environmental conditions for the in-depth analysis of physiological behavior. In addition to acquisition of standard process parameters, we also used DNA microarray analysis and differential gel electrophoresis (Ettan(TM) DIGE). Batch cultivations showed different yields of the distinct strains for cell dry mass and growth rate, which were highest for BL21. In addition, production of acetate, triggered by excess glucose supply, was much higher for the K-12 strains compared to the B strain. Analysis of transcriptome data showed significant alteration in 347 of 3882 genes common among all three hosts. These differentially expressed genes included, for example, those involved in transport, iron acquisition, and motility. The investigation of proteome patterns additionally revealed a high number of differentially expressed proteins among the investigated hosts. The subsequently selected 38 spots included proteins involved in transport and motility. The results of this comprehensive analysis delivered a full genomic picture of the three investigated strains. Differentially expressed groups for targeted host modification were identified like glucose transport or iron acquisition, enabling potential optimization of strains to improve yield and process quality. Dissimilar growth profiles of the strains confirm different genotypes. Furthermore, distinct transcriptome patterns support differential regulation at the genome level. The identified proteins showed high agreement with the transcriptome data and suggest similar regulation within a host at both levels for the identified groups. Such host attributes need to be considered in future process design and operation.

Comparative transcription profiling and in-depth characterization of plasmid-based and plasmid-free Escherichia coli expression systems under production conditions

Plasmid-based Escherichia coli BL21(DE3) expression systems are extensively used for the production of recombinant proteins. However, the combination of a high gene dosage with strong promoters exerts extremely stressful conditions on producing cells, resulting in a multitude of protective reactions and malfunctions in the host cell with a strong impact on yield and quality of the product. Here, we provide in-depth characterization of plasmid-based perturbations in recombinant protein production. A plasmid-free T7 system with a single copy of the gene of interest (GOI) integrated into the genome was used as a reference. Transcriptomics in combination with a variety of process analytics were used to characterize and compare a plasmid-free T7-based expression system to a conventional pET-plasmid-based expression system, with both expressing human superoxide dismutase in fed-batch cultivations. The plasmid-free system showed a moderate stress response on the transcriptional level, with only minor effects on cell growth. In contrast to this finding, comprehensive changes on the transcriptome level were observed in the plasmid-based expression system and cell growth was heavily impaired by recombinant gene expression. Additionally, we found that the T7 terminator is not a sufficient termination signal. Overall, this work reveals that the major metabolic burden in plasmid-based systems is caused at the level of transcription as a result of overtranscription of the multicopy product gene and transcriptional read-through of T7 RNA polymerase. We therefore conclude that the presence of high levels of extrinsic mRNAs, competing for the limited number of ribosomes, leads to the significantly reduced translation of intrinsic mRNAs.

An advanced monitoring platform for rational design of recombinant processes

Bioprocess engineering is an application-oriented science in an interdisciplinary environment, and a meaningful combination of different scientific disciplines is the only way to meet the challenges of bioprocess complexity. Setting up a reasoned process monitoring platform is the first step in an iterative procedure aiming at process and systems understanding, being the key to rational and innovative bioprocess design. This chapter describes a comprehensive process monitoring platform and how the resulting knowledge is translated into new strategies in process and/or host cell design.

Exploratory and inferential analysis of gene cluster neighborhood graphs

Background

Many different cluster methods are frequently used in gene expression data analysis to find groups of co-expressed genes. However, cluster algorithms with the ability to visualize the resulting clusters are usually preferred. The visualization of gene clusters gives practitioners an understanding of the cluster structure of their data and makes it easier to interpret the cluster results.

Results

In this paper recent extensions of R package gcExplorer are presented. gcExplorer is an interactive visualization toolbox for the investigation of the overall cluster structure as well as single clusters. The different visualization options including arbitrary node and panel functions are described in detail. Finally the toolbox can be used to investigate the quality of a given clustering graphically as well as theoretically by testing the association between a partition and a functional group under study.

Conclusion

It is shown that gcExplorer is a very helpful tool for a general exploration of microarray experiments. The identification of potentially interesting gene candidates or functional groups is substantially accelerated and eased. Inferential analysis on a cluster solution is used to judge its ability to provide insight into the underlying mechanistic biology of the experiment.