Understanding Cellular Stress Responses using Novel Transcriptomic Analytics

Industrial biotechnology has enabled the production of a wide range of products beneficial to society, including pharmaceuticals, chemicals, food and beverages. Current research investigates additional potential products that can be delivered through industrial biotechnology, such as alternative food proteins, sustainable materials with superior performance, and advanced therapeutics. These innovations may assist in solving global challenges, including emerging food and climate crises. Production is carried out in microbial cells that convert low-cost substrates into valuable products via fermentation. Using modern genetic engineering, cells can be engineered to overproduce these products through heterologous gene expression.

This Ph.D. thesis investigates how Escherichia coli production hosts respond to cellular stresses from heterologous gene expression. Applying independent component analysis (ICA) to large transcriptomic datasets, iModulons are used to
provide a data-driven perspective on transcriptional regulation. Systematic experiments are designed to investigate stress at the three key stages of heterologous expression: plasmid replication, mRNA transcription, and protein translation.

The findings revealed distinct stress responses at each stage. Hosts with increasing plasmid numbers triggered the DNA damage response, which provides DNA repair mechanisms while slowing replication and inhibiting cell division. The number of plasmids in a host stimulated cell filamentation that formed heterogeneously within the population. High-transcription systems activated the cold shock response that destabilize mRNA secondary structures to facilitate translation. Protein-related stresses were dependent on the protein being expressed. Cysteine-rich proteins elicited distinct responses that balance iron and redox homeostasis, including oxidative stress responses. Most proteins elicited the heat shock response that refold and degrade misfolded protein. This response was co-activated with another response that controls osmotic balance and possibly suppresses entry into the stationary phase. Based on our understanding of the latter response, strain, and media designs were formulated to enhance protein production.

This thesis demonstrates using big data and advanced analytics to reveal cellular stresses in production hosts. iModulon analysis is highlighted as a powerful approach for interpreting and understanding transcriptional responses. This data-driven approach may be used to engineer more predictable and optimized production hosts.

However, this imposes a substantial load and cellular stress on the production host. Therefore, the challenge is to develop robust hosts that express products optimally and predictably.