OBJECTIVE 1: The first objective of this project was to create a library of E. coli strains for transcriptional down-regulation (interference) of metabolic genes. We successfully created such a library that includes all 1515 metabolic genes in the latest genome-scale E. coli metabolic model. We achieve tight and inducible interference by expressing an enzymatically dead Cas9 (dCas9) protein from the genome, which is guided to the interference target by co-expressing a single guide RNA from a plasmid.
In total, we constructed 7177 sgRNAs, which we cloned in a pooled approach using array-synthesized oligonucleotides. From this pool of 7177 strains, we selected a panel of 30 strains for a pilot study to understand the cellular response during the transition phase from an unrepressed state into a repressed state for each of the 30 target enzymes (Donati et al. 2021). These data identified several regulatory mechanisms that compensate the decreases in enzymes levels. In the case of amino acids, this works via the known transcriptional and allosteric feedback mechanisms. In the case of the pentose phosphate pathway, we discovered a new regulatory mechanism that compensates decreases of an enzyme by activating alternative pathways that by-pass the critical enzyme. Thus, the 30-enzymes pilot study confirmed that downregulation of enzymes informs about regulatory mechanisms. Next, we arrayed 1515 CRISPRi strains (1 strain per metabolic gene) and measured the metabolome and proteome of 305 strains. By integrating these data we could infer feedback regulation in metabolic pathways, but also crosstalk between pathways (i.e. perturbations in one pathway that led to compensatory responses in another pathway).
OBJECTIVE 2: The analysis of the large metabolomics and proteomics datasets was the second challenge of this project, and we approached this by integrating the data with statistical analyses and computational models. Early in the project we realized that conventional models based on ordinary differential equations are not capable to handle a large amount of information and faithfully infer regulation. For this reason, we used alternative data-driven approaches to infer regulation. For example, a correlation analysis between transcription factors activities (based on transcriptome data and network component analysis), and metabolome data recovered known regulatory interactions between transcription factors and metabolites (Lempp et al. 2019). Similarly, correlation between proteins and metabolites in 305 CRISPRi knockdowns identified new regulatory mechanisms.
OBJECTIVE 3: The main goal of this project was to arrest growth of E. coli with CRISPR interference of essential genes and produce metabolites with these non-growing cells. It turned out that degradation of dCas9 and escape mutations are a major problem that prevents a stable and long-lasting growth arrest. To overcome this problem we used an alternative approach based on temperature-sensitive (TS) enzymes (Schramm et al. 2020). First we introduced gene deletions and then re-inserted TS enzyme variants that inactive at higher temperatures (42°C). With this approach, we were able to produce about 3 g/L of the α-amino acid L-citrulline in stationary E. coli. Next we inserted mutations that confer thermal-sensitive into the E. coli genome with CRISPR gene editing (Schramm et al. in revision). To this end, we created 15 120 E. coli mutants, each with one mutation and a pooled screen showed that more than 1000 of these mutants are thermo-sensitive. We analyzed 94 of these TS mutants in detail and used them as growth-switches in two-stage processes (e.g. for thermo-sensitive DNA replication in an arginine producing strain).
