Periodic Reporting for period 5 - MapMe (Mapping metabolic regulators at a genome-scale to switch bacteria from growth to overproduction of chemicals)
Periodo di rendicontazione: 2022-04-01 al 2022-12-31
Metabolic engineering creates improved microbes for industrial biotechnology. Rational design of industrial microbes revolves around modifications of genes with known roles in the production pathway of interest. However, genes that are unrelated to the production pathway are also known to substantially impact productivity. To date there are no methods that allow the prediction of such distal genes on a rational basis. Effects of distal genes are indirect and mediated through regulatory interactions between metabolites and proteins, most of which are currently unknown even in the well-studied microbe Escherichia coli. The lack of knowledge of metabolite-protein interactions thus effectively prohibits systematic exploration of distal regulatory relationships, with the consequence that models used to predict metabolic engineering targets are severely limited and rarely applied in industrial biotechnology.
The resulting gap between strain design and construction is a genuine problem for industrial biotechnology. Tight regulation of metabolism causes unpredictable responses to genetic modifications, which can substantially affect cellular fitness and robustness. Thus designing regulation of synthetic pathways on a rational basis will break new grounds in metabolic engineering and opens up novel applications in industrial biotechnology.
In this ERC funded project, we proposed to bridge the gap between strain design and construction by a genome-wide endeavor to map regulatory interactions between metabolites and proteins. The overall objective of this project is two-fold. First, we map regulation of the E. coli metabolic network by downregulating single enzymes with CRISPR interference (CRISPRi) and measuring the proteome and metabolome responses. Second, we use the knowledge about metabolic regulation to build superior E. coli strains that cease growth upon induction and focus all metabolic resources towards the synthesis of chemicals. A computational work package is at the interface of these primary objectives, for example computational methods to integrate metabolomics data with transcriptomics or proteomics data to learn interactions between these layers.
The resulting gap between strain design and construction is a genuine problem for industrial biotechnology. Tight regulation of metabolism causes unpredictable responses to genetic modifications, which can substantially affect cellular fitness and robustness. Thus designing regulation of synthetic pathways on a rational basis will break new grounds in metabolic engineering and opens up novel applications in industrial biotechnology.
In this ERC funded project, we proposed to bridge the gap between strain design and construction by a genome-wide endeavor to map regulatory interactions between metabolites and proteins. The overall objective of this project is two-fold. First, we map regulation of the E. coli metabolic network by downregulating single enzymes with CRISPR interference (CRISPRi) and measuring the proteome and metabolome responses. Second, we use the knowledge about metabolic regulation to build superior E. coli strains that cease growth upon induction and focus all metabolic resources towards the synthesis of chemicals. A computational work package is at the interface of these primary objectives, for example computational methods to integrate metabolomics data with transcriptomics or proteomics data to learn interactions between these layers.
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).
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).
We succeeded to map the metabolic landscape of a cell by measuring the concentration of >500 metabolites and >2500 proteins in 305 single gene knockdowns. These data show that metabolism responds locally to single gene knockdowns, and we rarely observe global growth rate responses, which contrast the response to environmental perturbations that we measured for example in Rados et al. 2022.
Another important progress was the construction of a genome-wide library of thermo-mutants that function as growth-switches (Schramm et al 2020 and Schramm et al in revision). These thermo-growth-switches allow to stop each cellular process (e.g. DNA replication, transcription or metabolic processes) by a small temperature shift. We demonstrated that the thermo-growth-switches can function as metabolic valves to engineer improved industrial microbes for two-stage bioprocesses.
Another important progress was the construction of a genome-wide library of thermo-mutants that function as growth-switches (Schramm et al 2020 and Schramm et al in revision). These thermo-growth-switches allow to stop each cellular process (e.g. DNA replication, transcription or metabolic processes) by a small temperature shift. We demonstrated that the thermo-growth-switches can function as metabolic valves to engineer improved industrial microbes for two-stage bioprocesses.