Organosilicons or compounds containing bonds between silicon and carbon and provide completely new structural moieties with altered properties and metabolism. By utilizing a well-known and previously engineered Cytochrome cKan.2016 as a catalyst, it is possible to synthesize carbon-silicon compounds suitable for medical and agricultural applications e.g. in Alzheimer’s disease or as insecticides. In our project, we are focusing on the application of novel organosilicon-forming organisms by evolving enhanced cytochrome c variants. This is implemented by the use of a phage-assisted continuous evolution (PACE) approach. In a stepwise proof of principle design, we can show 1) the production of two different organosilicons analyzed via the GC-MS method and 2) the viability of a riboswitch-coupled reporter system detecting one of the most valuable compounds derived from Organosilicon formation. This proof of principle will lead us to biocatalysts which are environmentally friendly and will greatly contribute to the production of novel carbon-silicon bonds as they are highly efficient.

Introduction

Organosilicons are organometallic compounds that consist of carbon-silicon bonds. They are comparable to their corresponding organic analogs but differ in their intrinsic properties. These differences, especially the chemical properties of silicon and the bond formation tendencies, have a significant impact on their bioavailability and their application in medicine. Recent publications cluster their unique features into three categories: The first category comprises the chemical properties of silicon bonds. Typically, silicon forms longer bonds at different angles, which leads to diverse ring conformations and thus, alterations in reactivity. Furthermore, its preference to form single bonds leads to chemical compounds that have a higher intrinsic stability than their carbon analogs. The second category represents the bioavailability of organosilicons. They are more likely to overcome the membrane barrier of cells as they are more lipophilic compared to their respective carbon counterparts. The third - and most important - category deals with the medical application of these compounds. Due to their aforementioned tendency to form single rather than double or triple bonds, they display a viable source for stable pharmaceuticals, which are inaccessible to carbon-based molecules. Additionally, the more electropositive nature of silicon facilitates hydrogen bond formation and conveniently increases the acidity of the compounds. As a result, organosilicons address the major issue in the synthesis of bioactive pharmaceuticals, the design of pro-drugs, as well as a safe medicine with a genuine biomedical benefit. Thus, their main advantage is to operate as pro-drugs due to their thermodynamic stability, but aqueous and acidic instability. On top, as we know so far, silicon is nonhazardous by itself, which makes it a valuable source for further biomedical research.

Our Idea

As a proof of principle, we wanted to show and harness the potential of organosilicon-forming proteins. Therefore, we use a previously engineered cytochrome c enzyme and couple organosilicon-production directly to a reporter expression. Thereby, we are focusing on the “small-molecule binding riboswitch” as proposed underlying mechanism. (citation) This riboswitch was designed in silico using the MAWS software, that was provided by the iGEM Team Heidelberg 2015. In a step-by-step approach, we wanted to produce an organosilicon which could, in the end, be tested with the designed riboswitch to express the NanoLuc reporter(Promega). NanoLuc is the most sensitive luciferaseuntil today and is able to show us a significant output despite using only a small amount of substrate .

Outlook

By establishing this proof of principle, we aim to further extend the use of organosilicon-producing proteins especially in combination with our PREDCEL approach and to bring silicon to life one big step closer.

Minmal Turbidostat Volume

Larger turbidostats or chemostats with a larger flow needs more medium for the same duration than smaller ones. When working with the minimal required volume or flow you can save medium and thus energy. The minimal flow that is required can be calculated using $$ \Phi_{T} = b \cdot V_{L} \cdot N_{L} \cdot \Phi_{L} $$ Which is simply the product of the lagoon volume, count and flow rate and a buffer.
In case of fluctuations in the generation time of the E. coli cells it is crucial to have a buffer so that the turbidostat is not diluted when the culture grows slower. A buffer of 50 %, means \(b\) is set to \(1.5\). For a turbidostat, the volume can be calculated from the flow using $$ V_{T} = \Phi_{T} \cdot \frac{t_{E} }{log(2)} $$ For chemostats only the needed flow rate \(\Phi_{T}\) is calculated, the volume needs to be chosen so that the flow rate can be reached.

Minimal Lagoon Volume

Obviously smaller lagoons require smaller turbidostats or chemostats with a lower flow rate and are therefore saving medium. However, there is a lower limit to lagoon size, if the phage population is too small, the sequence space that can be covered is insufficient to find variants that are better than previous ones. Lagoon sizes used by other vary from 15 mlRN158 over 40 mlRN31 to 100 mlRN63. There are a variety of possible ways to estimate the ideal size of the lagoons, one presented here. It is based on the sequence length and mutation rate. Alternatively, to adjust the size of the lagoons, it is possible to adjust the total duration of the experiment. But as that increases energy consumption for heating and stirring in addition to medium consumption, we decided to focus on the lagoon-size. The size of the phage population \(N_{P}\) per lagoon is $$ N_{P} = c_{P} \cdot V_{L} $$ The total sequence length \(L_{T}\) is $$ L_{T} = N_{P} \cdot L_{S} $$ when \(L{S}\) is the length of one sequence. The number of mutations that occur during one generation \(N_{M}\) is $$ N_{M} = L_{T} \cdot r_{M} $$ Here \(r_{M}\) is the mutation rate. It is reported to be \(5.3 \cdot 10^{-7}\), or when increased by an induced mutagenesis plasmids \(5 \cdot 10^{-5}\) RN63. The number of possible n-fold mutants of a sequence with length sl can be calculated by $$ N_{n} = 3^{n} \cdot r_{M} \cdot \frac{L_{S}!}{(L_{S} - n)!} $$ as there are three possibilities for each basepair to be exchanged to and with each additional mutation there is one possible position less. The number of n-fold mutants that can occur in a lagoon can be calculated using $$ M_{n} = N_{P} \cdot (r_{M} \cdot L_{S})^n $$ therefore, the required lagoon volume is $$ V_{L} = \frac{N_{n} \cdot t}{M_{n} } $$ With a theoretical coverage factor of the n-fold mutants of \(t\).

Practice

The PACE experiments we carried out ranged from 3 to 7 days with a turbidostat volume of about 1.2 l. We observed E. coli generation times of 30 to 40 min.

Since the goal was to improve the method reproducibility was prioritized over minimizing the medium consumption and we always used lagoon volumes of 100 ml. The turbidostat volume was then adjustet to the number of lagoons.