Team:Peking/Design
Design
Theoretical basis of our design
Inheritance of the genome is the basic prerequisite of all biological behaviors, and that the maintenance, replication and segregation of genome DNA must be coordinated with cell growth, division, volume, and shape. We find certain models trying to reveal such relationships with general and quantitative descriptions in basic research [1-2].
The most important factor, which has strong relation with all four parameters mentioned above, is the cell cycle (τ) of bacteria. A cell cycle can be divided into three periods: time from the end of the last cell cycle to DNA replication initiation (τini), time used to complete the DNA replication (C) and time from end of DNA replication to cell division (D). Considering the overlapping of cell cycle, we have
Since the efficacy of DNA polymerase is almost a constant under specific temperature and nutrition conditions, C can be treated as a period with fixed length (around 40 minutes for E. coli). Similarly, if enzymes related to cell division are all well produced and assembled, D is approximately a constant. Thus, under a given external conditions, the key parameter to adjust the cell cycle should be the initiation time (τini). A common physical picture for DNA replication initiation is that key proteins has to accumulate to a curtain threshold to initiate the replication. Hence, a concept of the initiation mass is put forward, which stands for the cell mass when DNA replication initiates. The volume of the cell at time t, denoted S(t), is then:
in which τm is the time a cell take to double its own mass. Therefore, we have:
Considering that during the initiation time, biosynthesis and metabolism are on the rails, so a longer initiation time would result in bigger initiation mass. For simplicity, we can write the formula as:
Thus, we can draw the conclusion that a longer initiation time would result in a bigger cell volume. As a simplest simulation, we assume that the DNA replication initiation time conforms to Poisson Distribution. By delaying the DNA initiation, we find a remarkable elongation in cell shape (Figure 0). The formula gives us a general picture about how a microcosmic DNA replication initiation connects with macroscopical cell morphology and cell cycle. Based on this, we came up with the idea of altering the overall states of bacteria by changing its DNA replication initiation time. In order to achieve precise and predictable adjustment of τini, we further looked into the detailed molecular mechanism of DNA replication initiation.
Figure 0. Simulation results of cell volume with different DNA replication initiation time.
Molecular basis of DNA replication initiation
Our design is based on basic knowledge about the regulation of genome replication initiation. On the global and systems level, concentration of cellular DnaA-ATP complex accurately regulates DNA replication initiation. The proportion of DnaA bound by ATP fluctuates during the cell cycle, defining periods of high initiating activity [3].
On the molecular level, DnaA protein performs its function by mediation of DNA melting. The consensus binding sequence for the DnaA protein in E. coli is a highly-conserved nine nucleotide-long DNA motif (DnaA box). E. coli genome replication origin (oriC) contains multiple copies of DnaA boxes positioned close to an AT-rich DNA unwinding element (DUE). These DnaA boxes function as nucleation points for DnaA oligomerization, and formation of DnaA filaments can exert helical torsion as it grows on DNA, resulting in the unwinding of DUE and recruitment of helicase as well as subsequent recruitment of other proteins in the replisome. In addition, accessory proteins, like integration host factor (IHF), modulate binding of DnaA at the weak DnaA boxes, thereby fine-tuning the initiation time [3]. Mechanism is briefly shown in Figure 1.
Figure 1. . Formation of DnaA filaments around oriC results in the unwinding of DUE and recruitment of the helicase. Accessory protein like IHF (integration host factor) is also needed. This figure is adapted from the review of Costa, A. et al [4].
Our design
Despite this knowledge, there have been very few works trying to directly exert control over genome replication. Here we introduce our method, a novel approach for prokaryotic genome replication interference (CRISPRri), similar to CRISPR-interference for transcription inhibition [5]. An array of sgRNAs is constructed, whose first 20-bp are complementary region for sequence-specific DNA binding. Most of them target DnaA boxes. Figure 2 shows the relative position of DnaA boxes within oriC, and the 20-bp sgRNA binding sites we use in our experiments. High-affinity DnaA boxes (R1, R2 and R4) are shown in orange, whereas weaker affinity boxes (R3 and M) are indicated in yellow.
Figure 2. Basic features of oriC in E. coli genome
In this way, we hope to interrogate the dynamics of cell cycle and those related properties, such as cell growth, division time, volume and morphology by controlling genome replication initiation.
To construct a fully versatile system, we think of the interactions between dCas9 and sgRNA as an AND gate. In order to adjust the effect of the gate, we finely tuned the inputs of CRISPRri on multiple aspects, including expression and degradation rate of dCas9, expression of sgRNA, targeted box, length of sgRNA, and other extension for wider and smarter use of the system. Figure 3 provide an overview of our efforts.
Figure 3. Multiple inputs are incorporated into our core design to achieve tunability.
Input 1: dCas9 expression level
As a proof of concept, we use pBAD promoter to drive the expression of dCas9 in most of our experiments, thus the concentration of dCas9 is directly linked to the concentration of arabinose in the medium. (Fig. 3 Input 1-1) See Results: CRISPR interference and Results: CRISPRri method for more details.
Fully-artificial interference system might not be feasible enough for medical use, as the state of bacteria cannot be supervised all the time to be adjusted immediately. In our expectation, we hope that our system has the ability to adjust in situ. When applied in therapeutic scenario, we hope that it would response when cell density reaches a certain threshold, namely, when a large number of cells gather and colonize near the tumor. Therefore, A classical quorum sensing system based on cell secretion of AHL synthesized by LuxI, which in turn activates the transcription factor LuxR, is combined with CRISPRri system to realize cell state control which itself senses the population density (Fig. 3 Input 1-2). See Results: Autoregulation for more details.
Small molecule inducers or inhibitors, antibiotics, and recently developed light control systems are wildly used in cell state control in basic research. However, small molecular drugs are always expensive, while the transmittance of light in culture medium is very low, making them less practical in real-world applications. Therefore, we designed a temperature controlled CRISPRri system. We collaborated with UCAS-China and learnt about the mechanism of temperature-based gene transcription switch. High temperature inhibits the function of TcI42, which make the promoter PTcI42 unsuppressed, and the downstream gene will express. We hope this switch can turn on or turn off the expression of dCas9 responding to temperature.
Input 2: dCas9 degradation rate
It has been observed on single molecule level that once dCas9 binds to specific DNA sequence, it will hardly drop off, which means that the inhibition of DNA replication by CRISPRri might be irreversible. A straightforward method is to degrade dCas9 faster. In order to build a relatively gentle, reversible control system, we fused a degradation tag named ssrA at the C terminal of dCas9(Fig. 3 Input 2). See Results: CRISPRri method for more details.
Input 3: sgRNA transcription level
The concentration of sgRNA is another key factor that affects CRISPRri efficiency. We place transcription level of sgRNA under the control of a T7 promoter, with a lactose inducible T7 polymerase knocked into the E. coli chromosome. Therefore, the concentration of sgRNA is directly linked to the concentration of IPTG in the medium (Fig. 3 Input 3). See Results: CRISPRri method for more details.
In fact, since the growth rate output is actually a function of dCas9 concentration and sgRNA concentration, no matter what the upstream regulatory module is, any input signal can be used to control the cell growth rate as we put the corresponding sensor module before dCas9 or sgRNA.
Input 4: sgRNA target sites
An excellent controllable system often supports different modes of control, including coarse tuning and fine tuning. As is mentioned before, the oriC region contains multiple copies of DnaA boxes whose affinity to DnaA protein vary a lot (Fig. 2). Functions and essence of different DNA boxes on the OriC and their contributions to genome replication vary accordingly [3]. Therefore, we built a library of sgRNAs targeting to these DnaA boxes, as well as the regions between two functional boxes (sgRNA M-R2+ in Figure 2), the IHF binding region (sgRNA IHF in Figure 2), the DNA unwinding elements (sgRNA MR13+ in Figure 2), and a poly-adenine sgRNA used as control group in our experiment(Fig. 3 Input 4). See Results: sgRNA Library for more details.
Input 5: sgRNA length
In CRISPRi, different complementary lengths of sgRNA will bring different degrees of inhibition. Normally, the complementary region of sgRNA are of 20-bp in length. As we observed that the effect of dCas9 targeting to R1 box can be too strong to sustain normal cell growth, we developed sgRNAs with 18 or 19 base pairs, which may make the system gentler and more reversible (Figure 3 and Input 5). See Results: CRISPRri method for more details.
In a word, we found several models to provide theoretical basis for the feasibility of our design. To control the DNA replication process, we designed a novel method based on CRISPR-dCas9. In order to allow our control system to regulate DNA replication initiation more accurately and sensitively, we introduced five different mechanisms to regulate this system.
However, even if our experiment results are quite consistent with predictions of the general growth law, the formula is only one of the models for control of DNA replication and cell division. Although it explains much phenomena in basic researches, it is still restricted by certain conditions and hypothesis, and much is still unknown about the molecular mechanism of cell cycle coordination, especially when there are very few efforts to control DNA replication initiation itself. Hopefully our design and results can provide more insights into this process.
References
1. Zheng, H. et al. Interrogating the Escherichia coli cell cycle by cell dimension perturbations. Proc. Natl. Acad. Sci. U. S. A. 113, 15000–15005 (2016).
2. Wallden, M., Fange, D., Lundius, E. G., Baltekin, Ö. & Elf, J. The Synchronization of Replication and Division Cycles in Individual E. coli Cells. Cell 166, 729–739 (2016).
3. Reyes-Lamothe, R. & Sherratt, D. J. The bacterial cell cycle, chromosome inheritance and cell growth. Nat. Rev. Microbiol. 17 (2019).
4. Costa, A., Hood, I. V., & Berger, J. M. Mechanisms for initiating cellular DNA replication. Annual review of biochemistry, 82, 25-54(2013).
5. Wiktor, J., Lesterlin, C., Sherratt, D. J. & Dekker, C. CRISPR-mediated control of the bacterial initiation of replication. Nucleic Acids Res. 44, 3801–3810 (2016).
6. Lilly, J. & Camps, M. Mechanisms of Theta Plasmid Replication. Microbiol. Spectr. 10, 1–11 (2014).