A synthetic oscillatory network of transcriptional regulators
A synthetic oscillatory network of transcriptional regulators
Michael B. Elowitz & Stanislas Leibler
Departments of Molecular Biology and Physics, Princeton University, Princeton,
New Jersey 08544, USA
The passing of the millennia marked a profound paradigm shift in biological sciences. High-profile multinational projects were beginning to be organised but there was another meme on the move. A quieter, subtler movement was formed. It was not confined to research institutions – it spread to general population… To garages and basements, to inquisitive minds of people not strictly linked to biotechnology. Thirty, forty or perhaps even fifty years ago a storm of digital enthusiasm was raging through general population, making space for new mentality. In those bygone times the advance of electronic technology represented means to achievement, while the cause was always striving for something – for ideals and philosophies. Much like those bygone times were the first years of millennia and much like those bygone times are the passing years still. Except the technology has changed. Previously, digital was new, now new became biological. A tipping point was reached when enough analytical data accumulated to begin knitting it together. Much like a toddler breaks apart your carefully constructed plastic block houses, and does it again and again until some day he begins to stack the bricks together and sees that they stick. Not soon after he finds out that he’s quite good at it, that what he thinks will stick together actually sticks together. And so he begins to stack the foundations of what could someday become a house grander that everything you have ever shown him. They called the at the time emerging science Synthetic biology, an elusive field to grasp and confine still. It strives to create biological systems, the ones we observed in nature and the ones with functions we can only imagine. By biological systems we mean human-logical components of living organisms with distinct function like temperature sensors on our wrists, circadian clock that drives us to sleep or flagella on bacteria that enable them to swim. You will notice we will also use the term biological device throughout this chapter. A biological device is simply a less advanced biological system, but the terms are sometimes interchangeably used.
In the year 2000 a short paper was published in the Letters to nature. In it two physicists by education, Dr. Michael B. Elowitz and Dr. Stanislas Leiber, described the path to creation of an artificial oscillator based on known genetic components. They set off by making a simplified mathematical model of transcriptional regulation. The model, using a specific network of transcriptional regulators, predicted sustained oscillatory behaviour in some instances (figure 1b in the article), such that can be observed in many natural systems like circadian clock and similar. Their model accounts for mRNA and protein synthesis and degradation and the binding kinetics of operator proteins. Using numerical simulations, the authors have shown that such configurations do produce sustained oscillations while accounting for all known and predictable variables. Using numerical simulations they have also shown that previously reported stochastic variations can be responsible for the observed noise in gene expression networks. The short article helped initiate the emerging field of synthetic biology.
The inner working of an organism can be represented by networks of interacting molecules, carrying out a certain function in living cells. Such networks can be seen as cellular subsystems – independent mechanisms in intricate clockwork of a cell. They are created by scientists as human-readable representations of systems of biochemical reactions, constructed from data obtained in analytical experiments. Many, hundreds if not thousands and more of such discriminate networks, could possibly be combined to form an in silico model of a living organism. While the identification of such networks and the elucidation of their functions have been accomplished throughout the history of biological sciences, the underlying principles of their working are not so easy to uncover. Using modern synthetic approach to bioengineering, such understanding is not always required in order to replicate the work of nature.
Introduction
In the above mentioned article the authors present the design and construction of an artificial biological system that implements a particular cellular function, existence of which was discovered by analysis of naturally occurring organisms. The system they chose to reverse engineer is a biological oscillator – a functional component of some living cells with a periodical output in a form of protein concentrations, not unlike a musical metronome or a clock. Many organisms use similar biological devices to adapt to periodic variations in environment such as the cycle of ebbs and tides or the day and night cycle. The most ground-breaking novelty described in the article is the fact that the artificial biological system was built using knowledge from a wide variety of biological sciences without intentionally, or knowingly, copying the nature’s design of a system with analogous function. This pioneering work is why this article ranks top in synthetic biology by the amount of citations. Such feat was accomplished by coupling a great deal of cleverness with accurate characterisation of a large amount of components of biological systems, such as promoters of RNA synthesis, binding proteins of various functional sites on DNA and similar. Only previously obtaining such high quality data could this task be accomplished and such data lies at the heart of at the time emerging science of synthetic biology. Its aim is to simplify work in the field of biological sciences and is known by experiments that aim to mimic or compliment nature’s work for the benefit of humanity. The vast majority of them use cutting-edge gene-manipulation technologies which produce transgenic organisms disfavoured by general public. A notable novelty of synthetic biology is the paradigm which favours simplicity – grab a basic DNA sequence, analyse what it does, change it slightly to ease manipulation with it, wrap it in a neat black box with all the properties easily accessible in an open database and call it a BioBrick or similar. These parts can be easily combined using simple yet clever molecular manipulation methods in order to create more complex molecular devices. Most advanced ideas involved in synthetic biology are regarded way ahead of our time; for more information reader should consult reports from iGEM (international Genetically Engineered Machine) competitions, which date back to 2003, or perhaps take a dive into the subculture of biohacking.
Overview
The artificial oscillatory system, designed by the authors, will be inserted into host’s genomic material, also termed the chassis, and will essentially become just another system in the host organism. They chose Escherichia coli, the best studied organism as well as a simple prokaryote, as a biological chassis for their experiment. The reasoning behind this choice is solid – according to understanding at the time, E. coli regulatory systems should not interfere with the components of the artificial system, some of which are also, by nature, parts of regulatory systems. The design of the oscillator presumes no or benign interactions between its components and the components of host organism. For advanced readers the strain of the host E. coli should be mentioned – all experiments described in the article use strain MC4100. Its genotype is marked by substantial mutation that encompasses full lac operon; this removes the interference of additional binding sites for LacI repressor or induced or leaked expression of endogenous repressor protein. Other parts used in the design of the represillator are not found in E. coli genome and therefore should not harbour interactions with host matrix.
Composition
At this point we should introduce the individual components of the oscillatory system termed ‘the represillator’ by the authors. Firstly, the name represillator suits the artificial system well – it is composed of three genes which code for proteins that act as repressors, expression of which is controlled in a cyclical way by promoters combined with operators they repress. The working of the circuit, told in a single sentence, may sound confusing for a general reader, much like some of the synthetic biology’s terminology used in the entire chapter. This chapter is written for semi-specialised public and many concepts are explained on site – despite this, the reader is encouraged to get a firm grasp on the meaning of terms before continuing. Great efforts were made, cross my heart, by student deep in the study of synthetic biology to write in an understanding manner, but sometimes one gets sloppy with the language for which most sincere apologies should suffice.
The represillator’s construction is evidently tripartite – it is formed by combining three similar parts, composition of which is as follows: first comes the promoter which is simply a sequence of DNA which promotes transcription of the sequence that immediately follows it. In and around it are the binding sites for the repressor proteins, also called operator sites – these are merely short sequences of DNA which assume such conformation that selectively binds repressor protein of certain type. When a repressor protein is bound to this part of DNA, the resulting complex inhibits the activity of a nearby promoter and greatly reduces transcription of the following genetic material. At the end of the promoter lie some sequences that are crucial for protein synthesis and are usually mentioned separately from the promoter – these are the ribosome binding site, more often than not shortened to an acronym RBS, and the starting codon. Immediately following the start codon is the coding region of the repressor protein. Information about amino acid sequence of the protein is stored in this comparatively large piece of DNA. It is followed by a stop codon and a transcription terminator, the first one interrupts the translation and the latter transcription of the gene at a specific spot; both are considered a mere bagatelle in most applications. The idea of cyclical repression is best conveyed with the help of figure 1a in the article. We have added a simplified representation of the aforementioned figure below, in the form of a list of components, for your convenience.
• promoter(λPR, repressed by λ cI)- LacI-lite (repressor)
• promoter(PLlacO1, repressed by LacI) - TetR-lite (repressor)
• promoter (PLtetO1, repressed by TetR) - λ cI-lite (repressor)
This list represents a simplified representation of a represillator. Each component represses the expression of the next component in line and the last one represses the first one closing the circle. This, under certain circumstances, produces temporal oscillations of components’ protein concentrations. The first repressor protein LacI inhibits the formation of second repressor protein TetR at the stage of transcription. In a similar manner expression of λ cI gene from λ phage is repressed by TetR repressor. The λ cI repressor closes the circle by repressing expression of LacI repressor protein thus forming a cyclical negative-feedback loop. Such system configuration has at least two types of solutions: convergence towards a stable steady state or an unstable steady state leading to sustained temporal oscilations. The system will fall into its native state immediately after introduction of transgenic material into host cell without exogenic interference. The authors have tried to use IPTG as an inhibitor of activity of LacI to synchronize states of all represillators in population but the results were marked by rapid decorrelation of states due to endogenous factors.
There are some prerequisites for such a system to behave as an oscillator and not sink into a stable stationary, or non-oscillatory, state. The authors have found that the oscillatory behavior is favored by strong promoters together with efficient ribosome-binding sites, which together produces a large amount of protein product in short amount of time. Tight repression, meaning low ‘leakiness’ of the promoters in repressed state, also contributes to such behavior as it increases the difference in protein concentrations in repressed vs. active state. Cooperative repression characteristics as well as comparable mRNA and protein degradation times are also beneficial to the prevalence of an oscillatory state. Reader can find more information on the subject by consulting figure 1b and 1c in the article.
At the beginning, construction of a tripartite negative-feedback loop was envisioned using exclusively naturally occurring components – the so called wild-type forms. The authors’ estimates of relevant parameters of the system indicated that such configuration should by nature favour oscillatory regime and should not collapse to a steady-state. However, there are some issues that can easily be overcome by use of simple molecular manipulation methods, such as leakiness of lacI promoter and high half-lives of involved proteins compared to their RNA counterparts. They have chosen to increase their chances by adding several modifications to the natural components. Instead of wild-type versions of promoters they used hybrid versions which are stronger yet still tightly repressible and combine λ PL promoter with lac and tet operator sequences termed PLlac01 and PLtet01 respectively (be reminded to consult figure 1a in article or figure I in this chapter if required). The third promoter used is termed λPR and is naturally occurring right promoter of lambda phage. Additionally they lowered the repressor protein lifetimes to values closer to expected mRNA lifetimes (about 2 min on average in E. coli) by inserting carboxy-terminal tag originating from ssrA RNA, which is recognized by host’s proteases and targets the protein for recycling. Data from previous experiments indicates that they can expect such tag to reduce the half-lives of repressor proteins to an order of magnitude of minutes, close to expected half-lives of mRNA. Repressors tagged in such manner were denoted LacI-, TetR- and λ cl-lite repressors due to their shortened life span in the cell.
At this stage the authors have constructed and characterized well-performing repressible promoters and repressors and combined all appropriate constructs onto a low copy-number plasmid to be transferred into E. coli. There is yet still a most foundamental component missing – the way to see what is happening in the cell. The constructed circuit is expected to produce oscillations of all involved proteins’ concentrations, yet the technology to measure them in an easy and real-time fashion is missing. Therefore the authors coupled oscillations of TetR-lite repressor’s concentrations with expression of intermediate stability variant of green fluorescent protein (GFP) by putting the latter under TetR responsive promoter PLtetO1 (the same is used in the device itself to controll the expression of λ cI-lite repressor). They have constructed this, so called reporter, construct on a high copy number plasmid and used it to transfect E. coli cells already harboring the oscillator. Now the oscillations from the represillator are transferred to the reporter and can be easily observed under a fluorescent microscope as oscillations of fluorescence due to changing concentration of GFP in the cells.
Results
As mentioned previously, the authors have used intermediate stability variant of GFP, coupled to one of the promoters within the represillator, to quantitatively measure the state of the oscillatory system. This reporter system was introduced in a high copy plasmid in contrast to represillator system, which was encoded on a low-copy plasmid. Due to the modifications introduced to promote oscillatory behaviour over 40 % of the observed cells were shown to follow such regime. Despite that the noise in individual cells due to stochastic effects coupled with inability of cells to synchronize oscillations across population caused rapid desynchronisation and inability to measure fluorescence fluctuations in bulk. Because of such effects the measurements of represillator behavior must occur on individual-cell level and was done under a fluorescent microscope. Using it the authors measured fluorescence of a large number of individual cells as a function of time. The oscillation period was shown to be on average longer than the replication period of cells; the measurements were therefore continued on daughter cells. Interestingly, the desynchronisation seems to be relatively slow in sister cells as shown in figures 3a-c, therefore we can assume that the replication process doesn’t interfere substantially with the oscillatory system. In such experiments, total observation time was limited to about 10 hours (at 30°C) due to colonies entering stationary phase. After entering stationary phase the represillator grinds to a halt, indicating its direct dependence on endogenous global regulatory mechanisms.
The timecourse of the fluorescence of on cell harboring the represillator and reporter system is showed in figure 2 in the article. We can see that the amplitude of fluorescence oscillations is large compared to the background noise, with peak-to-peak intervals of 160±40 min (mean ± s.d.). Typical cell division time under used conditions was 50 – 70 min. As mentioned before, sibling decorrelation time was relatively long, measured at 95 ± 10 min. Cell duplication was observed to cause other effects, such as substantial changes in oscillation frequency and amplitude as well as producing phase delay in one cell relative to the other. Interestingly, when the experiment was repeated using the same genetic constructs in the same strain of host cells under similar conditions, oscillation frequencies observed were remarkably different.
Theoretical work
At the end of the article authors contemplate the theoretical basis behind the work, described in this article (box 1 in the article). They mention that the stochastic effects may be the culprit responsible for noisy operation in natural gene-expression networks as well as their own. We should be reminded at this stage that all events, chemical by nature, are inherently stochastic as well as of discreteness of the network components. Corpuscularity itself is sufficient to greatly increase noise if the number of involved molecules is low enough. They have conducted simulations that show those effects reduce the correlation time of individual cells to about two periods. As we know that natural circadian clocks exhibit great noise resistance; the authors ask whether construction of similar circuits that would retain this highly valued property is possible as a logical continuation of their work. Such network would use both positive and negative regulation elements which could lead to a formation of a bistable behavior with high noise-resistance.
References:
Michael B. Elowitz & Stanislas Leibler,A synthetic oscillatory network of transcriptional regulators, Letters to Nature, 2010 and references therein.
