"Metabolic gene regulation in a dynamically changing environment".

Matthew R. Bennett ^{1, 2, 3}, Wyming Lee Pang ^{1, 3, 4}, Natalie A. Ostroff ¹, Bridget L. Baumgartner ¹, Sujata Nayak ¹, Lev S. Tsimring ², and Jeff Hasty ^{1, 2}

¹ Department of Bioengineering, and,
² Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093, USA
³ These authors contributed equally to this work.
⁴ Present address: Institute for Systems Biology, Seattle, Washington 98103, USA.

Correspondence to: Jeff Hasty ^{1, 2} Correspondence and requests for materials should be addressed to J.H. (Email: hasty@ucsd.edu).

Abstract:

Natural selection dictates that cells constantly adapt to dynamically changing environments in a context-dependent manner. Gene-regulatory networks often mediate the cellular response to perturbation (1, 2, 3), and an understanding of cellular adaptation will require experimental approaches aimed at subjecting cells to a dynamic environment that mimics their natural habitat (4, 5, 6, 7, 8, 9). Here we monitor the response of Saccharomyces cerevisiae metabolic gene regulation to periodic changes in the external carbon source by using a microfluidic platform that allows precise, dynamic control over environmental conditions. We show that the metabolic system acts as a low-pass filter that reliably responds to a slowly changing environment, while effectively ignoring fast fluctuations. The sensitive low-frequency response was significantly faster than in predictions arising from our computational modelling, and this discrepancy was resolved by the discovery that two key galactose transcripts possess half-lives that depend on the carbon source. Finally, to explore how induction characteristics affect frequency response, we compare two S. cerevisiae strains and show that they have the same frequency response despite having markedly different induction properties. This suggests that although certain characteristics of the complex networks may differ when probed in a static environment, the system has been optimized for a robust response to a dynamically changing environment.

Figures:

RESEARCH
"A fast, robust and tunable synthetic gene oscillator"
Nature Letters to Editor (28 Aug 2008).

PERSPECTIVE
"Medical Systems Biology in Health and Disease".

http://www.nature.com/nature/journal/v454/n7208/fig_tab/nature07211_ft.html

Figure 1: Design and implementation of the microfluidic platform developed for our study.

a, Conceptual design of the imaging chamber. The chamber is coupled to the switch output channel by means of multiple 'feeding' channels 1 mum tall. The feeding channels are fed by a controllable waveform generator that creates sinusoidal perturbations in the glucose concentration while maintaining constant background levels of galactose.

b, An overview of the design showing the layout of the device. The device makes use of three flow networks for loading cells (middle, black), generating microenvironmental waveforms (bottom, green), and controlling on-chip temperature (top, orange). The imaging chamber (centre, grey region) is designed to be about 4 mum tall to constrain a population of yeast cells to grow in a monolayer.

c, Representative bright-field image of cells growing in the imaging chamber. These images were used to measure the total size of the colony. Large circles are support posts in the chamber. Scale bar, 25 mum.

d, Green fluorescence image of the same cells as in c. These images allowed us to measure the amount of Gal1 in each cell. FIGURE 2. Regulation in the galactose utilization network.

e, Red fluorescence image of the chamber. The glucose medium also contained a red fluorescent dye; the intensity of the red fluorescence was therefore proportional to the amount of glucose in the chamber at any given time.

FIGURE 2. Regulation in the galactose utilization network.

a, Diagram of the gene regulatory networks involved. The regulatory genes in the galactose network are activated by the Gal4 protein, which binds to upstream activation sites. The GAL80 gene provides negative feedback in the system by prohibiting the inducing affects of Gal4. Positive feedback is provided by both GAL2 and GAL3. Internalized galactose can bind to Gal3, and the resulting complex binds to Gal80. Gal80 bound to the Gal3–galactose complex is incapable of repressing Gal4. In addition, the transporter Gal2 increases the amount of internal galactose, which stimulates the galactose network. The glucose network inhibits the transport of galactose and represses transcription of the galactose network in the presence of glucose through the action of Mig1, which can bind to upstream regulatory sites of GAL1, GAL3 and GAL4 (ref. 19). The glucose network also regulates the hexose transporter genes (HXT), responsible for transporting glucose into the cell (27), which then activates the glucose network.

b, Experimentally measured decay of GAL1 transcripts in galactose (circles) and glucose (squares). Also shown are the best-fit lines corresponding to half-lives of about 17 min in galactose (solid line) and 4 min in glucose (dashed line), which are similar to the values predicted by the numerical model. Data are normalized to the initial concentration of mRNA predicted by the best-fit lines. Similar results for GAL3 transcripts are shown in Supplementary Information.

FIGURE 3. Experimental and computational results for cells of two yeast strains expressing a GAL1–yECFP fusion gene in response to alternating glucose and galactose media.

a, Strain K699; b, strain YPH499. The top row for each strain depicts the input glucose signal that was measured during each experimental run and was also used to simulate the responses. The mean fluorescence of a red tracer dye, representing the glucose concentration in the medium, is normalized and subtracted from 1 to represent the 'induction' signal used in the experimental and computational runs above. The middle rows show normalized and detrended fluorescence trajectories for a population of cells as they respond to glucose waves of various frequencies over a galactose background. In the absence of glucose, galactose induces the transcription of GAL1–yECFP, causing an increase in cellular fluorescence. However, as glucose is introduced into the extracellular environment, transcription of the galactose enzymes is shut off, causing a decrease in fluorescence signal as the Gal1–yECFP protein is degraded. Oscillation periods are (from left to right) 4.5, 3.0, 2.25, 1.5, 1.125 and 0.75 h. For input waves with a period shorter than 1.125 h, cells no longer responded to sinusoidal repression in a periodic fashion, demonstrating their ability to 'filter' out high-frequency environmental fluctuations. The bottom rows show simulation results for the same frequencies as above. The model, calibrated to experimental induction and repression data, accurately reproduces the cellular responses over a large range of frequencies.

FIGURE 4. Experimental and computational comparison of two yeast strains.

The amplitude (top row) and phase shift (bottom row) of the response of cells to sinusoidal repression at various frequencies are shown for both K699 (red) and YPH499 (blue) strains (error bars represent s.d.). Strain YPH499 is known to have a deficiency in the galactose utilization network. For the highest-frequency trial, reliable phases could not be calculated because of noise; these results have been omitted from the graphs. The experimental data (left column) show that the amplitude responses of the two strains are strikingly similar, especially considering their significantly different induction curves (see Supplementary Information). This phenomenon was predicted by model simulations, because slight modifications to the model parameters that affected the induction and repression curves did not affect the cell population's robust response to a dynamic environment. This suggests that the complex structure of the glucose and galactose networks may confer robustness on cells even if faced with seemingly detrimental network deficiencies. The phase responses (bottom row) of the two strains showed a marked difference, with YPH499 cells having a greater phase lag than K699 cells.

http://www.nature.com/nature/journal/v454/n7208/suppinfo/nature07211.html

http://www.nature.com/nature/journal/v454/n7208/full/4541059a.html

 "Systems biology: Reverse engineering the cell".

Nicholas T. Ingolia1 and Jonathan S. Weissman1

¹ Howard Hughes Medical Institute, Department of Cellular and Molecular Pharmacology, University of California, San Francisco, and California Institute for Quantitative Biomedical Research, San Francisco, California 94158-2542, USA.

Email: weissman@cmp.ucsf.edu

Borrowing ideas that were originally developed to study electronic circuits, two reports decipher how yeast reacts to changes in its environment by analysing the organism's responses to oscillating input signals.

Systems biology has certainly caught people's attention. But when pressed to define precisely what it is, let alone how it will complement classic reductionist approaches, concrete answers can be hard to find.

Jesse Stricker ^{1, 3}, Scott Cookson ^{1, 3}, Matthew R. Bennett ^{1. 2, 3}, William H. Mather ¹, Lev S. Tsimring ², and Jeff Hasty ^{1, 2}

¹ Department of Bioengineering, University of California, San Diego, La Jolla, California 92093, USA
² Institute for Nonlinear Science, University of California, San Diego, La Jolla, California 92093, USA
³ These authors contributed equally to this work.

Correspondence to: Jeff Hasty ^{1, 2} Correspondence and requests for materials should be addressed to J.H. (Email: hasty@bioeng.ucsd.edu).

One defining goal of synthetic biology is the development of engineering-based approaches that enable the construction of gene-regulatory networks according to 'design specifications' generated from computational modelling (1, 2, 3, 4, 5, 6). This approach provides a systematic framework for exploring how a given regulatory network generates a particular phenotypic behaviour. Several fundamental gene circuits have been developed using this approach, including toggle switches (7) and oscillators (8, 9, 10), and these have been applied in new contexts such as triggered biofilm development (11) and cellular population control (12). Here we describe an engineered genetic oscillator in Escherichia coli that is fast, robust and persistent, with tunable oscillatory periods as fast as 13 min. The oscillator was designed using a previously modelled network architecture comprising linked positive and negative feedback loops (1, 13). Using a microfluidic platform tailored for single-cell microscopy, we precisely control environmental conditions and monitor oscillations in individual cells through multiple cycles. Experiments reveal remarkable robustness and persistence of oscillations in the designed circuit; almost every cell exhibited large-amplitude fluorescence oscillations throughout observation runs. The oscillatory period can be tuned by altering inducer levels, temperature and the media source. Computational modelling demonstrates that the key design principle for constructing a robust oscillator is a time delay in the negative feedback loop, which can mechanistically arise from the cascade of cellular processes involved in forming a functional transcription factor. The positive feedback loop increases the robustness of the oscillations and allows for greater tunability. Examination of our refined model suggested the existence of a simplified oscillator design without positive feedback, and we construct an oscillator strain confirming this computational prediction.

http://www.nature.com/nature/journal/vaop/ncurrent/fig_tab/nature07389_F1.html#figure-title

http://www.nature.com/nature/journal/vaop/ncurrent/suppinfo/nature07389.html

FIGURE 1. Oscillations in the dual-feedback circuit.

a, Network diagram of the dual-feedback oscillator. A hybrid promoter plac/ara-1 drives transcription of araC and lacI, forming positive and negative feedback loops.

b, Single-cell fluorescence trajectories induced with 0.7% arabinose and 2 mM IPTG. Points represent experimental fluorescence values, and solid curves are smoothed by a Savitsky–Golay filter (for unsmoothed trajectories, see Supplementary Fig. 3). The trajectory in red corresponds to the density map above the graph. Density maps for trajectories in grey are shown in g. a.u., arbitrary units.

c–h, Single-cell density map trajectories for various IPTG conditions (c, 0 mM IPTG; d, 0.25 mM; e, 0.5 mM; f, 1 mM; g, 2 mM; h, 5 mM).

FIGURE 2. Robust oscillations.

a–c, Oscillatory periods on transects with 0.7% arabinose and varying IPTG (a), 2 mM IPTG and varying arabinose (b), or 0.7% arabinose, 2 mM IPTG, and varying temperature (c). Mean periods from single-cell microscopy (red diamonds, mean plus/minus s.d.) or flow cytometry (green circles) are shown. Black curves are trend lines in a and b, or represent the theoretical prediction based on reference values at 30 °C in c (see Supplementary Information). Samples grown in minimal medium rather than LB are indicated by crosses. G represents the cell doubling period.

d, Oscillatory period and cell division time increase monotonically as the growth temperature decreases. Symbols are as described above, and the black line is a linear regression of samples grown in LB.

FIGURE 3. An oscillator with no positive feedback loop.

a, Network diagram of the negative feedback oscillator. This oscillator is similar to the dual-feedback oscillator except that the hybrid promoter pLlacO-1 (ref. 14) gives expression of lacI and yemGFP in the absence of LacI or in the presence of IPTG without requiring an activator.

b, Single-cell density map trajectories for cells containing this oscillator (see Supplementary Movie 11 and Supplementary Fig. 5).

FIGURE 4. Modelling the genetic oscillator.

a, Intermediate processes are explicitly modelled in the refined oscillator model.

b, c, Simulation results from Gillespie simulations (b) or deterministic modelling (c) at 0.7% arabinose and 2 mM IPTG. AraC dimers (green), LacI tetramers (red) and lacI mRNA (black) are shown.

d, e, Comparison of modelling and experiment for oscillation period at 0.7% arabinose (d) or 2 mM IPTG (e). Values from deterministic modelling (blue curve), stochastic simulations (grey symbols, Supplementary Fig. 18), and microscopy (red diamonds) or flow cytometry (green circles) are shown. Lower and upper error bars in d represent the 16th and 84th percentiles, respectively, of the stochastic data, corresponding to plus/minus1 s.d. for a normal distribution.

"Medical Systems Biology in Health and Disease".

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