of Biosystem Dynamics (LBD)
Research Interests of the LBD
I. Transcription Dynamics
Dissecting the rate-limiting steps in transcription initiation from time-lapse imaging of transcription events in live cells
One of the main research topics of our lab is the study of the dynamics of the process of transcription in E. coli. This process is essential in many aspects, including in determining the degree of cell-to-cell variability of monoclonal cell populations. Recently, we investigated the hypothesis that, while the concentration of RNA polymerases differs in different growth conditions, the fraction of RNA polymerases free for transcription remains approximately constant within a certain range of these conditions. Thus, it is possible to place E. coli cells in different conditions that will cause different concentrations of RNA polymerases and compare the rates of RNA production of a target gene. By doing this, one can next infer how much time of transcription is spent prior and after to commitment to transcription (i.e. in the closed and open complex formation). Applying this method, we found that, for the Plac/ara-1 promoter, under full induction, the closed complex lasts ∼788 s while subsequent steps last ∼193 s, on average. Furthermore, the promoter intermittently switches to an inactive state that, on average, lasts ∼87 s, due to the intermittent repression of the promoter by LacI. The new methods can be used to resolve the rate-limiting steps governing the in vivo dynamics of initiation of prokaryotic promoters, similar to established steady-state assays to resolve the in vitro dynamics.
Schematic representation of the in vivo measurement of the initiation kinetics, using simulated data. (A) First, several conditions are selected, labelled I–IV, differing in intracellular RNAp concentration, R. (B) Next, we obtain timeseries of ﬂuorescence and phase contrast ( for cell segmentation purposes) images of cells expressing MS2d-GFP and target RNA under the control of the promoter of interest in each condition, from which time intervals between individual transcription events are determined. This is done by jump detection in the total RNA spot intensity of each cell (lower-left in B), from which the interval distribution is obtained (lower-right in B). (C) Mean interval durations are then estimated from these interval distributions for each condition. (D) Finally, the mean interval durations and measurements of R are combined into a τ-plot,22 from which estimates of the mean times taken by the closed complex and open complex formation are obtained for each condition.
J Lloyd-Price, S Startceva, V Kandavalli, JG Chandraseelan, N Goncalves, SMD Oliveira, A Häkkinen and AS Ribeiro (2016) Dissecting the stochastic transcription initiation process in live Escherichia coli. DNA Res 23 (3): 203-214. DOI: 10.1093/dnares/dsw009
Indirect means of global regulation of transcription in E. coli making use of the existence of rate-limiting steps in transcription
In E. coli, the expression of a σ factor is expected to indirectly down-regulate the expression of genes recognized by another σ factor, due to σ factor competition for a limited pool of RNA polymerase core enzymes. Interestingly, the sensitivity of genes to indirect down-regulation differs widely. We studied the variability in this sensitivity, and found that the smaller is the time-scale of the closed complex formation relative to the open complex formation of a promoter, the weaker is its responsiveness to changes in other σ factor numbers. We concluded that a promoter’s responsiveness to indirect regulation by σ factor competition is determined by the sequence-dependent kinetics of the rate limiting steps of transcription initiation.
VK Kandavalli, H Tran, and AS Ribeiro (2016) Effects of σ factor competition on the in vivo kinetics of transcription initiation in Escherichia coli. BBA Gene Regulatory Mechanisms. In press.
II. Small Genetic Circuits
Bacteria process information so as to bind past events to future actions, making them adaptable to environment changes. This is made possible by a timely organized execution of multiple tasks such as counting time, sensing the environment, and decision making, which are performed in parallel, by semi-independent, tuned small genetic circuits. These circuits differ in structure, which defines the function. Meanwhile, the properties of their internal components, e.g. the kinetics of transcription initiation of the promoters, define the efficiency with how functions are performed.
We constructed a single-copy repressilator (SCR) by implementing the original repressilator circuit on a single-copy F-plasmid. After verifying its functionality, we studied its behaviour as a function of temperature and compared it with that of the original low-copy-number repressilator (LCR). Under optimal temperature conditions, the dynamics of the two systems differs significantly, although qualitatively they respond similarly to temperature changes. Interestingly, the SCR, the first functional, synthetic, single-copy, ring-type genetic clock, is more robust to lower temperatures and to external perturbations than the original LCRWe have also studied in detail why the Repressilator loses functionality as temperature increases beyond 30 C. Namely, we showed that it is due to the loss of functionality of one of its component proteins, CI.
III. Cellular Aging at the Molecular LevelThe accumulation of non-functional protein aggregates is believed to be a major cause for cellular aging. It has been found that cell lineages are able to distribute these aggregates unevenly by the cells of each generation, causing rejuvenation in many cells and acelerated aging in a few others.
Using the ability of tracking individual fluorescent proteins and protein aggregates in live cells, we studied their short- and long-term dynamics and spatial distribution in the cells' cytoplasm. One of our key finds is that the nucleoid excludes the aggregates from midcell, forcing them to preferencially remain at the poles. Subsequently, following cell divisions, this will lead to the accumulation of aggregates in only some of the individuals of a cell lineage at each generation, which will exhibit slower division rates i.e. aging.
Example microscopy images prior and after segmentation. (A) DAPI-stained nucleoids in cells, (B) cells with visible cytoplasm (filled with MS2-GFP proteins) along with MS2-GFP tagged RNA molecules (synthetic aggregates), visible as bright white “spots”, and (C) segmentation of the images in (A) and (B) merged into one image. Dark grey areas show segmented cells while segmented nucleoids are shown in lighter grey and synthetic aggregates are shown as small white spots.
This figure shows, as a function of the distance from the cell center, the anisotropy in the protein aggregates motions. Note the positive peak at 0.6, which means that the aggregates preferential move away from midcell. This peak occurs exactly where the nucleoid 'border' is. Meanwhile, another peak (negative) occurs at the cell borders, forcing the aggregates to remain inside the cell, as expected.
More recently, we studied this process in cells subject to low temperatures. We show that the process of segregation of aggregates to the poles is hampered, due to increased cytoplasm viscosity.
IV. Models and Stochastic SimulatorsFor a while now, we have been developing ever more detailed models of transcription and models of small genetic circuits. With these, we have made several predictions on how transcription and genetic circuits operate, i.e. how their mechanisms control their dynamics and what are the limits of this dynamics. This has provide us several tangible hypotheses on how genetic circuits operate and how can they be modified to perform desired functions. Most of our studies on real cells have been based on these hypotheses and models, which continue to guide us in our present research.
For this, we have developed both modelling strategies as well as simulators to implement these models.
Selected Publications on modelling strategies:
Selected Publications on simulators:
V. Signal Processing Methods for Single-Cell Single-Molecule Biology Techniques
MS2-GFP-tagging of RNA is currently the only method to measure intervals between consecutive transcription events in live cells. For this, new transcripts must be accurately detected from intensity time traces. We have therefore been developing methods for this detection. We also developed methods for counting RNA molecules from single images.
Different methods tested on 100 series (first of which is shown), each with a duration of 2 h, sampled every 1 min.
A Häkkinen and AS Ribeiro (2015) Estimation of GFP-tagged RNA numbers from temporal fluorescence intensity data. Bioinformatics 31 (1): 69-75. DOI: 10.1093/bioinformatics/btu592
A Häkkinen and AS Ribeiro (2016) Characterizing Rate Limiting Steps in Transcription from RNA Production Times in Live Cells. Bioinformatics. 32(9): 1346-1352. DOI: 10.1093/bioinformatics/btv744.
VI. Learning and Behavioral Changes using Mouse Paw-Preference as a case-studyWe have being much interested in Learning and Behavior. Paw preference is a very interesting case-study since the amount of learning (information) can be quantified from measurements such as the one here shown, where the choice of which paw the mouse uses at each reach, as he learns, can be observed, and the number of possible choices (left or right) can also be quantified.
So far, some of our interesting results by observing the behavior of many mice of difference strains include:
AS Ribeiro, B.A. Eales, and F.G. Biddle (2013) Short-term and long-term memory deficits in handedness learning in mice with absent corpus callosum and reduced hippocampal commissure. Behavioural Brain Research 245, 145–151. DOI: 10.1016/j.bbr.2013.02.021
AS Ribeiro, BA Eales, J Lloyd-Price, and FG Biddle (2014) Predictability and randomness of paw choices are critical elements in the behavioural plasticity of mouse paw preference. Animal Behavior 98, 167-176. DOI: 10.1016/j.anbehav.2014.10.008
The data is all collected by our long-time collaborators Fred G. Biddle (picture below) and Brenda Eales:
Fred in his lab, at the University of Calgary, Canada :-)