Research
Genome-wide Transcription Dynamics
Tracking the levels of the global regulators of gene expression of E. coli
Dash S, Jagadeesan R, Baptista ISC, Chauhan V, Kandavalli V, Oliveira SMD, Ribeiro AS. 0. A library of reporters of the global regulators of gene expression in Escherichia coli. mSystems 0:e00065-24.
Escherichia coli have evolved hundreds of transcription factors to tune the expression of thousands of genes. Interestingly, a few transcription factors control almost half of all genes and are thus named global regulators (GRs). Tracking the numbers of these GRs over time is essential to understand phenotypic modifications, e.g., under stress conditions. We have engineered a library of strains to track GR levels. Each strain has a single-copy plasmid coding for a fast-maturing green fluorescent protein whose transcription is controlled by a copy of the natural promoter of the GR. This library should become useful in scientific research, and future applications in therapeutics and the bioindustries. For more information, please visit: 10.1128/msystems.00065-24.
DNA supercoiling as a trigger of short-term, cold shock repressed genes of E. coli
Dash S*, Palma CSD*, et al. (2022) Alteration of DNA supercoiling serves as a trigger of short-term cold shock repressed genes of E. coli. Nucleic Acids Research 50(15) 8512–8528. DOI: 10.1093/nar/gkac643. *Equal authorship.
Adapting to cold is a key skill of gut bacteria of warm-blooded animals. We hypothesized that cold affects DNA supercoiling, which is regulated by gyrase. We performed two RNA-seqs, one after cold-shock, the other after adding Novobiocin, an antibiotic that represses gyrase. We found that many cold-shock repressed genes are similarly sensitive to Novobiocin. Next, we observed similar changes in the protein numbers of these genes to both perturbations. Moreover, under cold shock, nucleoid density increases, and gyrases and nucleoid become colocalized. Also, the cell energy decreases, which hinders the resolution of positive supercoils. We suggest that responsiveness to low negative supercoiling is a core feature of E. coli’s short-term, cold shock transcriptional response program, and could be used to regulate the temperature sensitivity of synthetic circuits.
The transcription factor network of E. coli steers global responses to shifts in RNAP concentration
B Almeida et al (2022) The transcription factor network of E. coli steers global responses to shifts in RNAP concentration. Nucleic Acids Research 50(12), 6801-6819. DOI: 10.1093/nar/gkac540.
The role of transcription factor networks (TFNs) on the robustness, sensitivity, plasticity, and overall adaptability of microorganisms to environmental stresses remains largely unexplored. We studied how the TFN of E. coli responds to genome-wide perturbations caused by quick changes in medium richness, known to alter RNA polymerase (RNAP) concentrations.
We found that, at the gene cohort level, the average magnitude of the mid-term transcriptional responses can be explained by the average absolute difference between the numbers of activating and repressing input TFs of a gene. Interestingly, this difference is strongly positively correlated with the average number of input TFs of a gene. Our result suggests that the global topological traits of TFNs influence their global response dynamics to genome-wide stresses. This may be a general principle by which TFNs regulate genome-wide information propagation.
Analytical kinetic model of native tandem promoters in E. coli
Chauhan V*, Bahrudeen MNM*, Palma CSD, Baptista ISC, Almeida BLB, Dash S, et al. (2022) Analytical kinetic model of native tandem promoters in E. coli. PLoS Comput Biol 18(1): e1009824. https://doi.org/10.1371/journal.pcbi.1009824. *Equal authorship.
We studied how tandem promoters interfere with each other, depending on their strengths and on the nucleotide distances between them., taking into consideration the nucleotide length occupied by an RNAP when bound to the transcription start site region.
Illustration of tandem promoters, when and when not overlapping.
Events leading to transcriptional interference between tandem promoters. (A) Sequence of events in transcription in isolated promoters. A similar set of events occurs in tandem promoters, if only one RNAP interacts with them at any given time. (B / C) Interference due to the occlusion of the downstream / upstream promoter by a bound RNAP, which will impede the incoming RNAP from binding to the TSS. (D) Interference of the activity of the RNAP from the upstream promoter by the RNAP on the downstream promoter. One of these RNAPs will be dislodged by the collision. Created with BioRender.com.
Workflow. (I) We identified genes controlled by tandem promoters in RegulonDB. (II) We measured their single-cell protein levels and RNA fold changes over time. (III) We used the data to tune the model. (IV) We used the model to explore the state space of protein expression.
This is our first study where we combine RNA-seq, flow-cytometry of natural promoters, info from RegulonDB on transcription factor interactions and promoter sequences, strain libraries, and stochastic and analytical models.
We proposed an analytical model of gene expression with RNAP-promoter occupancy times and distance between promoters, dTSS as the key regulators of transcription interference.
This model can assist to predict the dynamics of new pairs of tandem promoters and, thus, contribute to the expansion of synthetic genetic libraries.
Single-gene Transcription Dynamics
Selected Publication:
CSD Palma, V Kandavalli, MNM Bahrudeen, M Minoia, V Chauhan and AS Ribeiro (2020) Dissecting the in vivo dynamics of transcription locking due to positive supercoiling buildup. Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms 1863(5) 194515. doi: 10.1016/j.bbagrm.2020.194515.
We proposed a new strategy to dissect the contribution of transcription initiation locking due to PSB on the kinetics of RNA production. We started by validating a stochastic model of transcription (A) that accounts for positive supercoiling accumulation (reaction 4 and 5 in (A)) and removal (reaction 6 in (A)) due to Gyrase intervention. We then derived an analytical solution for the inverse of the mean rate of RNA production as a function of Gyrase levels. From qPCR and microscopy data at different concentrations of Gyrase, we applied a Lineweaver-Burk plot to dissect the in vivo transcription rate in absence of PSB (D). We validated this using the same gene when single-copy plasmid-borne (shown to be impervious to supercoiling). We then estimated the time in locked states (τLocked) and the number of transcription events prior to locking (N), which we validated by measurements after adding Novobiocin. Finally, we inferred a range of transcription initiation locking kinetics in a chromosomal location, obtainable by tuning the basal transcription rate, and validate it by measurements at different induction levels. This strategy for dissecting the transcription initiation locking kinetics due to PSB may contribute to resolving transcriptional programs of E. coli and in the engineering of synthetic genetic circuits.
Selected Publication:
J Lloyd-Price et al (2016) Dissecting the stochastic transcription initiation process in live Escherichia coli. DNA Res 23 (3): 203-214. DOI: 10.1093/dnares/dsw009
One topic of our lab is transcription dynamics, which affects cell-to-cell variability. Having E. coli cells to naturally change their RNA polymerase concentrations, we compared RNA production rates of a target gene and inferred the time RNAP spends prior and after commitment to transcription. For the Plac/ara-1 promoter, under full induction, the closed complex took ∼788 s while subsequent steps took ∼193 s, on average. Also, the promoter intermittently switches to an inactive state that, on average, last ∼87 s, due to the repression by LacI. This method can be used to resolve the rate-limiting steps of the in vivo dynamics of initiation of prokaryotic promoters.
Selected Publication
VK Kandavalli, H Tran, and AS Ribeiro (2016) Effects of σ factor competition are promoter initiation kinetics dependent. Biochimica et Biophysica Acta 1859, 1281–1288
Small Synthetic Genetic Circuits
Bacteria process information to bind past events to future actions, so as to adapt 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 of Elowitz et al on a single-copy F-plasmid. We studied its behaviour as a function of temperature and compared to the original low-copy-number repressilator (LCR). In optimal temperature, the dynamics of the two systems differ, but respond similarly to temperature changes. Interestingly, the SCR is more robust to lower temperatures and perturbations than the original LCR, which loses functionality as temperature increases beyond 30 C, due to the loss of functionality of one of its proteins, CI.
Selected Publications:
JG Chandraseelan, SMD Oliveira, A Häkkinen, H Tran, I Potapov, A Sala, M Kandhavelu, and AS Ribeiro (2013) Effects of temperature on the dynamics of the LacI-TetR-CI repressilator. Mol. BioSyst. 9 (12), 3117-3123. DOI:10.1039/c3mb70203k
SMD Oliveira*, JG Chandraseelan*, A Häkkinen, NSM Goncalves, O Yli-Harja, S Startceva, and AS Ribeiro (2015) Single-cell kinetics of the Repressilator when inserted into a single-copy plasmid. Mol. BioSyst. 11, 1939-1945. *Equal contributions. DOI: 10.1039/C5MB00012B
Inner Cellular Biophysics
Cells have a complex internal spatial organization, ruled by biophysical laws. While E. coli is likely one of the simplest case-studies, there are still many questions about the mechanisms that regulate their internal organization. We have so far focused on three issues, namely, the temporal spatial organization of: protein aggregates, Z-rings, and Serine Chemoreceptors.
Protein aggregates and cellular aging
The accumulation of non-functional protein aggregates is believed to be a 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 accelerated aging in others.
Using tracking of 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 finds is that the nucleoid excludes aggregates from midcell, forcing them to mostly remain at the poles. Following cell divisions, this leads to accumulation of aggregates in only some individuals of a lineage at each generation, which will exhibit slower division rates i.e. aging.
We also 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.
Selected Publications:
A Gupta, J Lloyd-Price, R Neeli-Venkata, SMD Oliveira, and AS Ribeiro (2014) In vivo kinetics of segregation and polar retention of MS2-GFP-RNA complexes in Escherichia coli. Biophysical Journal, 106(9), 1928–1937. DOI: 10.1016/j.bpj.2014.03.035
A Gupta, J Lloyd-Price, SMD Oliveira, O Yli-Harja, A-B Muthukrishnan and AS Ribeiro (2014) Robustness of the division symmetry in Escherichia coli and functional consequences of symmetry breaking. Physical Biology 11(6):066005. DOI: 10.1088/1478-3975/11/6/066005
SMD Oliveira, R Neeli-Venkata, N Goncalves, JA Santinha, L Martins, H Tran, J Mäkelä, A Gupta, M Barandas, A Häkkinen, J Lloyd-Price, JM Fonseca, and AS Ribeiro (2016) Increased cytoplasm viscosity hampers aggregate polar segregation in Escherichia coli. Mol. Microbiol. 99(4), 686–699. DOI: 10.1111/mmi.13257
Z-rings and Cell Division
Cell division in E coli is morphologically symmetric due to, among other, their ability to place the Z-ring at midcell. At sub-optimal temperatures, this symmetry decreases at the single-cell level. Using fluorescence microscopy, we observed FtsZ-GFP and DAPI-stained nucleoids to assess the robustness of the symmetry of Z-ring formation and positioning in cells at sub-optimal and critical temperatures. We found that the Z-ring formation and positioning is robust at sub-optimal temperatures, as the Z-ring ’ s mean width, density and displacement from midcell maintain correlation to one another. However, at critical temperatures, the Z-ring displacement from midcell is greatly increased. This is due to enhanced distance between the replicated nucleoids and, thus, reduced Z-ring density, which explains the weaker precision in setting a morphologically symmetric division site. This also occurs in rich media and is cumulative, i.e. combining richer media and critically high temperatures enhances the asymmetries in division, which is evidence that the causes are biophysical. To support this, we showed that the effects are reversible, i.e. shifting cells from optimal to critical, and then to optimal again, reduces and then enhances the symmetry in Z-ring positioning, respectively. Overall, we found that Z-ring positioning in E. coli is a robust biophysical process under sub-optimal temperatures, and that critical temperatures cause significant asymmetries in division.
Selected Publication:
R Neeli-Venkata, SMD Oliveira, L Martins, S Startceva, M Bahrudeen, JM Fonseca, M Minoia and AS Ribeiro (2018) The precision of the symmetry in Z-ring placement in Escherichia coli is hampered at critical temperatures. Physical Biology 15(5):056002. DOI: 10.1088/1478-3975/aac1cb
Serine Chemoreceptors
We studied whether nucleoid exclusion contributes to the segregation and retention of Tsr chemoreceptor clusters at the cell poles. We used live time-lapse, single-cell microscopy measurements, mutant cells, etc.
Overall, we found that the single-cell spatial distributions of Tsr clusters have heterogeneities and symmetries that are consistent with nucleoid exclusion, being one of the mechanisms by which they are positioned in the cells.
R Neeli-Venkata, S Startceva, T Annila, and AS Ribeiro (2016) Polar Localization of the Serine Chemoreceptor of Escherichia coli is Nucleoid Exclusion-Dependent. Biophysical Journal 111(11), 2512–2522. DOI: 10.1016/j.bpj.2016.10.024
Stochastic Simulators
We have been developing detailed models of transcription and small genetic circuits. With these, we made predictions on how transcription and genetic circuits operate. This has provide us 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. For this, we developed modelling strategies and simulators.
Selected Publications on modelling strategies:
AS Ribeiro, R Zhu, and SA Kauffman (2006) A General Modeling Strategy for Gene Regulatory Networks with Stochastic Dynamics, Journal of Computational Biology 13(9), 1630-1639. DOI:10.1089/cmb.2006.13.1630
AS Ribeiro (2010) Stochastic and delayed stochastic models of gene expression and regulation, Mathematical Biosciences 223(1), 1-11 (invited review article). DOI: 10.1016/j.mbs.2009.10.007
J Mäkelä, J Lloyd-Price, O Yli-Harja, and AS Ribeiro (2011) Stochastic sequence-level model of coupled transcription and translation in prokaryotes. BMC Bioinformatics 2011, 12:121. DOI: 10.1186/1471-2105-12-121
Selected Publications on simulators:
AS Ribeiro and J Lloyd-Price (2007) SGN Sim, a Stochastic Genetic Networks Simulator, Bioinformatics, 23(6):777-779. DOI: 10.1093/bioinformatics/btm004
J Lloyd-Price, A. Gupta, and AS Ribeiro (2012) SGNS2: A Compartmentalized Stochastic Chemical Kinetics Simulator for Dynamic Cell Populations. Bioinformatics, 28(22):3004-5. DOI: 10.1093/bioinformatics/bts556
Software for image analysis of microscopy data
We have developed software for analyzing microscopy data:
Selected Publications :
E Lihavainen, J Mäkelä, JN Spelbrink, and AS Ribeiro (2012) Mytoe: Automatic analysis of mitochondrial dynamics. Bioinformatics 7(28), 1050-1051. DOI: 10.1093/bioinformatics/bts073
A Häkkinen, A-B Muthukrishnan, A Mora, JM Fonseca, and AS Ribeiro (2013) CellAging: A tool to study segregation and partitioning in division in cell lineages of Escherichia coli. Bioinformatics 29 (13): 1708-1709. DOI: 10.1093/bioinformatics/btt194
L Martins, R Neeli-Venkata, SMD Oliveira, A Häkkinen, AS Ribeiro, and JM Fonseca (2018) SCIP: A Single-Cell Image Processor toolbox. Bioinformatics 34(24), 4318–4320. DOI: 10.1093/bioinformatics/bty505
Signal Processing of Single-Cell, Single-Molecule Biology Data
Estimating RNA numbers in single cells by RNA fluorescent tagging and flow-cytometry
We proposed a new methodology for quantifying mean, standard deviation, and skewness of single-cell distributions of RNA numbers, from flow cytometry data on cells expressing RNA tagged with MS2d-GFP. The quantification, based on scaling flow-cytometry data from microscopy single-cell data on integer-valued RNA numbers, produces precise, big data on in vivo single-cell distributions of RNA numbers and can assist in studies of transcription.
Selected Publication:
MNM Bahrudeen*, V Chauhan*, CSD Palma, SMD Oliveira, VK Kandavalli, and AS Ribeiro (2019) Estimating RNA numbers in single cells by RNA fluorescent tagging and flow cytometry. J Microbiological Methods 166, 105745. DOI: 10.1016/j.mimet.2019.105745 *Equal contributions.
VIII) Learning and Behavioral Changes using Mouse Paw-Preference as a case-study
We have being 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 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 include:
Hand-preference behavior in mice is inherently probabilistic. Likely, this provides robustness and constant adaptability to ever-changing environments.
Strong biases in paw preference of individual mice result from weak biases that appear by chance early in training and are reinforced with training over time. Constitutive behaviors play a minor or no role.
The degree of learning with training depends on the amount of short-term and long-term memory of past choices, which are genetically determined.
The corpus callosum (CC) and the hippocampal commissure (HC) contribute heavily to memory function and formation of long-term, but not short-term, paw-preference biases.
There is a degree of randomness in paw choice that is not removed by training, which may be a critical element for behavioural plasticity in paw preference in changing environments, supplying constant adaptability in paw preferences.
Selected Publications:
AS Ribeiro, J Lloyd-Price, B Eales, and FG Biddle (2010) Dynamic Agent-Based Model of Hand-Preference Behavior Patterns in the Mouse. Adaptive Behavior 18(2), 116-131. DOI: 10.1177/1059712309339859
AS Ribeiro, BA Eales and FG Biddle, (2011) Learning of paw preference in mice is strain dependent, gradual and based on short-term memory of previous reaches, Animal Behavior, 81(1), 249-257. DOI: 10.1016/j.anbehav.2010.10.014
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 collected by our long-time collaborators Fred G. Biddle (picture below) and Brenda Eales.
Fred in his lab at the University of Calgary, Canada :-)