133 research outputs found
Measuring fast gene dynamics in single cells with time-lapse luminescence microscopy.
Time-lapse fluorescence microscopy is an important tool for measuring in vivo gene dynamics in single cells. However, fluorescent proteins are limited by slow chromophore maturation times and the cellular autofluorescence or phototoxicity that arises from light excitation. An alternative is luciferase, an enzyme that emits photons and is active upon folding. The photon flux per luciferase is significantly lower than that for fluorescent proteins. Thus time-lapse luminescence microscopy has been successfully used to track gene dynamics only in larger organisms and for slower processes, for which more total photons can be collected in one exposure. Here we tested green, yellow, and red beetle luciferases and optimized substrate conditions for in vivo luminescence. By combining time-lapse luminescence microscopy with a microfluidic device, we tracked the dynamics of cell cycle genes in single yeast with subminute exposure times over many generations. Our method was faster and in cells with much smaller volumes than previous work. Fluorescence of an optimized reporter (Venus) lagged luminescence by 15-20 min, which is consistent with its known rate of chromophore maturation in yeast. Our work demonstrates that luciferases are better than fluorescent proteins at faithfully tracking the underlying gene expression
Programming stress-induced altruistic death in engineered bacteria.
Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is 'altruistic': the killing of some cells can benefit the survivors through release of 'public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the 'Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment
The evolution of a G1/S transcriptional network in yeasts
The G1-to-S cell cycle transition is promoted by the periodic expression of a large set of genes. In Saccharomyces cerevisiae G1/S gene expression is regulated by two transcription factor (TF) complexes, the MBF and SBF, which bind to specific DNA sequences, the MCB and SCB, respectively. Despite extensive research little is known regarding the evolution of the G1/S transcription regulation including the co-evolution of the DNA binding domains with their respective DNA binding sequences. We have recently examined the co-evolution of the G1/S TF specificity through the systematic generation and examination of chimeric Mbp1/Swi4 TFs containing different orthologue DNA binding domains in S. cerevisiae (Hendler et al. in PLoS Genet 13:e1006778. doi:10.1371/journal.pgen.1006778, 2017). Here, we review the co-evolution of G1/S transcriptional network and discuss the evolutionary dynamics and specificity of the MBF–MCB and SBF–SCB interactions in different fungal species
Designing sequential transcription logic: a simple genetic circuit for conditional memory
The ability to learn and respond to recurrent events depends on the capacity
to remember transient biological signals received in the past. Moreover, it may
be desirable to remember or ignore these transient signals conditioned upon
other signals that are active at specific points in time or in unique
environments. Here, we propose a simple genetic circuit in bacteria that is
capable of conditionally memorizing a signal in the form of a transcription
factor concentration. The circuit behaves similarly to a "data latch" in an
electronic circuit, i.e. it reads and stores an input signal only when
conditioned to do so by a "read command". Our circuit is of the same size as
the well-known genetic toggle switch (an unconditional latch) which consists of
two mutually repressing genes, but is complemented with a "regulatory front
end" involving protein heterodimerization as a simple way to implement
conditional control. Deterministic and stochastic analysis of the circuit
dynamics indicate that an experimental implementation is feasible based on
well-characterized genes and proteins. It is not known, to which extent
molecular networks are able to conditionally store information in natural
contexts for bacteria. However, our results suggest that such sequential logic
elements may be readily implemented by cells through the combination of
existing protein-protein interactions and simple transcriptional regulation.Comment: 20 pages, 5 figures; supplementary material available upon request
from the author
A modular analysis of the Auxin signalling network
Auxin is essential for plant development from embryogenesis onwards. Auxin acts in large part through regulation of transcription. The proteins acting in the signalling pathway regulating transcription downstream of auxin have been identified as well as the interactions between these proteins, thus identifying the topology of this network implicating 54 Auxin Response Factor (ARF) and Aux/IAA (IAA) transcriptional regulators. Here, we study the auxin signalling pathway by means of mathematical modeling at the single cell level. We proceed analytically, by considering the role played by five functional modules into which the auxin pathway can be decomposed: the sequestration of ARF by IAA, the transcriptional repression by IAA, the dimer formation amongst ARFs and IAAs, the feedback loop on IAA and the auxin induced degradation of IAA proteins. Focusing on these modules allows assessing their function within the dynamics of auxin signalling. One key outcome of this analysis is that there are both specific and overlapping functions between all the major modules of the signaling pathway. This suggests a combinatorial function of the modules in optimizing the speed and amplitude of auxin-induced transcription. Our work allows identifying potential functions for homo- and hetero-dimerization of transcriptional regulators, with ARF:IAA, IAA:IAA and ARF:ARF dimerization respectively controlling the amplitude, speed and sensitivity of the response and a synergistic effect of the interaction of IAA with transcriptional repressors on these characteristics of the signaling pathway. Finally, we also suggest experiments which might allow disentangling the structure of the auxin signaling pathway and analysing further its function in plants
Prediction of lethal and synthetically lethal knock-outs in regulatory networks
The complex interactions involved in regulation of a cell's function are
captured by its interaction graph. More often than not, detailed knowledge
about enhancing or suppressive regulatory influences and cooperative effects is
lacking and merely the presence or absence of directed interactions is known.
Here we investigate to which extent such reduced information allows to forecast
the effect of a knock-out or a combination of knock-outs. Specifically we ask
in how far the lethality of eliminating nodes may be predicted by their network
centrality, such as degree and betweenness, without knowing the function of the
system. The function is taken as the ability to reproduce a fixed point under a
discrete Boolean dynamics. We investigate two types of stochastically generated
networks: fully random networks and structures grown with a mechanism of node
duplication and subsequent divergence of interactions. On all networks we find
that the out-degree is a good predictor of the lethality of a single node
knock-out. For knock-outs of node pairs, the fraction of successors shared
between the two knocked-out nodes (out-overlap) is a good predictor of
synthetic lethality. Out-degree and out-overlap are locally defined and
computationally simple centrality measures that provide a predictive power
close to the optimal predictor.Comment: published version, 10 pages, 6 figures, 2 tables; supplement at
http://www.bioinf.uni-leipzig.de/publications/supplements/11-01
Dynamic modeling of gene expression in prokaryotes: application to glucose-lactose diauxie in Escherichia coli
Coexpression of genes or, more generally, similarity in the expression
profiles poses an unsurmountable obstacle to inferring the gene regulatory
network (GRN) based solely on data from DNA microarray time series. Clustering
of genes with similar expression profiles allows for a course-grained view of
the GRN and a probabilistic determination of the connectivity among the
clusters. We present a model for the temporal evolution of a gene cluster
network which takes into account interactions of gene products with genes and,
through a non-constant degradation rate, with other gene products. The number
of model parameters is reduced by using polynomial functions to interpolate
temporal data points. In this manner, the task of parameter estimation is
reduced to a system of linear algebraic equations, thus making the computation
time shorter by orders of magnitude. To eliminate irrelevant networks, we test
each GRN for stability with respect to parameter variations, and impose
restrictions on its behavior near the steady state. We apply our model and
methods to DNA microarray time series' data collected on Escherichia coli
during glucose-lactose diauxie and infer the most probable cluster network for
different phases of the experiment.Comment: 20 pages, 4 figures; Systems and Synthetic Biology 5 (2011
High-Precision, In Vitro Validation of the Sequestration Mechanism for Generating Ultrasensitive Dose-Response Curves in Regulatory Networks
Our ability to recreate complex biochemical mechanisms in designed, artificial systems provides a stringent test of our understanding of these mechanisms and opens the door to their exploitation in artificial biotechnologies. Motivated by this philosophy, here we have recapitulated in vitro the “target sequestration” mechanism used by nature to improve the sensitivity (the steepness of the input/output curve) of many regulatory cascades. Specifically, we have employed molecular beacons, a commonly employed optical DNA sensor, to recreate the sequestration mechanism and performed an exhaustive, quantitative study of its key determinants (e.g., the relative concentrations and affinities of probe and depletant). We show that, using sequestration, we can narrow the pseudo-linear range of a traditional molecular beacon from 81-fold (i.e., the transition from 10% to 90% target occupancy spans an 81-fold change in target concentration) to just 1.5-fold. This narrowing of the dynamic range improves the sensitivity of molecular beacons to that equivalent of an oligomeric, allosteric receptor with a Hill coefficient greater than 9. Following this we have adapted the sequestration mechanism to steepen the binding-site occupancy curve of a common transcription factor by an order of magnitude over the sensitivity observed in the absence of sequestration. Given the success with which the sequestration mechanism has been employed by nature, we believe that this strategy could dramatically improve the performance of synthetic biological systems and artificial biosensors
Cumulative signal transmission in nonlinear reaction-diffusion networks
Quantifying signal transmission in biochemical systems is key to uncover the mechanisms that cells use to control their responses to environmental stimuli. In this work we use the time-integral of chemical species as a measure of a network’s ability to cumulatively transmit signals encoded in spatiotemporal concentrations. We identify a class of nonlinear reaction-diffusion networks in which the time-integrals of some species can be computed analytically. The derived time-integrals do not require knowledge of the solution of the reaction-diffusion equation, and we provide a simple graphical test to check if a given network belongs to the proposed class. The formulae for the time-integrals reveal how the kinetic parameters shape signal transmission in a network under spatiotemporal stimuli. We use these to show that a canonical complex-formation mechanism behaves as a spatial low-pass filter, the bandwidth of which is inversely proportional to the diffusion length of the ligand
Formation of regulatory modules by local sequence duplication
Turnover of regulatory sequence and function is an important part of
molecular evolution. But what are the modes of sequence evolution leading to
rapid formation and loss of regulatory sites? Here, we show that a large
fraction of neighboring transcription factor binding sites in the fly genome
have formed from a common sequence origin by local duplications. This mode of
evolution is found to produce regulatory information: duplications can seed new
sites in the neighborhood of existing sites. Duplicate seeds evolve
subsequently by point mutations, often towards binding a different factor than
their ancestral neighbor sites. These results are based on a statistical
analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome,
and a comparison set of intergenic regulatory sequence in Saccharomyces
cerevisiae. In fly regulatory modules, pairs of binding sites show
significantly enhanced sequence similarity up to distances of about 50 bp. We
analyze these data in terms of an evolutionary model with two distinct modes of
site formation: (i) evolution from independent sequence origin and (ii)
divergent evolution following duplication of a common ancestor sequence. Our
results suggest that pervasive formation of binding sites by local sequence
duplications distinguishes the complex regulatory architecture of higher
eukaryotes from the simpler architecture of unicellular organisms
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