Robustness of DNA looping across multiple cell divisions in individual bacteria.

DNA looping has emerged as a central paradigm of transcriptional regulation, as it is shared across many living systems. One core property of DNA looping-based regulation is its ability to greatly enhance repression or activation of genes with only a few copies of transcriptional regulators. However, this property based on a small number of proteins raises the question of the robustness of such a mechanism with respect to the large intracellular perturbations taking place during growth and division of the cell. Here we address the issue of sensitivity to variations of intracellular parameters of gene regulation by DNA looping. We use the lac system as a prototype to experimentally identify the key features of the robustness of DNA looping in growing Escherichia coli cells. Surprisingly, we observe time intervals of tight repression spanning across division events, which can sometimes exceed 10 generations. Remarkably, the distribution of such long time intervals exhibits memoryless statistics that is mostly insensitive to repressor concentration, cell division events, and the number of distinct loops accessible to the system. By contrast, gene regulation becomes highly sensitive to these perturbations when DNA looping is absent. Using stochastic simulations, we propose that the observed robustness to division emerges from the competition between fast, multiple rebinding events of repressors and slow initiation rate of the RNA polymerase. We argue that fast rebinding events are a direct consequence of DNA looping that ensures robust gene repression across a range of intracellular perturbations.

All-or-none amyloid disassembly via chaperone-triggered fibril unzipping favors clearance of α-synuclein toxic species.

α-synuclein aggregation is present in Parkinson's disease and other neuropathologies. Among the assemblies that populate the amyloid formation process, oligomers and short fibrils are the most cytotoxic. The human Hsc70-based disaggregase system can resolve α-synuclein fibrils, but its ability to target other toxic assemblies has not been studied. Here, we show that this chaperone system preferentially disaggregates toxic oligomers and short fibrils, while its activity against large, less toxic amyloids is severely impaired. Biochemical and kinetic characterization of the disassembly process reveals that this behavior is the result of an all-or-none abrupt solubilization of individual aggregates. High-speed atomic force microscopy explicitly shows that disassembly starts with the destabilization of the tips and rapidly progresses to completion through protofilament unzipping and depolymerization without accumulation of harmful oligomeric intermediates. Our data provide molecular insights into the selective processing of toxic amyloids, which is critical to identify potential therapeutic targets against increasingly prevalent neurodegenerative disorders.

Clearly Detectable, Kinetically Restricted Solid-Solid Phase Transition in cis-Ceramide Monolayers.

Sphingosine [(2 S,3 R,4 E)-2-amino-4-octadecene-1,3-diol] is the most common sphingoid base in mammals. Ceramides are N-acyl sphingosines. Numerous small variations on this canonical structure are known, including the 1-deoxy, the 4,5-dihydro, and many others. However, whenever there is a Δ4 double bond, it adopts the trans (or E) configuration. We synthesized a ceramide containing 4 Z-sphingosine and palmitic acid ( cis-pCer) and studied its behavior in the form of monolayers extended on an air-water interface. cis-pCer acted very differently from the trans isomer in that, upon lateral compression of the monolayer, a solid-solid transition was clearly observed at a mean molecular area ≤44 Å2·molecule-1, whose characteristics depended on the rate of compression. The solid-solid transition, as well as states of domain coexistence, could be imaged by atomic force microscopy and by Brewster-angle microscopy. Atomistic molecular dynamics simulations provided results compatible with the experimentally observed differences between the cis and trans isomers. The data can help in the exploration of other solid-solid transitions in lipids, both in vitro and in vivo, that have gone up to now undetected because of their less obvious change in surface properties along the transition, as compared to cis-pCer.

Communication: system-size scaling of Boltzmann and alternate Gibbs entropies.

It has recurrently been proposed that the Boltzmann textbook definition of entropy S(E) = k ln Ω(E) in terms of the number of microstates Ω(E) with energy E should be replaced by the expression S(G)(E) = k ln Σ(E' < E)Ω(E') examined by Gibbs. Here, we show that SG either is equivalent to S in the macroscopic limit or becomes independent of the energy exponentially fast as the system size increases. The resulting exponential scaling makes the realistic use of SG unfeasible and leads in general to temperatures that are inconsistent with the notions of hot and cold.

The unreasonable effectiveness of equilibrium gene regulation through the cell cycle.

Systems like the prototypical lac operon can reliably hold repression of transcription upon DNA replication across cell cycles with just 10 repressor molecules per cell and behave as if they were at equilibrium. The origin of this phenomenology is still an unresolved question. Here, we develop a general theory to analyze strong perturbations in quasi-equilibrium systems and use it to quantify the effects of DNA replication in gene regulation. We find a scaling law linking actual with predicted equilibrium transcription via a single kinetic parameter. We show that even the lac operon functions beyond the physical limits of naive regulation through compensatory mechanisms that suppress non-equilibrium effects. Synthetic systems without adjuvant activators, such as the cAMP receptor protein (CRP), lack this reliability. Our results provide a rationale for the function of CRP, beyond just being a tunable activator, as a mitigator of cell cycle perturbations.

Systems biophysics of gene expression.

Gene expression is a process central to any form of life. It involves multiple temporal and functional scales that extend from specific protein-DNA interactions to the coordinated regulation of multiple genes in response to intracellular and extracellular changes. This diversity in scales poses fundamental challenges to the use of traditional approaches to fully understand even the simplest gene expression systems. Recent advances in computational systems biophysics have provided promising avenues to reliably integrate the molecular detail of biophysical process into the system behavior. Here, we review recent advances in the description of gene regulation as a system of biophysical processes that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. There is now basic mechanistic understanding on how promoters controlled by multiple, local and distal, DNA binding sites for transcription factors can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including precision and flexibility of the transcriptional responses.

Control of gene expression by modulated self-assembly.

Numerous transcription factors self-assemble into different order oligomeric species in a way that is actively regulated by the cell. Until now, no general functional role has been identified for this widespread process. Here, we capture the effects of modulated self-assembly in gene expression with a novel quantitative framework. We show that this mechanism provides precision and flexibility, two seemingly antagonistic properties, to the sensing of diverse cellular signals by systems that share common elements present in transcription factors like p53, NF-κB, STATs, Oct and RXR. Applied to the nuclear hormone receptor RXR, this framework accurately reproduces a broad range of classical, previously unexplained, sets of gene expression data and corroborates the existence of a precise functional regime with flexible properties that can be controlled both at a genome-wide scale and at the individual promoter level.

Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series.

The ability to infer the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is crucial for multiple applications. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions (NPIs) with 0.93-day accuracy over the time span of a year.

Multi-landmark alignment of genomic signals reveals conserved expression patterns across transcription start sites.

The prevalent one-dimensional alignment of genomic signals to a reference landmark is a cornerstone of current methods to study transcription and its DNA-dependent processes but it is prone to mask potential relations among multiple DNA elements. We developed a systematic approach to align genomic signals to multiple locations simultaneously by expanding the dimensionality of the genomic-coordinate space. We analyzed transcription in human and uncovered a complex dependence on the relative position of neighboring transcription start sites (TSSs) that is consistently conserved among cell types. The dependence ranges from enhancement to suppression of transcription depending on the relative distances to the TSSs, their intragenic position, and the transcriptional activity of the gene. Our results reveal a conserved hierarchy of alternative TSS usage within a previously unrecognized level of genomic organization and provide a general methodology to analyze complex functional relationships among multiple types of DNA elements.

Far-from-equilibrium processes without net thermal exchange via energy sorting.

Many important processes at the microscale require far-from-equilibrium conditions to occur, as in the functioning of mesoscopic bioreactors, nanoscopic rotors, and nanoscale mass conveyors. Achieving such conditions, however, is typically based on energy inputs that strongly affect the thermal properties of the environment and the controllability of the system itself. Here, we present a general class of far-from-equilibrium processes that suppress the net thermal exchange with the environment by maintaining the Maxwell-Boltzmann velocity distribution intact. This new phenomenon, referred to as ghost equilibrium, results from the statistical cancellation of superheated and subcooled nonequilibrated degrees of freedom that are autonomously generated through a microscale energy sorting process. We provide general conditions to observe this phenomenon and study its implications for manipulating energy at the microscale. The results are applied explicitly to two mechanistically different cases, an ensemble of rotational dipoles and a gas of trapped particles, which encompass a great variety of common situations involving both rotational and translational degrees of freedom.

Trafficking coordinate description of intracellular transport control of signaling networks.

Many cellular networks rely on the regulated transport of their components to transduce extracellular information into precise intracellular signals. The dynamics of these networks is typically described in terms of compartmentalized chemical reactions. There are many important situations, however, in which the properties of the compartments change continuously in a way that cannot naturally be described by chemical reactions. Here, we develop an approach based on transport along a trafficking coordinate to precisely describe these processes and we apply it explicitly to the TGF-ß signal transduction network, which plays a fundamental role in many diseases and cellular processes. The results of this newly introduced approach accurately capture the distinct TGF-ß signaling dynamics of cells with and without cancerous backgrounds and provide an avenue to predict the effects of chemical perturbations in a way that closely recapitulates the observed cellular behavior.

CplexA: a Mathematica package to study macromolecular-assembly control of gene expression.

SUMMARY: Macromolecular assembly coordinates essential cellular processes, such as gene regulation and signal transduction. A major challenge for conventional computational methods to study these processes is tackling the exponential increase of the number of configurational states with the number of components. CplexA is a Mathematica package that uses functional programming to efficiently compute probabilities and average properties over such exponentially large number of states from the energetics of the interactions. The package is particularly suited to study gene expression at complex promoters controlled by multiple, local and distal, DNA binding sites for transcription factors. AVAILABILITY: CplexA is freely available together with documentation at http://sourceforge.net/projects/cplexa/.

Ascertaining the initiation of epidemic resurgences: an application to the COVID-19 second surges in Europe and the Northeast United States.

Assessing a potential resurgence of an epidemic outbreak with certainty is as important as it is challenging. The low number of infectious individuals after a long regression, and the randomness associated with it, makes it difficult to ascertain whether the infectious population is growing or just fluctuating. We have developed an approach to compute confidence intervals for the switching time from decay to growth and to compute the corresponding multiple-location aggregated quantities over a region to increase the precision of the determination. We estimated the aggregate prevalence over time for Europe and the northeast United States to characterize the COVID-19 second surge in these regions during year 2020. We find a starting date as early as 3 July (95% confidence interval (CI): 1-6 July) for Europe and 19 August (95% CI: 16-23 August) for the northeast United States; subsequent infectious populations that, as of 31 December, have always increased or remained stagnant; and the resurgences being the collective effect of each overall region with no location, either country or state, dominating the regional dynamics by itself.

Reliably quantifying the evolving worldwide dynamic state of the COVID-19 outbreak from death records, clinical parametrization, and demographic data.

The dynamic characterization of the COVID-19 outbreak is critical to implement effective actions for its control and eradication but the information available at a global scale is not sufficiently reliable to be used directly. Here, we develop a quantitative approach to reliably quantify its temporal evolution and controllability through the integration of multiple data sources, including death records, clinical parametrization of the disease, and demographic data, and we explicitly apply it to countries worldwide, covering 97.4% of the human population, and to states within the United States (US). The validation of the approach shows that it can accurately reproduce the available prevalence data and that it can precisely infer the timing of nonpharmaceutical interventions. The results of the analysis identified general patterns of recession, stabilization, and resurgence. The diversity of dynamic behaviors of the outbreak across countries is paralleled by those of states and territories in the US, converging to remarkably similar global states in both cases. Our results offer precise insights into the dynamics of the outbreak and an efficient avenue for the estimation of the prevalence rates over time.

Accurate prediction of gene expression by integration of DNA sequence statistics with detailed modeling of transcription regulation.

Gene regulation involves a hierarchy of events that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. The effects of DNA sequence on these processes have typically been studied based either on its quantitative connection with single-domain binding free energies or on empirical rules that combine different DNA motifs to predict gene expression trends on a genomic scale. The middle-point approach that quantitatively bridges these two extremes, however, remains largely unexplored. Here, we provide an integrated approach to accurately predict gene expression from statistical sequence information in combination with detailed biophysical modeling of transcription regulation by multidomain binding on multiple DNA sites. For the regulation of the prototypical lac operon, this approach predicts within 0.3-fold accuracy transcriptional activity over a 10,000-fold range from DNA sequence statistics for different intracellular conditions.

From molecular noise to behavioural variability in a single bacterium.

The chemotaxis network that governs the motion of Escherichia coli has long been studied to gain a general understanding of signal transduction. Although this pathway is composed of just a few components, it exhibits some essential characteristics of biological complexity, such as adaptation and response to environmental signals. In studying intracellular networks, most experiments and mathematical models have assumed that network properties can be inferred from population measurements. However, this approach masks underlying temporal fluctuations of intracellular signalling events. We have inferred fundamental properties of the chemotaxis network from a noise analysis of behavioural variations in individual bacteria. Here we show that certain properties established by population measurements, such as adapted states, are not conserved at the single-cell level: for timescales ranging from seconds to several minutes, the behaviour of non-stimulated cells exhibit temporal variations much larger than the expected statistical fluctuations. We find that the signalling network itself causes this noise and identify the molecular events that produce it. Small changes in the concentration of one key network component suppress temporal behavioural variability, suggesting that such variability is a selected property of this adaptive system.

Ab initio thermodynamic modeling of distal multisite transcription regulation.

Transcription regulation typically involves the binding of proteins over long distances on multiple DNA sites that are brought close to each other by the formation of DNA loops. The inherent complexity of assembling regulatory complexes on looped DNA challenges the understanding of even the simplest genetic systems, including the prototypical lac operon. Here we implement a scalable approach based on thermodynamic molecular properties to model ab initio systems regulated through multiple DNA sites with looping. We show that this approach applied to the lac operon accurately predicts the system behavior for a wide range of cellular conditions, which include the transcription rate over five orders of magnitude as a function of the repressor concentration for wild type and all seven combinations of deletions of three operators, as well as the observed induction curves for cells with and without active catabolite activator protein. Our results provide new insights into the detailed functioning of the lac operon and reveal an efficient avenue to incorporate the required underlying molecular complexity into fully predictive models of gene regulation.

Extraction and Refolding Determinants of Chaperone-Driven Aggregated Protein Reactivation.

Reactivation of protein aggregates plays a fundamental role in numerous situations, including essential cellular processes, hematological and neurological disorders, and biotechnological applications. The molecular details of the chaperone systems involved are known to a great extent but how the overall reactivation process is achieved has remained unclear. Here, we quantified reactivation over time through a predictive mechanistic model and identified the key parameters that control the overall dynamics. We performed new targeted experiments and analyzed classical data, covering multiple types of non-ordered aggregates, chaperone combinations, and experimental conditions. We found that, irrespective of the behavior observed, the balance of surface disaggregation and refolding in solution universally determines the reactivation dynamics, which is broadly described by two characteristic times. This characterization makes it possible to use activity measurements to accurately infer the underlying loss of aggregated protein and to quantify, for the first time, the refolding rates of the soluble intermediates.

DNA looping in gene regulation: from the assembly of macromolecular complexes to the control of transcriptional noise.

The formation of DNA loops by the binding of proteins and protein complexes at distal DNA sites plays a central role in many cellular processes, such as transcription, recombination and replication. Important thermodynamic concepts underlie the assembly of macromolecular complexes on looped DNA. The effects that this process has on the properties of gene regulation extend beyond the traditional view of DNA looping as a mechanism to increase the affinity of regulatory molecules for their cognate sites. Recent developments indicate that DNA looping can also lead to the suppression of cell-to-cell variability, the control of transcriptional noise, and the activation of cooperative interactions on demand.

Modeling network dynamics: the lac operon, a case study.

We use the lac operon in Escherichia coli as a prototype system to illustrate the current state, applicability, and limitations of modeling the dynamics of cellular networks. We integrate three different levels of description (molecular, cellular, and that of cell population) into a single model, which seems to capture many experimental aspects of the system.