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.

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.

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.

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.

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.

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.

Regulation of Human Hsc70 ATPase and Chaperone Activities by Apg2: Role of the Acidic Subdomain.

Protein aggregate reactivation in metazoans is accomplished by the combined activity of Hsp70, Hsp40 and Hsp110 chaperones. Hsp110s support the refolding of aggregated polypeptides acting as specialized nucleotide exchange factors of Hsp70. We have studied how Apg2, one of the three human Hsp110s, regulates the activity of Hsc70 (HspA8), the constitutive Hsp70 in our cells. Apg2 shows a biphasic behavior: at low concentration, it stimulates the ATPase cycle of Hsc70, binding of the chaperone to protein aggregates and the refolding activity of the system, while it inhibits these three processes at high concentration. When the acidic subdomain of Apg2, a characteristic sequence present in the substrate binding domain of all Hsp110s, is deleted, the detrimental effects occur at lower concentration and are more pronounced, which concurs with an increase in the affinity of the Apg2 mutant for Hsc70. Our data support a mechanism in which Apg2 arrests the chaperone cycle through an interaction with Hsc70(ATP) that might lead to premature ATP dissociation before hydrolysis. In this line, the acidic subdomain might serve as a conformational switch to support dissociation of the Hsc70:Apg2 complex.

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.

Determinants of population responses to environmental fluctuations.

Environmental fluctuations, such as changing conditions and variable nutrient availability, are an unavoidable component of the dynamics of virtually all populations. They affect populations in ways that are often difficult to predict and sometimes lead to paradoxical outcomes. Here, we present a general analytical approach to examine how populations respond to fluctuations. We show that there exist general explicit conditions that determine to what extent fluctuations propagate to the variability of the responses and how they change the behavior of the system, including whether they promote proliferation or death and whether they facilitate coexistence or exclusion of competing species. These conditions depend on linear and nonlinear terms of the growth rate and on the characteristic times of the fluctuations. We validated our general approach through computational experiments for both stochastic and chaotic fluctuations and for multiple types of systems. From an applied point of view, our results provide an avenue for the precise control of the population behavior through fluctuations in addition to just through average properties.

Computing at the Front-End by Receptor Networks.

Cells use receptors at their surface not just to transduce signals but also to perform computations before relaying them downstream.

Suppression and enhancement of transcriptional noise by DNA looping.

DNA looping has been observed to enhance and suppress transcriptional noise but it is uncertain which of these two opposite effects is to be expected for given conditions. Here, we derive analytical expressions for the main quantifiers of transcriptional noise in terms of the molecular parameters and elucidate the role of DNA looping. Our results rationalize paradoxical experimental observations and provide the first quantitative explanation of landmark individual-cell measurements at the single molecule level on the classical lac operon genetic system [Choi, L. Cai, K. Frieda, and X. S. Xie, Science 322, 442 (2008)].

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.

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.

Reliable prediction of complex phenotypes from a modular design in free energy space: an extensive exploration of the lac operon.

The basic methodology for designing, altering, and constructing biological systems is increasingly relying on well-established engineering principles to move forward from trial and error approaches to reliably predicting the system behavior from the properties of the components and their interactions. The inherent complexity of even the simplest biological systems, however, often precludes achieving such predictive power. A prototypical example is the lac operon, one of the best-characterized genetic systems, which still poses serious challenges for understanding the results of combining its parts into novel setups. The reason is the pervasive complex hierarchy of events involved in gene regulation that extend from specific protein-DNA interactions to the combinatorial assembly of nucleoprotein complexes. Here, we integrate such complexity into a few-parameter model to accurately predict gene expression from a few simple rules to connect the parts. The model accurately reproduces the observed transcriptional activity of the lac operon over a 10,000-fold range for 21 different operator setups, different repressor concentrations, and tetrameric and dimeric forms of the repressor. Incorporation of the calibrated model into more complex scenarios accurately captures the induction curves for key operator configurations and the temporal evolution of the ß-galactosidase activity of cell populations.

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.

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.

Optimal resting-growth strategies of microbial populations in fluctuating environments.

Bacteria spend most of their lifetime in non-growing states which allow them to survive extended periods of stress and starvation. When environments improve, they must quickly resume growth to maximize their share of limited nutrients. Cells with higher stress resistance often survive longer stress durations at the cost of needing more time to resume growth, a strong disadvantage in competitive environments. Here we analyze the basis of optimal strategies that microorganisms can use to cope with this tradeoff. We explicitly show that the prototypical inverse relation between stress resistance and growth rate can explain much of the different types of behavior observed in stressed microbial populations. Using analytical mathematical methods, we determine the environmental parameters that decide whether cells should remain vegetative upon stress exposure, downregulate their metabolism to an intermediate optimum level, or become dormant. We find that cell-cell variability, or intercellular noise, is consistently beneficial in the presence of extreme environmental fluctuations, and that it provides an efficient population-level mechanism for adaption in a deteriorating environment. Our results reveal key novel aspects of responsive phenotype switching and its role as an adaptive strategy in changing environments.

Noisy-threshold control of cell death.

BACKGROUND: Cellular responses to death-promoting stimuli typically proceed through a differentiated multistage process, involving a lag phase, extensive death, and potential adaptation. Deregulation of this chain of events is at the root of many diseases. Improper adaptation is particularly important because it allows cell sub-populations to survive even in the continuous presence of death conditions, which results, among others, in the eventual failure of many targeted anticancer therapies. RESULTS: Here, I show that these typical responses arise naturally from the interplay of intracellular variability with a threshold-based control mechanism that detects cellular changes in addition to just the cellular state itself. Implementation of this mechanism in a quantitative model for T-cell apoptosis, a prototypical example of programmed cell death, captures with exceptional accuracy experimental observations for different expression levels of the oncogene Bcl-xL and directly links adaptation with noise in an ATP threshold below which cells die. CONCLUSIONS: These results indicate that oncogenes like Bcl-xL, besides regulating absolute death values, can have a novel role as active controllers of cell-cell variability and the extent of adaptation.