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.

Computational design of single-stranded DNA hairpin aptamers immobilized on a biosensor substrate.

Aptamer interactions with a surface of attachment are central to the design and performance of aptamer-based biosensors. We have developed a computational modeling approach to study different system designs-including different aptamer-attachment ends, aptamer surface densities, aptamer orientations, and solvent solutions-and applied it to an anti MUC1 aptamer tethered to a silica biosensor substrate. Amongst all the system designs explored, we found that attaching the anti MUC1 aptamer through the 5' terminal end, in a high surface density configuration, and solvated in a 0.8 M NaCl solution provided the best exposure of the aptamer MUC1 binding regions and resulted in the least amount of aptamer backbone fluctuations. Many of the other designs led to non-functional systems, with the aptamer collapsing onto the surface. The computational approach we have developed and the resulting analysis techniques can be employed for the rational design of aptamer-based biosensors and provide a valuable tool for improving biosensor performance and repeatability.

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.

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.

Three-dimensional modeling of single stranded DNA hairpins for aptamer-based biosensors.

Aptamers consist of short oligonucleotides that bind specific targets. They provide advantages over antibodies, including robustness, low cost, and reusability. Their chemical structure allows the insertion of reporter molecules and surface-binding agents in specific locations, which have been recently exploited for the development of aptamer-based biosensors and direct detection strategies. Mainstream use of these devices, however, still requires significant improvements in optimization for consistency and reproducibility. DNA aptamers are more stable than their RNA counterparts for biomedical applications but have the disadvantage of lacking the wide array of computational tools for RNA structural prediction. Here, we present the first approach to predict from sequence the three-dimensional structures of single stranded (ss) DNA required for aptamer applications, focusing explicitly on ssDNA hairpins. The approach consists of a pipeline that integrates sequentially building ssDNA secondary structure from sequence, constructing equivalent 3D ssRNA models, transforming the 3D ssRNA models into ssDNA 3D structures, and refining the resulting ssDNA 3D structures. Through this pipeline, our approach faithfully predicts the representative structures available in the Nucleic Acid Database and Protein Data Bank databases. Our results, thus, open up a much-needed avenue for integrating DNA in the computational analysis and design of aptamer-based biosensors.

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)].

Literature-based automated reconstruction, expansion, and refinement of the TGF-ß superfamily ligand-receptor network.

The TGF-ß pathway transduces a variety of extracellular signals into intracellular responses that control multiple cellular processes, including cell growth, apoptosis, and differentiation. It encompasses 33 ligands that interact with 7 type II receptors and 5 type I receptors at the plasma membrane to potentially form 1,155 ligand-receptor complexes in mammalian cells. Retrieving the information of the complexes that are actually formed from reading the literature might be tedious and prone to missing links. Here, we have developed an automated literature-mining procedure to obtain the interactions of the TGF-ß ligand-receptor network. By querying the Information Hyperlinked over Proteins (iHOP) online service and processing the results, we were able to find pairwise interactions between ligands and receptors that allowed us to build the network automatically from the literature. Comparison with available published review papers indicates that this method is able to automatically reconstruct and expand the TGF-ß superfamily ligand-receptor network. Retrieving and parsing the full text of the manuscripts containing the interactions allowed us to refine the network interactions for specific cell lines.

In silico identification of potential therapeutic targets in the TGF-ß signal transduction pathway.

The transforming growth factor-ß (TGF-ß) superfamily of cytokines controls fundamental cellular processes, such as proliferation, motility, differentiation, and apoptosis. This fundamental role is emphasized by the widespread presence of mutations of the core components of the TGF-ß signal transduction pathway in a number of human diseases. Therefore, there is an increasing interest in the development of therapies to specifically target this pathway. Here we develop a computational approach to identify potential intervention points that are capable of restoring the normal signaling dynamics to the mutated system while maintaining the behavior of normal cells substantially unperturbed. We apply this approach explicitly to the TGF-ß pathway to study the signaling dynamics of mutated and normal cells treated with inhibitory drugs and identify the processes in the pathway that are most susceptible to therapeutic intervention.

Computational modelling of Smad-mediated negative feedback and crosstalk in the TGF-ß superfamily network.

The transforming growth factor-ß (TGF-ß) signal transduction pathway controls many cellular processes, including differentiation, proliferation and apoptosis. It plays a fundamental role during development and it is dysregulated in many diseases. The factors that control the dynamics of the pathway, however, are not fully elucidated yet and so far computational approaches have been very limited in capturing the distinct types of behaviour observed under different cellular backgrounds and conditions into a single-model description. Here, we develop a detailed computational model for TGF-ß signalling that incorporates elements of previous models together with crosstalking between Smad1/5/8 and Smad2/3 channels through a negative feedback loop dependent on Smad7. The resulting model accurately reproduces the diverse behaviour of experimental datasets for human keratinocytes, bovine aortic endothelial cells and mouse mesenchymal cells, capturing the dynamics of activation and nucleocytoplasmic shuttling of both R-Smad channels. The analysis of the model dynamics and its system properties revealed Smad7-mediated crosstalking between Smad1/5/8 and Smad2/3 channels as a major determinant in shaping the distinct responses to single and multiple ligand stimulation for different cell types.

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.

Characterization of negative feedback network motifs in the TGF-ß signaling pathway.

The transforming growth factor-ß (TGF-ß) superfamily of cytokines plays a fundamental role in a wide variety of cellular processes, including growth, differentiation, apoptosis, and tissue homeostasis [corrected]. Its relevance is emphasized by the mutations of its core components that are associated with diverse human diseases, such as cancer and cardiovascular pathologies. A prominent regulator of the pathway is Smad7, which attenuates the signal and controls its duration in a cell-type-dependent manner through a negative feedback loop. Here, we characterize all the potential Smad7-mediated negative feedback network motifs and investigate their effects on the signaling dynamics upon stimulation with TGF-ß and bone morphogenetic protein (BMP) ligands. The results show that the specific negative feedback implementation is a key determinant of both the response of the system to single and multiple ligands of the TGF-ß superfamily and its robustness and sensitivity to parameter perturbations.

The physics of protein-DNA interaction networks in the control of gene expression.

Protein-DNA interaction networks play a central role in many fundamental cellular processes. In gene regulation, physical interactions and reactions among the molecular components together with the physical properties of DNA control how genes are turned on and off. A key player in all these processes is the inherent flexibility of DNA, which provides an avenue for long-range interactions between distal DNA elements through DNA looping. Such versatility enables multiple interactions and results in additional complexity that is remarkably difficult to address with traditional approaches. This topical review considers recent advances in statistical physics methods to study the assembly of protein-DNA complexes with loops, their effects in the control of gene expression, and their explicit application to the prototypical lac operon genetic system of the E. coli bacterium. In the last decade, it has been shown that the underlying physical properties of DNA looping can actively control transcriptional noise, cell-to-cell variability, and other properties of gene regulation, including the balance between robustness and sensitivity of the induction process. These physical properties are largely dependent on the free energy of DNA looping, which accounts for DNA bending and twisting effects. These new physical methods have also been used in reverse to uncover the actual in vivo free energy of looping double-stranded DNA in living cells, which was not possible with existing experimental techniques. The results obtained for DNA looping by the lac repressor inside the E. coli bacterium showed a more malleable DNA than expected as a result of the interplay of the simultaneous presence of two distinct conformations of looped DNA.

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.