Disrupting autorepression circuitry generates "open-loop lethality" to yield escape-resistant antiviral agents.

Across biological scales, gene-regulatory networks employ autorepression (negative feedback) to maintain homeostasis and minimize failure from aberrant expression. Here, we present a proof of concept that disrupting transcriptional negative feedback dysregulates viral gene expression to therapeutically inhibit replication and confers a high evolutionary barrier to resistance. We find that nucleic-acid decoys mimicking cis-regulatory sites act as "feedback disruptors," break homeostasis, and increase viral transcription factors to cytotoxic levels (termed "open-loop lethality"). Feedback disruptors against herpesviruses reduced viral replication >2-logs without activating innate immunity, showed sub-nM IC50, synergized with standard-of-care antivirals, and inhibited virus replication in mice. In contrast to approved antivirals where resistance rapidly emerged, no feedback-disruptor escape mutants evolved in long-term cultures. For SARS-CoV-2, disruption of a putative feedback circuit also generated open-loop lethality, reducing viral titers by >1-log. These results demonstrate that generating open-loop lethality, via negative-feedback disruption, may yield a class of antimicrobials with a high genetic barrier to resistance.

Biosensors based on peptide exposure show single molecule conformations in live cells.

We describe an approach to study the conformation of individual proteins during single particle tracking (SPT) in living cells. "Binder/tag" is based on incorporation of a 7-mer peptide (the tag) into a protein where its solvent exposure is controlled by protein conformation. Only upon exposure can the peptide specifically interact with a reporter protein (the binder). Thus, simple fluorescence localization reflects protein conformation. Through direct excitation of bright dyes, the trajectory and conformation of individual proteins can be followed. Simple protein engineering provides highly specific biosensors suitable for SPT and FRET. We describe tagSrc, tagFyn, tagSyk, tagFAK, and an orthogonal binder/tag pair. SPT showed slowly diffusing islands of activated Src within Src clusters and dynamics of activation in adhesions. Quantitative analysis and stochastic modeling revealed in vivo Src kinetics. The simplicity of binder/tag can provide access to diverse proteins.

Identification of a therapeutic interfering particle-A single-dose SARS-CoV-2 antiviral intervention with a high barrier to resistance.

Viral-deletion mutants that conditionally replicate and inhibit the wild-type virus (i.e., defective interfering particles, DIPs) have long been proposed as single-administration interventions with high genetic barriers to resistance. However, theories predict that robust, therapeutic DIPs (i.e., therapeutic interfering particles, TIPs) must conditionally spread between cells with R0 >1. Here, we report engineering of TIPs that conditionally replicate with SARS-CoV-2, exhibit R0 >1, and inhibit viral replication 10- to 100-fold. Inhibition occurs via competition for viral replication machinery, and a single administration of TIP RNA inhibits SARS-CoV-2 sustainably in continuous cultures. Strikingly, TIPs maintain efficacy against neutralization-resistant variants (e.g., B.1.351). In hamsters, both prophylactic and therapeutic intranasal administration of lipid-nanoparticle TIPs durably suppressed SARS-CoV-2 by 100-fold in the lungs, reduced pro-inflammatory cytokine expression, and prevented severe pulmonary edema. These data provide proof of concept for a class of single-administration antivirals that may circumvent current requirements to continually update medical countermeasures against new variants.

A single-administration therapeutic interfering particle reduces SARS-CoV-2 viral shedding and pathogenesis in hamsters.

The high transmissibility of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a primary driver of the COVID-19 pandemic. While existing interventions prevent severe disease, they exhibit mixed efficacy in preventing transmission, presumably due to their limited antiviral effects in the respiratory mucosa, whereas interventions targeting the sites of viral replication might more effectively limit respiratory virus transmission. Recently, intranasally administered RNA-based therapeutic interfering particles (TIPs) were reported to suppress SARS-CoV-2 replication, exhibit a high barrier to resistance, and prevent serious disease in hamsters. Since TIPs intrinsically target the tissues with the highest viral replication burden (i.e., respiratory tissues for SARS-CoV-2), we tested the potential of TIP intervention to reduce SARS-CoV-2 shedding. Here, we report that a single, postexposure TIP dose lowers SARS-CoV-2 nasal shedding, and at 5 days postinfection, infectious virus shed is below detection limits in 4 out of 5 infected animals. Furthermore, TIPs reduce shedding of Delta variant or WA-1 from infected to uninfected hamsters. Cohoused "contact" animals exposed to infected, TIP-treated animals exhibited significantly lower viral loads, reduced inflammatory cytokines, no severe lung pathology, and shortened shedding duration compared to animals cohoused with untreated infected animals. TIPs may represent an effective countermeasure to limit SARS-CoV-2 transmission.

Ratiometric GPCR signaling enables directional sensing in yeast.

Accurate detection of extracellular chemical gradients is essential for many cellular behaviors. Gradient sensing is challenging for small cells, which can experience little difference in ligand concentrations on the up-gradient and down-gradient sides of the cell. Nevertheless, the tiny cells of the yeast Saccharomyces cerevisiae reliably decode gradients of extracellular pheromones to find their mates. By imaging the behavior of polarity factors and pheromone receptors, we quantified the accuracy of initial polarization during mating encounters. We found that cells bias the orientation of initial polarity up-gradient, even though they have unevenly distributed receptors. Uneven receptor density means that the gradient of ligand-bound receptors does not accurately reflect the external pheromone gradient. Nevertheless, yeast cells appear to avoid being misled by responding to the fraction of occupied receptors rather than simply the concentration of ligand-bound receptors. Such ratiometric sensing also serves to amplify the gradient of active G protein. However, this process is quite error-prone, and initial errors are corrected during a subsequent indecisive phase in which polarity clusters exhibit erratic mobile behavior.

A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement.

Cells polarize their movement or growth toward external directional cues in many different contexts. For example, budding yeast cells grow toward potential mating partners in response to pheromone gradients. Directed growth is controlled by polarity factors that assemble into clusters at the cell membrane. The clusters assemble, disassemble, and move between different regions of the membrane before eventually forming a stable polarity site directed toward the pheromone source. Pathways that regulate clustering have been identified but the molecular mechanisms that regulate cluster mobility are not well understood. To gain insight into the contribution of chemical noise to cluster behavior we simulated clustering using the reaction-diffusion master equation (RDME) framework to account for molecular-level fluctuations. RDME simulations are a computationally efficient approximation, but their results can diverge from the underlying microscopic dynamics. We implemented novel concentration-dependent rate constants that improved the accuracy of RDME-based simulations, allowing us to efficiently investigate how cluster dynamics might be regulated. Molecular noise was effective in relocating clusters when the clusters contained low numbers of limiting polarity factors, and when Cdc42, the central polarity regulator, exhibited short dwell times at the polarity site. Cluster stabilization occurred when abundances or binding rates were altered to either lengthen dwell times or increase the number of polarity molecules in the cluster. We validated key results using full 3D particle-based simulations. Understanding the mechanisms cells use to regulate the dynamics of polarity clusters should provide insights into how cells dynamically track external directional cues.

Particle-based simulations of polarity establishment reveal stochastic promotion of Turing pattern formation.

Polarity establishment, the spontaneous generation of asymmetric molecular distributions, is a crucial component of many cellular functions. Saccharomyces cerevisiae (yeast) undergoes directed growth during budding and mating, and is an ideal model organism for studying polarization. In yeast and many other cell types, the Rho GTPase Cdc42 is the key molecular player in polarity establishment. During yeast polarization, multiple patches of Cdc42 initially form, then resolve into a single front. Because polarization relies on strong positive feedback, it is likely that the amplification of molecular-level fluctuations underlies the generation of multiple nascent patches. In the absence of spatial cues, these fluctuations may be key to driving polarization. Here we used particle-based simulations to investigate the role of stochastic effects in a Turing-type model of yeast polarity establishment. In the model, reactions take place either between two molecules on the membrane, or between a cytosolic and a membrane-bound molecule. Thus, we developed a computational platform that explicitly simulates molecules at and near the cell membrane, and implicitly handles molecules away from the membrane. To evaluate stochastic effects, we compared particle simulations to deterministic reaction-diffusion equation simulations. Defining macroscopic rate constants that are consistent with the microscopic parameters for this system is challenging, because diffusion occurs in two dimensions and particles exchange between the membrane and cytoplasm. We address this problem by empirically estimating macroscopic rate constants from appropriately designed particle-based simulations. Ultimately, we find that stochastic fluctuations speed polarity establishment and permit polarization in parameter regions predicted to be Turing stable. These effects can operate at Cdc42 abundances expected of yeast cells, and promote polarization on timescales consistent with experimental results. To our knowledge, our work represents the first particle-based simulations of a model for yeast polarization that is based on a Turing mechanism.

Capturing Unknown Substrates via in Situ Formation of Tightly Bound Bisubstrate Adducts: S-Adenosyl-vinthionine as a Functional Probe for AdoMet-Dependent Methyltransferases.

Identifying an enzyme's substrates is essential to understand its function, yet it remains challenging. A fundamental impediment is the transient interactions between an enzyme and its substrates. In contrast, tight binding is often observed for multisubstrate-adduct inhibitors due to synergistic interactions. Extending this venerable concept to enzyme-catalyzed in situ adduct formation, unknown substrates were affinity-captured by an S-adenosyl-methionine (AdoMet, SAM)-dependent methyltransferase (MTase). Specifically, the electrophilic methyl sulfonium (alkyl donor) in AdoMet is replaced with a vinyl sulfonium (Michael acceptor) in S-adenosyl-vinthionine (AdoVin). Via an addition reaction, AdoVin and the nucleophilic substrate form a covalent bisubstrate-adduct tightly complexed with thiopurine MTase (2.1.1.67). As such, an unknown substrate was readily identified from crude cell lysates. Moreover, this approach is applicable to other systems, even if the enzyme is unknown.

Evidence for Behavioral Autorepression in Covid-19 Epidemiological Dynamics.

It has long been hypothesized that behavioral reactions to epidemic severity autoregulate infection dynamics, for example when susceptible individuals self-sequester based on perceived levels of circulating disease. However, evidence for such 'behavioral autorepression' has remained elusive, and its presence could significantly affect epidemic forecasting and interventions. Here, we analyzed early COVID-19 dynamics at 708 locations over three epidemiological scales (96 countries, 50 US states, and 562 US counties). Signatures of behavioral autorepression were identified through: (i) a counterintuitive mobility-death correlation, (ii) fluctuation-magnitude analysis, and (iii) dynamics of SARS-CoV-2 infection waves. These data enabled calculation of the average behavioral-autorepression strength (i.e., negative feedback 'gain') across different populations. Surprisingly, incorporating behavioral autorepression into conventional models was required to accurately forecast COVID-19 mortality. Models also predicted that the strength of behavioral autorepression has the potential to alter the efficacy of non-pharmaceutical interventions. Overall, these results provide evidence for the long-hypothesized existence of behavioral autorepression, which could improve epidemic forecasting and enable more effective application of non-pharmaceutical interventions during future epidemics. Significance: Challenges with epidemiological forecasting during the COVID-19 pandemic suggested gaps in underlying model architecture. One long-held hypothesis, typically omitted from conventional models due to lack of empirical evidence, is that human behaviors lead to intrinsic negative autoregulation of epidemics (termed 'behavioral autorepression'). This omission substantially alters model forecasts. Here, we provide independent lines of evidence for behavioral autorepression during the COVID-19 pandemic, demonstrate that it is sufficient to explain counterintuitive data on 'shutdowns', and provides a mechanistic explanation of why early shutdowns were more effective than delayed, high-intensity shutdowns. We empirically measure autorepression strength, and show that incorporating autorepression dramatically improves epidemiological forecasting. The autorepression phenomenon suggests that tailoring interventions to specific populations may be warranted.