Multimodal surveillance of SARS-CoV-2 at a university enables development of a robust outbreak response framework.

BACKGROUND: Universities are vulnerable to infectious disease outbreaks, making them ideal environments to study transmission dynamics and evaluate mitigation and surveillance measures. Here, we analyze multimodal COVID-19-associated data collected during the 2020-2021 academic year at Colorado Mesa University and introduce a SARS-CoV-2 surveillance and response framework. METHODS: We analyzed epidemiological and sociobehavioral data (demographics, contact tracing, and WiFi-based co-location data) alongside pathogen surveillance data (wastewater and diagnostic testing, and viral genomic sequencing of wastewater and clinical specimens) to characterize outbreak dynamics and inform policy. We applied relative risk, multiple linear regression, and social network assortativity to identify attributes or behaviors associated with contracting SARS-CoV-2. To characterize SARS-CoV-2 transmission, we used viral sequencing, phylogenomic tools, and functional assays. FINDINGS: Athletes, particularly those on high-contact teams, had the highest risk of testing positive. On average, individuals who tested positive had more contacts and longer interaction durations than individuals who never tested positive. The distribution of contacts per individual was overdispersed, although not as overdispersed as the distribution of phylogenomic descendants. Corroboration via technical replicates was essential for identification of wastewater mutations. CONCLUSIONS: Based on our findings, we formulate a framework that combines tools into an integrated disease surveillance program that can be implemented in other congregate settings with limited resources. FUNDING: This work was supported by the National Science Foundation, the Hertz Foundation, the National Institutes of Health, the Centers for Disease Control and Prevention, the Massachusetts Consortium on Pathogen Readiness, the Howard Hughes Medical Institute, the Flu Lab, and the Audacious Project.

Time-resolved phosphoproteomics reveals scaffolding and catalysis-responsive patterns of SHP2-dependent signaling.

SHP2 is a protein tyrosine phosphatase that normally potentiates intracellular signaling by growth factors, antigen receptors, and some cytokines, yet is frequently mutated in human cancer. Here, we examine the role of SHP2 in the responses of breast cancer cells to EGF by monitoring phosphoproteome dynamics when SHP2 is allosterically inhibited by SHP099. The dynamics of phosphotyrosine abundance at more than 400 tyrosine residues reveal six distinct response signatures following SHP099 treatment and washout. Remarkably, in addition to newly identified substrate sites on proteins such as occludin, ARHGAP35, and PLCγ2, another class of sites shows reduced phosphotyrosine abundance upon SHP2 inhibition. Sites of decreased phospho-abundance are enriched on proteins with two nearby phosphotyrosine residues, which can be directly protected from dephosphorylation by the paired SH2 domains of SHP2 itself. These findings highlight the distinct roles of the scaffolding and catalytic activities of SHP2 in effecting a transmembrane signaling response.

Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells.

Targeted inhibition of oncogenic pathways can be highly effective in halting the rapid growth of tumors but often leads to the emergence of slowly dividing persister cells, which constitute a reservoir for the selection of drug-resistant clones. In BRAFV600E melanomas, RAF and MEK inhibitors efficiently block oncogenic signaling, but persister cells emerge. Here, we show that persister cells escape drug-induced cell-cycle arrest via brief, sporadic ERK pulses generated by transmembrane receptors and growth factors operating in an autocrine/paracrine manner. Quantitative proteomics and computational modeling show that ERK pulsing is enabled by rewiring of mitogen-activated protein kinase (MAPK) signaling: from an oncogenic BRAFV600E monomer-driven configuration that is drug sensitive to a receptor-driven configuration that involves Ras-GTP and RAF dimers and is highly resistant to RAF and MEK inhibitors. Altogether, this work shows that pulsatile MAPK activation by factors in the microenvironment generates a persistent population of melanoma cells that rewires MAPK signaling to sustain non-genetic drug resistance.

Considerations for the Selection and Use of Disinfectants Against SARS-CoV-2 in a Health Care Setting.

Proper disinfection using adequate disinfecting agents will be necessary for infection control strategies against coronavirus disease 2019 (COVID-19). However, limited guidance exists on effective surface disinfectants or best practices for their use against severe acute respiratory coronavirus 2. We outlined a process of fully characterizing over 350 products on the Environmental Protection Agency List N, including pH, method of delivery, indication for equipment sterilization, and purchase availability. We then developed a streamlined set of guidelines to help rapidly evaluate and select suitable disinfectants from List N, including practicality, efficacy, safety, and cost/availability. This resource guides the evaluation of ideal disinfectants amidst practical considerations posed by the COVID-19 pandemic.

Using Mechanistic Models for Analysis of Proteomic Data.

A goal of systems biology is to develop an integrated picture of how the myriad components of a biological system work together to produce responses to environmental inputs. Achieving this goal requires (1) assembling a list of the component parts of a cellular regulatory system, and (2) understanding how the connections between these components enable information processing. To work toward these ends, a number of methods have matured in parallel. The compilation of a cellular parts list has been accelerated by the advent of omics technologies, which enable simultaneous characterization of a large collection of biomolecules. A particular type of omics technology that is useful for understanding protein-protein interaction networks is proteomics, which can give information about a number of dimensions of the state of the cell's proteins: quantification of protein abundances within the cell, characterization of the posttranslational modification state of the proteome through phosphopeptide enrichment, and identification of protein-protein interactions through co-immunoprecipitation. Mathematical models can be useful in analyzing proteomic data.

Using Equation-Free Computation to Accelerate Network-Free Stochastic Simulation of Chemical Kinetics.

The chemical kinetics of many complex systems can be concisely represented by reaction rules, which can be used to generate reaction events via a kinetic Monte Carlo method that has been termed network-free simulation. Here, we demonstrate accelerated network-free simulation through a novel approach to equation-free computation. In this process, variables are introduced that approximately capture system state. Derivatives of these variables are estimated using short bursts of exact stochastic simulation and finite differencing. The variables are then projected forward in time via a numerical integration scheme, after which a new exact stochastic simulation is initialized and the whole process repeats. The projection step increases efficiency by bypassing the firing of numerous individual reaction events. As we show, the projected variables may be defined as populations of building blocks of chemical species. The maximal number of connected molecules included in these building blocks determines the degree of approximation. Equation-free acceleration of network-free simulation is found to be both accurate and efficient.

Timescale Separation of Positive and Negative Signaling Creates History-Dependent Responses to IgE Receptor Stimulation.

The high-affinity receptor for IgE expressed on the surface of mast cells and basophils interacts with antigens, via bound IgE antibody, and triggers secretion of inflammatory mediators that contribute to allergic reactions. To understand how past inputs (memory) influence future inflammatory responses in mast cells, a microfluidic device was used to precisely control exposure of cells to alternating stimulatory and non-stimulatory inputs. We determined that the response to subsequent stimulation depends on the interval of signaling quiescence. For shorter intervals of signaling quiescence, the second response is blunted relative to the first response, whereas longer intervals of quiescence induce an enhanced second response. Through an iterative process of computational modeling and experimental tests, we found that these memory-like phenomena arise from a confluence of rapid, short-lived positive signals driven by the protein tyrosine kinase Syk; slow, long-lived negative signals driven by the lipid phosphatase Ship1; and slower degradation of Ship1 co-factors. This work advances our understanding of mast cell signaling and represents a generalizable approach for investigating the dynamics of signaling systems.

BioNetFit: a fitting tool compatible with BioNetGen, NFsim and distributed computing environments.

UNLABELLED: Rule-based models are analyzed with specialized simulators, such as those provided by the BioNetGen and NFsim open-source software packages. Here, we present BioNetFit, a general-purpose fitting tool that is compatible with BioNetGen and NFsim. BioNetFit is designed to take advantage of distributed computing resources. This feature facilitates fitting (i.e. optimization of parameter values for consistency with data) when simulations are computationally expensive. AVAILABILITY AND IMPLEMENTATION: BioNetFit can be used on stand-alone Mac, Windows/Cygwin, and Linux platforms and on Linux-based clusters running SLURM, Torque/PBS, or SGE. The BioNetFit source code (Perl) is freely available (http://bionetfit.nau.edu). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: bionetgen.help@gmail.com.

Modeling for (physical) biologists: an introduction to the rule-based approach.

Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions.

Modeling biomolecular site dynamics in immunoreceptor signaling systems.

The immune system plays a central role in human health. The activities of immune cells, whether defending an organism from disease or triggering a pathological condition such as autoimmunity, are driven by the molecular machinery of cellular signaling systems. Decades of experimentation have elucidated many of the biomolecules and interactions involved in immune signaling and regulation, and recently developed technologies have led to new types of quantitative, systems-level data. To integrate such information and develop nontrivial insights into the immune system, computational modeling is needed, and it is essential for modeling methods to keep pace with experimental advances. In this chapter, we focus on the dynamic, site-specific, and context-dependent nature of interactions in immunoreceptor signaling (i.e., the biomolecular site dynamics of immunoreceptor signaling), the challenges associated with capturing these details in computational models, and how these challenges have been met through use of rule-based modeling approaches.

Phosphorylation site dynamics of early T-cell receptor signaling.

In adaptive immune responses, T-cell receptor (TCR) signaling impacts multiple cellular processes and results in T-cell differentiation, proliferation, and cytokine production. Although individual protein-protein interactions and phosphorylation events have been studied extensively, we lack a systems-level understanding of how these components cooperate to control signaling dynamics, especially during the crucial first seconds of stimulation. Here, we used quantitative proteomics to characterize reshaping of the T-cell phosphoproteome in response to TCR/CD28 co-stimulation, and found that diverse dynamic patterns emerge within seconds. We detected phosphorylation dynamics as early as 5 s and observed widespread regulation of key TCR signaling proteins by 30 s. Development of a computational model pointed to the presence of novel regulatory mechanisms controlling phosphorylation of sites with central roles in TCR signaling. The model was used to generate predictions suggesting unexpected roles for the phosphatase PTPN6 (SHP-1) and shortcut recruitment of the actin regulator WAS. Predictions were validated experimentally. This integration of proteomics and modeling illustrates a novel, generalizable framework for solidifying quantitative understanding of a signaling network and for elucidating missing links.

An Interaction Library for the FcεRI Signaling Network.

Antigen receptors play a central role in adaptive immune responses. Although the molecular networks associated with these receptors have been extensively studied, we currently lack a systems-level understanding of how combinations of non-covalent interactions and post-translational modifications are regulated during signaling to impact cellular decision-making. To fill this knowledge gap, it will be necessary to formalize and piece together information about individual molecular mechanisms to form large-scale computational models of signaling networks. To this end, we have developed an interaction library for signaling by the high-affinity IgE receptor, FcεRI. The library consists of executable rules for protein-protein and protein-lipid interactions. This library extends earlier models for FcεRI signaling and introduces new interactions that have not previously been considered in a model. Thus, this interaction library is a toolkit with which existing models can be expanded and from which new models can be built. As an example, we present models of branching pathways from the adaptor protein Lat, which influence production of the phospholipid PIP3 at the plasma membrane and the soluble second messenger IP3. We find that inclusion of a positive feedback loop gives rise to a bistable switch, which may ensure robust responses to stimulation above a threshold level. In addition, the library is visualized to facilitate understanding of network circuitry and identification of network motifs.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems.

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and posttranslational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation).

Decoding the language of phosphorylation site dynamics.

Immune defenses depend on the ability of immunoreceptors to recognize foreign antigens and initiate intracellular signaling when a pathogen is detected. Signal initiation requires spatial reorganization of proteins and site-specific receptor phosphorylation, which leads to engagement of feedback loops. This Journal Club discusses recent work using combined experimental and computational approaches to investigate these processes in B cell antigen receptor (BCR) signaling. Specifically, the roles of different kinases in the presence and absence of BCR clustering were evaluated. Results indicated that spleen tyrosine kinase (SYK) can compensate for loss of Src-family kinase activity when receptors are spatially clustered, in part because receptor clustering enables SYK to trigger a positive feedback loop. This study and its implications suggest additional uses for computational models in studies of immunoreceptor signaling and highlight areas where extensions of current methodology are needed to better understand the complexities of biomolecular interactions.

Guidelines for visualizing and annotating rule-based models.

Rule-based modeling provides a means to represent cell signaling systems in a way that captures site-specific details of molecular interactions. For rule-based models to be more widely understood and (re)used, conventions for model visualization and annotation are needed. We have developed the concepts of an extended contact map and a model guide for illustrating and annotating rule-based models. An extended contact map represents the scope of a model by providing an illustration of each molecule, molecular component, direct physical interaction, post-translational modification, and enzyme-substrate relationship considered in a model. A map can also illustrate allosteric effects, structural relationships among molecular components, and compartmental locations of molecules. A model guide associates elements of a contact map with annotation and elements of an underlying model, which may be fully or partially specified. A guide can also serve to document the biological knowledge upon which a model is based. We provide examples of a map and guide for a published rule-based model that characterizes early events in IgE receptor (FcεRI) signaling. We also provide examples of how to visualize a variety of processes that are common in cell signaling systems but not considered in the example model, such as ubiquitination. An extended contact map and an associated guide can document knowledge of a cell signaling system in a form that is visual as well as executable. As a tool for model annotation, a map and guide can communicate the content of a model clearly and with precision, even for large models.