Proof of concept for quantitative adverse outcome pathway modeling of chronic toxicity in repeated exposure.

Adverse Outcome Pathway (AOP) is a useful tool to glean mode of action (MOE) of a chemical. However, in order to use it for the purpose of risk assessment, an AOP needs to be quantified using in vitro or in vivo data. Majority of quantitative AOPs developed so far, were for single exposure to progressively higher doses. Limited attempts were made to include time in the modeling. Here as a proof-of concept, we developed a hypothetical AOP, and quantified it using a virtual dataset for six repeated exposures using a Bayesian Network Analysis (BN) framework. The virtual data was generated using realistic assumptions. Effects of each exposure were analyzed separately using a static BN model and analyzed in combination using a dynamic BN (DBN) model. Our work shows that the DBN model can be used to calculate the probability of adverse outcome when other upstream KEs were observed earlier. These probabilities can help in identification of early indicators of AO. In addition, we also developed a data driven AOP pruning technique using a lasso-based subset selection, and show that the causal structure of AOP is itself dynamic and changes over time. This proof-of-concept study revealed the possibility for expanding the applicability of the AOP framework to incorporate biological dynamism in toxicity appearance by repeated insults.

Development of a biofidelic computational model of human pelvis for predicting biomechanical responses and pelvic fractures.

BACKGROUND AND OBJECTIVE: The pelvis, a crucial structure for human locomotion, is susceptible to injuries resulting in significant morbidity and disability. This study aims to introduce and validate a biofidelic computational pelvis model, enhancing our understanding of pelvis injury mechanisms under lateral loading conditions. METHODS: The Finite Element (FE) pelvic model, representing a mid-sized male, was developed with variable cortical thickness in pelvis bones. Material properties were determined through a synthesis of existing constitutive models, parametric studies, and multiple validations. Comprehensive validation included various tests, such as load-displacement assessments of sacroiliac joints, quasi-static and dynamic lateral compression on the acetabulum, dynamic side impacts on the acetabulum and iliac wing using defleshed pelvis, and lateral impacts by a rigid plate on the full body's pelvis region. RESULTS: Simulation results demonstrated a reasonable correlation between the pelvis model's overall response and cadaveric testing data. Predicted fracture patterns of the isolated pelvis exhibited fair agreement with experimental results. CONCLUSIONS: This study introduces a credible computational model, providing valuable biomechanical insights into the pelvis' response under diverse lateral loading conditions and fracture patterns. The work establishes a robust framework for developing and enhancing the biofidelity of pelvis FE models through a multi-level validation approach, stimulating further research in modeling, validation, and experimental studies related to pelvic injuries. The findings are expected to offer critical perspectives for predicting, preventing, and mitigating pelvic injuries from vehicular accidents, contributing to advancements in clinical research on medical treatments for pelvic fractures.

Descriptor-Free Deep Learning QSAR Model for the Fraction Unbound in Human Plasma.

Chemical-specific parameters are either measured in vitro or estimated using quantitative structure-activity relationship (QSAR) models. The existing body of QSAR work relies on extracting a set of descriptors or fingerprints, subset selection, and training a machine learning model. In this work, we used a state-of-the-art natural language processing model, Bidirectional Encoder Representations from Transformers, which allowed us to circumvent the need for calculation of these chemical descriptors. In this approach, simplified molecular-input line-entry system (SMILES) strings were embedded in a high-dimensional space using a two-stage training approach. The model was first pre-trained on a masked SMILES token task and then fine-tuned on a QSAR prediction task. The pre-training task learned meaningful high-dimensional embeddings based upon the relationships between the chemical tokens in the SMILES strings derived from the "in-stock" portion of the ZINC 15 dataset─a large dataset of commercially available chemicals. The fine-tuning task then perturbed the pre-trained embeddings to facilitate prediction of a specific QSAR endpoint of interest. The power of this model stems from the ability to reuse the pre-trained model for multiple different fine-tuning tasks, reducing the computational burden of developing multiple models for different endpoints. We used our framework to develop a predictive model for fraction unbound in human plasma (fu,p). This approach is flexible, requires minimum domain expertise, and can be generalized for other parameters of interest for rapid and accurate estimation of absorption, distribution, metabolism, excretion, and toxicity.

McSNAC: A software to approximate first-order signaling networks from mass cytometry data.

Background: Mass cytometry (CyTOF) gives unprecedented opportunity to simultaneously measure up to 40 proteins in single cells, with a theoretical potential to reach 100 proteins. This high-dimensional single-cell information can be very useful in dissecting mechanisms of cellular activity. In particular, measuring abundances of signaling proteins like phospho-proteins can provide detailed information on the dynamics of single-cell signaling processes. However, computational analysis is required to reconstruct such networks with a mechanistic model. Methods: We propose our Mass cytometry Signaling Network Analysis Code (McSNAC), a new software capable of reconstructing signaling networks and estimating their kinetic parameters from CyTOF data. McSNAC approximates signaling networks as a network of first-order reactions between proteins. This assumption often breaks down as signaling reactions can involve binding and unbinding, enzymatic reactions, and other nonlinear constructions. Furthermore, McSNAC may be limited to approximating indirect interactions between protein species, as cytometry experiments are only able to assay a small fraction of protein species involved in signaling. Results: We carry out a series of in silico experiments here to show (1) McSNAC is capable of accurately estimating the ground-truth model in a scalable manner when given data originating from a first-order system; (2) McSNAC is capable of qualitatively predicting outcomes to perturbations of species abundances in simple second-order reaction models and in a complex in silico nonlinear signaling network in which some proteins are unmeasured. Conclusions: These findings demonstrate that McSNAC can be a valuable screening tool for generating models of signaling networks from time-stamped CyTOF data.

Generating Human Arm Kinematics Using Reinforcement Learning to Train Active Muscle Behavior in Automotive Research.

Computational human body models (HBMs) are important tools for predicting human biomechanical responses under automotive crash environments. In many scenarios, the prediction of the occupant response will be improved by incorporating active muscle control into the HBMs to generate biofidelic kinematics during different vehicle maneuvers. In this study, we have proposed an approach to develop an active muscle controller based on reinforcement learning (RL). The RL muscle activation control (RL-MAC) approach is a shift from using traditional closed-loop feedback controllers, which can mimic accurate active muscle behavior under a limited range of loading conditions for which the controller has been tuned. Conversely, the RL-MAC uses an iterative training approach to generate active muscle forces for desired joint motion and is analogous to how a child develops gross motor skills. In this study, the ability of a deep deterministic policy gradient (DDPG) RL controller to generate accurate human kinematics is demonstrated using a multibody model of the human arm. The arm model was trained to perform goal-directed elbow rotation by activating the responsible muscles and investigated using two recruitment schemes: as independent muscles or as antagonistic muscle groups. Simulations with the trained controller show that the arm can move to the target position in the presence or absence of externally applied loads. The RL-MAC trained under constant external loads was able to maintain the desired elbow joint angle under a simplified automotive impact scenario, implying the robustness of the motor control approach.

Evaluation and Validation of Thorax Model Responses: A Hierarchical Approach to Achieve High Biofidelity for Thoracic Musculoskeletal System.

As one of the most frequently occurring injuries, thoracic trauma is a significant public health burden occurring in road traffic crashes, sports accidents, and military events. The biomechanics of the human thorax under impact loading can be investigated by computational finite element (FE) models, which are capable of predicting complex thoracic responses and injury outcomes quantitatively. One of the key challenges for developing a biofidelic FE model involves model evaluation and validation. In this work, the biofidelity of a mid-sized male thorax model has been evaluated and enhanced by a multi-level, hierarchical strategy of validation, focusing on injury characteristics, and model improvement of the thoracic musculoskeletal system. At the component level, the biomechanical responses of several major thoracic load-bearing structures were validated against different relevant experimental cases in the literature, including the thoracic intervertebral joints, costovertebral joints, clavicle, sternum, and costal cartilages. As an example, the thoracic spine was improved by accurate representation of the components, material properties, and ligament failure features at tissue level then validated based on the quasi-static response at the segment level, flexion bending response at the functional spinal unit level, and extension angle of the whole thoracic spine. At ribcage and full thorax levels, the thorax model with validated bony components was evaluated by a series of experimental testing cases. The validation responses were rated above 0.76, as assessed by the CORA evaluation system, indicating the model exhibited overall good biofidelity. At both component and full thorax levels, the model showed good computational stability, and reasonable agreement with the experimental data both qualitatively and quantitatively. It is expected that our validated thorax model can predict thorax behavior with high biofidelity to assess injury risk and investigate injury mechanisms of the thoracic musculoskeletal system in various impact scenarios. The relevant validation cases established in this study shall be directly used for future evaluation of other thorax models, and the validation approach and process presented here may provide an insightful framework toward multi-level validating of human body models.

Development and multi-level validation of a computational model to predict traumatic aortic injury.

Traumatic aortic injury (TAI) is one of the leading causes of fatalities in blunt impact. However, there is no consensus on the injury mechanism of TAI in traffic accidents, mainly due to the complexity of occurrence scenarios and limited real-world crash data relevant to TAI. In this study, a computational model of the aorta with nonlinear mechanical characteristics and accurate morphology was developed and integrated within a thorax finite element model that included all major anatomical structures. To maximize the model's capability for predicting TAI, a multi-level process was presented to validate the model comprehensively. At the component level, the in vitro aortic pressurization testing was simulated to mimic the aortic burst pressure. Then, a sled test of a truncated cadaver was modeled to evaluate aorta response under posterior acceleration. The frontal chest pendulum impact was utilized to validate the performance of the aorta within full body model under direct chest compression. A parametric study was implemented to determine an injury tolerance for the aorta under these different loading conditions. The simulated peak pressure before aortic rupture was within the range of the experimental burst pressure. For the sled test, the simulated chest deflection and cross-sectional pressure of the aorta were correlated with the experimental measurement. No aorta injury was observed in simulated results of both sled test and chest pendulum impact, which matched the experimental findings. The present model will be a useful tool for understanding the TAI mechanisms, evaluating injury tolerance, and developing prevention strategies for aortic injuries.

Role of nanoscale antigen organization on B-cell activation probed using DNA origami.

Vaccine efficacy can be increased by arraying immunogens in multivalent form on virus-like nanoparticles to enhance B-cell activation. However, the effects of antigen copy number, spacing and affinity, as well as the dimensionality and rigidity of scaffold presentation on B-cell activation remain poorly understood. Here, we display the clinical vaccine immunogen eOD-GT8, an engineered outer domain of the HIV-1 glycoprotein-120, on DNA origami nanoparticles to systematically interrogate the impact of these nanoscale parameters on B-cell activation in vitro. We find that B-cell signalling is maximized by as few as five antigens maximally spaced on the surface of a 40-nm viral-like nanoparticle. Increasing antigen spacing up to ~25-30 nm monotonically increases B-cell receptor activation. Moreover, scaffold rigidity is essential for robust B-cell triggering. These results reveal molecular vaccine design principles that may be used to drive functional B-cell responses.

Finite Element Model of a Deformable American Football Helmet Under Impact.

Despite the use of helmets in American football, brain injuries are still prevalent. To reduce the burden of these injuries, novel impact mitigation systems are needed. The Vicis Zero1 (VZ1) American football helmet is unique in its use of multi-directional buckling structures sandwiched between a deformable outer shell and a stiff inner shell. The objective of this study was to develop a model of the VZ1 and to assess this unique characteristic for its role in mitigating head kinematics. The VZ1 model was developed using a bottom-up framework that emphasized material testing, constitutive model calibration, and component-level validation. Over 50 experimental tests were simulated to validate the VZ1 model. CORrelation and Analysis (CORA) was used to quantify the similarity between experimental and model head kinematics, neck forces, and impactor accelerations and forces. The VZ1 model demonstrated good correlation with an overall mean CORA score of 0.86. A parametric analysis on helmet compliance revealed that the outer shell and column stiffness influenced translational head kinematics more than rotational. For the material parameters investigated, head linear acceleration ranged from 80 to 220 g, whereas angular velocity ranged from 37 to 40 rad/s. This helmet model is open-source and serves as an in silico design platform for helmet innovation.

Corrigendum: Spatial Clustering of Receptors and Signaling Molecules Regulates NK Cell Response to Peptide Repertoire Changes.

[This corrects the article DOI: 10.3389/fimmu.2019.00605.].

Spatial Clustering of Receptors and Signaling Molecules Regulates NK Cell Response to Peptide Repertoire Changes.

Natural Killer (NK) cell activation requires integration of inhibitory and activating signaling. Inhibitory signals are determined by members of the killer cell immunoglobulin-like receptor (KIR) family, which have major histocompatibility complex (MHC) class I ligands. Loss of this inhibitory signal leads to NK cell activation. Thus, down-regulation of MHC I during viral infection or cancer induces NK cell activation. However, NK cell activation in the presence of MHC-I has been demonstrated for HLA-C*0102 through changes in its peptide content: "peptide antagonism." Here we identify an antagonist peptide for HLA-C*0304 suggesting that peptide antagonism is a generalizable phenomenon and, using a combination of mathematical modeling, confocal imaging, and immune-assays, we quantitatively determine mechanisms that underlie peptide antagonism in inhibitory KIR2DL2/3 signaling. These data provide a mechanism for NK cell activation based on a reduction of inhibitory signaling in the presence of preserved levels of MHC class I.

Mutations in bacterial genes induce unanticipated changes in the relationship between bacterial pathogens in experimental otitis media.

Otitis media (OM) is a common polymicrobial infection of the middle ear in children under the age of 15 years. A widely used experimental strategy to analyse roles of specific phenotypes of bacterial pathogens of OM is to study changes in co-infection kinetics of bacterial populations in animal models when a wild-type bacterial strain is replaced by a specific isogenic mutant strain in the co-inoculating mixtures. As relationships between the OM bacterial pathogens within the host are regulated by many interlinked processes, connecting the changes in the co-infection kinetics to a bacterial phenotype can be challenging. We investigated middle ear co-infections in adult chinchillas (Chinchilla lanigera) by two major OM pathogens: non-typeable Haemophilus influenzae (NTHi) and Moraxella catarrhalis (Mcat), as well as isogenic mutant strains in each bacterial species. We analysed the infection kinetic data using Lotka-Volterra population dynamics, maximum entropy inference and Akaike information criteria-(AIC)-based model selection. We found that changes in relationships between the bacterial pathogens that were not anticipated in the design of the co-infection experiments involving mutant strains are common and were strong regulators of the co-infecting bacterial populations. The framework developed here allows for a systematic analysis of host-host variations of bacterial populations and small sizes of animal cohorts in co-infection experiments to quantify the role of specific mutant strains in changing the infection kinetics. Our combined approach can be used to analyse the functional footprint of mutant strains in regulating co-infection kinetics in models of experimental OM and other polymicrobial diseases.

Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data.

Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to probe single-cell signalling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 to more than 40) of signalling species but are unable to track single cells. Thus, cytometry experiments provide detailed time-stamped snapshots of single-cell signalling kinetics. Is it possible to use the time-stamped cytometry data to reconstruct single-cell signalling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signalling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signalling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations, live-cell imaging measurements, and, synthetic flow cytometry datasets. The application of invariants and slow variables to reconstruct trajectories provides a radically different way to track objects using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines.

In silico modeling identifies CD45 as a regulator of IL-2 synergy in the NKG2D-mediated activation of immature human NK cells.

Natural killer (NK) cells perform immunosurveillance of virally infected and transformed cells, and their activation depends on the balance between signaling by inhibitory and activating receptors. Cytokine receptor signaling can synergize with activating receptor signaling to induce NK cell activation. We investigated the interplay between the signaling pathways stimulated by the cytokine interleukin-2 (IL-2) and the activating receptor NKG2D in immature (CD56bright) and mature (CD56dim) subsets of human primary NK cells using mass cytometry experiments and in silico modeling. Our analysis revealed that IL-2 changed the abundances of several key proteins, including NKG2D and the phosphatase CD45. Furthermore, we found differences in correlations between protein abundances, which were associated with the maturation state of the NK cells. The mass cytometry measurements also revealed that the signaling kinetics of key protein abundances induced by NKG2D stimulation depended on the maturation state and the pretreatment condition of the NK cells. Our in silico model, which described the multidimensional data with coupled first-order reactions, predicted that the increase in CD45 abundance was a major enhancer of NKG2D-mediated activation in IL-2-treated CD56bright NK cells but not in IL-2-treated CD56dim NK cells. This dependence on CD45 was verified by measurement of CD107a mobilization to the NK cell surface (a marker of activation). Our mathematical framework can be used to glean mechanisms underlying synergistic signaling pathways in other activated immune cells.

Non-canonical antagonism of PI3K by the kinase Itpkb delays thymocyte ß-selection and renders it Notch-dependent.

ß-selection is the most pivotal event determining αß T cell fate. Here, surface-expression of a pre-T cell receptor (pre-TCR) induces thymocyte metabolic activation, proliferation, survival and differentiation. Besides the pre-TCR, ß-selection also requires co-stimulatory signals from Notch receptors - key cell fate determinants in eukaryotes. Here, we show that this Notch-dependence is established through antagonistic signaling by the pre-TCR/Notch effector, phosphoinositide 3-kinase (PI3K), and by inositol-trisphosphate 3-kinase B (Itpkb). Canonically, PI3K is counteracted by the lipid-phosphatases Pten and Inpp5d/SHIP-1. In contrast, Itpkb dampens pre-TCR induced PI3K/Akt signaling by producing IP4, a soluble antagonist of the Akt-activating PI3K-product PIP3. Itpkb(-/-) thymocytes are pre-TCR hyperresponsive, hyperactivate Akt, downstream mTOR and metabolism, undergo an accelerated ß-selection and can develop to CD4(+)CD8(+) cells without Notch. This is reversed by inhibition of Akt, mTOR or glucose metabolism. Thus, non-canonical PI3K-antagonism by Itpkb restricts pre-TCR induced metabolic activation to enforce coincidence-detection of pre-TCR expression and Notch-engagement.

Maximum Entropy Estimation of Probability Distribution of Variables in Higher Dimensions from Lower Dimensional Data.

A common statistical situation concerns inferring an unknown distribution Q(x) from a known distribution P(y), where X (dimension n), and Y (dimension m) have a known functional relationship. Most commonly, n ≤ m, and the task is relatively straightforward for well-defined functional relationships. For example, if Y1 and Y2 are independent random variables, each uniform on [0, 1], one can determine the distribution of X = Y1 + Y2; here m = 2 and n = 1. However, biological and physical situations can arise where n > m and the functional relation Y→X is non-unique. In general, in the absence of additional information, there is no unique solution to Q in those cases. Nevertheless, one may still want to draw some inferences about Q. To this end, we propose a novel maximum entropy (MaxEnt) approach that estimates Q(x) based only on the available data, namely, P(y). The method has the additional advantage that one does not need to explicitly calculate the Lagrange multipliers. In this paper we develop the approach, for both discrete and continuous probability distributions, and demonstrate its validity. We give an intuitive justification as well, and we illustrate with examples.

Peptide selectivity discriminates NK cells from KIR2DL2- and KIR2DL3-positive individuals.

Natural killer cells are controlled by peptide selective inhibitory receptors for MHC class I, including the killer cell immunoglobulin-like receptors (KIRs). Despite having similar ligands, KIR2DL2 and KIR2DL3 confer different levels of protection to infectious disease. To investigate how changes in peptide repertoire may differentially affect NK cell reactivity, NK cells from KIR2DL2 and KIR2DL3 homozygous donors were tested for activity against different combinations of strong inhibitory (VAPWNSFAL), weak inhibitory (VAPWNSRAL), and antagonist peptide (VAPWNSDAL). KIR2DL3-positive NK cells were more sensitive to changes in the peptide content of MHC class I than KIR2DL2-positive NK cells. These differences were observed for the weakly inhibitory peptide VAPWNSRAL in single peptide and double peptide experiments (p < 0.01 and p < 0.03, respectively). More significant differences were observed in experiments using all three peptides (p < 0.0001). Mathematical modeling of the experimental data demonstrated that VAPWNSRAL was dominant over VAPWNSFAL in distinguishing KIR2DL3- from KIR2DL2-positive donors. Donors with different KIR genotypes have different responses to changes in the peptide bound by MHC class I. Differences in the response to the peptide content of MHC class I may be one mechanism underlying the protective effects of different KIR genes against infectious disease.

Host-to-host variation of ecological interactions in polymicrobial infections.

Host-to-host variability with respect to interactions between microorganisms and multicellular hosts are commonly observed in infection and in homeostasis. However, the majority of mechanistic models used to analyze host-microorganism relationships, as well as most of the ecological theories proposed to explain coevolution of hosts and microbes, are based on averages across a host population. By assuming that observed variations are random and independent, these models overlook the role of differences between hosts. Here, we analyze mechanisms underlying host-to-host variations of bacterial infection kinetics, using the well characterized experimental infection model of polymicrobial otitis media (OM) in chinchillas, in combination with population dynamic models and a maximum entropy (MaxEnt) based inference scheme. We find that the nature of the interactions between bacterial species critically regulates host-to-host variations in these interactions. Surprisingly, seemingly unrelated phenomena, such as the efficiency of individual bacterial species in utilizing nutrients for growth, and the microbe-specific host immune response, can become interdependent in a host population. The latter finding suggests a potential mechanism that could lead to selection of specific strains of bacterial species during the coevolution of the host immune response and the bacterial species.

Cell responses only partially shape cell-to-cell variations in protein abundances in Escherichia coli chemotaxis.

Cell-to-cell variations in protein abundance in clonal cell populations are ubiquitous in living systems. Because protein composition determines responses in individual cells, it stands to reason that the variations themselves are subject to selective pressures. However, the functional role of these cell-to-cell differences is not well understood. One way to tackle questions regarding relationships between form and function is to perturb the form (e.g., change the protein abundances) and observe the resulting changes in some function. Here, we take on the form-function relationship from the inverse perspective, asking instead what specific constraints on cell-to-cell variations in protein abundance are imposed by a given functional phenotype. We develop a maximum entropy-based approach to posing questions of this type and illustrate the method by application to the well-characterized chemotactic response in Escherichia coli. We find that full determination of observed cell-to-cell variations in protein abundances is not inherent in chemotaxis itself but, in fact, appears to be jointly imposed by the chemotaxis program in conjunction with other factors (e.g., the protein synthesis machinery and/or additional nonchemotactic cell functions, such as cell metabolism). These results illustrate the power of maximum entropy as a tool for the investigation of relationships between biological form and function.

Data-driven quantification of the robustness and sensitivity of cell signaling networks.

Robustness and sensitivity of responses generated by cell signaling networks has been associated with survival and evolvability of organisms. However, existing methods analyzing robustness and sensitivity of signaling networks ignore the experimentally observed cell-to-cell variations of protein abundances and cell functions or contain ad hoc assumptions. We propose and apply a data-driven maximum entropy based method to quantify robustness and sensitivity of Escherichia coli (E. coli) chemotaxis signaling network. Our analysis correctly rank orders different models of E. coli chemotaxis based on their robustness and suggests that parameters regulating cell signaling are evolutionary selected to vary in individual cells according to their abilities to perturb cell functions. Furthermore, predictions from our approach regarding distribution of protein abundances and properties of chemotactic responses in individual cells based on cell population averaged data are in excellent agreement with their experimental counterparts. Our approach is general and can be used to evaluate robustness as well as generate predictions of single cell properties based on population averaged experimental data in a wide range of cell signaling systems.