Computational approach to characterize causative factors and molecular indicators of chronic wound inflammation

A recombinant signalling-selective activated protein C that lacks anticoagulant activity is efficacious and safe in cutaneous wound preclinical models

Various preclinical and clinical studies have demonstrated the robust wound healing capacity of the natural anticoagulant activated protein C (APC). A bioengineered APC variant designated 3K3A-APC retains APC's cytoprotective cell signalling actions with <10% anticoagulant activity. This study was aimed to provide preclinical evidence that 3K3A-APC is efficacious and safe as a wound healing agent. 3K3A-APC, like wild-type APC, demonstrated positive effects on proliferation of human skin cells (keratinocytes, endothelial cells and fibroblasts). Similarly it also increased matrix metollaproteinase-2 activation in keratinocytes and fibroblasts. Topical 3K3A-APC treatment at 10 or 30 μg both accelerated mouse wound healing when culled on Day 11. And at 10 μg, it was superior to APC and had half the dermal wound gape compared to control. Further testing was conducted in excisional porcine wounds due to their congruence to human skin. Here, 3K3A-APC advanced macroscopic healing in a dose-dependent manner (100, 250 and 500 μg) when culled on Day 21. This was histologically corroborated by greater collagen maturity, suggesting more advanced remodelling. A non-interference arm of this study found no evidence that topical 3K3A-APC caused either any significant systemic side-effects or any significant leakage into the circulation. However the female pigs exhibited transient and mild local reactions after treatments in week three, which did not impact healing. Overall these preclinical studies support the hypothesis that 3K3A-APC merits future human wound studies.

COMMBINI: an experimentally-informed COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse

Bone fracture healing is a well-orchestrated but complex process that involves numerous regulations at different scales. This complexity becomes particularly evident during the inflammatory stage, as immune cells invade the healing region and trigger a cascade of signals to promote a favorable regenerative environment. Thus, the emergence of criticalities during this stage might hinder the rest of the process. Therefore, the investigation of the many interactions that regulate the inflammation has a primary importance on the exploration of the overall healing progression. In this context, an in silico model named COMMBINI (COmputational Model of Macrophage dynamics in the Bone INjury Immunoresponse) has been developed to investigate the mechano-biological interactions during the early inflammatory stage at the tissue, cellular and molecular levels. An agent-based model is employed to simulate the behavior of immune cells, inflammatory cytokines and fracture debris as well as their reciprocal multiscale biological interactions during the development of the early inflammation (up to 5 days post-injury). The strength of the computational approach is the capacity of the in silico model to simulate the overall healing process by taking into account the numerous hidden events that contribute to its success. To calibrate the model, we present an in silico immunofluorescence method that enables a direct comparison at the cellular level between the model output and experimental immunofluorescent images. The combination of sensitivity analysis and a Genetic Algorithm allows dynamic cooperation between these techniques, enabling faster identification of the most accurate parameter values, reducing the disparity between computer simulation and histological data. The sensitivity analysis showed a higher sensibility of the computer model to the macrophage recruitment ratio during the early inflammation and to proliferation in the late stage. Furthermore, the Genetic Algorithm highlighted an underestimation of macrophage proliferation by in vitro experiments. Further experiments were conducted using another externally fixated murine model, providing an independent validation dataset. The validated COMMBINI platform serves as a novel tool to deepen the understanding of the intricacies of the early bone regeneration phases. COMMBINI aims to contribute to designing novel treatment strategies in both the biological and mechanical domains.

Predictive analytics identifies key factors driving hyperalgesic priming of muscle sensory neurons

Hyperalgesic priming, a form of neuroplasticity induced by inflammatory mediators, in peripheral nociceptors enhances the magnitude and duration of action potential (AP) firing to future inflammatory events and can potentially lead to pain chronification. The mechanisms underlying the development of hyperalgesic priming are not well understood, limiting the identification of novel therapeutic strategies to combat chronic pain. In this study, we used a computational model to identify key proteins whose modifications caused priming of muscle nociceptors and made them hyperexcitable to a subsequent inflammatory event. First, we extended a previously validated model of mouse muscle nociceptor sensitization to incorporate Epac-mediated interaction between two G protein-coupled receptor signaling pathways commonly activated by inflammatory mediators. Next, we calibrated and validated the model simulations of the nociceptor's AP response to both innocuous and noxious levels of mechanical force after two subsequent inflammatory events using literature data. Then, by performing global sensitivity analyses that simulated thousands of nociceptor-priming scenarios, we identified five ion channels and two molecular processes (from the 18 modeled transmembrane proteins and 29 intracellular signaling components) as potential regulators of the increase in AP firing in response to mechanical forces. Finally, when we simulated specific neuroplastic modifications in Kv1.1 and Nav1.7 alone as well as with simultaneous modifications in Nav1.7, Nav1.8, TRPA1, and Kv7.2, we observed a considerable increase in the fold change in the number of triggered APs in primed nociceptors. These results suggest that altering the expression of Kv1.1 and Nav1.7 might regulate the neuronal hyperexcitability in primed mechanosensitive muscle nociceptors.

Modeling breast cancer proliferation, drug synergies, and alternating therapies

Estrogen receptor positive (ER+) breast cancer is responsive to a number of targeted therapies used clinically. Unfortunately, the continuous application of targeted therapy often results in resistance, driving the consideration of combination and alternating therapies. Toward this end, we developed a mathematical model that can simulate various mono, combination, and alternating therapies for ER + breast cancer cells at different doses over long time scales. The model is used to look for optimal drug combinations and predicts a significant synergism between Cdk4/6 inhibitors in combination with the anti-estrogen fulvestrant, which may help explain the clinical success of adding Cdk4/6 inhibitors to anti-estrogen therapy. Furthermore, the model is used to optimize an alternating treatment protocol so it works as well as monotherapy while using less total drug dose.

Identification of key factors driving inflammation-induced sensitization of muscle sensory neurons

Sensory neurons embedded in muscle tissue that initiate pain sensations, i.e., nociceptors, are temporarily sensitized by inflammatory mediators during musculoskeletal trauma. These neurons transduce peripheral noxious stimuli into an electrical signal [i.e., an action potential (AP)] and, when sensitized, demonstrate lower activation thresholds and a heightened AP response. We still do not understand the relative contributions of the various transmembrane proteins and intracellular signaling processes that drive the inflammation-induced hyperexcitability of nociceptors. In this study, we used computational analysis to identify key proteins that could regulate the inflammation-induced increase in the magnitude of AP firing in mechanosensitive muscle nociceptors. First, we extended a previously validated model of a mechanosensitive mouse muscle nociceptor to incorporate two inflammation-activated G protein-coupled receptor (GPCR) signaling pathways and validated the model simulations of inflammation-induced nociceptor sensitization using literature data. Then, by performing global sensitivity analyses that simulated thousands of inflammation-induced nociceptor sensitization scenarios, we identified three ion channels and four molecular processes (from the 17 modeled transmembrane proteins and 28 intracellular signaling components) as potential regulators of the inflammation-induced increase in AP firing in response to mechanical forces. Moreover, we found that simulating single knockouts of transient receptor potential ankyrin 1 (TRPA1) and reducing the rates of G_αq-coupled receptor phosphorylation and G_αq subunit activation considerably altered the excitability of nociceptors (i.e., each modification increased or decreased the inflammation-induced fold change in the number of triggered APs compared to when all channels were present). These results suggest that altering the expression of TRPA1 or the concentration of intracellular G_αq might regulate the inflammation-induced increase in AP response of mechanosensitive muscle nociceptors.

Mathematically Modeling the Effect of Endocrine and Cdk4/6 Inhibitor Therapies on Breast Cancer Cells

Mathematical modeling of cancer systems is beginning to be used to design better treatment regimens, especially in chemotherapy and radiotherapy. The effectiveness of mathematical modeling to inform treatment decisions and identify therapy protocols, some of which are highly nonintuitive, is because it enables the exploration of a huge number of therapeutic possibilities. Considering the immense cost of laboratory research and clinical trials, these nonintuitive therapy protocols would likely never be found by experimental approaches. While much of the work to date in this area has involved high-level models, which look simply at overall tumor growth or the interaction of resistant and sensitive cell types, mechanistic models that integrate molecular biology and pharmacology can contribute greatly to the discovery of better cancer treatment regimens. These mechanistic models are better able to account for the effect of drug interactions and the dynamics of therapy. The aim of this chapter is to demonstrate the use of ordinary differential equation-based mechanistic models to describe the dynamic interactions between the molecular signaling of breast cancer cells and two key clinical drugs. In particular, we illustrate the procedure for building a model of the response of MCF-7 cells to standard therapies used in the clinic. Such mathematical models can be used to explore the vast number of potential protocols to suggest better treatment approaches.

Preparation and characterization of vaccarin, hypaphorine and chitosan nanoparticles and their promoting effects on chronic wounds healing

Chronic wounds have become an important factor hindering human health, affecting tens of millions of people worldwide, especially diabetic wounds. Based on the antibacterial properties of chitosan, the angiogenesis promoting effect of vaccarin (VAC) and the anti-inflammatory effect of hypaphorine (HYP), nanoparticles with high bioavailability were prepared. VAC, HYP and chitosan nanoparticles (VAC + HYP-NPS) were used to the treatment of chronic wounds. Transmission electron microscopy (TEM) images showed the nanoparticles were spherical. ZetaPALS showed the potential of nanoparticles were -12.8 ± 5.53 mV and the size were 166.8 ± 29.95 nm. Methyl thiazolyl tetrazolium (MTT) assay showed that VAC + HYP-NPS had no toxicity and the biocompatibility was satisfactory. In the treatment of chronic wounds in diabetic rats, VAC + HYP-NPS significantly promoted the re-epithelialization of chronic wounds and accelerated the healing of chronic wounds. In the process of chronic wounds healing, VAC + HYP-NPS played the antibacterial effect of chitosan, the angiogenic effect of VAC and the anti-inflammatory effect of HYP, and finally promoted the chronic wounds healing. Overall, the developed VAC + HYP-NPS have potential application in chronic wounds healing. In view of the complexity of the causes of chronic wounds, multi-target drug administration may be an effective way to treat chronic wounds.

Paper-Based Interleukin-6 Test Strip for Early Detection of Wound Infection

The early stage of wound infection is always non-specific. Prompt intervention may help to prevent the wound from worsening. We developed a new protocol, based on previous research, that employs a paper-based IL-6 test strip used in combination with a spectrum-based optical reader to detect IL-6 in normal tissue (n = 19), acute wounds (n = 31), and chronic wounds (n = 32). Our data indicated the presence of significantly higher levels of IL-6 in acute wound tissues, but no significant difference in serum CRP. Receiver operating characteristics were used to determine clinical sensitivity and specificity of tissue IL-6 and systemic CRP. The area under the curve values were 0.87 and 0.63, respectively. The cut-off value of 30 pg/mL for IL-6 provided good sensitivity (75.0%) and superior specificity (88.9%). We found a high correlation between the IL-6 test strip and conventional ELISA results (R2 = 0.85, p < 0.001), and good agreement was also observed according to Bland-Altman analysis. We showed a promising role of tissue IL-6 to help early diagnosis of wound infection when clinical symptoms were non-specific. The advantages of this wound detection protocol included minimal invasiveness, small sample requirements, speed, sample preparation ease, and user-friendliness. This methodology could help care providers quickly clarify wound infection status and implement timely, optimal management.

A method for the inference of cytokine interaction networks

Cell-cell communication is mediated by many soluble mediators, including over 40 cytokines. Cytokines, e.g. TNF, IL1β, IL5, IL6, IL12 and IL23, represent important therapeutic targets in immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel disease (IBD), psoriasis, asthma, rheumatoid and juvenile arthritis. The identification of cytokines that are causative drivers of, and not just associated with, inflammation is fundamental for selecting therapeutic targets that should be studied in clinical trials. As in vitro models of cytokine interactions provide a simplified framework to study complex in vivo interactions, and can easily be perturbed experimentally, they are key for identifying such targets. We present a method to extract a minimal, weighted cytokine interaction network, given in vitro data on the effects of the blockage of single cytokine receptors on the secretion rate of other cytokines. Existing biological network inference methods typically consider the correlation structure of the underlying dataset, but this can make them poorly suited for highly connected, non-linear cytokine interaction data. Our method uses ordinary differential equation systems to represent cytokine interactions, and efficiently computes the configuration with the lowest Akaike information criterion value for all possible network configurations. It enables us to study indirect cytokine interactions and quantify inhibition effects. The extracted network can also be used to predict the combined effects of inhibiting various cytokines simultaneously. The model equations can easily be adjusted to incorporate more complicated dynamics and accommodate temporal data. We validate our method using synthetic datasets and apply our method to an experimental dataset on the regulation of IL23, a cytokine with therapeutic relevance in psoriasis and IBD. We validate several model predictions against experimental data that were not used for model fitting. In summary, we present a novel method specifically designed to efficiently infer cytokine interaction networks from cytokine perturbation data in the context of IMIDs.

Cytokines are the messenger molecules of the immune system, allowing intercellular communication and mediating effective immune responses. They are an important therapeutic target in immune mediated inflammatory diseases such as inflammatory bowel disease (IBD) and rheumatoid arthritis. Cytokines interact in a tightly regulated network and depending on the context a particular cytokine can be involved in anti-inflammatory or inflammatory activities. In order to determine which cytokines to target in specific disease types and patient subsets, it is critical to study the effects of the inhibition of one or more cytokines on the larger cytokine interaction network. We present a novel method to extract a minimal, weighted network from cytokine interaction data. Existing biological network inference methods typically consider the correlation structure of the underlying dataset and/or make further assumptions of the dataset such as the existence of a small core of regulators. This can make them poorly suited for highly connected, non-linear cytokine interaction data. We validated our method using synthetic data and applied our method to a dataset on the regulation of IL23, a cytokine implicated in IBD pathogenesis. Predictions of the extracted IL23 network were validated using additional experimental data and were used to support the view of the cytokines IL1 and IL23 as promising targets for those patients that fail to respond to TNFα inhibition, the current golden standard in IBD treatment.

MATHEMATICAL INSIGHTS INTO THE DYNAMICS OF INNATE IMMUNE RESPONSE DURING INFLAMMATION

Innate immune system cells activate in response to infection and trigger an acute inflammatory reaction to restore tissue homeostasis and promote subsequent tissue repair. Their activation and functions must be very well regulated to avoid tissue damage, organ dysfunction, or even death. In this work, a new set of mathematical models is presented to examine the dynamics of the innate immune system response to tissue damage and provide further understanding of the role of the innate immune system during the early stages of an inflammatory response. Different damaged cells production functions are proposed to represent the effect of secondary tissue damage by the innate immune system. The stability and bifurcation analyses of the model reveal that there is an important threshold parameter that can be controlled in order to avoid sustained chronic inflammation and secure a successful healing outcome. A set of numerical simulations is also performed to support the presented theoretical results and demonstrate the medical applicability of the new mathematical model.

Antibacterial peptide NZ2114-loaded hydrogel accelerates Staphylococcus aureus-infected wound healing

Wound infection caused by Staphylococcus aureus (S. aureus) is a great challenge which has caused significant burden and economic loss to the medical system. NZ2114, a plectasin-derived peptide, is an antibacterial agent for preventing and treating S. aureus infection, especially for methicillin-resistant S. aureus (MRSA) infection. Here, three-dimensional reticulated antimicrobial peptide (AMP) NZ2114 hydrogels were developed based on hydroxypropyl cellulose (HPC) and sodium alginate (SA); they displayed sustained and stable release properties (97.88 ± 1.79% and 91.1 ± 10.52% release rate in 72 h, respectively) and good short-term cytocompatibility and hemocompatibility. But the HPC-NZ2114 hydrogel had a smaller pore size (diameter 0.832 ± 0.420 μm vs. 3.912 ± 2.881 μm) and better mechanical properties than that of the SA-NZ2114 hydrogel. HPC/SA-NZ2114 hydrogels possess efficient antimicrobial activity in vitro and in vivo. In a full-thickness skin defect model, the wound closure of the 1.024 mg/g HPC-NZ2114 hydrogel group was superior to those of the SA-NZ2114 hydrogel and antibiotic groups on day 7. The HPC-NZ2114 hydrogel accelerated wound healing by reducing inflammation and promoting the production of vascular endothelial growth factor (VEGF), endothelial growth factor (EGF) and angiogenesis (CD31) through histological and immunohistochemistry evaluation. These data indicated that the HPC-NZ2114 hydrogel is an excellent candidate for S. aureus infection wound dressing. KEY POINTS: •NZ2114 hydrogels showed potential in vitro bactericidal activity against S. aureus •NZ2114 hydrogels could release continuously for 72 h and had good biocompatibility •NZ2114 hydrogels could effectively promote S. aureus-infected wound healing.

Towards in silico Models of the Inflammatory Response in Bone Fracture Healing

In silico modeling is a powerful strategy to investigate the biological events occurring at tissue, cellular and subcellular level during bone fracture healing. However, most current models do not consider the impact of the inflammatory response on the later stages of bone repair. Indeed, as initiator of the healing process, this early phase can alter the regenerative outcome: if the inflammatory response is too strongly down- or upregulated, the fracture can result in a non-union. This review covers the fundamental information on fracture healing, in silico modeling and experimental validation. It starts with a description of the biology of fracture healing, paying particular attention to the inflammatory phase and its cellular and subcellular components. We then discuss the current state-of-the-art regarding in silico models of the immune response in different tissues as well as the bone regeneration process at the later stages of fracture healing. Combining the aforementioned biological and computational state-of-the-art, continuous, discrete and hybrid modeling technologies are discussed in light of their suitability to capture adequately the multiscale course of the inflammatory phase and its overall role in the healing outcome. Both in the establishment of models as in their validation step, experimental data is required. Hence, this review provides an overview of the different in vitro and in vivo set-ups that can be used to quantify cell- and tissue-scale properties and provide necessary input for model credibility assessment. In conclusion, this review aims to provide hands-on guidance for scientists interested in building in silico models as an additional tool to investigate the critical role of the inflammatory phase in bone regeneration.

In silico Identification of Key Factors Driving the Response of Muscle Sensory Neurons to Noxious Stimuli

Nociceptive nerve endings embedded in muscle tissue transduce peripheral noxious stimuli into an electrical signal [i.e., an action potential (AP)] to initiate pain sensations. A major contributor to nociception from the muscles is mechanosensation. However, due to the heterogeneity in the expression of proteins, such as ion channels, pumps, and exchangers, on muscle nociceptors, we currently do not know the relative contributions of different proteins and signaling molecules to the neuronal response due to mechanical stimuli. In this study, we employed an integrated approach combining a customized experimental study in mice with a computational model to identify key proteins that regulate mechanical nociception in muscles. First, using newly collected data from somatosensory recordings in mouse hindpaw muscles, we developed and then validated a computational model of a mechanosensitive mouse muscle nociceptor. Next, by performing global sensitivity analyses that simulated thousands of nociceptors, we identified three ion channels (among the 17 modeled transmembrane proteins and four endoplasmic reticulum proteins) as potential regulators of the nociceptor response to mechanical forces in both the innocuous and noxious range. Moreover, we found that simulating single knockouts of any of the three ion channels, delayed rectifier voltage-gated K⁺ channel (Kv1.1) or mechanosensitive channels Piezo2 or TRPA1, considerably altered the excitability of the nociceptor (i.e., each knockout increased or decreased the number of triggered APs compared to when all channels were present). These results suggest that altering expression of the gene encoding Kv1.1, Piezo2, or TRPA1 might regulate the response of mechanosensitive muscle nociceptors.

Current Trends in Advanced Alginate-Based Wound Dressings for Chronic Wounds

Chronic wounds represent a major public health issue, with an extremely high cost worldwide. In healthy individuals, the wound healing process takes place in different stages: inflammation, cell proliferation (fibroblasts and keratinocytes of the dermis), and finally remodeling of the extracellular matrix (equilibrium between metalloproteinases and their inhibitors). In chronic wounds, the chronic inflammation favors exudate persistence and bacterial film has a special importance in the dynamics of chronic inflammation in wounds that do not heal. Recent advances in biopolymer-based materials for wound healing highlight the performance of specific alginate forms. An ideal wound dressing should be adherent to the wound surface and not to the wound bed, it should also be non-antigenic, biocompatible, semi-permeable, biodegradable, elastic but resistant, and cost-effective. It has to give protection against bacterial, infectious, mechanical, and thermal agents, to modulate the level of wound moisture, and to entrap and deliver drugs or other molecules This paper explores the roles of alginates in advanced wound-dressing forms with a particular emphasis on hydrogels, nanofibers networks, 3D-scaffolds or sponges entrapping fibroblasts, keratinocytes, or drugs to be released on the wound-bed. The latest research reports are presented and supported with in vitro and in vivo studies from the current literature.

A robust computational pipeline for model-based and data-driven phenotype clustering

MOTIVATION

Precision medicine is a promising field that proposes, in contrast to a one-size-fits-all approach, the tailoring of medical decisions, treatments or products. In this context, it is crucial to introduce innovative methods to stratify a population of patients on the basis of an accurate system-level knowledge of the disease. This is particularly important in very challenging conditions, where the use of standard statistical methods can be prevented by poor data availability or by the need of oversimplifying the processes regulating a complex disease.

RESULTS

We define an innovative method for phenotype classification that combines experimental data and a mathematical description of the disease biology. The methodology exploits the mathematical model for inferring additional subject features relevant for the classification. Finally, the algorithm identifies the optimal number of clusters and classifies the samples on the basis of a subset of the features estimated during the model fit. We tested the algorithm in two test cases: an in silico case in the context of dyslipidemia, a complex disease for which a large population of patients has been generated, and a clinical test case, in the context of a lysosomal rare disorder, for which the amount of available data was limited. In both the scenarios, our methodology proved to be accurate and robust, and allowed the inference of an additional phenotype division that the experimental data did not show.

AVAILABILITY AND IMPLEMENTATION

The code to reproduce the in silico results has been implemented in MATLAB v.2017b and it is available in the Supplementary Material.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Aging-induced fragility of the immune system

The adaptive and innate branches of the vertebrate immune system work in close collaboration to protect organisms from harmful pathogens. As an organism ages its immune system undergoes immunosenescence, characterized by declined performance or malfunction in either immune branch, which can lead to disease and death. In this study we develop a mathematical framework of coupled innate and adaptive immune responses, namely the integrated immune branch (IIB) model. This model describes dynamics of immune components in both branches, uses a shape-space representation to encode pathogen-specific immune memory, and exhibits three steady states - health, septic death, and chronic inflammation - qualitatively similar to clinically-observed immune outcomes. In this model, the immune system (initialized in the health state) is subjected to a sequence of pathogen encounters, and we use the number of prior pathogen encounters as a proxy for the "age" of the immune system. We find that repeated pathogen encounters may trigger a fragility in which any encounter with a novel pathogen will cause the system to irreversibly switch from health to chronic inflammation. This transition is consistent with the onset of "inflammaging", a condition observed in aged individuals who experience chronic low-grade inflammation even in the absence of pathogens. The IIB model predicts that the onset of chronic inflammation strongly depends on the history of encountered pathogens; the timing of onset differs drastically when the same set of infections occurs in a different order. Lastly, the coupling between the innate and adaptive immune branches generates a trade-off between rapid pathogen clearance and a delayed onset of immunosenescence. Overall, by considering the complex feedback between immune compartments, our work suggests potential mechanisms for immunosenescence and provides a theoretical framework at the system level and on the scale of an organism's lifetime to account for clinical observations.

Anti-inflammatory, wound healing and antioxidant potential of compounds from Dioscorea bulbifera L. bulbils

BACKGROUND

Dioscorea bulbifera L. (Dioscoreaceae) has been traditionally used in Thai folk medicine as a diuretic and anthelmintic, for longevity preparations, and for wound and inflammation treatment. This plant is also commonly used in traditional Indian and Chinese medicines in the treatment of sore throat, gastric cancer, rectal carcinoma and goiters. However, the wound healing effects of the active compounds in this plant have not been investigated.

OBJECTIVE

This study aimed to identify compounds responsible for the wound healing activity of D. bulbifera and determine their potential anti-inflammatory and antioxidant activities.

METHODS

Crude extracts of D. bulbifera bulbils, their derived fractions and eleven purified compounds were tested for anti-inflammatory activity against LPS-induced NO production in RAW264.7 macrophages. The wound healing effects were evaluated via cell proliferation and migration assays using human dermal fibroblasts (HDFs), and the antioxidant effects were determined using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and hydroxyl radical (•OH) scavenging activity assays.

RESULTS

15,16-Epoxy-6α-O-acetyl-8β-hydroxy-19-nor-clero-13(16),14-diene-17,12;18,2-diolide (2), (+)-catechin (5), quercetin (6) and myricetin (11) exhibited significantly potent wound healing effects and promoted marked cell proliferation, resulting in % viabilities of 107.4-137.6, 121.1-151.9, 98.0-131.9, 90.9-115.9, respectively. Among them, (+)-catechin produced the highest % cell migration, resulting in 100.0% wound closure sooner (at day 2) than the other compounds. In addition, 1 μg/ml (+)-catechin significantly increased fibroblast migration by 2.4-fold compared to that in the control after 24 h. Regarding anti-inflammatory properties, kaempferol (7) and quercetin (6) decreased (p < 0.005) NO production, with IC50 values of 46.6 and 56.2 μM, respectively. In addition, the crude extracts, solvent fractions and flavonoid compounds were also found to possess marked antioxidant activity in both DPPH and •OH radical scavenging assays.

CONCLUSIONS

These findings provide more evidence to support the traditional use of D. bulbifera for the treatment of wounds and inflammation.

Dynamical modeling of pro- and anti-inflammatory cytokines in the early stage of septic shock

A dynamical model of the pathophysiological behaviors of IL18 and IL10 cytokines with their receptors is tested against data for the case of early sepsis. The proposed approach considers the surroundings (organs and bone marrow) and the different subsystems (cells and cyctokines). The interactions between blood cells, cytokines and the surroundings are described via mass balances. Cytokines are adsorbed onto associated receptors at the cell surface. The adsorption is described by the Langmuir model and gives rise to the production of more cytokines and associated receptors inside the cell. The quantities of pro and anti-inflammatory cytokines present in the body are combined to give global information via an inflammation level function which describes the patient’s state. Data for parameter estimation comes from the Sepsis 48 H database. Comparisons between patient data and simulations are presented and are in good agreement. For the IL18/IL10 cytokine pair, 5 key parameters have been found. They are linked to pro-inflammatory IL18 cytokine and show that the early sepsis is driven by components of inflammatory character.

Microbiome-skin-brain axis: A novel paradigm for cutaneous wounds

Chronic wounds cause a significant burden on society financially, medically, and psychologically. Unfortunately, patients with nonhealing wounds often suffer from comorbidities that further compound their disability. Given the high rate of depressive symptoms experienced by patients with chronic wounds, further studies are needed to investigate the potentially linked pathophysiological changes in wounds and depression in order to improve patient care. The English literature on wound healing, inflammatory and microbial changes in chronic wounds and depression, and antiinflammatory and probiotic therapy was reviewed on PubMed. Chronic wound conditions and depression were demonstrated to share common pathologic features of dysregulated inflammation and altered microbiome, indicating a possible relationship. Furthermore, alternative treatment strategies such as immune-targeted and probiotic therapy showed promising potential by addressing both pathophysiological pathways. However, many existing studies are limited to a small study population, a cross-sectional design that does not establish temporality, or a wide range of confounding variables in the context of a highly complex and multifactorial disease process. Therefore, additional preclinical studies in suitable wound models, as well as larger clinical cohort studies and trials are necessary to elucidate the relationship between wound microbiome, healing, and depression, and ultimately guide the most effective therapeutic and management plan for chronic wound patients.

Interleukin-17: Potential Target for Chronic Wounds

Chronic wounds exhibit persistent inflammation with markedly delayed healing. The significant burden of chronic wounds, which are often resistant to standard therapy, prompts further research on novel therapies. Since the interleukin-17 family has been implicated as a group of proinflammatory cytokines in immune-mediated diseases in the gut and connective tissue, as well as inflammatory skin conditions, we consider here if it may contribute to the pathogenesis of chronic wounds. In this review, we discuss the interleukin-17 family's signaling pathways and role in tissue repair. A PubMed review of the English literature on interleukin-17, wound healing, chronic wounds, and inflammatory skin conditions was conducted. Interleukin-17 family signaling is reviewed in the context of tissue repair, and preclinical and clinical studies examining its role in the skin and other organ systems are critically reviewed. The published work supports a pathologic role for interleukin-17 family members in chronic wounds, though this needs to be more conclusively proven. Clinical studies using monoclonal interleukin-17 antibodies to improve healing of chronic skin wounds have not yet been performed, and only a few studies have examined interleukin-17 family expression in chronic skin wounds. Furthermore, different interleukin-17 family members could be playing selective roles in the repair process. These studies suggest a therapeutic role for targeting interleukin-17A to promote wound healing; therefore, interleukin-17A may be a target worthy of pursuing in the near future.

Predictive Approach Identifies Molecular Targets and Interventions to Restore Angiogenesis in Wounds With Delayed Healing

Impaired angiogenesis is a hallmark of wounds with delayed healing, and currently used therapies to restore angiogenesis have limited efficacy. Here, we employ a computational simulation-based approach to identify influential molecular and cellular processes, as well as protein targets, whose modulation may stimulate angiogenesis in wounds. We developed a mathematical model that captures the time courses for platelets, 9 cell types, 29 proteins, and oxygen, which are involved in inflammation, proliferation, and angiogenesis during wound healing. We validated our model using previously published experimental data. By performing global sensitivity analysis on thousands of simulated wound-healing scenarios, we identified six processes (among the 133 modeled in total) whose modulation may improve angiogenesis in wounds. By simulating knockouts of 25 modeled proteins and by simulating different wound-oxygenation levels, we identified four proteins [namely, transforming growth factor (TGF)-β, vascular endothelial growth factor (VEGF), fibroblast growth factor-2 (FGF-2), and angiopoietin-2 (ANG-2)], as well as oxygen, as therapeutic targets for stimulating angiogenesis in wounds. Our modeling results indicated that simultaneous inhibition of TGF-β and supplementation of either FGF-2 or ANG-2 could be more effective in stimulating wound angiogenesis than the modulation of either protein alone. Our findings suggest experimentally testable intervention strategies to restore angiogenesis in wounds with delayed healing.

Modeling the Macrophage-Mediated Inflammation Involved in the Bone Fracture Healing Process

A new mathematical model is presented to study the effects of macrophages on the bone fracture healing process. The model consists of a system of nonlinear ordinary differential equations that represents the interactions among classically and alternatively activated macrophages, mesenchymal stem cells, osteoblasts, and pro- and anti-inflammatory cytokines. A qualitative analysis of the model is performed to determine the equilibria and their corresponding stability properties. Numerical simulations are also presented to support the theoretical results, and to monitor the evolution of a broken bone for different types of fractures under various medical interventions. The model can be used to guide clinical experiments and to explore possible medical treatments that accelerate the bone fracture healing process, either by surgical interventions or drug administrations.

In silico study of principal sex hormone effects on post-injury synovial inflammatory response

Following an anterior cruciate ligament injury, premenopausal females tend to experience poorer outcomes than males, and sex hormones are thought to contribute to the disparity. Evidence seems to suggest that the sex hormones estrogen, progesterone, and testosterone may regulate the inflammation caused by macrophages, which invade the knee after an injury. While the individual effects of hormones on macrophage inflammation have been studied in vitro, their combined effects on post-injury inflammation in the knee have not been examined, even though both males and females have detectable levels of both estrogen and testosterone. In the present work, we developed an in silico kinetic model of the post-injury inflammatory response in the human knee joint and the hormonal influences that may shape that response. Our results indicate that post-injury, sex hormone concentrations observed in females may lead to a more pro-inflammatory, catabolic environment, while the sex hormone concentrations observed in males may lead to a more anti-inflammatory environment. These findings suggest that the female hormonal milieu may lead to increased catabolism, potentially worsening post-injury damage to the cartilage for females compared to males. The model developed herein may inform future in vitro and in vivo studies that seek to uncover the origins of sex differences in outcomes and may ultimately serve as a starting point for developing targeted therapies to prevent or reduce the cartilage damage that results from post-injury inflammation, particularly for females.

A Systems and Treatment Perspective of Models of Influenza Virus-Induced Host Responses

Severe influenza infections are often characterized as having unique host responses (e.g., early, severe hypercytokinemia). Neuraminidase inhibitors can be effective in controlling the severe symptoms of influenza but are often not administered until late in the infection. Several studies suggest that immune modulation may offer protection to high risk groups. Here, we review the current state of mathematical models of influenza-induced host responses. Selecting three models with conserved immune response components, we determine if the immune system components which most affect virus replication when perturbed are conserved across the models. We also test each model’s response to a pre-induction of interferon before the virus is administered. We find that each model emphasizes the importance of controlling the infected cell population to control viral replication. Moreover, our work shows that the structure of current models does not allow for significant responses to increased interferon concentrations. These results suggest that the current library of available published models of influenza infection does not adequately represent the complex interactions of the virus, interferon, and other aspects of the immune response. Specifically, the method used to model virus-resistant cells may need to be adapted in future work to more realistically represent the immune response to viral infection.

Computational analysis identifies putative prognostic biomarkers of pathological scarring in skin wounds

Background

Pathological scarring in wounds is a prevalent clinical outcome with limited prognostic options. The objective of this study was to investigate whether cellular signaling proteins could be used as prognostic biomarkers of pathological scarring in traumatic skin wounds.

Methods

We used our previously developed and validated computational model of injury-initiated wound healing to simulate the time courses for platelets, 6 cell types, and 21 proteins involved in the inflammatory and proliferative phases of wound healing. Next, we analysed thousands of simulated wound-healing scenarios to identify those that resulted in pathological (i.e., excessive) scarring. Then, we identified candidate proteins that were elevated (or decreased) at the early stages of wound healing in those simulations and could therefore serve as predictive biomarkers of pathological scarring outcomes. Finally, we performed logistic regression analysis and calculated the area under the receiver operating characteristic curve to quantitatively assess the predictive accuracy of the model-identified putative biomarkers.

Results

We identified three proteins (interleukin-10, tissue inhibitor of matrix metalloproteinase-1, and fibronectin) whose levels were elevated in pathological scars as early as 2 weeks post-wounding and could predict a pathological scarring outcome occurring 40 days after wounding with 80% accuracy.

Conclusion

Our method for predicting putative prognostic wound-outcome biomarkers may serve as an effective means to guide the identification of proteins predictive of pathological scarring.

Electronic supplementary material

The online version of this article (10.1186/s12967-018-1406-x) contains supplementary material, which is available to authorized users.

Curcumin-mediated bone marrow mesenchymal stem cell sheets create a favorable immune microenvironment for adult full-thickness cutaneous wound healing

Background

Adult full-thickness cutaneous wound repair suffers from an imbalanced immune response, leading to nonfunctional reconstructed tissue and fibrosis. Although various treatments have been reported, the immune-mediated tissue regeneration driven by biomaterial offers an attractive regenerative strategy for damaged tissue repair.

Methods

In this research, we investigated a specific bone marrow-derived mesenchymal stem cell (BMSC) sheet that was induced by the Traditional Chinese Medicine curcumin (CS-C) and its immunomodulatory effects on wound repair. Comparisons were made with the BMSC sheet induced without curcumin (CS-N) and control (saline).

Results

In vitro cultured BMSC sheets (CS-C) showed that curcumin promoted the proliferation of BMSCs and modified the features of produced extracellular matrix (ECM) secreted by BMSCs, especially the contents of ECM structural proteins such as fibronectin (FN) and collagen I and III, as well as the ratio of collagen III/I. Two-photon fluorescence (TPF) and second-harmonic generation (SHG) imaging of mouse implantation revealed superior engraftment of BMSCs, maintained for 35 days in the CS-C group. Most importantly, CS-C created a favorable immune microenvironment. The chemokine stromal cell-derived factor 1 (SDF1) was abundantly produced by CS-C, thus facilitating a mass migration of leukocytes from which significantly increased expression of signature T_H1 cells (interferon gamma) and M1 macrophages (tumor necrosis factor alpha) genes were confirmed at 7 days post-operation. The number of T_H1 cells and associated pro-inflammatory M1 macrophages subsequently decreased sharply after 14 days post-operation, suggesting a rapid type I immune regression. Furthermore, the CS-C group showed an increased trend towards M2 macrophage polarization in the early phase. CS-C led to an epidermal thickness and collagen deposition that was closer to that of normal skin.

Conclusions

Curcumin has a good regulatory effect on BMSCs and this promising CS-C biomaterial creates a pro-regenerative immune microenvironment for cutaneous wound healing.

Cardiac macrophage biology in the steady-state heart, the aging heart, and following myocardial infarction

Macrophages play critical roles in homeostatic maintenance of the myocardium under normal conditions and in tissue repair after injury. In the steady-state heart, resident cardiac macrophages remove senescent and dying cells and facilitate electrical conduction. In the aging heart, the shift in macrophage phenotype to a proinflammatory subtype leads to inflammaging. Following myocardial infarction (MI), macrophages recruited to the infarct produce both proinflammatory and anti-inflammatory mediators (cytokines, chemokines, matrix metalloproteinases, and growth factors), phagocytize dead cells, and promote angiogenesis and scar formation. These diverse properties are attributed to distinct macrophage subtypes and polarization status. Infarct macrophages exhibit a proinflammatory M1 phenotype early and become polarized toward an anti-inflammatory M2 phenotype later post-MI. Although this classification system is oversimplified and needs to be refined to accommodate the multiple different macrophage subtypes that have been recently identified, general concepts on macrophage roles are independent of subtype classification. This review summarizes current knowledge about cardiac macrophage origins, roles, and phenotypes in the steady state, with aging, and after MI, as well as highlights outstanding areas of investigation.

Computational identification and analysis of signaling subnetworks with distinct functional roles in the regulation of TNF production

Inflammation is a complex process driven by the coordinated action of a vast number of pro- and anti-inflammatory molecular mediators. While experimental studies have provided an abundance of information about the properties and mechanisms of action of individual mediators, essential system-level regulatory patterns that determine the time-course of inflammation are not sufficiently understood. In particular, it is not known how the contributions from distinct signaling pathways involved in cytokine regulation combine to shape the overall inflammatory response over different time scales. We investigated the kinetics of the intra- and extracellular signaling network controlling the production of the essential pro-inflammatory cytokine, tumor necrosis factor (TNF), and its anti-inflammatory counterpart, interleukin 10 (IL-10), in a macrophage culture. To tackle the intrinsic complexity of the network, we employed a computational modeling approach using the available literature data about specific molecular interactions. Our computational model successfully captured experimentally observed short- and long-term kinetics of key inflammatory mediators. Subsequent model analysis showed that distinct subnetworks regulate IL-10 production by impacting different temporal phases of the cAMP response element-binding protein (CREB) phosphorylation. Moreover, the model revealed that functionally similar inhibitory control circuits regulate the early and late activation phases of nuclear factor κB and CREB. Finally, we identified and investigated distinct signaling subnetworks that independently control the peak height and tail height of the TNF temporal trajectories. The knowledge of such subnetwork-specific regulatory effects may facilitate therapeutic interventions aimed at precise modulation of the inflammatory response.

Inflammation in Chronic Wounds

Non-healing chronic wounds present a major biological, psychological, social, and financial burden on both individual patients and the broader health system. Pathologically extensive inflammation plays a major role in the disruption of the normal healing cascade. The causes of chronic wounds (venous, arterial, pressure, and diabetic ulcers) can be examined through a juxtaposition of normal healing and the rogue inflammatory response created by the common components within chronic wounds (ageing, hypoxia, ischaemia-reperfusion injury, and bacterial colonisation). Wound bed care through debridement, dressings, and antibiotics currently form the basic mode of treatment. Despite recent setbacks, pharmaceutical adjuncts form an interesting area of research.

Mathematical Models for Immunology: Current State of the Art and Future Research Directions

The advances in genetics and biochemistry that have taken place over the last 10 years led to significant advances in experimental and clinical immunology. In turn, this has led to the development of new mathematical models to investigate qualitatively and quantitatively various open questions in immunology. In this study we present a review of some research areas in mathematical immunology that evolved over the last 10 years. To this end, we take a step-by-step approach in discussing a range of models derived to study the dynamics of both the innate and immune responses at the molecular, cellular and tissue scales. To emphasise the use of mathematics in modelling in this area, we also review some of the mathematical tools used to investigate these models. Finally, we discuss some future trends in both experimental immunology and mathematical immunology for the upcoming years.

Knowledge gaps to understanding cardiac macrophage polarization following myocardial infarction

Following myocardial infarction (MI), macrophages coordinate both pro-inflammatory and reparative responses of the left ventricle (LV) by reacting to and secreting cytokines, chemokines, and growth factors and by stimulating endothelial cells and fibroblasts to modulate neovascularization and scar formation. Healing of the infarcted LV can be divided into three distinct, but overlapping phases: inflammatory, proliferative, and maturation. Macrophages are involved in all phases. Despite macrophages being a major leukocyte cell type in the post-MI LV, how this cell type regulates LV remodeling over the post-MI time continuum is not completely understood. In this review, we summarize the current literature as a foundation to discuss the major knowledge gaps that remain. Defining the post-MI temporal macrophage phenotypes to establish a classification system is the first step in exploring how macrophage phenotypes are regulated, how temporal stimulation and secretion profiles evolve, and how best to modify stimuli to yield predictable cell responses. This article is part of a Special Issue entitled: The role of post-translational protein modifications on heart and vascular metabolism edited by Jason R.B. Dyck & Jan F.C. Glatz.

Modeling keratinocyte wound healing dynamics: Cell-cell adhesion promotes sustained collective migration

The in vitro migration of keratinocyte cell sheets displays behavioral and biochemical similarities to the in vivo wound healing response of keratinocytes in animal model systems. In both cases, ligand-dependent Epidermal Growth Factor Receptor (EGFR) activation is sufficient to elicit collective cell migration into the wound. Previous mathematical modeling studies of in vitro wound healing assays assume that physical connections between cells have a hindering effect on cell migration, but biological literature suggests a more complicated story. By combining mathematical modeling and experimental observations of collectively migrating sheets of keratinocytes, we investigate the role of cell-cell adhesion during in vitro keratinocyte wound healing assays. We develop and compare two nonlinear diffusion models of the wound healing process in which cell-cell adhesion either hinders or promotes migration. Both models can accurately fit the leading edge propagation of cell sheets during wound healing when using a time-dependent rate of cell-cell adhesion strength. The model that assumes a positive role of cell-cell adhesion on migration, however, is robust to changes in the leading edge definition and yields a qualitatively accurate density profile. Using RNAi for the critical adherens junction protein, α-catenin, we demonstrate that cell sheets with wild type cell-cell adhesion expression maintain migration into the wound longer than cell sheets with decreased cell-cell adhesion expression, which fails to exhibit collective migration. Our modeling and experimental data thus suggest that cell-cell adhesion promotes sustained migration as cells pull neighboring cells into the wound during wound healing.

Computational Identification of Mechanistic Factors That Determine the Timing and Intensity of the Inflammatory Response

Timely resolution of inflammation is critical for the restoration of homeostasis in injured or infected tissue. Chronic inflammation is often characterized by a persistent increase in the concentrations of inflammatory cells and molecular mediators, whose distinct amount and timing characteristics offer an opportunity to identify effective therapeutic regulatory targets. Here, we used our recently developed computational model of local inflammation to identify potential targets for molecular interventions and to investigate the effects of individual and combined inhibition of such targets. This was accomplished via the development and application of computational strategies involving the simulation and analysis of thousands of inflammatory scenarios. We found that modulation of macrophage influx and efflux is an effective potential strategy to regulate the amount of inflammatory cells and molecular mediators in both normal and chronic inflammatory scenarios. We identified three molecular mediators − tumor necrosis factor-α (TNF-α), transforming growth factor-β (TGF-β), and the chemokine CXCL8 − as potential molecular targets whose individual or combined inhibition may robustly regulate both the amount and timing properties of the kinetic trajectories for neutrophils and macrophages in chronic inflammation. Modulation of macrophage flux, as well as of the abundance of TNF-α, TGF-β, and CXCL8, may improve the resolution of chronic inflammation.

A recent approach to quantitatively characterize the timing and intensity of the inflammatory response relies on the use of four quantities termed inflammation indices. The values of the inflammation indices may reflect the differences between normal and pathological inflammation, and may be used to gauge the effects of therapeutic interventions aimed to control inflammation. Yet, the specific inflammatory mechanisms that can be targeted to selectively control these indices remain unknown. Here, we developed and applied a computational strategy to identify potential target mechanisms to regulate such indices. We used our recently developed model of local inflammation to simulate thousands of inflammatory scenarios. We then subjected the corresponding inflammation index values to sensitivity and correlation analysis. We found that the inflammation indices may be significantly influenced by the macrophage influx and efflux rates, as well as by the degradation rates of three specific molecular mediators. These results suggested that the indices can be effectively regulated by individual or combined inhibition of those molecular mediators, which we confirmed by computational experiments. Taken together, our results highlight possible targets of therapeutic intervention that can be used to control both the timing and the intensity of the inflammatory response.

Overview of Methods for Overcoming Hindrance to Drug Delivery to Tumors, with Special Attention to Tumor Interstitial Fluid

Every drug used to treat cancer (chemotherapeutics, immunological, monoclonal antibodies, nanoparticles, radionuclides) must reach the targeted cells through the tumor environment at adequate concentrations, in order to exert their cell-killing effects. For any of these agents to reach the goal cells, they must overcome a number of impediments created by the tumor microenvironment (TME), beginning with tumor interstitial fluid pressure (TIFP), and a multifactorial increase in composition of the extracellular matrix (ECM). A primary modifier of TME is hypoxia, which increases the production of growth factors, such as vascular endothelial growth factor and platelet-derived growth factor. These growth factors released by both tumor cells and bone marrow recruited myeloid cells form abnormal vasculature characterized by vessels that are tortuous and more permeable. Increased leakiness combined with increased inflammatory byproducts accumulates fluid within the tumor mass (tumor interstitial fluid), ultimately creating an increased pressure (TIFP). Fibroblasts are also up-regulated by the TME, and deposit fibers that further augment the density of the ECM, thus, further worsening the TIFP. Increased TIFP with the ECM are the major obstacles to adequate drug delivery. By decreasing TIFP and ECM density, we can expect an associated rise in drug concentration within the tumor itself. In this overview, we will describe all the methods (drugs, nutraceuticals, and physical methods of treatment) able to lower TIFP and to modify ECM used for increasing drug concentration within the tumor tissue.

A Computational, Tissue-Realistic Model of Pressure Ulcer Formation in Individuals with Spinal Cord Injury

People with spinal cord injury (SCI) are predisposed to pressure ulcers (PU). PU remain a significant burden in cost of care and quality of life despite improved mechanistic understanding and advanced interventions. An agent-based model (ABM) of ischemia/reperfusion-induced inflammation and PU (the PUABM) was created, calibrated to serial images of post-SCI PU, and used to investigate potential treatments in silico. Tissue-level features of the PUABM recapitulated visual patterns of ulcer formation in individuals with SCI. These morphological features, along with simulated cell counts and mediator concentrations, suggested that the influence of inflammatory dynamics caused simulations to be committed to "better" vs. "worse" outcomes by 4 days of simulated time and prior to ulcer formation. Sensitivity analysis of model parameters suggested that increasing oxygen availability would reduce PU incidence. Using the PUABM, in silico trials of anti-inflammatory treatments such as corticosteroids and a neutralizing antibody targeted at Damage-Associated Molecular Pattern molecules (DAMPs) suggested that, at best, early application at a sufficiently high dose could attenuate local inflammation and reduce pressure-associated tissue damage, but could not reduce PU incidence. The PUABM thus shows promise as an adjunct for mechanistic understanding, diagnosis, and design of therapies in the setting of PU.

Innate immune programing by endotoxin and its pathological consequences

Monocytes and macrophages play pivotal roles in inflammation and homeostasis. Recent studies suggest that dynamic programing of macrophages and monocytes may give rise to distinct "memory" states. Lipopolysaccharide (LPS), a classical pattern recognition molecule, dynamically programs innate immune responses. Emerging studies have revealed complex dynamics of cellular responses to LPS, with high doses causing acute, resolving inflammation, while lower doses are associated with low-grade and chronic non-resolving inflammation. These phenomena hint at dynamic complexities of intra-cellular signaling circuits downstream of the Toll-like receptor 4 (TLR4). In this review, we examine pathological effects of varying LPS doses with respect to the dynamics of innate immune responses and key molecular regulatory circuits responsible for these effects.

Simulation of B cell affinity maturation explains enhanced antibody cross-reactivity induced by the polyvalent malaria vaccine AMA1

Polyvalent vaccines use a mixture of Ags representing distinct pathogen strains to induce an immune response that is cross-reactive and protective. However, such approaches often have mixed results, and it is unclear how polyvalency alters the fine specificity of the Ab response and what those consequences might be for protection. In this article, we present a coarse-grain theoretical model of B cell affinity maturation during monovalent and polyvalent vaccinations that predicts the fine specificity and cross-reactivity of the Ab response. We stochastically simulate affinity maturation using a population dynamics approach in which the host B cell repertoire is represented explicitly, and individual B cell subpopulations undergo rounds of stimulation, mutation, and differentiation. Ags contain multiple epitopes and are present in subpopulations of distinct pathogen strains, each with varying degrees of cross-reactivity at the epitope level. This epitope- and strain-specific model of affinity maturation enables us to study the composition of the polyclonal response in granular detail and identify the mechanisms driving serum specificity and cross-reactivity. We applied this approach to predict the Ab response to a polyvalent vaccine based on the highly polymorphic malaria Ag apical membrane antigen-1. Our simulations show how polyvalent apical membrane Ag-1 vaccination alters the selection pressure during affinity maturation to favor cross-reactive B cells to both conserved and strain-specific epitopes and demonstrate how a polyvalent vaccine with a small number of strains and only moderate allelic coverage may be broadly neutralizing. Our findings suggest that altered fine specificity and enhanced cross-reactivity may be a universal feature of polyvalent vaccines.