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Background. Current management efforts of COVID-19 include: early diagnosis, use of antivirals and immune modulation. After the initial viral phase of the illness, identification of the patients developing cytokine storm syndrome is critical. Treatment of this hyper-inflammation in these patients using existing, approved therapies with proven safety profiles could address the immediate need to reduce the rising mortality. Methods. Using data from an A549 cell line, primary human bronchial epithelial (NBHE), as well as from COVID-19-infected lung, we compare the changes in the gene expression, pathways and mechanisms between SARS-CoV2, influenza A, and respiratory syncytial virus. Results. We identified FDA-approved drugs that could be repurposed to help COVID-19 patients with severe symptoms related to hyper-inflammation. An important finding is that drugs in the same class will not achieve similar effects. For instance methylprednisolone and prednisolone were predicted to be effective in reverting many of the changes triggered by COVID-19, while other closely related steroids, such as prednisone or dexamethasone, were not. An independent clinical study evaluated 213 subjects, 81 (38%) and 132 (62%) in pre-and post-methylprednisolone groups, respectively. The composite end point was composed of escalation to intensive care units, need for mechanical ventilation, and death. The composite endpoint occurred at a significantly lower rate in post-methylprednisolone group compared to pre-methylprednisolone group (34.9% vs. 54.3%, p=0.005). Conclusion. Clinical results confirmed the efficacy of the in silico prediction that indicated methyl- prednisolone could improve outcomes in severe COVID-19. These findings are important for any future pandemic regardless of the virus.

Current management efforts of COVID-19 include: early diagnosis, use of antivirals, and immune modulation. After the initial viral phase of the illness, identification of the patients developing cytokine storm syndrome is critical.1, 2 Treatment of hyper- inflammation in these patients using existing, approved therapies with proven safety profiles could address the immediate need to reduce the rising mortality.3 The identification of existing drugs that could modulate the immune response is an immediate need. Here we show that an analysis of the changes in the gene expression, path- ways and putative mechanisms between SARS-CoV2, influenza A, and respiratory syncytial virus can be used to identify FDA-approved drugs that could be repurposed to help COVID-19 patients with severe symptoms related to hyper-inflammation. An important finding is that drugs in the same class may not achieve similar effects. An independent clinical study evaluated 213 subjects, 81 (38%) and 132 (62%) in pre-and post-methylprednisolone groups, respectively. Thirty-day all-cause mortality occurred at a significantly lower rate in the post-methylprednisolone group compared to pre-methylprednisolone group (29.6% vs. 16.6%, p=0.027). Clinical results con- firmed the in silico prediction that methylprednisolone could improve outcomes in severe cases of COVID-19. These findings are important for any future pandemic regardless of the virus.

Inflammation is both central to proper wound healing and a key driver of chronic tissue injury via a positive-feedback loop incited by incidental cell damage. We seek to derive actionable insights into the role of inflammation in wound healing in order to improve outcomes for individual patients.To date, dynamic computational models have been used to study the time evolution of inflammation in wound healing. Emerging clinical data on histo-pathological and macroscopic images of evolving wounds, as well as noninvasive measures of blood flow, suggested the need for tissue-realistic, agent-based, and hybrid mechanistic computational simulations of inflammation and wound healing.We developed a computational modeling system, Simple Platform for Agent-based Representation of Knowledge, to facilitate the construction of tissue-realistic models.A hybrid equation-agent-based model (ABM) of pressure ulcer formation in both spinal cord-injured and -uninjured patients was used to identify control points that reduce stress caused by tissue ischemia/reperfusion. An ABM of arterial restenosis revealed new dynamics of cell migration during neointimal hyperplasia that match histological features, but contradict the currently prevailing mechanistic hypothesis. ABMs of vocal fold inflammation were used to predict inflammatory trajectories in individuals, possibly allowing for personalized treatment.The intertwined inflammatory and wound healing responses can be modeled computationally to make predictions in individuals, simulate therapies, and gain mechanistic insights.

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., Author Summary A virtual pressure ulcer was created as a platform to test therapies and determine the mechanisms most correlated with unfavorable outcomes. A layer of tissue fed with oxygen and diffusible molecules via blood vessels could develop an ulcer if pressure was applied, by simulating constriction of blood vessels in a circular region. Simulated ulcers were visually similar to digital photographs of ulcers in individuals with spinal cord injury in their irregular shapes, jagged edges, and overall progression in time. Statistical analyses of simulation outputs revealed that inflammation was an important determinant of ulcer severity and overall tissue damage. However, simulated clinical trials revealed that blocking the negative effects of inflammation could not prevent ulceration, and in order to be beneficial at all for this specific type of ulcer, anti-inflammatory treatments must be applied during the earliest stages of ulcer formation—before many clinical signs of ulceration appear.

Wounds can arise either from a disease process or are surgically created as part of therapy. The healing of damaged tissue is a fundamental biological process, involving a complex set of cellular and molecular components acting within a specific spatial context. Impairment or aberration of the wound healing process is a considerable source of morbidity and mortality, likely only to increase in clinical significance given an aging population. Despite considerable advances in the mechanistic knowledge of wound healing, as with all complex biological processes converting that knowledge into effective therapeutics is a substantial translational challenge. As result, wound healing has been a major focus in the field of Translational Systems Biology, and, in particular, been a subject of agent-based mechanistic computational modeling. The intuitive mapping between biological knowledge and the rules in an agent-based model (ABM), the ability of an ABM to readily represent stochastic processes, and the inherent spatial representation capability of an ABM all facilitate the utilization of this method in the practice of Translational Systems Biology. Presented herein are a series of ABMs of wound healing that demonstrate the translational potential and utility of this methodology in advancing the rational development of wound healing therapeutics.

Inflammation can drive both homeostasis and disease via dynamic, multiscale processes. The inflammatory response can be studied using multiplexed platforms, but there is no straightforward means by which to deal with the consequent “data deluge” in order to glean basic insights and clinically useful applications. Systems approaches, including data-driven and mechanistic computational modeling, have been employed in order to study the acute inflammatory response in the settings of trauma/hemorrhage and sepsis. Through combined data-driven and mechanistic modeling based on such “meso-dimensional” datasets, computational models of acute inflammation applicable to multiple preclinical species as well as humans were generated. A key hypothesis derived from these studies is that inflammation may be regulated via positive feedback loops that control switching between beneficial and detrimental inflammatory responses. Self-resolving inflammation may occur when specific signals feedback in a positive fashion to drive anti-inflammatory responses, while proinflammatory signals remain below certain thresholds. In contrast, self-amplifying, detrimental inflammation may occur when different signals feedback in a positive fashion to drive proinflammatory responses, setting in motion the positive feedback loop of inflammation → tissue damage/dysfunction → inflammation driven by damage-associated molecular pattern molecules. These insights may drive a future generation of targeted, personalized therapies for acute inflammation.

Objective To gain insights into individual variations in acute inflammation and physiology. Design Large-animal study combined with mathematical modeling. Setting Academic large-animal and computational laboratories. Subjects Outbred juvenile swine. Interventions Four swine were instrumented and subjected to endotoxemia (100 µg/kg), followed by serial plasma sampling. Measurements and main results Swine exhibited various degrees of inflammation and acute lung injury, including one death with severe acute lung injury (PaO(2)/FIO(2) ratio μ200 and static compliance μ10 L/cm H(2)O). Plasma interleukin-1β, interleukin-4, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α, high mobility group box-1, and NO(2)/NO(3) were significantly (p μ .05) elevated over the course of the experiment. Principal component analysis was used to suggest principal drivers of inflammation. Based in part on principal component analysis, an ordinary differential equation model was constructed, consisting of the lung and the blood (as a surrogate for the rest of the body), in which endotoxin induces tumor necrosis factor-α in monocytes in the blood, followed by the trafficking of these cells into the lung leading to the release of high mobility group box-1, which in turn stimulates the release of interleukin-1β from resident macrophages. The ordinary differential equation model also included blood pressure, PaO(2), and FIO(2), and a damage variable that summarizes the health of the animal. This ordinary differential equation model could be fit to both inflammatory and physiologic data in the individual swine. The predicted time course of damage could be matched to the oxygen index in three of the four swine. Conclusions The approach described herein may aid in predicting inflammation and physiologic dysfunction in small cohorts of subjects with diverse phenotypes and outcomes.

Background: Complex biological processes such as acute inflammation induced by trauma/hemorrhagic shock/ (T/HS) are dynamic and multi-dimensional. We utilized multiplexing cytokine analysis coupled with data-driven modeling to gain a systems perspective into T/HS. Methodology/Principal Findings: Mice were subjected to surgical cannulation trauma (ST) 6 hemorrhagic shock (HS; 25 mmHg), and followed for 1, 2, 3, or 4 h in each case. Serum was assayed for 20 cytokines and NO2 2 /NO3 2 . These data were analyzed using four data-driven methods (Hierarchical Clustering Analysis [HCA], multivariate analysis [MA], Principal Component Analysis [PCA], and Dynamic Network Analysis [DyNA]). Using HCA, animals subjected to ST vs. ST + HS could be partially segregated based on inflammatory mediator profiles, despite a large overlap. Based on MA, interleukin [IL]-12p40/ p70 (IL-12.total), monokine induced by interferon-c (CXCL-9) [MIG], and IP-10 were the best discriminators between ST and ST/HS. PCA suggested that the inflammatory mediators found in the three main principal components in animals subjected to ST were IL-6, IL-10, and IL-13, while the three principal components in ST + HS included a large number of cytokines including IL-6, IL-10, keratinocyte-derived cytokine (CXCL-1) [KC], and tumor necrosis factor-a [TNF-a]. DyNA suggested that the circulating mediators produced in response to ST were characterized by a high degree of interconnection/complexity at all time points; the response to ST + HS consisted of different central nodes, and exhibited zero network density over the first 2 h with lesser connectivity vs. ST at all time points. DyNA also helped link the conclusions from MA and PCA, in that central nodes consisting of IP-10 and IL-12 were seen in ST, while MIG and IL-6 were central nodes in ST + HS. Conclusions/Significance: These studies help elucidate the dynamics of T/HS-induced inflammation, complementing other forms of dynamic mechanistic modeling. These methods should be applicable to the analysis of other complex biological processes.

T cells carrying receptors specific to an epitope are primed and differentiate in lymph nodes when they encounter their peptide-MHC counterpart displayed on dendritic cells. These epitopes are subunits of proteins derived from pathogens, but very few of the epitopes from any given pathogen elicit a measureable cell-mediated immune response. For infectious diseases lacking a protective vaccine, concurrent cellular immunity to several peptides may be necessary to target multiple stages of disease. In this work, we expand upon our previously developed computational model of T cell priming and differentiation in LNs to explore simultaneous induction of memory for multiple antigens. The model incorporates key parameters describing cell interactions, including pMHC-TCR binding parameters, cognate T cell frequency, and fraction of pMHCs specific to a TCR. Our results point to the importance of binding thresholds and pMHC fractions in determining immunodominance hierarchies. These findings are relevant to design of multi-subunit vaccines, where the aim is to simultaneously induce memory to several peptides.

Introduction The development of autoimmune diseases, cancer, and transplant outcomes are all influenced by the ability of regulatory T cells (Treg) to suppress the activity of conventional T cells (Tconv). Therefore, rational modulation of Tconv’ susceptibility to suppression by Tregs is highly desirable. However, our understanding of the factors and mechanisms that underlie this modulation is limited. We believe that inflammatory mediators, by contributing to the activation of Tconv while reducing Treg activity, limit the effectiveness of tolerogenic regimens. We aimed to identify principal inflammatory mediators, released by innate immune cells, that lessen T cell regulation and infer their mechanism of action. Methods Using a CFSE-based, in vitro mouse T cell activation system, we quantified Treg suppressive activity and assessed the influence of the supernatant of maturing dendritic cells sampled at multiple times after stimulation with LPS. In parallel, 24 cytokines were quantified using Luminex™ in each sample tested. Principal Component Analysis (PCA) was utilized to delineate likely modulators of Treg suppression; the results were subsequently validated experimentally. Results PCA suggested IL-6, a known modulator of Treg suppression, as one of the cytokines with the most relevant contribution to the variance of the system. Interestingly, the same analysis revealed a similar pattern of variance between IL-1 α and IL-1 β with IL-6 suggesting a modulatory role. The CFSE assay indicated that IL-6 did not directly interfere with the susceptibility of Tconv to suppression (as originally suggested), but counteracted regulation by promoting a higher frequency of proliferating cells (that remained susceptible to suppression). Conversely, IL-1 α and IL-1 β each interfered with Treg suppression by directly affecting their suppressive activity. Using combinations of IL-1R-knock-out Tconv or Treg, we established that this effect was exerted by acting on both cells. As the in vitro activity of Treg is counteracted by IL-2 availability, we next tested if the differential effect of IL-6 and IL-1 correlated with differences in IL-2 secretion by Tconv following activation (in the absence of Treg). While IL-6 did not significantly increase the frequency of IL-2-secreting cells, IL-1 doubled this number to values comparable to those seen upon exogenous costimulation, further underscoring the separate mechanisms of action of these cytokines. In a mouse model of inflammatory bowel disease, preliminary results indicate that blockade of IL-6 signaling delays disease development through inhibition of colitogenic T cells expansion allowing tighter control by Treg. Conclusion Our results suggest that combining computation with experimentation may help to dissect the complex inflammatory milieu that determines T cell susceptibility to suppression. Specifically, this approach identified complementary as well as synergistic activities of IL-6 and IL-1 ( α and β ). Elucidation of the differential intracellular mechanisms used will aid in identifying targets for therapeutic approaches to selectively promote (autoimmunity and transplantation) or prevent (cancer and vaccination) immuneregulation.

Results: Mortality was 14% (76 patients). Increases in pressures were associated with small but statistically significant decreases in HR complexity; associations between RR and HR complexity were not significant (Table 1). Decreased HR complexity remained a significant explanatory variable in logistic regression, after incorporating ventilator parameters (Table 2). Conclusions: 1. Pressure, but not rate, of mechanical ventilation may be important in interpreting integer HR complexity.