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Mice from different genetic backgrounds have widely diverse responses to the same metabolic challenges. Only PWK/PhJ mice show significant mitochondrial dysfunction and progression to fibrotic NASH that resembles human NASH. The PWK/PhJ strain is a novel NASH mouse model., Non-alcoholic steatohepatitis (NASH) is a global health concern without treatment. The challenge in finding effective therapies is due to the lack of good mouse models and the complexity of the disease, characterized by gene-environment interactions. We tested the susceptibility of seven mouse strains to develop NASH. The severity of the clinical phenotypes observed varied widely across strains. PWK/PhJ mice were the most prone to develop hepatic inflammation and the only strain to progress to NASH with extensive fibrosis, while CAST/EiJ mice were completely resistant. Levels of mitochondrial transcripts and proteins as well as mitochondrial function were robustly reduced specifically in the liver of PWK/PhJ mice, suggesting a central role of mitochondrial dysfunction in NASH progression. Importantly, the NASH gene expression profile of PWK/PhJ mice had the highest overlap with the human NASH signature. Our study exposes the limitations of using a single mouse genetic background in metabolic studies and describes a novel NASH mouse model with features of the human NASH.

Human malaria can be caused by the parasite Plasmodium falciparum that is transmitted by female Anopheles mosquitoes. “Immunological crosstalk” between the mammalian and anopheline hosts for Plasmodium functions to control parasite numbers. Key to this process is the mammalian cytokine transforming growth factor-β1 (TGF-β1). In mammals, TGF-β1 regulates inducible nitric oxide (NO) synthase (iNOS) both positively and negatively. In some settings, high levels of NO activate latent TGF-β1, which in turn suppresses iNOS expression. In the mosquito, ingested TGF-β1 induces A. stephensi NOS (AsNOS), which limits parasite development and which in turn is suppressed by activation of the mosquito homolog of the mitogen-activated protein kinases MEK and ERK. Computational models linking TGF-β1, AsNOS, and MEK/ERK were developed to provide insights into this complex biology. An initial Boolean model suggested that, as occurs in mammalian cells, MEK/ERK and AsNOS would oscillate upon ingestion of TGF-β1. In support of this hypothesis, statistical models of multiphasic behavior were a best fit for data derived from time courses of Anopheles ERK activation in vitro and of AsNOS expression in vivo following treatment with TGF-β1. An ordinary differential equation (ODE) model further supported the hypothesis of TGF-β1-induced multiphasic behavior of MEK/ERK and AsNOS. To achieve this multiphasic behavior, the ODE model was predicated on the presence of constant levels of TGF-β1 in the mosquito midgut. Ingested TGF-β1, however, did not exhibit this behavior. Accordingly, we hypothesized and experimentally verified that ingested TGF-β1 induces the expression of the endogenous mosquito TGF-β superfamily ligand As60A. Computational simulation of these complex, cross-species interactions suggested that TGF-β1 and NO-mediated induction of As60A expression together may act to maintain multiphasic AsNOS expression via MEK/ERK-dependent signaling. We hypothesize that multiphasic behavior as represented in this model allows the mosquito to balance the conflicting demands of parasite killing and metabolic homeostasis in the face of damaging inflammation.

Blunt trauma patients may present with similar demographics and injury severity yet differ with regard to survival. We hypothesized that this divergence was due to different trajectories of systemic inflammation and utilized computational analyses to define these differences.Retrospective clinical study and experimental study in mice.Level 1 trauma center and experimental laboratory.From a cohort of 493 victims of blunt trauma, we conducted a pairwise, retrospective, case-control study of patients who survived over 24 hours but ultimately died (nonsurvivors; n = 19) and patients who, after ICU admission, went on to be discharged(survivors; n = 19).None in patients. Neutralizing anti-interleukin-17A antibody in mice.Data on systemic inflammatory mediators assessed within the first 24 hours and over 7 days were analyzed with computational modeling to infer dynamic networks of inflammation. Network density among inflammatory mediators in nonsurvivors increased in parallel with organ dysfunction scores over 7 days, suggesting the presence of early, self-sustaining, pathologic inflammation involving high-mobility group protein B1, interleukin-23, and the Th17 pathway. Survivors demonstrated a pattern commensurate with a self-resolving, predominantly lymphoid response, including higher levels of the reparative cytokine interleukin-22. Mice subjected to trauma/hemorrhage exhibited reduced organ damage when treated with anti-interleukin-17A.Variable type 17 immune responses are hallmarks of organ damage, survival, and mortality after blunt trauma and suggest a lymphoid cell-based switch from self-resolving to self-sustaining inflammation.

Extracellular β-nicotinamide adenine dinucleotide (NAD+) is anti-inflammatory. We hypothesized that NAD+ would modulate the anti-inflammatory cytokine Transforming Growth Factor (TGF)-β1. Indeed, NAD+ led to increases in both active and latent cell-associated TGF-β1 in RAW 264.7 mouse macrophages as well as in primary peritoneal macrophages isolated from both C3H/HeJ (TLR4-mutant) and C3H/HeOuJ (wild-type controls for C3H/HeJ) mice. NAD+ acts partially via cyclic ADP-ribose (cADPR) and subsequent release of Ca2+. Treatment of macrophages with the cADPR analog 3-deaza-cADPR or Ca2+ ionophores recapitulated the effects of NAD+ on TGF-β1, whereas the cADPR antagonist 8-Br-cADPR, Ca2+ chelation, and antagonism of L-type Ca2+ channels suppressed these effects. The time and dose effects of NAD+ on TGF-β1 were complex and could be modeled both statistically and mathematically. Model-predicted levels of TGF-β1 protein and mRNA were largely confirmed experimentally but also suggested the presence of other mechanisms of regulation of TGF-β1 by NAD+. Thus, in vitro and in silico evidence points to NAD+ as a novel modulator of TGF-β1.

Significance: Traumatic injury elicits a complex, dynamic, multidimensional inflammatory response that is intertwined with complications such as multiple organ dysfunction and nosocomial infection. The complex interplay between inflammation and physiology in critical illness remains a challenge for translational research, including the extrapolation to human disease from animal models. Recent Advances: Over the past decade, we and others have attempted to decipher the biocomplexity of inflammation in these settings of acute illness, using computational models to improve clinical translation. In silico modeling has been suggested as a computationally based framework for integrating data derived from basic biology experiments as well as preclinical and clinical studies. Critical Issues: Extensive studies in cells, mice, and human blunt trauma patients have led us to suggest (i) that while an adequate level of inflammation is required for healing post-trauma, inflammation can be harmful when it becomes self-sustaining via a damage-associated molecular pattern/Toll-like receptor-driven feed-forward circuit; (ii) that chemokines play a central regulatory role in driving either self-resolving or self-maintaining inflammation that drives the early activation of both classical innate and more recently recognized lymphoid pathways; and (iii) the presence of multiple thresholds and feedback loops, which could significantly affect the propagation of inflammation across multiple body compartments. Future Directions: These insights from data-driven models into the primary drivers and interconnected networks of inflammation have been used to generate mechanistic computational models. Together, these models may be used to gain basic insights as well as serving to help define novel biomarkers and therapeutic targets. Antioxid. Redox Signal. 23, 1370–1387.

The combination of liquid biopsies and ultra-sensitive next-generation sequencing (NGS) holds tremendous promise in oncology for the detection and surveillance of somatic mutations that correlate with diagnosis, prognosis, response prediction, resistance monitoring and tumor burden. We have developed a capture-based NGS workflow that provides ultra-sensitive detection of 4 mutation classes - single nucleotide variants (SNVs), insertions/deletions, copy number amplifications (CNAs) and fusions - from low input cell-free DNA isolated from plasma. Sensitivity and specificity of 2 different AVENIO ctDNA Analysis Kits (for Research Use Only) across 3 sites for 48 samples that included contrived DNA blends with known mutations validated by digital PCR and normal reference plasma samples were assessed. The AVENIO ctDNA Targeted Kit (Targeted) was designed to detect mutations in 17 genes, across 80kb, included in NCCN Guidelines for solid tumors plus additional variants relevant for therapy selection. The AVENIO ctDNA Expanded Kit (Expanded) was designed to detect mutations in 77 genes, across 200kb, associated with solid tumor including variants relevant for clinical trial research. Testing sites achieved 100% sensitivity for SNVs and fusions at 0.5% and 1% allelic frequency, respectively, from 30 ng of input DNA for both kits. Median coverage of 4,200-6,400X was greater with the Targeted kit relative to that of 2,700-3,800X with the Expanded kit. Specificity was 100% for the Targeted kit either by sample or variant. The Expanded kit had a specificity of 91.7-100% by sample and 99.7-100% by variant. At an expected 5X or 8X copy number amplification for multiple genes, both the Targeted and Expanded kits were 100% sensitive and 93.1-98.4% specific per gene. The specificity for fusion detection was 100% for both kits down to 1% mutant allele frequency. The AVENIO ctDNA Analysis Kits for plasma and NGS applications provide exquisite sensitivity and specificity for the simultaneous detection of 4 different mutation classes. Citation Format: Stephanie Yaung, Alex Lovejoy, Dan Klass, Fergal Casey, Aruna Arcot, Melissa Loyzer, William Chiu, Nabil Azhar, Sophie Beckert, Maria Lange, Sandra Siemann, Sebastian Froehler, Shelly Gunn, John F. Palma. Inter-site comparison of performance of an ultra-sensitive sequencing technology for circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2744. doi:10.1158/1538-7445.AM2017-2744

Blunt trauma and traumatic spinal cord injury induce systemic inflammation that contributes to morbidity. Dysregulated neural control of systemic inflammation postinjury is likely exaggerated in patients with traumatic spinal cord injury. We used in silico methods to discern dynamic inflammatory networks that could distinguish systemic inflammation in traumatic spinal cord injury from blunt trauma.Retrospective study.Tertiary care institution.Twenty-one severely injured thoracocervical traumatic spinal cord injury patients and matched 21 severely injured blunt trauma patients without spinal cord injury.None.Serial blood samples were obtained from days 1 to 14 postinjury. Twenty-four plasma inflammatory mediators were quantified. Statistical significance between the two groups was determined by two-way analysis of variance. Dynamic Bayesian network inference was used to suggest dynamic connectivity and central inflammatory mediators. Circulating interleukin-10 was significantly elevated in thoracocervical traumatic spinal cord injury group versus non-spinal cord injury group, whereas interleukin-1β, soluble interleukin-2 receptor-α, interleukin-4, interleukin-5, interleukin-7, interleukin-13, interleukin-17, macrophage inflammatory protein 1α and 1β, granulocyte-macrophage colony-stimulating factor, and interferon-γ were significantly reduced in traumatic spinal cord injury group versus non-spinal cord injury group. Dynamic Bayesian network suggested that post-spinal cord injury interleukin-10 is driven by inducible protein-10, whereas monocyte chemotactic protein-1 was central in non-spinal cord injury dynamic networks. In a separate validation cohorts of 356 patients without spinal cord injury and 85 traumatic spinal cord injury patients, individuals with plasma inducible protein-10 levels more than or equal to 730 pg/mL had significantly prolonged hospital and ICU stay and days on mechanical ventilator versus patients with plasma inducible protein-10 level less than 730 pg/mL.This is the first study to compare the dynamic systemic inflammatory responses of traumatic spinal cord injury patients versus patients without spinal cord injury, suggesting a key role for inducible protein-10 in driving systemic interleukin-10 and morbidity and highlighting the potential utility of in silico tools to identify key inflammatory drivers.

The acute inflammatory response is a complex defense mechanism that has evolved to respond rapidly to injury, infection, and other disruptions in homeostasis. This robust responsiveness to biological stress likely endows the host with increased fitness, but over-robust or inadequate inflammation predisposes the host to various diseases. Importantly, well-compartmentalized inflammation is generally beneficial, but spillover of inflammation into the blood is a hallmark—and likely also a driver—of self-maintaining inflammation. The blood is also a key entry point and immunological interface for vectors of parasitic diseases, diseases that themselves incite systemic inflammation. The complex role of inflammation in health and disease has made this biological system difficult to understand comprehensively and modulate rationally for therapeutic purposes. Consequently, systems approaches have been applied in order to characterize dynamical properties and identify key control points in inflammation. This process begins with the collection of high-dimensional, experimental, and clinical data, followed by data reduction and data-driven modeling that finally informs mechanistic computational models for analysis, prediction, and rational modulation. These studies have suggested that the overall architecture of the inflammatory response includes a multiscale positive feedback from inflammation → tissue damage → inflammation, which is often inadequately controlled by negative feedback from anti-inflammatory mediators. Given the importance of the blood interface for the inflammatory response, and the accessibility of this compartment both as an immunological sampling reservoir for vectors as well as for diagnosis and therapy, we suggest that any rational efforts at modulating inflammation via the blood compartment must involve computational modeling.

Half of the global population is at risk for malaria, which results in nearly one million deaths annually, 86 % of which are in children [1]. Plasmodium falciparum, the most important human malaria parasite, is transmitted by female Anopheles mosquitoes. Parasite development in the mosquito begins with the ingestion of blood containing sexualstage gametocytes. Mobile ookinetes penetrate the midgut epithelium 24–36 h later and transform into midgut-bound oocysts within the open circulatory system of the mosquito. Oocysts grow and develop for 10–12 days and then release thousands of sporozoites, which invade the salivary glands and are released during later blood feeding by the mosquito.

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.

Exposure to mathematical pattern tasks is often deemed important for developing children's algebraic thinking skills. Yet, there is a dearth of evidence on the cognitive underpinnings of pattern tasks and how early competencies on these tasks are related to later development.We examined the domain-specific and domain-general determinants of performances on pattern tasks by using (a) a standardized test of numerical and arithmetic proficiency and (b) measures of executive functioning, respectively.Participants were 163 6-year-olds enrolled in primary schools that typically serve families from low to middle socioeconomic backgrounds.Children were administered a battery of executive functioning (inhibitory, switching, updating), numerical and arithmetic proficiency (the Numerical Operations task from the Wechsler Individual Achievement Test-II), and three types of pattern tasks.Contrary to findings from the adult literature, we found all the executive functioning measures coalesced into two factors: updating and an inhibition/switch factor. Only the updating factor predicted performances on the pattern tasks. Although performance on the pattern tasks were correlated with numerical and arithmetic proficiency, findings from structural equation modelling showed that there were no direct or independent relationships between them.The findings suggest that the bivariate relationships between pattern, numeracy, and arithmetic tasks are likely due to their shared demands on updating resources. Unlike older children, these findings suggest that for 6-year-olds, better numerical and arithmetic proficiency, without accompanying advantages in updating capacities, will no more likely lead to better performance on the pattern tasks.

Milk fat globule epidermal growth factor 8 (Mfge8) is a soluble glycoprotein known to regulate inflammation and immunity by mediating apoptotic cell clearance. Since fibrosis can occur as a result of exaggerated apoptosis and inflammation, we set out to investigate the hypothesis that Mfge8 might negatively regulate tissue fibrosis. We report here that Mfge8 does decrease the severity of tissue fibrosis in a mouse model of pulmonary fibrosis; however, it does so not through effects on inflammation and apoptotic cell clearance, but by binding and targeting collagen for cellular uptake through its discoidin domains. Initial analysis revealed that Mfge8–/– mice exhibited enhanced pulmonary fibrosis after bleomycin-induced lung injury. However, they did not have increased inflammation or impaired apoptotic cell clearance after lung injury compared with Mfge8+/+ mice; rather, they had a defect in collagen turnover. Further experiments indicated that Mfge8 directly bound collagen and that Mfge8–/– macrophages exhibited defective collagen uptake that could be rescued by recombinant Mfge8 containing at least one discoidin domain. These data demonstrate a critical role for Mfge8 in decreasing the severity of murine tissue fibrosis by facilitating the removal of accumulated collagen.

13 Combined network and mathematical modeling suggests novel inflammation positive feedback circuit and role for IL-1 in experimental trauma/hemorrhage and hepatocyte hypoxia Nabil Azhar, Rami Namas, Bahiyyah Jefferson, Derek Barclay, Ruben Zamora, Yoram Vodovotz University of Pittsburgh, Pittsburgh, PA, USA Objectives: Trauma/hemorrhagic shock (T/HS) is the most common cause of death for young people in the United States, costing more than $400 billion annually. Most deaths from T/HS occur due to the multiple organ dysfunction syndrome. Multiple organ dysfunction syndrome is thought to be due, in part, to dysregulated inflammation. We have used computational modeling to define key networks and mechanisms of post-T/HS acute inflammation. We used a Dynamic Bayesian Network (DBN) algorithm (adapted from Grzegorczyk and Husmeier, Bioinformatics 2010;27:693). Given time-series data, DBNs provide a way of inferring causal relationships based on probabilistic measures. This approximation helps to suggest the overall network structure, including central nodes that exhibit feedback. Methods: C57Bl/6 mice were subjected to T/HS for 0 to 4 hours. Separately, C57Bl/6 hepatocytes were subjected to an in vitro surrogate of T/HS, namely, hypoxia (culture at 1% O2 for 1 to 72 hours). Cytokines/chemokines were assayed in the plasma (in vivo T/HS) as well as supernatants and lysates (in vitro hypoxia) by Luminex (MiraiBio, Alameda, CA), and the data were subjected to DBN analysis. Results: Dynamic Bayesian Network analysis suggested a core network that involves the cytokines IL-10 and IL-1α, as well as the chemokines IP-10 and MIG. Assigning biologically plausible functions to directed edges of the network resulted in a so-called incoherent type I feed forward loop that predicted pulsatile, “gatekeeper” behavior for the cytokine IL-1α—a behavior that was indeed observed in preliminary studies in mice subjected to T/ HS. An initial ordinary differential equation (ODE) model was constructed to analyze this behavior in further detail. Simulated trajectories of cytokines and chemokines in the ODE model corresponded closely with the data. Conclusions: Data-driven (DBN) and mechanistic (ODE) analyses suggest a novel inflammatory circuit with a central role for IL-1α in T/HS and hepatocyte hypoxia. http://dx.doi.org/10.1016/j.jcrc.2012.10.029

Results: Sevoflurane pharmacodynamic was in close agreement with a sigmoid dose-response relationship for SEF95 (correlation coefficient r = 0.62 ± 0.17), FD (r = 0.73 ± 0.11), and H (r = 0.78 ± 0.12). The probability of H to predict sevoflurane effectside concentration (Pk = 0.86 ± 0.05) was significantly higher (P b .05) as compared with FD (Pk = 0.81 ± 0.06) or SEF95 (Pk = 0.76 ± 0.08). Conclusions: The Hurst coefficient is superior to fractal dimension and spectral edge frequency in predicting the depth of sevoflurane anesthesia. Nonlinear EEG analysis seems to be a promising tool to improve the monitoring of anesthetic depth and to investigate anesthesia-related EEG decomplexification.