Severe trauma, including burns, triggers a systemic response that significantly impacts on the liver, which plays a key role in the metabolic and immune responses aimed at restoring homeostasis. While many of these changes are likely regulated at the gene expression level, there is a need to better understand the dynamics and expression patterns of burn injury-induced genes in order to identify potential regulatory targets in the liver. Herein we characterized the response within the first 24 h in a standard animal model of burn injury using a time series of microarray gene expression data.
Rats were subjected to a full thickness dorsal scald burn injury covering 20% of their total body surface area while under general anesthesia. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Sham controls consisted of animals handled similarly but not burned. After identifying differentially expressed probesets between sham and burn conditions over time, the concatenated data sets corresponding to these differentially expressed probesets in burn and sham groups were combined and analyzed using a “consensus clustering” approach.
The clustering method of expression data identified 621 burn-responsive probesets in 4 different co-expressed clusters. Functional characterization revealed that these 4 clusters are mainly associated with pro-inflammatory response, anti-inflammatory response, lipid biosynthesis, and insulin-regulated metabolism. Cluster 1 pro-inflammatory response is rapidly up-regulated (within the first 2 h) following burn injury, while Cluster 2 anti-inflammatory response is activated later on (around 8 h post burn). Cluster 3 lipid biosynthesis is downregulated rapidly following burn, possibly indicating a shift in the utilization of energy sources to produce acute phase proteins which serve the anti-inflammatory response. Cluster 4 insulin-regulated metabolism was down-regulated late in the observation window (around 16 h postburn), which suggests a potential mechanism to explain the onset of hypermetabolism, a delayed but well-known response that is characteristic of severe burns and trauma with potential adverse outcome.
Simultaneous analysis and comparison of gene expression profiles for both burn and sham control groups provided a more accurate estimation of the activation time, expression patterns, and characteristics of a certain burn-induced response based on which the cause-effect relationship among responses were revealed.
Burn; gene expression; microarray; inflammation; liver
Burn injuries together with its subsequent complications, mainly bacterial infections originating from gastrointestinal tract, activate the host immune system through stimulation of a series of local and systemic responses, including the release of inflammatory mediators. To gain a more comprehensive understanding of these complex physiological changes and to propose therapeutic approaches to combat the deleterious consequences of burn and septic shocks, it is essential to analyze animal models of burn and sepsis. In this study, we analyzed the long term profiles of cytokines and chemokines in rat models which received burn injury followed two days later by cecal ligation and puncture (CLP) to induce sepsis and were sacrificed at different time points within 10 days (0, 1, 2, 3, 4, 7 and 10 days). It was observed that MCP-1 concentrations were elevated in all animal models following the burn injury or CLP treatment. IP-10 concentration was persistently decreased after CLP or sham-CLP treatments. GRO/KC concentration was also increased following the burn injury and CLP. It was elucidated that, in more severe injury model which received both burn and CLP treatments, GMCSF and MIP-1α (chemokines), IL-1α (a pro-inflammatory cytokine) and IL-6 (exhibiting both pro- and anti-inflammatory behaviors) were upregulated on the 7th and 10th days, which might be to protect the host system from the subsequent complications caused by burn and sepsis. In order to elucidate critical regulatory interactions, putative transcription factors of the inflammatory mediators which have been significantly changed following the injuries were further identified by analyzing the conserved regions of the promoters of cytokines and chemokines. In conclusion, the long term profiles of the inflammatory mediators were profoundly characterized in this study to gain a comprehensive understanding of inflammatory mediators’ behaviors in various injury models.
Burn; Cecal ligation and puncture; Cytokines; Chemokines; Long term inflammatory response; Transcription Factors
The control and management of inflammation is a key aspect of clinical care for critical illnesses such as sepsis. In an ideal reaction to injury, the inflammatory response provokes a strong enough response to heal the injury and then restores homeostasis. When inflammation becomes dysregulated, a persistent inflammatory state can lead to significant deleterious effects and clinical challenges. Thus, gaining a better biological understanding of the mechanisms driving the inflammatory response is of the utmost importance. In this review, we discuss our work with the late Stephen F. Lowry to investigate systemic inflammation through systems biology of human endotoxemia. We present our efforts in modeling the human endotoxemia response with a particular focus on physiologic variability. Through modeling, with a focus ultimately on translational applications, we obtain more fundamental understanding of relevant physiological processes. And by taking advantage of the information embedded in biological rhythms, ranging in time scale from high-frequency autonomic oscillations reflected in heart rate variability to circadian rhythms in inflammatory mediators, we gain insight into the underlying physiology.
systems biology; inflammation; mathematical modeling
The inflammatory response, and its subsequent resolution, are the result of a very complex cascade of events originating at the site of injury or infection. When the response is severe and persistent, Systemic Inflammatory Response Syndrome can set in, which is associated with a severely debilitating systemic hypercatabolic state. This complex behavior, mediated by cytokines and chemokines, needs to be further explored to better understand its systems properties and potentially identify multiple targets that could be addressed simultaneously. In this context, short term responses of serum cytokines and chemokines were analyzed in two types of insults: rats receiving a “sterile” cutaneous dorsal burn on 20% of the total body surface area (TBSA); rats receiving a cecum ligation and puncture treatment (CLP) to induce infection. Considering the temporal variability observed in the baseline corresponding to the control group, the concept of area under the curve (AUC) was explored to assess the dynamic responses of cytokines and chemokines. MCP-1, GROK/KC, IL-12, IL-18 and IL-10 were observed in both burn and CLP groups. While IL-10 concentration was only increased in the burn group, Eotaxin was only elevated in CLP group. It was also observed that Leptin and IP-1 concentrations were decreased in both CLP and sham-CLP groups. The link between the circulating protein mediators and putative transcription factors regulating the cytokine/chemokine gene expression was explored by searching the promoter regions of cytokine/chemokine genes in order to characterize and differentiate the inflammatory responses based on the dynamic data. Integrating multiple sources together with the bioinformatics tools identified mediators sensitive to type and extent of injury, and provided putative regulatory mechanisms. This is essential to gain a better understanding for the important regulatory points that can be used to modulate the inflammatory state at molecular level.
Cytokines; Chemokines; Burn injury; Cecum ligation and puncture
The systemic inflammatory response syndrome (SIRS) often accompanies critical illnesses and can be an important cause of morbidity and mortality. Marked abnormalities in cardiovascular function accompany acute illnesses manifested as sustained tachyarrhythmias which are but one component of systemic dysregulation. The realization that cardiac pacemaker activity is under control of the autonomic nervous system has promoted the analysis of heart rate variation for assessing autonomic activities. In acute illnesses, autonomic imbalance manifesting in part as parasympathetic attenuation is associated with increased morbidity in patients who manifest SIRS phenotype. Driven by the premise that biological phenotypes emerge as the outcome of the coordinated action of network elements across the host, a multiscale model of human endotoxemia, as a prototype model of systemic inflammation in humans, is developed that quantifies critical aspects of the complex relationship between inflammation and autonomic heart rate regulation. In the present study, changes in heart rate response to acute injury, phenotypically expressed as tachycardia, are simulated as a result of autonomic imbalance that reflects sympathetic activity excess and parasympathetic attenuation. The proposed model assesses both the anti-inflammatory and cardiovascular effects of antecedent stresses upon the systemic inflammatory manifestations of human endotoxemia as well as a series of non-linear inflammatory relevant scenarios. Such a modeling approach provides a comprehensive conceptual framework linking inflammation and physiological complexity via a multiscale model that may advance the translational potential of systems modeling in clinical research.
mathematical modeling; infection; humans; inflammation; autonomic nervous system; heart
Severe burns are among the most common causes of death from unintentional injury. The induction and resolution of the burn-induced systemic inflammatory response are mediated by a network of factors and regulatory proteins. Numerous mechanisms operate simultaneously, thus requiring a systems level approach to characterize their overall impact. Towards this goal, we propose an in silico semi-mechanistic model of burn-induced systemic inflammation using liver specific gene expression from a rat burn model. Transcriptional responses are coupled with extracellular signals through a receptor mediated indirect response (IDR) and transit compartment model. The activation of the innate immune system in response to the burn stimulus involves the interaction between extracellular signals and critical receptors which triggers downstream signal transduction cascades leading to transcriptional changes. The resulting model consists of fifteen (15) coupled ordinary differential equations capturing key aspects of inflammation such as pro-inflammation, anti-inflammation and hypermetabolism. The model was then evaluated through a series of biologically relevant scenarios aiming at revealing the non-linear behavior of acute inflammation including: investigating the implication of effect of different severity of thermal injury; examining possible mechanistic dysregulation of IKK-NFκB system which may reflect secondary effects that lead to potential malfunction of the response; and exploring the outcome of administration of receptor antagonist or anti-body to significant cytokines.
thermal injury; trauma; inflammation; hypermetabolism; modeling; liver; rat
Insufficient physical activity is the 4th leading risk factor for mortality. Methods for assessing the individual daily life activity (DLA) are of major interest in order to monitor the current health status and to provide feedback about the individual quality of life. The conventional assessment of DLAs with self-reports induces problems like reliability, validity, and sensitivity. The assessment of DLAs with small and light-weight wearable sensors (e.g. inertial measurement units) provides a reliable and objective method. State-of-the-art human physical activity classification systems differ in e.g. the number and kind of sensors, the performed activities, and the sampling rate. Hence, it is difficult to compare newly proposed classification algorithms to existing approaches in literature and no commonly used dataset exists. We generated a publicly available benchmark dataset for the classification of DLAs. Inertial data were recorded with four sensor nodes, each consisting of a triaxial accelerometer and a triaxial gyroscope, placed on wrist, hip, chest, and ankle. Further, we developed a novel, hierarchical, multi-sensor based classification system for the distinction of a large set of DLAs. Our hierarchical classification system reached an overall mean classification rate of 89.6% and was diligently compared to existing state-of-the-art algorithms using our benchmark dataset. For future research, the dataset can be used in the evaluation process of new classification algorithms and could speed up the process of getting the best performing and most appropriate DLA classification system.
Non-contact and low-cost measurements of heart and respiration rates are highly desirable for telemedicine. Here, we describe a novel technique to extract blood volume pulse and respiratory wave from a single channel images captured by a video camera for both day and night conditions. The principle of our technique is to uncover the temporal dynamics of heart beat and breathing rate through delay-coordinate transformation and independent component analysis-based deconstruction of the single channel images. Our method further achieves robust elimination of false positives via applying ratio-variation probability distributions filtering approaches. Moreover, it enables a much needed low-cost means for preventing sudden infant death syndrome in new born infants and detecting stroke and heart attack in elderly population in home environments. This noncontact-based method can also be applied to a variety of animal model organisms for biomedical research.
This study assesses the pharmacokinetics (PK) and pharmacodynamics (PD) of Nrf2-mediated increased expression of Phase II drug metabolizing enzyme (DME) and antioxidant enzymes which represents an important component of cancer chemoprevention in rat lymphocytes following intravenous (i.v.) administration of an anti-cancer phytochemical sulforaphane (SFN)
SFN was administered intravenously to four groups of male Sprague-Dawley JVC rats each group comprising four animals. Blood samples were drawn at selected time points. Plasma were obtained from half of the blood samples and analyzed using a validated LC-MS/MS method. Lymphocytes were collected from the remaining blood samples using Ficoll-Paque™ Plus centrifuge medium. Lymphocyte RNAs were extracted, converted to cDNA, and quantitative real-time PCR analyses were performed and fold changes were calculated against those at time zero for the relative expression of Nrf2-target genes of phase II DME/antioxidant enzymes. PK-PD modeling was conducted based on Jusko’s indirect response model (IDR) using GastroPlus™ and Bootstrap Method.
SFN plasma concentration declined biexponentially and the pharmacokinetic parameters were generated. Rat lymphocyte mRNA expression levels showed no change for GSTM1, SOD, NF-κB, UGT1A1, or UGT1A6. Moderate increases (2-5 folds) over the time zero were seen for HO-1, Nrf2, and NQO1, and significant increase (> 5 folds) for GSTT1, GPx1, and Maf. PK-PD analyses using GastroPlus™ and Bootstrap method provided reasonable fitting for the PK and PD profiles and parameter estimates.
Our present study shows that SFN could induce Nrf2-mediated phase II DME/antioxidant mRNA expression for NQO1, GSTT1, Nrf2, GPx, Maf, and HO-1 in rat lymphocytes after i.v. administration, suggesting that Nrf2-mediated mRNA expression in lymphocytes may serve as surrogate biomarkers. The PK-PD IDR model simultaneously linking the plasma concentrations of SFN and the PD response of lymphocyte mRNA expression is valuable for quantitating Nrf2 mediated effects of SFN. This study may provide a conceptual framework for future clinical PK-PD studies of dietary cancer chemopreventive agents in human.
sulforaphane; pharmacokinetics; pharmacodynamics; lymphocyte; phase II genes; Nrf2
Sepsis remains a major clinical challenge in intensive care units. The difficulty in developing new and more effective treatments for sepsis exemplifies our incomplete understanding of the underlying pathophysiology of it. One of the more widely used rodent models for studying polymicrobial sepsis is cecal ligation and puncture (CLP). While a number of CLP studies investigated the ensuing systemic inflammatory response, they usually focus on a single time point post CLP and therefore fail to describe the dynamics of the response. Furthermore, previous studies mostly use surgery without infection (herein referred to as Sham CLP, SCLP) as a control for the CLP model, however SCLP represents an aseptic injurious event that also stimulates a systemic inflammatory response. Thus, there is a need to better understand the dynamics and expression patterns of both injury- and sepsis- induced gene expression alterations to identify potential regulatory targets. In this direction, we characterized the response of the liver within the first 24 h in a rat model of SCLP and CLP using a time series of microarray gene expression data.
Rats were randomly divided into three groups, sham, SCLP and CLP. Rats in SCLP group are subjected to laparotomy, cecal ligation and puncture while those in CLP group are subjected to the similar procedures without cecal ligation and puncture. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Unoperated animals (Sham) serve as negative controls. After identifying differentially expressed probesets between sham and SCLP or CLP conditions over time, the concatenated data sets corresponding to these differentially expressed probesets in sham and SCLP or CLP groups were combined and analyzed using a “consensus clustering” approach. Promoters of genes that share common characteristics were extracted, and compared with gene batteries comprised of co expressed genes in order to identify putatative transcription factors which could be responsible for the co regulation of those genes.
The SCLP/CLP genes whose expression patterns significantly changed compared to sham over time were identified, clustered, and finally analyzed for pathway enrichment. Our results indicate that both CLP and SCLP triggered the activation of a pro-inflammatory response, enhanced synthesis of acute-phase proteins, increased metabolism and tissue damage markers. Genes triggered by CLP which can be directly linked to bacteria removal functions were absent in SCLP injury. In addition, genes relevant to oxidative stress induced damage were unique to CLP injury, which may be due to the increased severity of CLP injury vs. SCLP injury. Pathway enrichment identified pathways with similar functionality but different dynamics in the two injury models, indicating that the functions controlled by those pathways are under the influence of different transcription factors and regulatory mechanisms. Putatively identified transcription factors, notably including CREB, NF-KB and STAT, were obtained through analysis of the promoter regions in the SCLP/CLP genes. Our results show that while transcription factors such as NF-KB, HOMF, and GATA were common in both injuries for the IL-6 signaling pathway, there were many other transcription factors associated with that pathway which were unique to CLP, including FKHD, HESF and IRFF. There were 17 transcription factors that were identified as important in at least 2 pathways in the CLP injury, but only 7 transcription factors with that property in the SCLP injury. This also supports the hypothesis of unique regulatory modules that govern the pathways present in both the CLP and SCLP response.
By using microarrays to assess multiple genes in a high throughput manner, we demonstrate that an inflammatory response involving different dynamics and different genes is triggered by SCLP and CLP. From our analysis of the CLP data, the key characteristics of sepsis are a pro inflammatory response which drives hypermetabolism, immune cell activation, and damage from oxidative stress. This contrasts with SCLP, which triggers a modified inflammatory response leading to no immune cell activation, decreased detoxification potential, and hyper metabolism. Many of the identified transcription factors that drive the CLP-induced response are not found in the SCLP group, suggesting that SCLP and CLP induce different types of inflammatory responses via different regulatory pathways.
sepsis; trauma; gene expression; transcription factor; microarray; inflammation; liver
Sepsisis a clinical syndrome characterized by a multi-system response to a microbial pathogenic insult consisting of a mosaic of interconnected biochemical, cellular, and organ-organ interaction networks. A central thread that connects these responses is inflammation, which, while attempting to defend the body and prevent further harm, causes further damage through the feed-forward, pro-inflammatory effects of damage-associated molecular pattern molecules. In this review, we address the epidemiology and current definitions of sepsis, and focus specifically on the biological cascades that comprise the inflammatory response to sepsis. We suggest that attempts to improve clinical outcomes by targeting specific components of this network have been unsuccessful due to the lack of an integrative, predictive, and individualized systems-based approach to define the time-varying, multi-dimensional state of the patient. We highlight the translational impact of computational modeling and other complex systems approaches as applied to sepsis, including in silico clinical trials, patient-specific models, and complexity-based assessments of physiology.
Sepsis; Inflammatory Response; Physiologic Variability; Mathematical Model
Metabolic engineering tools have been widely applied to living organisms to gain a comprehensive understanding about cellular networks and to improve cellular properties. Metabolic flux analysis (MFA), flux balance analysis (FBA), and metabolic pathway analysis (MPA) are among the most popular tools in stoichiometric network analysis. Although application of these tools into well-known microbial systems is extensive in the literature, various barriers prevent them from being utilized in mammalian cells. Limited experimental data, complex regulatory mechanisms, and the requirement of more complex nutrient media are some major obstacles in mammalian cell systems. However, mammalian cells have been used to produce therapeutic proteins, to characterize disease states or related abnormal metabolic conditions, and to analyze the toxicological effects of some medicinally important drugs. Therefore, there is a growing need for extending metabolic engineering principles to mammalian cells in order to understand their underlying metabolic functions. In this review article, advanced metabolic engineering tools developed for stoichiometric analysis including MFA, FBA, and MPA are described. Applications of these tools in mammalian cells are discussed in detail, and the challenges and opportunities are highlighted.
Metabolic flux analysis; flux balance analysis; metabolic pathway analysis; mammalian cells
Liver metabolism is altered after systemic injuries such as burns and trauma. These changes have been elucidated in rat models of experimental burn injury where the liver was isolated and perfused ex vivo. Because these studies were performed in fasted animals to deplete glycogen stores, thus simplifying quantification of gluconeogenesis, these observations reflect the combined impact of fasting and injury on liver metabolism. Herein we asked whether the metabolic response to experimental burn injury is different in fed vs. fasted animals. Rats were subjected to a cutaneous burn covering 20% of the total body surface area, or to similar procedures without administering the burn, hence a sham-burn. Half of the animals in the burn and sham-burn groups were fasted starting on postburn day 3, and the others allowed to continue ad libitum. On postburn day 4, livers were isolated and perfused for 1 hour in physiological medium supplemented with 10% hematocrit red blood cells. The uptake/release rates of major carbon and nitrogen sources, oxygen, and carbon dioxide were measured during the perfusion and the data fed into a mass balance model to estimate intracellular fluxes. The data show that in fed animals, injury increased glucose output mainly from glycogen breakdown and minimally impacted amino acid metabolism. In fasted animals, injury did not increase glucose output but increased urea production and the uptake of several amino acids, namely glutamine, arginine, glycine, and methionine. Furthermore, sham-burn animals responded to fasting by triggering gluconeogenesis from lactate; however, in burned animals the preferred gluconeogenic substrate was amino acids. Taken together, these results suggest that the fed state prevents the burn-induced increase in hepatic amino acid utilization for gluconeogenesis. The role of glycogen stores and means to increase and/or maintain internal sources of glucose to prevent increased hepatic amino acid utilization warrant further studies.
As cellular variability and circadian rhythmicity play critical roles in immune and inflammatory responses, we present in this study an agent-based model of human endotoxemia to examine the interplay between circadian controls, cellular variability and stochastic dynamics of inflammatory cytokines. The model is qualitatively validated by its ability to reproduce circadian dynamics of inflammatory mediators and critical inflammatory responses after endotoxin administration in vivo. Novel computational concepts are proposed to characterize the cellular variability and synchronization of inflammatory cytokines in a population of heterogeneous leukocytes. Our results suggest that there is a decrease in cell-to-cell variability of inflammatory cytokines while their synchronization is increased after endotoxin challenge. Model parameters that are responsible for IκB production stimulated by NFκB activation and for the production of anti-inflammatory cytokines have large impacts on system behaviors. Additionally, examining time-dependent systemic responses revealed that the system is least vulnerable to endotoxin in the early morning and most vulnerable around midnight. Although much remains to be explored, proposed computational concepts and the model we have pioneered will provide important insights for future investigations and extensions, especially for single-cell studies to discover how cellular variability contributes to clinical implications.
Previously, we have shown that systemic insults in single injury models produced immunosuppressive effects in burn, and a strong acute phase response in sepsis through hepatic gene expression. In order to investigate the implications of these effects on a combined injury, a double hit model was explored to mimic the progression of clinical burn-sepsis. Rodents were subjected to a 20% total body surface area (TSA) full-thickness burn injury, and 48 hours later underwent cecal ligation and puncture (CLP) to induce sepsis. Pathways related to innate immune signaling through cytokines and NF-KB were co regulated with xenobiotic metabolism genes and acute phase protein genes, and that these genes were suppressed early, and then activated. Furthermore, we were able to identify that, in addition to amino acid metabolism, pyruvate metabolism, fatty acid metabolism and NRF-2 mediated oxidative stress genes were down regulated over the time course. Overall, these observed trends within the double hit burn-sepsis model represent unique immune and metabolic pathways and dynamics not found in either injury, including an early suppression followed by overreaction of pro inflammatory mediators, and an increase in amino acid metabolism at the expense of central carbon pathways.
Sepsis; burns; microarray analysis; RT-PCR analysis; liver
Isolated liver perfusion systems have been used to characterize intrinsic metabolic changes in liver as a result of various perturbations, including systemic injury, hepatotoxin exposure, and warm ischemia. Most of these studies were done using hyperoxic conditions (95% O2) but without the use of oxygen carriers in the perfusate. Prior literature data do not clearly establish the impact of oxygenation, and in particular that of adding oxygen carriers to the perfusate, on the metabolic functions of the liver. Therefore, herein the effects of oxygen delivery in the perfusion system on liver metabolism were investigated by comparing three modes of oxygenation. Rat livers were perfused via the portal and hepatic veins at a constant flow rate of 3 mL/min/g liver in a recirculating perfusion system. In the first group, the perfusate was equilibrated in a membrane oxygenator with room air (21% O2) before entering the liver. In the second group, the perfusate was equilibrated with a 95% O2/5% CO2 gas mixture. In the third group, the perfusate was supplemented with washed bovine red blood cells (RBCs) at 10% hematocrit and also equilibrated with the 95% O2/5% CO2 gas mixture. Oxygen and CO2 gradients across the liver were measured periodically with a blood gas analyzer. The rate of change in the concentration of major metabolites in the perfusate was measured over time. Net extracellular fluxes were calculated from these measurements and applied to a stoichiometric-based optimization problem to determine the intracellular fluxes and active pathways in the perfused livers. Livers perfused with RBCs consumed oxygen at twice the rate observed using hyperoxic (95% O2) perfusate without RBCs, and also produced more urea and ketone bodies. At the flow rate used, the oxygen supply in perfusate without RBCs was just sufficient to meet the average oxygen demand of the liver but would be insufficient if it increased above baseline, as is often the case in response to environmental perturbations. Metabolic pathway analysis suggests that significant anaerobic glycolysis occurred in the absence of RBCs even using hyperoxic perfusate. Conversely, when RBCs were used, glucose production from lactate and glutamate, as well as pathways related to energy metabolism were upregulated. RBCs also reversed an increase in PPP fluxes induced by the use of hyperoxic perfusate alone. In conclusion, the use of oxygen carriers is required to investigate the effect of various perturbations on liver metabolism.
liver; perfusion; oxygenation; metabolism; flux balance analysis
Severe burn trauma is generally associated with bacterial infections, which causes a more persistent inflammatory response with an ongoing hypermetabolic and catabolic state. This complex biological response, mediated by chemokines and cytokines, can be more severe when excessive interactions between the mediators take place. In this study, the early inflammatory response following the cecum ligation and puncture (CLP) or its corresponding control treatment (sham-CLP or SCLP) in burn (B) male rats was analyzed by measuring 23 different cytokines and chemokines. Cytokines and chemokines, including MCP-1, IP-10, Leptin, TNF-α, MIP-1α, IL-18, GMCSF, RANTES and GCSF were significantly altered in both B+CLP and B+SCLP groups. IL-10 and IL-6 were significantly up-regulated in the B+CLP group when compared to the B+SCLP group. Down regulation of Leptin and IP-10 concentrations were found to be related to surgery and/or infection. IL-18 and MCP-1 were elevated in all groups including previously published single injury models receiving similar treatments. In this study, insult-specific mediators with their characteristic temporal patterns were elucidated in double hit models.
Cytokines; Chemokines; Double-hit injury models; Burn injury; Cecum ligation and puncture
A key ingredient to modern data analysis is probability density estimation. However, it is well known that the curse of dimensionality prevents a proper estimation of densities in high dimensions. The problem is typically circumvented by using a fixed set of assumptions about the data, e.g., by assuming partial independence of features, data on a manifold or a customized kernel. These fixed assumptions limit the applicability of a method. In this paper we propose a framework that uses a flexible set of assumptions instead. It allows to tailor a model to various problems by means of 1d-decompositions. The approach achieves a fast runtime and is not limited by the curse of dimensionality as all estimations are performed in 1d-space. The wide range of applications is demonstrated at two very different real world examples. The first is a data mining software that allows the fully automatic discovery of patterns. The software is publicly available for evaluation. As a second example an image segmentation method is realized. It achieves state of the art performance on a benchmark dataset although it uses only a fraction of the training data and very simple features.
Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.
mathematical model; inflammation; physiologic variability; heart rate variability; neural control
Pathway analysis is a useful tool which reveals important metabolic network properties. However, the big challenge is to propose an objective function for estimating active pathways which represent the actual state of network. In order to provide weight values for all possible pathways within the metabolic network, this study presents different approaches, considering the structural and physiological properties of metabolic network, aiming at a unique decomposition of the flux vector into pathways. These methods were used to analyze the hepatic metabolism considering available data sets obtained from perfused livers of fasted rats receiving burn injury. Utilizing unique decomposition techniques and different fluxes revealed that higher weights were always attributed to short pathways. Specific pathways including pyruvate, glutamate and oxaloacetate pools, and urea production from arginine were found to be important or essential in all methods and experimental conditions. Moreover, the pathways including serine production from glycine and conversion between acetoacetate and B-OH-butyrate were assigned higher weights. Pathway analysis was also used to identify the main sources for the production of certain products in the hepatic metabolic network to gain a better understanding of the effects of burn injury on liver metabolism.
Pathway analysis; Liver metabolism; Burn injury
The objective of identifying transcriptional regulatory networks is to provide insights as to what governs an organism’s long term response to external stimuli. We explore the coupling of the living cell array (LCA), a novel microfluidics device which utilizes fluorescence levels as a surrogate for transcription factor activity with reverse Euler deconvolution (RED) a computational technique proposed in this work to decipher the dynamics of the interactions. It is hypothesized that these two methods will allow us to first assess the underlying network architecture associated with the transcription factor network as well as specific mechanistic consequences of transcription factor activation such as receptor dimerization or tolerance.
The overall approach identifies evidence of time-lagged response which may be indicative of mechanisms such as receptor dimerization, tolerance mechanisms which are evidence of various receptor mediated dynamics, and feedback loops which regulate the response of an organism to changing environmental conditions. Furthermore, through the exploration of multiple network architectures, we were able to obtain insights as to the role each transcription factor plays in the overall response and their overall redundancy in the organism’s response to external perturbations. Thus, the LCA along with the proposed analysis technique is a valuable tool for identifying the possible architectures and mechanisms underlying the transcriptional response.
Systems biology; Network reconstruction
A mathematical model of interstitial fluid flow is developed, based on the application of the governing equations for fluid flow, i.e., the conservation laws for mass and momentum, to physiological systems containing solid tumors. The discretized form of the governing equations, with appropriate boundary conditions, is developed for a predefined tumor geometry. The interstitial fluid pressure and velocity are calculated using a numerical method, element based finite volume. Simulations of interstitial fluid transport in a homogeneous solid tumor demonstrate that, in a uniformly perfused tumor, i.e., one with no necrotic region, because of the interstitial pressure distribution, the distribution of drug particles is non-uniform. Pressure distribution for different values of necrotic radii is examined and two new parameters, the critical tumor radius and critical necrotic radius, are defined. Simulation results show that: 1) tumor radii have a critical size. Below this size, the maximum interstitial fluid pressure is less than what is generally considered to be effective pressure (a parameter determined by vascular pressure, plasma osmotic pressure, and interstitial osmotic pressure). Above this size, the maximum interstitial fluid pressure is equal to effective pressure. As a consequence, drugs transport to the center of smaller tumors is much easier than transport to the center of a tumor whose radius is greater than the critical tumor radius; 2) there is a critical necrotic radius, below which the interstitial fluid pressure at the tumor center is at its maximum value. If the tumor radius is greater than the critical tumor radius, this maximum pressure is equal to effective pressure. Above this critical necrotic radius, the interstitial fluid pressure at the tumor center is below effective pressure. In specific ranges of these critical sizes, drug amount and therefore therapeutic effects are higher because the opposing force, interstitial fluid pressure, is low in these ranges.
Microarray experiments generate massive amounts of data, necessitating innovative algorithms to distinguish biologically relevant information from noise. Because the variability of gene expression data is an important factor in determining which genes are differentially expressed, analysis techniques that take into account repeated measurements are critically important. Additionally, the selection of informative genes is typically done by searching for the individual genes that vary the most across conditions. Yet because genes tend to act in groups rather than individually, it may be possible to glean more information from the data by searching specifically for concerted behavior in a set of genes. Applying a symbolic transformation to the gene expression data allows the detection overrepresented patterns in the data, in contrast to looking only for genes that exhibit maximal differential expression. These challenges are approached by introducing an algorithm based on a new symbolic representation that searches for concerted gene expression patterns; furthermore, the symbolic representation takes into account the variance in multiple replicates and can be applied to long time series data. The proposed algorithm's ability to discover biologically relevant signals in gene expression data is exhibited by applying it to three datasets that measure gene expression in the rat liver.
One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes.
Differentiation of oligodendrocyte progenitor cells (OPCs) into mature oligodendrocytes requires extensive changes in gene expression, which are partly mediated by post-translational modifications of nucleosomal histones. An essential modification for oligodendrocyte differentiation is the removal of acetyl groups from lysine residues which is catalyzed by histone deacetylases (HDACs). The transcriptional targets of HDAC activity within OPCs however, have remained elusive and have been identified in this study by interrogating the oligodendrocyte transcriptome. Using a novel algorithm that allows clustering of gene transcripts according to expression kinetics and expression levels, we defined major waves of co-regulated genes. The initial overall decrease in gene expression was followed by the up-regulation of genes involved in lipid metabolism and myelination. Functional annotation of the down-regulated gene clusters identified transcripts involved in cell cycle regulation, transcription, and RNA processing. To define whether these genes were the targets of HDAC activity, we cultured rat OPCs in the presence of trichostatin A (TSA), an HDAC inhibitor previously shown to inhibit oligodendrocyte differentiation. By overlaying the defined oligodendrocyte transcriptome with the list of ‘TSA sensitive’ genes, we determined that a high percentage of ‘TSA sensitive’ genes are part of a normal program of oligodendrocyte differentiation. TSA treatment increased the expression of genes whose down-regulation occurs very early after induction of OPC differentiation, but did not affect the expression of genes with a slower kinetic. Among the increased ‘TSA sensitive’ genes we detected several transcription factors including Id2, Egr1, and Sox11, whose down-regulation is critical for OPC differentiation. Thus, HDAC target genes include clusters of co-regulated genes involved in transcriptional repression. These results support a de-repression model of oligodendrocyte lineage progression that relies on the concurrent down-regulation of several inhibitors of differentiation.