Demystifying the cytokine network: Mathematical models point the way

Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis

Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.

Distribution modeling quantifies collective TH cell decision circuits in chronic inflammation

Immune responses are tightly regulated by a diverse set of interacting immune cell populations. Alongside decision-making processes such as differentiation into specific effector cell types, immune cells initiate proliferation at the beginning of an inflammation, forming two layers of complexity. Here, we developed a general mathematical framework for the data-driven analysis of collective immune cell dynamics. We identified qualitative and quantitative properties of generic network motifs, and we specified differentiation dynamics by analysis of kinetic transcriptome data. Furthermore, we derived a specific, data-driven mathematical model for T helper 1 versus T follicular helper cell-fate decision dynamics in acute and chronic lymphocytic choriomeningitis virus infections in mice. The model recapitulates important dynamical properties without model fitting and solely by using measured response-time distributions. Model simulations predict different windows of opportunity for perturbation in acute and chronic infection scenarios, with potential implications for optimization of targeted immunotherapy.

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.

Markers of Neuroinflammation in the Serum of Prepubertal Children with Fetal Alcohol Spectrum Disorders

BACKGROUND

Fetal Alcohol Spectrum Disorders (FASD) are the manifestation of the damage caused by alcohol consumption during pregnancy. Children with Fetal Alcohol Syndrome (FAS), the extreme FASD manifestation, show both facial dysmorphology and mental retardation. Alcohol consumed during gestational age prejudices brain development by reducing, among others, the synthesis and release of neurotrophic factors and neuroinflammatory markers. Alcohol drinking also induces oxidative stress.

HYPOTHESIS/OBJECTIVE

The present study aimed to investigate the potential association between neurotrophins, neuroinflammation, and oxidative stress in 12 prepubertal male and female FASD children diagnosed as FAS or partial FAS (pFAS).

METHODS

Accordingly, we analyzed, in the serum, the level of BDNF and NGF and the oxidative stress, as Free Oxygen Radicals Test (FORT) and Free Oxygen Radicals Defense (FORD). Moreover, serum levels of inflammatory mediators (IL-1α, IL-2, IL-6, IL-10, IL-12, MCP-1, TGF-β, and TNF-α) involved in neuroinflammatory and oxidative processes have been investigated.

RESULTS

We demonstrated low serum levels of NGF and BDNF in pre-pubertal FASD children with respect to healthy controls. These changes were associated with higher serum presence of TNF- α and IL-1α. Quite interestingly, an elevation in the FORD was also found despite normal FORT levels. Moreover, we found a potentiation of IL-1α, IL-2, IL-10, and IL-1α1 in the analyzed female compared to male children.

CONCLUSION

The present investigation shows an imbalance in the peripheral neuroimmune pathways that could be used in children as early biomarkers of the deficits observed in FASD.

A Mathematical Model of the Dynamics of Cytokine Expression and Human Immune Cell Activation in Response to the Pathogen Staphylococcus aureus

Cell-based mathematical models have previously been developed to simulate the immune system in response to pathogens. Mathematical modeling papers which study the human immune response to pathogens have predicted concentrations of a variety of cells, including activated and resting macrophages, plasma cells, and antibodies. This study aims to create a comprehensive mathematical model that can predict cytokine levels in response to a gram-positive bacterium, S. aureus by coupling previous models. To accomplish this, the cytokines Tumor Necrosis Factor Alpha (TNF-α), Interleukin 6 (IL-6), Interleukin 8 (IL-8), and Interleukin 10 (IL-10) are included to quantify the relationship between cytokine release from macrophages and the concentration of the pathogen, S. aureus, ex vivo. Partial differential equations (PDEs) are used to model cellular response and ordinary differential equations (ODEs) are used to model cytokine response, and interactions between both components produce a more robust and more complete systems-level understanding of immune activation. In the coupled cellular and cytokine model outlined in this paper, a low concentration of S. aureus is used to stimulate the measured cellular response and cytokine expression. Results show that our cellular activation and cytokine expression model characterizing septic conditions can predict ex vivo mechanisms in response to gram-negative and gram-positive bacteria. Our simulations provide new insights into how the human immune system responds to infections from different pathogens. Novel applications of these insights help in the development of more powerful tools and protocols in infection biology.

Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

The maintenance and function of tissues in health and disease depends on cell-cell communication. This work shows how high-level features, representing cell-cell communication, can be defined and used to associate certain signaling "axes" with clinical outcomes. We generated a scaffold of cell-cell interactions and defined a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for The Cancer Genome Atlas (TCGA) patient samples, each representing likely channels of intercellular communication in the tumor microenvironment (TME). It was shown that cell-cell edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types, and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

Elevated levels of IL-18 associated with schizophrenia and first episode psychosis: A systematic review and meta-analysis

AIM

To determine whether interleukin 18 (IL-18) is elevated in the blood of schizophrenia (SCZ) and first episode psychosis patients, as well as investigate whether this potential relationship is causal.

METHOD

We conducted a systematic review and meta-analysis of IL-18 levels in the blood of SCZ patients, comprising of both chronic and first episode psychosis (FEP) cohorts. To investigate causality, we undertook the two-sample Mendelian randomization (MR) study.

RESULTS

A total of eight studies were included in our meta-analysis, our results did indeed show an association between elevated levels of IL-18 and SCZ compared to healthy controls (Z = 3.50, P = .0005). This association remained significant in subsequent subgroup analyses for chronic (Z = 3.15, P = .002) and achieved borderline significance in FEP (Z = 1.93, P = .05) SCZ. Our MR analysis failed to detect any causal relationship between IL-18 levels and SCZ.

CONCLUSION

The results of our study demonstrate that even though IL-18 levels are elevated in SCZ patients, IL-18 levels do not seem to cause of the disorder itself. Our findings suggest that IL-18 may have utility as a biomarker of SCZ and aid in research into the early intervention of the disease.

Modeling the Th17 and Tregs Paradigm: Implications for Cancer Immunotherapy

CD4 + T cell differentiation is governed by gene regulatory and metabolic networks, with both networks being highly interconnected and able to adapt to external stimuli. Th17 and Tregs differentiation networks play a critical role in cancer, and their balance is affected by the tumor microenvironment (TME). Factors from the TME mediate recruitment and expansion of Th17 cells, but these cells can act with pro or anti-tumor immunity. Tregs cells are also involved in tumor development and progression by inhibiting antitumor immunity and promoting immunoevasion. Due to the complexity of the underlying molecular pathways, the modeling of biological systems has emerged as a promising solution for better understanding both CD4 + T cell differentiation and cancer cell behavior. In this review, we present a context-dependent vision of CD4 + T cell transcriptomic and metabolic network adaptability. We then discuss CD4 + T cell knowledge-based models to extract the regulatory elements of Th17 and Tregs differentiation in multiple CD4 + T cell levels. We highlight the importance of complementing these models with data from omics technologies such as transcriptomics and metabolomics, in order to better delineate existing Th17 and Tregs bifurcation mechanisms. We were able to recompilate promising regulatory components and mechanisms of Th17 and Tregs differentiation under normal conditions, which we then connected with biological evidence in the context of the TME to better understand CD4 + T cell behavior in cancer. From the integration of mechanistic models with omics data, the transcriptomic and metabolomic reprograming of Th17 and Tregs cells can be predicted in new models with potential clinical applications, with special relevance to cancer immunotherapy.

Cytokine Profiling in Plasma from Patients with Brain Tumors Versus Healthy Individuals using 2 Different Multiplex Immunoassay Platforms

We compared the performance of two 96-well multiplex immunoassay platforms in assessing plasma cytokine concentrations in patients with glioblastoma (GBM; n = 27), individuals with melanoma, breast or lung cancer metastases to the brain (n = 17), and healthy volunteers (n = 11). Assays included a bead-based fluorescence MILLIPLEX^® assay/Luminex (LMX) platform and 4 planar electrochemiluminescence kits from Meso Scale Discovery (MSD). The LMX kit evaluated 21 cytokines and the 3 MSD kits evaluated 20 cytokines in total, with 19 overlapping human cytokines between platforms (GM-CSF, IFNγ, IL-1β, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12p70, IL-13, IL-17A, IL-21, IL-23, MIP-1α, MIP-1β, MIP-3α, TNFα). The MSD platform had lower LLoQs (lower limits of quantification) than LMX for 17/19 cytokines, and higher LLoQs for IFN-γ and IL-21. The ULoQs were higher in LMX versus MSD assays for 17/19 shared analytes, but lower than MSD for IL-17A and IL-21. With LMX, all 19 shared analytes were quantifiable in each of 55 samples. Although MSD recombinant protein standard curves indicated lower LLoQs than LMX for most cytokines, MSD detected 7/19 (37%) native analytes in <75% of samples, including 0% detection for IL-21 and 8% for IL-23. The LMX platform categorized identical samples at greater concentrations than the MSD system for most analytes (MIP-1β the sole exception), sometimes by orders of magnitude. This mismatched quantification paradigm was supported by Bland-Altman analysis. LMX identified significantly elevated levels of 10 of 19 circulating cytokines in GBM: GM-CSF, IFN-γ, IL-1β, IL-5, IL-10, IL-17A, IL-21, IL-23, MIP-1α, and MIP-3α, consistent with prior findings and confirming the utility of applying appropriate multiplex immunoassay technologies toward developing a cytokine signature profile for GBM.

Impact of Cytokines and Phosphoproteins in Response to Chronic Joint Infection

The early cellular response to infection has been investigated extensively, generating valuable information regarding the mediators of acute infection response. Various cytokines have been highlighted for their critical roles, and the actions of these cytokines are related to intracellular phosphorylation changes to promote infection resolution. However, the development of chronic infections has not been thoroughly investigated. While it is known that wound healing processes are disrupted, the interactions of cytokines and phosphoproteins that contribute to this dysregulation are not well understood. To investigate these relationships, this study used a network centrality approach to assess the impact of individual cytokines and phosphoproteins during chronic inflammation and infection. Tissues were taken from patients undergoing total knee arthroplasty (TKA) and total knee revision (TKR) procedures across two tissue depths to understand which proteins are contributing most to the dysregulation observed at the joint. Notably, p-c-Jun, p-CREB, p-BAD, IL-10, IL-12p70, IL-13, and IFN-γ contributed highly to the network of proteins involved in aseptic inflammation caused by implants. Similarly, p-PTEN, IL-4, IL-10, IL-13, IFN-γ, and TNF-α appear to be central to signaling disruptions observed in septic joints. Ultimately, the network centrality approach provided insight into the altered tissue responses observed in chronic inflammation and infection.

Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination

Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library.

Wide Cytokine Analysis in Cerebrospinal Fluid at Diagnosis Identified CCL-3 as a Possible Prognostic Factor for Multiple Sclerosis

Background: Apart from IgG oligoclonal bands, no other biomarker has, to date, been validated for diagnostic and/or prognostic purposes in multiple sclerosis (MS). Aim: To investigate a wide panel of cytokines and chemokines in the cerebrospinal fluid (CSF) of relapsing-remitting MS (RRMS) patients and evaluate their association with clinical and magnetic resonance imaging (MRI) parameters, as well as their predictive clinical value. Methods: Fifty-one RRMS at clinical onset and 17 other not inflammatory neurological disorders (ONINDs) underwent brain MRI (including 3D-T1, 3D-FLAIR, and 3-DIR sequences) and CSF examination. Eighty-seven cytokines and chemokines were analyzed in CSF by Multiplex technology. Results: Compared to ONIND, CXCL-10, CXCL-11, CXCL-13, CCL-1, CCL-2, CCL-3, CCL-22, IL-16, and BAFF were significantly (p < 0.05) increased in RRMS CSF. However, only CCL-3 was associated with both MS diagnosis and IgGOB detection. Based on a 95%CI in ONIND (cut-off value: 0.798 pg/ml) and ROC analysis (cut-off value: 0.495 pg/ml), RRMS patients were stratified in CCL-3^high (>0.736 pg/mL), CCL-3^medium, and CCL-3^low (<0.495 pg/ml). Survival analysis disclosed a strong association between high CCL-3 values and disease reactivation (OR = 4.9, 95%CI: 1.8-13.3, p < 0.005) in the following 2 years. Conclusions: CCL-3 deserves further investigation as a candidate prognostic biomarker for RRMS.

Human spermatozoa of male patients with subfertility express the interleukin-6 receptor

Male subfertility has been associated with bacterial infections and chronic inflammation. In this context, several studies investigated cytokine levels in seminal plasma, whereas interleukin-6 (IL-6) appears to be crucial. However, little is known about its receptor, the IL-6R expression on human spermatozoa. Thus, the aim of the present study was to screen spermatozoa for IL-6R expression and to identify its localisation. Semen samples of 137 patients (median age 37.69, SD ± 7.82) with subfertility were analysed. Sperm analysis including determination of IL-6 was performed following the World Health Organization criteria. Also, flow cytometry was performed for sperm IL-6R expression. IL-6R⁺ cells were used for immunofluorescence staining to identify receptor localisation. The results showed positive staining for IL-6R in the midpiece of spermatozoa. Furthermore, a significant correlation between sperm IL-6R expression, seminal plasma IL-6 and total sperm count could be demonstrated, whereas a negative correlation was observed in sperm IL-6R expression and motility. However, no statistical significance could be observed between IL-6R expression, vitality and morphology. Moreover, incubation of spermatozoa with IL-6 led to a slight but significant decrease in motility after 24 hr. These data suggest that IL-6R expression may play a role in impaired sperm function during inflammation.

Immune network dysregulation associated with child neurodevelopmental delay: modulatory role of prenatal alcohol exposure

Background

Evidence suggests that cytokine imbalances may be at the root of deficits that occur in numerous neurodevelopmental disorders, including schizophrenia and autism spectrum disorder. Notably, while clinical studies have demonstrated maternal cytokine imbalances with alcohol consumption during pregnancy—and data from animal models have identified immune disturbances in alcohol-exposed offspring—to date, immune alterations in alcohol-exposed children have not been explored. Thus, here we hypothesized that perturbations in the immune environment as a result of prenatal alcohol exposure will program the developing immune system, and result in immune dysfunction into childhood. Due to the important role of cytokines in brain development/function, we further hypothesized that child immune profiles might be associated with their neurodevelopmental status.

Methods

As part of a longitudinal study in Ukraine, children of mothers reporting low/no alcohol consumption or moderate-to-heavy alcohol consumption during pregnancy were enrolled in the study and received neurodevelopmental assessments. Group stratification was based on maternal alcohol consumption and child neurodevelopmental status resulting in the following groups: A/TD, alcohol-consuming mother, typically developing child; A/ND, alcohol-consuming mother, neurodevelopmental delay in the child; C/TD, control mother (low/no alcohol consumption), typically development child; and C/ND, control mother, neurodevelopmental delay in the child. Forty cytokines/chemokines were measured in plasma and data were analyzed using regression and constrained principle component analysis.

Results

Analyses revealed differential cytokine network activity associated with both prenatal alcohol exposure and neurodevelopmental status. Specifically, alcohol-exposed children showed activation of a cytokine network including eotaxin-3, eotaxin, and bFGF, irrespective of neurodevelopmental status. However, another cytokine network was differentially activated based on neurodevelopmental outcome: A/TD showed activation of MIP-1β, MDC, and MCP-4, and inhibition of CRP and PlGF, with opposing pattern of activation/inhibition detected in the A/ND group. By contrast, in the absence of alcohol-exposure, activation of a network including IL-2, TNF-β, IL-10, and IL-15 was associated with neurodevelopmental delay.

Conclusions

Taken together, this comprehensive assessment of immune markers allowed for the identification of unique immune milieus that are associated with alcohol exposure as well as both alcohol-related and alcohol-independent neurodevelopmental delay. These findings are a critical step towards establishing unique immune biomarkers for alcohol-related and alcohol-independent neurodevelopmental delay.

Network topology dynamics of circulating biomarkers and cognitive performance in older Cytomegalovirus-seropositive or -seronegative men and women

Background

Cytokines are signaling molecules operating within complex cascade patterns and having exceptional modulatory functions. They impact various physiological processes such as neuroendocrine and metabolic interactions, neurotrophins’ metabolism, neuroplasticity, and may affect behavior and cognition. In our previous study, we found that sex and Cytomegalovirus (CMV)-serostatus may modulate levels of circulating pro- and anti-inflammatory cytokines, metabolic factors, immune cells, and cognitive performance, as well as associations between them.

Results

In the present study, we used a graph-theoretical approach to investigate the network topology dynamics of 22 circulating biomarkers and 11 measures of cognitive performance in 161 older participants recruited to undergo a six-months training intervention. For network construction, we applied coefficient of determination (R²) that was calculated for all possible pairs of variables (N = 33) in four groups (CMV⁻ men and women; CMV⁺ men and women). Network topology has been evaluated by clustering coefficient (CC) and characteristic path length (CPL) as well as local (E_local) and global (E_global) efficiency, showing the degree of network segregation (CC and E_local) and integration (CPL and E_global). We found that networks under consideration showed small-world networks properties with more random characteristics. Mean CC, as well as local and global efficiency were highest and CPL shortest in CMV⁻ males (having lowest inflammatory status and highest cognitive performance). CMV⁻ and CMV⁺ females did not show any significant differences. Modularity analyses showed that the networks exhibit in all cases highly differentiated modular organization (with Q-value ranged between 0.397 and 0.453).

Conclusions

In this work, we found that segregation and integration properties of the network were notably stronger in the group with balanced inflammatory status. We were also able to confirm our previous findings that CMV-infection and sex modulate multiple circulating biomarkers and cognitive performance and that balanced inflammatory and metabolic status in elderly contributes to better cognitive functioning. Thus, network analyses provide a useful strategy for visualization and quantitative description of multiple interactions between various circulating pro- and anti-inflammatory biomarkers, hormones, neurotrophic and metabolic factors, immune cells, and measures of cognitive performance and can be in general applied for analyzing interactions between different physiological systems.

Mechanistic Models of Cellular Signaling, Cytokine Crosstalk, and Cell-Cell Communication in Immunology

The cells of the immune system respond to a great variety of different signals that frequently reach them simultaneously. Computational models of signaling pathways and cellular behavior can help us explore the biochemical mechanisms at play during such responses, in particular when those models aim at incorporating molecular details of intracellular reaction networks. Such detailed models can encompass hypotheses about the interactions among molecular binding domains and how these interactions are modulated by, for instance, post-translational modifications, or steric constraints in multi-molecular complexes. In this way, the models become formal representations of mechanistic immunological hypotheses that can be tested through quantitative simulations. Due to the large number of parameters (molecular abundances, association-, dissociation-, and enzymatic transformation rates) the goal of simulating the models can, however, in many cases no longer be the fitting of particular parameter values. Rather, the simulations perform sweeps through parameter space to test whether a model can account for certain experimentally observed features when allowing the parameter values to vary within experimentally determined or physiologically reasonable ranges. We illustrate how this approach can be used to explore possible mechanisms of immunological pathway crosstalk. Probing the input-output behavior of mechanistic pathway models through systematic simulated variations of receptor stimuli will soon allow us to derive cell population behavior from single-cell models, thereby bridging a scale gap that currently still is frequently addressed through heuristic phenomenological multi-scale models.

High interleukin-8 and/or extracellular signal-regulated kinase 2 expression predicts poor prognosis in patients with hepatocellular carcinoma

Interleukin (IL)-8 and extracellular signal-regulated kinase (ERK) 2 play key roles in tumor progression, but the relationship between IL-8 and/or ERK2 expression in hepatocellular carcinoma (HCC) tissues and postoperative recurrence or survival is unclear. The expression levels of IL-8 and ERK2 in both HCC tissues and non-tumor liver tissues were analyzed using the Oncomine™ database and immunohistochemistry assay. Reverse transcription-quantitative PCR was then used to evaluate the expression levels of IL-8 and ERK2 in the tumor tissues of 67 patients with HCC undergoing radical hepatectomy. Pearson's correlation, Kaplan-Meier, Cox univariate and multivariate survival analyses were utilized to determine the correlation between IL-8 and ERK2 expression in HCC tissues, and their potential prognostic significance. As indicated by the data from the Oncomine™ database, and the patient samples, IL-8 and ERK2 were expressed at significantly higher levels in HCC tissues than in non-tumor liver tissues (P<0.05). The rates of high IL-8 and ERK2 expression in HCC tissues were 43.28 (29/67) and 34.33% (23/67), respectively, and the IL-8 and ERK2 expression levels were positively correlated (r=0.764; P<0.001). Both ERK2 expression and IL-8/ERK2 co-expression were significantly associated with tumor size and differentiation (P<0.05). Additionally, high expression levels of IL-8, ERK2 and IL-8/ERK2 co-expression were all significantly associated with poor overall survival (OS; P<0.05) and disease-free survival (DFS; P<0.05). Multivariate Cox regression analysis also showed that high expression levels of IL-8, ERK2, and IL-8 and ERK2 were independent prognostic factors for OS and DFS (P<0.05). The results of the present study indicate a significant increase in the risk of recurrence and mortality in HCC patients with high expression levels of IL-8 and/or ERK2, compared with patients with low expression. Therefore, IL-8 and ERK2 may be predictors of postoperative prognosis in patients with HCC.

A Modeling Framework to Characterize Cytokine Release upon T-Cell-Engaging Bispecific Antibody Treatment: Methodology and Opportunities

T‐cell–engaging bispecific antibodies (T‐BsAbs) are an important class of antibody therapeutics in immuno‐oncology. T‐BsAbs simultaneously bind to CD3 on T cells and a tumor‐associated antigen on tumor cells, activate T cells, and redirect T cells’ cytotoxicity against tumor cells. Cytokine release syndrome (CRS), a common dose‐limiting adverse event for T‐BsAbs, is associated with T‐cell activation. A “priming” dose strategy (i.e., a lower initial dose followed by a higher maintenance dose) has been implemented in the clinic to mitigate CRS and to achieve efficacious doses with T‐BsAbs. So far, the selection of the optimal priming dosing regimen is largely empirical. A “fit‐for‐purpose” semimechanistic pharmacokinetic/pharmacodynamic model was developed to characterize the cytokine release profiles upon T‐BsAb treatment, including the priming effect observed with repeated dosing. This model can be utilized to simulate cytokine profiles following various dosing regimens and may assist the design of clinical dosing strategies for T‐BsAbs programs.

Neuroimmune Activation Drives Multiple Brain States

Neuroimmune signaling is increasingly identified as a critical component of neuronal processes underlying memory, emotion and cognition. The interactions of microglia and astrocytes with neurons and synapses, and the individual cytokines and immune signaling molecules that mediate these interactions are a current focus of much research. Here, we discuss neuroimmune activation as a mechanism triggering different states that modulate cognitive and affective processes to allow for appropriate behavior during and after illness or injury. We propose that these states lie on a continuum from a naïve homeostatic baseline state in the absence of stimulation, to acute neuroimmune activity and chronic activation. Importantly, consequences of illness or injury including cognitive deficits and mood impairments can persist long after resolution of immune signaling. This suggests that neuroimmune activation also results in an enduring shift in the homeostatic baseline state with long lasting consequences for neural function and behavior. Such different states can be identified in a multidimensional way, using patterns of cytokine and glial activation, behavioral and cognitive changes, and epigenetic signatures. Identifying distinct neuroimmune states and their consequences for neural function will provide a framework for predicting vulnerability to disorders of memory, cognition and emotion both during and long after recovery from illness.

The impact of cytokine responses in the intra- and extracellular signaling network of a traumatic injury

Investigations of cellular responses involved in injury and repair processes have generated valuable information contributing to the advancement of wound healing and treatments. Intra- and extracellular regulators of healing mechanisms, such as cytokines, signaling proteins, and growth factors, have been described to possess significant roles in facilitating optimal recovery. This study explored a collection of 30 spatiotemporal responses comprised of cytokines (IL-1α, IL-1ß, IL-2, IL-6, TNF-α, MIP-1α), intracellular proteins (Akt, c-Jun, CREB, ERK1/2, JNK, MEK1, p38, p53, p90RSK), phosphorylated proteins (p-Akt, p-c-Jun, p-CREB, p-ERK1/2, p-GSK-3α/ß, p-HSP27, p-IκBα, p-JNK, p-MEK1, p-p38, p-p70S6K, p-p90RSK, p-STAT2, p-STAT3), and a protease (Caspase-3), measured in skeletal muscle tissue following a traumatic injury (rodent Gustilo IIIB fracture). To optimize the analysis of context-specific data sets, a network centrality parameter approach was used to assess the impact of each response in relation to all other measured responses. This approach identified proteins that were substantially amplified and potentially central in the wound healing network by evaluation of their corresponding centrality parameter, radiality. Network analysis allowed us to distinguish the progression of healing that occurred at certain time points and regions of injury. Notably, new tissue formation was proposed to occur by 168 h post-injury in severely injured tissue, while tissue 1-cm away from the site of injury that experienced relatively minor injury appeared to exhibit signs of new tissue formation as early as 24 h post-injury. In particular, hallmarks of inflammation, cytokines IL-1ß, IL-6, and IL-2, appear to have a pronounced impact at earlier time points (0-24 h post-injury), while intracellular proteins involved in cell proliferation, differentiation, or proteolysis (c-Jun, CREB, JNK, p38, p-c-Jun; p-MEK1, p-p38, p-STAT3) are more significant at later times (24-168 h). Overall, this study demonstrates the feasibility of a network analysis approach to extract significant information and also offers a spatiotemporal visualization of the intra- and extracellular signaling responses that regulate healing mechanisms.