Indigo naturalis (RIF) and its principal element realgar on 4 main cytochrome P450 enzymes activities in rats. A simple and efficient cocktail method was developed to detect the four probe drugs simultaneously. In this study, Wistar rats were administered intragastric RIF and realgar for 14 days; mixed probe drugs were injected into rats by caudal vein. Through analyzing the pharmacokinetic parameter of mixed probe drugs in rats, we can calculate the CYPs activities. The results showed that RIF could inhibit CYP1A2 enzyme activity and induce CYP2C11 enzyme activity significantly. Interestingly, in realgar high dosage group, CYP3A1/2 enzyme activity was inhibited significantly, and different dosage of realgar manifested a good dose-dependent manner. The RIF results indicated that drug coadministrated with RIF may need to be paid attention in relation to drug-drug interactions (DDIs). Realgar, a toxic traditional Chinese medicine (TCM), does have curative effect on acute promyelocytic leukemia (APL). Its toxicity studies should be focused on. We found that, in realgar high dosage group, CYP3A1/2 enzymes activity was inhibited. This phenomenon may explain its potential toxicity mechanism.The purpose of this work was to study the influences of Realgar- The complexity of medicine suggests that treatment protocols should be carefully designed, and construction of a prescription is an art in fighting disease. Increasing evidences demonstrate that, in treating diseases, especially cancer  and chro Blood Journal, describing the mechanism of action and clinical results of treating APL patients with ATO. At the time, little was known about ATO treatment of leukemia patients in western medicine, and these 2 papers helped accelerate the development of ATO as a leukemia drug. Today, ATO has become the first choice in APL therapy [Indigo naturalis formula (RIF), which was invented by Huang [4S4), while Indigo naturalis, Salvia miltiorrhiza, and Radix pseudostellariae serve as adjuvant. Despite the promising clinical studies and excellent curative effect in APL treatment, arsenic is well known for its toxic effects. RIF as an arsenic-containing preparation also raises public concern. Similar to realgar, many traditional medicines are forbidden in the US and European market because the p content of arsenic is higher than their tolerance limits [Acute promyelocytic leukemia (APL) is characterized by the accumulation of immature promyelocytes in bone marrow (BM) and the presence of a specific chromosome translocation t15:17)(q22:q21) in a vast majority of patients [(q22:q21) therapy . Realgarby Huang , a TCM dby Huang . The “eme limits . More inCytochrome P450 enzymes (CYPs) is responsible for the metabolism of a variety of drugs, xenobiotics and endogenous substances. Over 90% of the clinical drugs are metabolized by human CYPs . Among mThis paper aims at elucidating the potential influences of RIF and realgar on the activities of four CYP450 isozymes  in rats. A simplified and specific HPLC method was developed and validated to simultaneously quantify the four substrates in rat plasma as a cocktail. The changes of relative enzyme activity were evaluated as well.Indigo naturalis formula tablets (Product number 150402) were obtained from Tiankang Pharmaceutical Co., Ltd. . Realgar was purchased from Huamiao TCM Engineering Technology Center . Caffeine (Product number 150439) and midazolam (Product number 150402) were purchased from National Institutes for Food and Drug Control . Tolbutamide (Product number 1002039664) and chlorzoxazone (Product number 1001433956) were purchased from Sigma-Aldrich Co., Ltd. . Diazepam (Product number 1503141) as internal standard (IS) was obtained from Kingyork Group Co., Ltd. . HPLC-grade acetonitrile was supplied by Fisher Scientific Company Inc. . All other reagents were of analytical grade and purchased from Sinopharm Chemical Reagent Co., Ltd. .Realgar-30 Sprague-Dawley rats  were purchased from the animal experiment center of Academy of Military Medical Sciences of people's liberation army (Production license: SCXK(army)-2012-0004). Animal welfare and experimental procedures were strictly in accordance with the Guide for the Care and Use of Laboratory Animals . All animals were kept in an environmentally controlled breeding room  for at least one week before starting the experiments and fed with standard laboratory food and water. Prior to each experiment, all of the rats were fasted for 12 h with free access to water.μm). The mobile phase consisted of a mixture of acetonitrile (A) and 50 mM phosphate buffer (pH 3.0) (B). Gradient elution was performed as follows: (1) mobile phase A was at 60% at 0 min, (2) an isocratic elution was maintained at 60% A from 0 min to 9 min, (3) a linear gradient was increased to 75% A from 9 min to 13 min, and (4) mobile phase A maintained at 75% from 13 min to 15 min. The flow rate was 1.0 ml/min. UV detection wavelength was 230 nm.Chromatographic analysis was performed on a Waters 2695 Series  LC system containing a quaternary pump, an online degasser, an autosampler, and a thermostatic column compartment set at 30°C. Chromatographic separation was conducted on Waters XSelect HSS T3 C18 column . The mixture was vortexed for 1 min. 600 μl ethylacetate was added to centrifuge tube (1.5 ml). The mixture was vortex-mixed and centrifuged at 12000 ×g for 10 min to precipitate proteins. The organic layer was evaporated to dryness under a gentle stream of nitrogen at 40°C. Finally, the residue was dissolved in 200 μl of the mobile phase and centrifuged at 12000 ×g for 10 min. 20 μl of the supernatant was injected into HPLC system for analysis.In our study, a conventional liquid-liquid extraction method was used to prepare the plasma sample . After pTo investigate whether endogenous substances would interfere with the assay, blank plasma, blank plasma extracted by ethylacetate containing IS, blank plasma spiked with analytes, and a plasma sample collected at 45 min after administration intravenously  of mixed probe drugs to rats were processed according to the procedures described above and analyzed.μg/ml for caffeine and from 0.4 to 400 μg/ml for tolbutamide, chlorzoxazone, and midazolam. Using weighted least squares linear regression of the peak area of each probe drug, we can obtain the corresponding concentration (C). The lower limits of quantification (LLOQ) were defined as the plasma concentration giving a signal to noise ratio (S/N) > 10.The calibration curves of HPLC method were evaluated by analyzing a series of standard plasma samples at concentration from 0.2 to 200 Intraday/interday accuracies and precisions of the method were evaluated by analyzing the QC samples at 3 concentration levels in 6 replicates during a single day and in duplicate over 3 consecutive days. The precision was represented by relative standard deviation (RSD), which should be not be more than 15%. The accuracies were determined by calculating the percentage of measured concentrations to the nominal concentrations of each probe drug in QC samples and should be within 85–115%.The extraction recovery was determined by comparing the response of analytes in postextract QC samples to that obtained from preextraction spiked samples. Experiments were performed at 3 concentration levels in 6 replicates. The results of extraction recovery should be stable and repeatable.The stability experiments of analytes in biosamples were investigated under a variety of storage and process conditions. Sample freeze-thaw stability was determined after 3 freeze-thaw cycles by comparing the concentrations with their nominal concentrations. For room temperature stability, the QC samples were prepared in the reconstituted solution for 24 h at room temperature and analyzed along with the freshly prepared set of QC samples. Samples were considered stable if the accuracies of samples were within 15% at different levels, and the precisions should not exceed 15%.Indigo naturalis formula tablets and considering the conversion coefficient between rat and human, we have calculated the clinic equivalent dosage of RIF to be 720 mg/kg. In the RIF tablet, realgar accounts for 14.5% of the weight; based on the ratio, we selected the dose of different realgar groups, that is, 104.4, 1044, and 2088 mg/kg, and the 104.4 mg/kg was equivalent to the actual clinic dosage. Respectively, for consecutive 14 days, during this period, control group rats were administered oral normal pure water. In the 15th day, the mixed probe drugs containing caffeine, chlorzoxazone, tolbutamide, and midazolam were administered intravenously  at a dosage of 2.5, 5, 5, and 5 mg/kg. Approximately 0.50 ml of blood samples was collected in heparinized tubes following the specific schedule: 0.17, 0.33, 0.5, 0.75, 1, 1.5, 2, 4, 8, 12, and 24 h. Each blood sample was immediately centrifuged for 15 min at 5000 rpm. The obtained plasma layers were separated and stored in microcentrifuge tubes at −20°C before analysis was performed as the procedure in 30 male Sprague-Dawley rats were randomly divided into 5 groups: control group, RIF group, and realgar group . Each group contained 6 rats. All drugs were administered orally. According to the dispensatory of Realgar-p < 0.05 was considered statistically significant; p < 0.01 was considered as extremely statistically significant. GraphPad prism software (Version 5.0) was used to draw statistical figures.The data was generated and analyzed by Waters Empower Acquisition software (Waters Technologies). The pharmacokinetic parameters were analyzed by DAS pharmacokinetic program . All data were presented as mean ± SD One-way ANOVA test was employed to determine the differences of pharmacokinetic parameters by SAS statistical software. For all analyses, The retention time of relevant analytes was 3.41, 7.54, 9.02, and 11.58, respectively. No endogenous interfering peaks were observed at or near the retention time of analytes by comparing the chromatograms. Typical chromatograms of blank plasma, blank plasma extracted by ethylacetate containing IS, blank plasma spiked with analytes, and a plasma sample collected at 45 min after administration of mixed probe drugs to rats were shown in R2) larger than 0.990.The standard curve, correlation coefficient of each analyte in the selected concentration range, and LLOQ were shown in The obtained intraday/interday accuracy and precision data were summarized in As shown in A summary of stability of each analyte was given in Cmax in RIF group increased and CLz decreased. Both realgar low and middle dosage groups presented the same tendency as that of RIF group. However the realgar high dosage group showed the opposite tendency. Therefore, the pharmacokinetic behavior of caffeine indicated that RIF and realgar at low and middle dosage can inhibit the CYP1A2 activity, but high dosage realgar might induce the CYP1A2 activity.By comparing the pharmacokinetic parameter of caffeine in each group, we can calculate the CYP1A2 activity. As shown in Cmax. This maybe indicated that RIF and low dosage realgar had no significant influence on CYP2E1 activity. In realgar middle group, AUC was increased, t1/2 was prolonged, and CLz was decreased significantly; however, in realgar high dosage group, the tendency was the opposite. Therefore, the pharmacokinetic behavior of chlorzoxazone indicated that, in the realgar middle dosage group, the CYP2E1 activity was inhibited, but high dosage realgar might induce the CYP2E1 activity. The correlation between CYP2E1 activity and realgar dosage was nonlinear.By comparing the pharmacokinetic parameter of chlorzoxazone in each group, we can calculate the CYP2E1 activity. As shown in t1/2, and realgar middle and high groups showed the same tendency. The pharmacokinetic parameter of realgar low dosage group had no obvious difference compared with the control group. Therefore, the pharmacokinetic behavior of tolbutamide indicated that realgar had no significant influence on CYP2C11 activity at low dosage, but it might induce the CYP2C11 activity at middle and high dosage. The correlation between CYP2C11 and realgar dosage was nonlinear. This phenomenon may need further investigation.By comparing the pharmacokinetic parameter of tolbutamide in each group, we can calculate the CYP2C11 activity. As shown in Cmax were significantly increased; t1/2 was prolonged, while CLz had no obvious change. The realgar at different dosage manifested a good dose-dependent manner. Therefore, the pharmacokinetic behavior of midazolam indicated that RIF and realgar at low and middle dosage had no significant influence on CYP3A1/2 activity, but high dosage realgar might inhibit the CYP3A1/2 activity.By comparing the pharmacokinetic parameter of midazolam in each group, we can calculate the CYP3A1/2 activity. As shown in In this work, we first established a simple and useful method for probe drugs quantitative analysis and then verified it. Compared with the method proposed by Yang et al. , our metThrough evaluating the influence of RIFs on the activity of 4 CYP subtypes, we are able to predict and improve the effectiveness and reliability of RIF on clinical applications. The results showed that RIF of clinical dosage did not alter the CYP3A1/2 or CYP2E1 activity but inhibited the CYP1A2 activity and induced the CYP2C11 activity. The influence of RIF on the activity of the 4 enzymes can be used to predict drug-drug interactions and provide guidance for RIF's combination with other drugs.Another purpose of this study is to evaluate the influence of different doses of realgar on the 4 CYP enzymes. The research by Aihua et al.  showed tIncreasing evidences demonstrate that research on CYP450 enzymes not only can be used to predict the DDIs, but also can be applied in the study of the characteristics of TCM compatibility. Research results based on CYP450 enzymes can reveal the scientific connotation of classical TCM formula compatibility rationality, especially in the study of the attenuated toxic effects of TCM formula. Research on CYP450 enzymes has become a tool and plays a more and more important role in TCM synergism and toxicity reducing research. In the future study, we are looking forward to exploring the relationship between the toxicity of accumulated realgar and CYP3A enzyme activity.RIF at clinical dosage can inhibit CYP1A2 enzyme activity and induce CYP2C11 enzyme activity significantly. In realgar high dosage group, CYP3A1/2 enzyme activity was inhibited significantly, and different dosage of realgar manifested a good dose-dependent manner.


Antisense oligonucleotides (ASO) to MKK4, MKK7 and p38 (versus scrambled control) were used for in vivo knockdown, and in some experiments PMH were used after in vivo knockdown. Mice were treated with APAP or TNF/GalN and injury assessed. MKK4 and MKK7 were expressed in liver and each was efficiently knocked down with two different ASOs. Massive liver injury and ALT elevation were abrogated by MKK4 but not MKK7 ASO pretreatment in both injury models. The protection was confirmed in PMH. Knockdown of MKK4 completely inhibited basal P-p38 in both cytoplasm and mitochondria. However, ALT levels and histologic injury in APAP-treated mice were not altered with p38 knockdown versus scrambled control. p38 knockdown significantly increased P-JNK levels in cytoplasm but not mitochondria after APAP treatment. In conclusion, MKK4 is the major MAP2K, which activates JNK in acute liver injury. p38, the other downstream target of MKK4, does not contribute to liver injury from APAP or TNF/galactosamine.c-Jun N-terminal kinase (JNK) mediates hepatotoxicity through interaction of its phospho-activated form with a mitochondrial outer membrane protein, Sh3bp5 or Sab, leading to dephosphorylation of intermembrane Src and consequent impaired mitochondrial respiration and enhanced ROS release. ROS production from mitochondria activates MAP3 kinases, such as MLK3 and ASK1, which continue to activate a pathway to sustain JNK activation, and amplifies the toxic effect of acetaminophen (APAP) and TNF/galactosamine . Downstream of MAP3K, in various contexts MKK4 activates both JNK and p38 kinases and MKK7 activates only JNK. The relative role of MKK4  MKK4 activates both JNK and p38 kinases and MKK7 activates only JNK.12 Therefore, the current studies were conducted to gain further insight concerning this controversy by addressing the role of MAP2K to determine whether silencing the expression of either of the two MAP2K upstream of JNK would protect against acute liver injury. As we found that the MAP2K involved (MKK4) activates both JNK and p38, we also explored its possible role in liver injury.In the APAP model, many approaches to modulating MAP3K and JNK using small molecule inhibitors and genetic approaches have supported the role of JNK activation in liver injury. However, a recent study using liver-specific deletion of JNK1 in global JNK2 knockout mice has suggested the opposite, that is a protective role.in vivo. MKK4 and 7 expression in liver were efficiently knocked down by both their respective ASO: as shown by immunoblot and by qPCR.  with p38 knockdown versus scrambled control  directly impairs mitochondrial respiration and increases ROS, which requires binding to Sab.34 P-MKK4 plus ATP had no direct effect on mitochondrial function.1We have previously observed by P-MKK4 translocation to mitochondrial with P-JNK.et al.13 used a p38 inhibitor, which was dissolved in DMSO. Although the study controlled for DMSO vehicle, the APAP injury was markedly dampened by DMSO alone, which therefore complicates the interpretation of finding no additional protection by p38 inhibitor. Nakagawa et al.4 used p38+/− global hemizygotes and showed no protection against APAP but the decrease in p38 might not have been sufficient to exclude its role. However, in the current study, we used two different ASO targeted treatments to markedly suppress p38 expression in the liver by >90%. Both ASO molecules are specific for the p38α isoform and the marked loss of total p38 in immunoblots using antisera to total p38 indicates that nearly all the p38 in liver is p38α. The depletion of p38α had no effect on the toxicity of APAP or TNF/GalN as reflected in histology and serum ALT. Interestingly, some P-p38 was associated with mitochondria under basal conditions and tended to transiently increase after APAP. This association was the same in Sab knockout liver indicating that Sab is not required for the association of p38 with mitochondria. Furthermore, we previously showed that P-p38 plus ATP had no effect on mitochondrial respiration in contrast to P-JNK plus ATP.1 However, we did observe that knockdown of p38 lead to amplification of P-JNK levels in the first few hours after APAP dosing.As we identified MKK4 as the key MAP2K-mediating JNK activation and toxicity, we addressed the possibility that p38, the other MKK4 known substrate of MKK4, was involved in mediating toxicity. Prior work has suggested that p38 does not contribute to the toxic mechanism of APAP. Gunawan α deletion amplified JNK activation in response to TNF14 and a recent report showed that p38 inhibition in cancer cells amplified JNK signaling in response to SMAC mimetic.35 These findings might suggest that p38 normally dampens the JNK activation pathway. However, we did not observe an enhancement of the toxicity of APAP or TNF/GalN by depleting p38. Despite the increased P-JNK levels in p38 ASO-treated mice, we did not detect any difference in the association of P-JNK with mitochondria. However, further exploration of the potential protective role of p38 is warranted in future studies, particularly to determine whether p38 knockdown increases the susceptibility to lower nontoxic or minimally toxic doses of APAP and TNF/GalN.A previous study showed that liver-specific p38Overall, our findings indicate that MKK4 is the major MAP2K, which activates JNK in acute liver injury. p38, the other downstream target of MKK4, does not promote liver injury from APAP or TNF/galactosamine. Therefore, our findings support the critical role of JNK in hepatotoxicity.loxP-flanked Sab mutant mice (Sh3bp5tm1a(KOMP)Wtsi) were generated in C57BL/6N background by KOMP, UC Davis. Exon 5, 6 and 7 were flanked by loxP. Primers for genotyping of Sab mutant mice are: for floxed mice 5′-GAGATGGCGCAACGCAATTAATG-3′ and 5′-TGTGTAGCAGAGTAGACCCTCATGC-3′, for post-Cre mice 5′-GCTACCATTACCAGTTGGTCTGGTGTC-3′ and 5′-TGTGTAGCAGAGTAGACCCTCATGC-3′. To generate tamoxifen-induced hepatocyte-specific Sab KO mice (Tam-SabLPC-KO), Sab floxed mice were crossbred with Alb-CreERT2 mice (Albtm1(cre/ERT2)Mtz)37 kindly provided by Dr. Pierre CHAMBON, Institut de Génétique et de Biologie Moléculaire et Cellulaire (IGBMC), France. Sabf/f;Alb-CreERT2+/- were crossbred with Sabf/f floxed mice at least 10 times, and then 6-week-old male mice were fed with tamoxifen (Tam) 1 g/kg of diet pellet for 7 days and the experiment was performed 5–7 days later after the last day of Tam diet.1All animal experiments followed procedures approved by the Institutional Animal Care and Use Committee of the University of Southern California. Male C57BL/6 NHsd mice (6–8 weeks of age) were obtained from ENVIGO Bioproducts Inc. . α ASO (5′-GCAGCCTCTCTCTGTCACTG-3′) and its control ASO (5′-CCTTCCCTGAAGGTTCCTCC-3′) were provided by Ionis Pharmaceuticals. Experiments with a second set of ASO and control ASO were conducted to verify the findings: MKK4 ASO (5′-GTGGTTTAGTGCTTTC-3′) and its control ASO (5′-GGCCAATACGCCGTCA-3′); MKK7 ASO (5′-CCGTTCACAGTGTCTGTCGG-3′) or p38α ASO (5′-CCCGCTCTGAATCCAGATCT-3′) and its control ASO (5′-CCTTCCCTGAAGGTTCCTCC-3′). The results are shown in the supplemental section. ASO was dissolved in sterile PBS and oligonucleotide concentration was determined before aliquot for storage in −80 °C. Eight-week-old mice were injected 50 mg/kg intraperitoneally on alternate days for seven doses. The experiment was performed the next day after last injection.5MKK4 ASO (5′-TACGCTGGCTCTAAGC-3′), and its control ASO (5′-GGCCAATACGCCGTCA-3′); MKK7 ASO (5′-ACTTTGGTCTCTTCCTGTGA-3′), MKK7 ASO (5′-ACTTTGGTCTCTTCCTGTGA-3′), or p38α  was dissolved and diluted in PBS. Overnight fasted mice received GalN (800 mg/kg) 30 min before PBS or TNFα (12 μg/kg) intraperitoneally. The blood and samples were collected at 1, 2 or 24 h after injection.Acetaminophen  was dissolved in 50 °C PBS by vigorous vortexing and brought back to 25 °C before injection. Overnight fasted mice received PBS or APAP 300 mg/kg intraperitoneally and blood and samples were collected at 0.5, 1, 2, 4 or 24 h after injection. Galactosamine  was dissolved in PBS. TNFμm thick section. The tissue sections were stained for hematoxylin and eosin (H&E) and the images were taken with Nikon Eclipse 80i . For TUNEL staining, the tissue sections were stained using In Situ Cell Death Detection Kit, Fluorescein  and images were taken with Leica SP8 (LAS X) confocal microscope used with HC PL APO CS2 20X/0.75 DRY lens. Laser intensity used was 1% for both DAPI and FITC, gain was 752 to 800 g for DAPI and 726 to 750 g for FITC. . Exposure was identical in all cases based on one scan of the same duration for each fluorescence imaging.The liver sections were fixed in 10% neutral buffer formalin and embedded in paraffin for 5 Total mRNA was extracted from mouse liver tissue by RNeasy Plus Mini kit . cDNA was synthetized by QIAGEN OneStep RT-PCR Kit using 500 ng of mRNA. A total 2.5 ng of cDNA was applied for quantitive PCR in ABI 7900 HT Fast Real-Time PCR System using QuantiTect SYBR Green PCR Kit. Specific primer-amplified RNA amount was determined from standard curve generated by serial dilution of pool mRNA of normal three male mice. MKK4 or MKK7 mRNA was normalized by housekeeping gene TBP (TATA box binding protein). Primers for MKK4 mRNA are 5′-AATCGACAGCACGGTTTACTC-3′ and 5′-TGAAATCCCAGTGTTGTTCAGG-3′. Primers for MKK7 are 5′-GATGTCGCGTCCTGGTTTAAG-3′ and 5′-ACTTGGGAGAAGGTGGGGAA-3′. Primers for TBP are 5′-AGAACAATCCAGACTAGCAGCA-3′ and 5′-GGGAACTTCACATCACAGCTC-3′.2 Livers were homogenized in homogenizing buffer  and centrifuged at 1000X g for 10 min to collect fibrous tissue debris and nuclei. Mitochondria were collected by centrifugation at 9000 Xg for 10 min. The mitochondrial pellet was washed once. Mitochondrial protein was extracted in RIPA lysis buffer for Western blot analysis. The supernatant (cytoplasmic fraction) and mitochondrial lysate were stored in −80 °C.Mitochondria were isolated from mouse liver by differential centrifugation as described before.38Antisera to P-JNK (#4668), SEK1/MKK4 , Phospho-SEK1/MKK4 , P-Src, c-Src (2109), P-p38 (#4511), total p38 (#8690), MKK7 (#4172), PHB1 , total JNK , GAPDH  were used. ALT was determined by reagents from Teco Diagnostics . Covalent binding to liver proteins was assessed using antiserum to NAPQI protein adducts provided by Dr. Laura James of University of Arkansas for Medical Sciences. GSH was determined as described before.Aliquots of cytoplasmic or mitochondrial extracts were fractionated by electrophoresis on 7.5, 10 or 12% SDS-polyacrylamide gel . Subsequently, proteins were transferred to nitrocellulose membrane using iBlot Gel transfer device , and blots were blocked with 5% (w/v) nonfat milk or 5% (w/v) BSA dissolved in Tris buffered saline with 0.05% Tween 20 (TBS-T). The blots were then incubated with the desired primary and secondary antibodies. Finally, the proteins were detected by ECL reagent  using autoradiography film (Denville) or Amersham Hyperfilm ECL (GE Lifesciences). All gels shown are representative samples from at least three experiments.39 Three hours after plating of isolated hepatocytes, APAP (5 mM) dissolved in fresh prewarmed DMEM/F-12 culture medium was added. After 16 h of treatment, the cells were double-stained with Hoechst 33258  and SYTOX Green . In other experiments, hepatocytes from control or MKK4 ASO-injected mice were incubated with TNF-α (20 ng/ml) /actinomycin D  and, after 6 h, stained with Hoechst 33258 and apoptotic cells were counted. Quantification of total and apoptotic cells was performed by counting a minimum of 1000 cells in 10 different fields. Necrotic cells (SYTOX green-positive) were determined by counting the same field under Nikon Eclipse TE300 fluorescence microscope using MetaMorph imaging program.34Primary mouse hepatocytes (PMHs) from control or MKK4 ASO-injected mice were isolated and cultured as described previously.t-test and analysis of variance. P-value <0.05 was defined as statistically significant.A minimum of three biological replicates were considered for all the studies. The data are expressed as mean±S.D. Statistical analyses were performed using the paired two-tailed Student’s


Cytotoxic T lymphocytes (CTLs) have been suggested to play an important role in controlling human immunodeficiency virus (HIV-1 or simply HIV) infection. HIV, due to its high mutation rate, can evade recognition of T cell responses by generating escape variants that cannot be recognized by HIV-specific CTLs. Although HIV escape from CTL responses has been well documented, factors contributing to the timing and the rate of viral escape from T cells have not been fully elucidated. Fitness costs associated with escape and magnitude of the epitope-specific T cell response are generally considered to be the key in determining timing of HIV escape. Several previous analyses generally ignored the kinetics of T cell responses in predicting viral escape by either considering constant or maximal T cell response; several studies also considered escape from different T cell responses to be independent. Here, we focus our analysis on data from two patients from a recent study with relatively frequent measurements of both virus sequences and HIV-specific T cell response to determine impact of CTL kinetics on viral escape. In contrast with our expectation, we found that including temporal dynamics of epitope-specific T cell response did not improve the quality of fit of different models to escape data. We also found that for well-sampled escape data, the estimates of the model parameters including T cell killing efficacy did not strongly depend on the underlying model for escapes: models assuming independent, sequential, or concurrent escapes from multiple CTL responses gave similar estimates for CTL killing efficacy. Interestingly, the model assuming sequential escapes  was unable to accurately describe data on escapes occurring rapidly within a short-time window, suggesting that some of model assumptions must be violated for such escapes. Our results thus suggest that the current sparse measurements of temporal CTL dynamics in blood bear little quantitative information to improve predictions of HIV escape kinetics. More frequent measurements using more sensitive techniques and sampling in secondary lymphoid tissues may allow to better understand whether and how CTL kinetics impacts viral escape. Generavaccines . Howevervaccines –10. BettViral escape from CTL responses follows a somewhat predictive pattern with more dominant (larger magnitude) CTL responses leading to earlier viral escape , 12. HowHere, we explored whether including experimentally measured CTL kinetics improves description of the viral escape data. In doing so, we compared predictions of three alternative models of viral escape from CTL responses such as independent escapes, sequential escapes, and concurrent escapes. In the first model (independent escapes), we assumed that escape from any given CTL response occurs independently of other escapes and directly from the wild-type, i.e., we ignored the effects of clonal interference—in essence assuming high effective population size and/or high recombination rate. Of note, several recent experimental papers also assumed independent escapes –13. In t22.1+ T cell) responses were measured using peptide-stimulated IFN-γ ELISPOT assay and/or intracellular cytokine staining (ICS) 6 months after enrollment using peptides matched to the founder virus sequence . Multiple viruses were sequenced by SGA/S, and all sequences were compared at cites coding for CTL epitopes, and changes in the percentage of transmitted (wild-type) sequences were followed over time  is the density of uninfected target cells; wI(t) and mI(t) is the density of target cells infected by the wild-type or escape variant viruses, respectively; wV(t) and mV(t) is the density of wild-type or escape variant viruses, respectively; s is the turnover rate of uninfected target cells; T0 is the preinfection level of uninfected target cells; βw and βm is infection rate of wild-type or escape variant viruses, respectively; μ is the probability of mutation from wild-type to escape mutant during reverse transcription of viral RNA into proviral DNA; δ is the death rate of infected cells due to viral pathogenicity; k is the killing rate of wild-type virus infected cell due to CTL response; wp and mp is the rate at which cells infected by wild-type or escape mutant viruses produce viruses; and vc is the clearance rate of free viral particles.Models describing the dynamics of viral escape from a single cytotoxic T lymphocyte (CTL) response have been developed and adopted by different researchers . HerwV(t)) and escape viruses (mV(t)) differ by two factors: viral infectivity (wβ and mβ) and the rate of virus production (wp and mp). Given that in vivo viral particles are short-lived  (c implies fitness advantage of escape virus (In this model (equation ), we assrt-lived , 30, to  ≤ βwpw) , and negpe virus , 32. By w(t) or m(t), respectively, and assume that the wild-type and escape viruses differ only in the rate of infectivity (that is wβ ≥ mβ and wp = mp)  , the sys2.3Mathematical model given in equation  tracks cn CTL responses that control viral growth, and virus can potentially escape from all n responses. We use mi to denote the density of variants where i is a vector i =  denoting the positions of n epitopes, and we define ji = 0 if there is no mutation in the jth CTL epitope and ji = 1 if there is a mutation leading to an escape from the jth (1 ≤ j ≤ n) CTL response. We denote the set of escape variant as I, which is i ∈ I. The wild-type variant is then denoted as .To track the dynamics of viral escape from multiple responses, we assume that there are in total i, cik, and iμ to denote killing rate due to ith CTL response, cost of escape mutation from the ith CTL response and mutation rate for the ith epitope, respectively. Due to a small rate of double mutation  to m is μ2, and the mutation rate from m to m is 0. Assuming multiplicative fitness , that is, the fitness cost of a variant i =  is i =  due to remaining CTL responses is given by For our analysis, we neglect recombination and backward mutation from mutant to wild-type. We use mutation , we assuSimilar to equation , the dynjf(t)  is the fraction of viral variants that have escaped recognition from the jth CTL response. The frequency of a viral variant escaping from the jth response is given byWe define n CTL responses simultaneously along multiple different pathways. This is likely to happen when the HIV effective population size is large. With n CTL responses, there are n escape variants for “sequential” escape and 2n − 1 escape variants for “concurrent” escape in addition to the wild-type variant. For example, with n = 3 CTL responses, for “sequential” escape, there are 3 escape variants: m, m, and m with m being the wild-type virus. For “concurrent” escape, there are 7 escape variants: m, m, m, m, m, m, and m with m being the wild-type virus . It is interesting to note that “sequential” escape is a simplification of “concurrent” escape when the effective population size is small. Previous work did not fully resolve whether CTL escapes in HIV infection occur sequentially or concurrently    or time-dependent killing rate The killing rate TLs (Ei) . Previoug., Ref. , 18), org., Ref. , 36). Wig., Ref. , we adoptal data .iE(t), we used the first-order interpolation function (termed as “interpolated response”) or the fitted response function (termed as “fitted response”) by the Ton–Toff model  is given thus by the following differential equation:E(0) = E0. Here the “precursor frequency” E0 is a generalized recruitment parameter, which combines the true precursor frequency and the recruitment rate/time  is the predicted frequency of a specific viral escape variant at time jt. Model parameters were thus found by maximizing the log-likelihood function  fitL=∑j=1Ti[Random routine in Mathematica assuming beta distribution for sequencing data  scores . The moding data , fitted ic = 0. In all fits, we assumed that the rate of virus replication r = 1.5/day .While multiple models may be able to describe accurately experimental data, some models may do so at biologically unreasonable parameters. For example, estimated rate of mutation at different epitopes may be unrealistically large. Thus, in our analysis, we assume that mutation rates, which are above 10neration  and sizeTon–Toff model [equation . In such situations, we set the initial CTL density as 1 (the detection level for this data set) for simplicity. Other starting points  were also tested and led to similar results (not shown). This was largely due to the fact that, in our models, CTLs at low densities are not expected to exert large selective pressure on the virus population due to assumed mass-action killing term.33.1Given virus evolution data, we may be often interested in quantifying selecting pressures driving specific changes in the virus population. Following HIV-1 infection, the virus escapes from several cytotoxic T lymphocyte (CTL) responses , and mulf < 1 were rare). For the analysis, we, therefore, used the escape data from two patients, CH131 and CH159, where CTL and HIV sequence measurements were sufficiently frequent to address our modeling questions. We fitted a simple mathematical model describing escape of the virus from a single constant (non-changing) CTL response . It is interesting to note that, visually, the least squares method appear to describe the data better by accurately fitting the points with intermediate frequency of the escape variant in 20–30 days after the symptoms ). However, this visually better fit is not supported by the statistics: likelihood of the model for these data is −12.64 or −10.53 for normal  = ); removing this data point from the data led to identical estimates of the escape rate, k = 0.51/day, from two statistical models (results not shown). This is not surprising because with this data point removed, the information on escape rate is only coming from two data points when the frequency of the escape variant is intermediate (0 < f < 1).Unfortunately, in our cohort of 17 patients , very feequation  assumingf < 1) at one time point only. In particular, in our analysis of another escape in patient CH159  and ignore another data point (t = 56 days) leading to extremely high predicted escape rates (results not shown). Interestingly, using binomial distribution-based likelihood allowed for an accurate fit of the model to data and the fit compromised between describing early and late data points  predicts the timing of viral escape , 12. Immik was set constant over time), or when killing rate was proportional to the experimentally measured CTL frequency  and Nef DREVLIWKFDSSLARRHL (Nef 177–194) satisfying our selection criteria Figure C. DespitTon–Toff model) did not improve the quality of the model fit to escape data  for the peptide Rev 65–82. Constraining this parameter to remain μ1 ≤ 10−3 led to fits of significantly lower quality . Due to large length of the peptide, the overall mutation rate in this region could indeed be slightly higher than our calculated high bound for the mutation rate . However, assuming a constant response allowed to obtain lower, more physiological estimates of the mutation rate. These results suggest that inability of the models, which explicitly incorporate CTL dynamics to explain kinetics of first escape with physiologically reasonable mutation rate is due to late appearance of the CTL response. Indeed, escape can only accumulate when CTL response is present and extending the time window for virus evolution but not having CTL response active will not significantly impact estimates of the mutation rate.Therefore, to test whether the timing of the start of the escape influences the estimate of the mutation rate we did the following. We shifted the data for two escapes forward by adding some initial zeroes to data and reverse extended the predicted CTL response curves. Then we refitted models 1 and 3 to the data under the constrain Given our results for one patient, we next sought to investigate whether our conclusions will remain robust when looking at data from another patient. Patient CH131 had 6 CTL responses, and there was escape from at least 5 of these responses in 2 years since symptoms Figure : Nef64-sWe thus fitted 3 different models of viral escape combined with 3 different models for the CTL dynamics to the data on viral escape Figure . Importaμ3 to be constrained (μ3 ≤ 10−3) significantly reduced the quality of the model fit to data . Furthermore, estimates for the CTL killing efficacy differed between model 2 and models 1 and 3 suggesting that model choice  may indeed influence estimates of the killing efficacy.Extending the observation made with the patient CH159 data, we found that model assuming sequential escape model 2) could not accurately describe the dynamics of viral escape for biologically reasonable parameter values specifically for the third escape in Gag156 although this inability was significant only for a constant killing efficacy  with interpolated response different nearly 100-fold between 3' Table . Knowing3.4μi ≤ 10−3 led to fits of significantly lower quality .In analysis of data from both patients, we found that model 2, describing sequential escape from CTL responses, was not able to accurately describe experimental data for biologically reasonable parameter values; these model fits predicted extremely high mutation rates , led to similar conclusions (results not shown). Because previous work suggested that kinetics of escape was independent of the specific mechanism of how CTLs suppress wild-type virus  , wed cells) , we did d cells) .Reasons of why a model with time-variable CTL response did not describe experimental data better than a model with a constant response remain unclear but several hypotheses could be generated. First, frequency of sampling of the viral sequences may not be high enough to detect change in the speed at which mutant viruses accumulate in the population. Indeed, in mathematical models, CTL dynamics has a direct impact on the rate of escape , and thOur results may be interpreted as contradictory to several previous studies that found a strong correlation between the time of viral escape  and a relative magnitude of CTL response  , 12. HowIn our analysis, we generally found that for well sampled data, the pathway of generation of escape mutants played a minor role in predicting overall CTL killing efficacy; assuming escapes that occur independently model 1) or concurrently (model 3) gave nearly identical estimates of the CTL killing efficacy  or models assuming concurrent escape from multiple CTL responses (model 3) fit the data well and provide very similar  estimates for the killing efficacy of CTL response. Thus, for well sampled data, assumption of independent escapes may be sufficient to accurately estimate HIV escape rates. Also, the model with data-driven time-dependent CTL response (interpolated or fitted response input) did not improve the quality of the model fit to data, so, at present, it appears to be unnecessary to incorporate the experimentally measured CTL response dynamics in the model describing viral escapes. Yet, because our results were found only for two patients, whether similar conclusions will be reached in other studies/patients remains to be determined. Our analysis nevertheless demonstrates how mathematical modeling may help to quantify HIV evolution in presence of CTL responses and to highlight potential limitations with experimental measurements.This paper uses experimental data obtained previously, and no new observations requiring patient consent or institutional review board approval have been performed.YY and VG designed the study, contributed to analysis, interpolation of data, and simulation results, and wrote the paper. YY performed the simulations.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We aimed to test the hypothesis that patients responding to anti-TNF treatments exhibit attenuated TNF-dependent immune responses at the site of an immune challenge. We developed and validated four context-specific TNF-inducible transcriptional signatures to quantify TNF bioactivity in transcriptomic data. In anti-TNF treated rheumatoid arthritis (RA) patients, we measured the expression of these biosignatures in blood, and in skin biopsies from the site of tuberculin skin tests (TSTs) as a human experimental model of multivariate cell-mediated immune responses. In blood, anti-TNF therapies attenuated TNF bioactivity following ex vivo stimulation. However, at the site of the TST, TNF-inducible gene expression and genome-wide transcriptional changes associated with cell-mediated immune responses were comparable to that of RA patients receiving methotrexate only. These data demonstrate that anti-TNF agents in RA patients do not inhibit inducible TNF activity at the site of an acute inflammatory challenge in vivo, as modeled by the TST. We hypothesize instead that their therapeutic effects are limited to regulating TNF activity in chronic inflammation or by alternative non-canonical pathways.The impact of anti-tumor necrosis factor (TNF) therapies on inducible TNF-dependent activity in humans has never been evaluated  TNF receptor (TNFR) 1 is expressed on almost all nucleated cells and TNFR2 (p75) expression is restricted to cells of the immune system (Tumor necrosis factor (TNF) is a pleiotropic cytokine that is transcriptionally activated in response to a variety of stimuli during inflammation, infection, and stress. Its non-redundant role in the immune system is apparent in TNF-deficient mice, which have no phenotypic abnormalities in the steady state, but are unable to mount organized immune responses and thus suffer from an increased susceptibility to infection –4. Upon d TNFR2 p5 expresse system , 9.in vivo inhibitory effect on TNF activity has yet to emerge. Variable effects on the level of TNF itself in serum or synovium of RA patients have been described, which do not necessarily correlate with the clinical response to anti-TNF therapy (Excessive TNF activity contributes to the complex pathogenesis of rheumatoid arthritis (RA) , associa therapy –16. In g therapy –19. Whil therapy , 21. ETN therapy , suggestMycobacterium tuberculosis (Mtb)  , 24, in is (Mtb) . The rolis (Mtb) , 27. Thiis (Mtb) , 29. Conis (Mtb) –32. Possis (Mtb) –35, or dis (Mtb) .in vivo human immune responses at the site of a standardized experimental challenge  to make molecular and systems level assessments of hallenge , 38. Clihallenge , but trahallenge . Importahallenge , 38. In This study was approved by UK National Research Ethics Service (reference no: 11/LO/1863).Healthy volunteers and adult patients with RA, treated with methotrexate (MTX), adalimumab, infliximab, or ETN, were invited to participate subject to selected criteria Table . Writtenex vivo stimulation with 10 ng/mL recombinant human TNF (Life Technologies). Demographic, clinical, and laboratory data for each study group are summarized in Table Participants with positive peripheral blood IFNγ release assays as evidence of antimycobacterial T cell memory (see below) received either intradermal TST or saline injections in the volar aspect of the forearm as previously described , 38. At 5 PBMC per well and PPD 10 µg/mL , soluble anti-CD3 (HIT 3a) (eBioscience) as a positive control, or vehicle control. Plates were incubated for 48 h at 37°C. IFNγ production was detected using 1 µg/mL of biotinylated detection antibody (eBioscience), 1 µg/mL of streptavidin alkaline phosphatase (ALP) conjugate  and BCIP/NBT substrate for ALP (Merck). The reaction was allowed to develop in the dark and stopped by addition of distilled water. Reactive foci were counted using AID ELISpot Software version 5.0 on the AID ELISpot Reader.Participants were screened for antimycobacterial T cell memory by peripheral blood IFNγ release assays using QuantiFERON-TB Gold tests (Qiagen) according to the manufacturer’s instructions or IFNγ ELISpot responses to purified protein derivative . Briefly, PBMC were obtained by density gradient centrifugation of heparinized whole blood with Ficoll-Paque PLUS . Sterile, clear 96-well filter plate with a PVDF base membrane (Merck Millipore) were coated with 1 µg/mL antihuman IFNγ antibody (eBioscience) for 24 h at 4°C, prior to the addition of 2 × 10t-tests were used to derive TNF modules from ex vivo and cell culture experiments. Mann–Whitney tests were used to compare TST and saline injection sites. A p value of <0.05 was considered significant. This statistical threshold to identify significant differentially expressed genes was complemented by using a twofold change filter and statistical enrichment in upstream regulator analysis, performed using Ingenuity Pathway Analysis (Qiagen); and pathway analysis using InnateDB,Total RNA from peripheral blood, monocyte-derived macrophages (MDM), and skin biopsy samples was purified for genome-wide transcriptional profiling as previously described , 38. Alln = 3 per group). Transcriptional profiling of stimulated MDM was then performed as previously reported (n = 17). Among these LPS response genes, those that were attenuated significantly (>2-fold) in the presence of ETN were used as the LPS/ETN-MDM module. To derive a TNF-specific module in keratinocytes (TNF-KC module), we made use of previously published transcriptomic data from primary human keratinocytes (KCs) stimulated with a selection of cytokines, including TNF  incubated at 37°C for 3 h ±10 ng/mL recombinant human TNF, to identify transcripts that were significantly upregulated by >2-fold. The gene lists that make up each TNF module are listed in Data Sheet S1 in Supplementary Material. A human T cell-specific transcriptional signature (module M19)   and geneule M19) , 40 have2), which is represented as a percentage of the total tissue/ROI area.Punch skin biopsies for histological analysis were snap frozen in OCT Compound (Tissue-Tek). Frozen sections were carefully thawed and fixed in 4% neutral buffered formalin, then embedded in paraffin wax (Sakura). 3 µM sections were cut and stained using the Leica Bond III automated immunostaining platform, with the Leica Bond Polymer Refine detection kit (Leica DS9800) and a DAB chromogen. More specifically, antihuman antibodies against CD3 (clone LN10) (Leica NCL-L-CD3-565) and matrix metalloproteinase (MMP) 9 (clone 56-2A4) (Millipore MAB3309) were used. Whole slide images of the histology sections were acquired with an Axio-Scan microscope using Zen 2 core software at ×20 magnification and are presented without any subsequent processing. Digital image analysis was performed using Definiens AG (Munich) Tissue Studio 4.3 software. Tissue detection automatically identified all the tissue within each image, then a machine learning method was used to separate the sample from background and non-tissue regions, and segment the sample into dermis and epidermis; manual correction was used to ensure valid separation of these regions of interest (ROI). A fixed threshold was then applied to each ROI to identify the chromogen positive areas  associated with TNF activity showed only modest overlap Figure . Importaex vivo stimulation with TNF. We quantified expression of the blood-TNF module before and after ex vivo TNF stimulation of blood from RA patients treated with anti-TNF therapies and compared these to blood samples from RA patients on MTX only. To avoid the confounding of differential disease activity, we restricted recruitment to patients on a stable treatment regimen for at least three months and who showed low levels of background inflammation, using C-reactive protein and the RA disease activity score of 28 joints (DAS28) (Table Previous reports have interpreted the reduction in circulating pro-inflammatory mediators following initiation of anti-TNF therapies in RA as evidence for inhibition of TNF activity –19. Ther8) Table A, as a b8) Table . All had8) Table  were notEx vivo whole blood stimulation with TNF upregulated the expression of each of the TNF-dependent modules in all groups of patients, but this increase was significantly attenuated in patients on anti-TNF therapies , and written informed consent was obtained from all participants.RB-M, MRE, and MN designed the experiments. RB-M, CT, NG, and LB conducted the experiments. RB-M, CT, GP, ME, and MN undertook the analysis. RB-M, CT, MRE, and MN wrote the paper with input from all other authors.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Thus, we conclude that TNF-α promotes the growth of breast cancer through the positive feedback loop of TNFR1/NF-κB (and/or p38)/p-STAT3/HBXIP/TNFR1.Our finding provides new insights into the mechanism by which TNF-α drives oncoprotein HBXIP in the development of breast cancer.In the connection between inflammation and cancer development, tumor necrosis factor-alpha (TNF-α) contributes to the tumorigenesis. However, the underlying mechanism remains poorly understood. In this study, we report that TNF-α enhances the growth of breast cancer through up-regulation of oncoprotein hepatitis B X-interacting protein (HBXIP). Our data showed that the levels of TNF-α were positively related to those of HBXIP in clinical breast cancer tissues. Moreover, TNF-α could up-regulate HBXIP in breast cancer cells. Interestingly, silencing of TNF-α receptor 1 (TNFR1) blocked the effect of TNF-α on HBXIP. Mechanistically, we revealed that TNF-α could increase the activities of HBXIP promoter through activating transcriptional factor signal transducer and activator of transcription 3 (STAT3). In addition, nuclear factor kappa B (NF-κB) and/or p38 signaling increased the levels of p-STAT3 in the cells. Strikingly, HBXIP could also up-regulate TNFR1, forming a positive feedback loop of TNFR1/NF-κB (and/or p38)/p-STAT3/HBXIP/TNFR1. Notably, TNF-α was able to up-regulate TNFR1 through driving the loop. In function, we demonstrated that the knockdown of HBXIP remarkably abolished the growth of breast cancer mediated by TNF-α  The links between inflammation and cancer become a hot topic for cancer. However, the underlying molecular mechanism remains unclear. The cancer-related inflammation involving in the progression of cancer includes the presence of inflammatory cells and inflammatory mediators which including chemokines, cytokines and prostaglandins in cancer microenvironment , 2. AmonNuclear factor kappa B (NF-κB) responded to the activation of TNFR-associated death domain protein (TRADD) mediating the recruitment of protein complex leads to cell survival and release of inflammatory mediators . NF-κB aMammalian hepatitis B X-interacting protein  is originally identified for its binding to the C terminus of the hepatitis B virus X protein . HBXIP sIn this study, we are interested in the significance of inflammatory factor TNF-α on oncoprotein HBXIP in the links between inflammation and cancer. Strikingly, we found that TNF-α was able to up-regulate HBXIP through driving a positive feedback loop of NF-κB (and/or p38)/p-STAT3/HBXIP/TNFR1, resulting in the enhancement of growth of breast cancer. Our finding provides new insights into the mechanism by which TNF-α up-regulates HBXIP in the development of breast cancer.It has been reported that TNF-α promotes gastric tumorigenesis and tumor lymphangiogenesis , 11. Thep<0.05, Wilcoxon's signed-rank test, Figure p< 0.01, Pearson's correlation, Figure TNF-α is able to promote the development of cancer through activating cancer-related pathways or up-regulating Noxo1 and Gna14 . Previouhttp://gpminer.mbc.nctu.edu.tw/). Interestingly, we observed that there were several binding elements of transcription factors involving inflammatory response in the region of HBXIP promoter, including STAT3, STAT5a and AP1. To further validate whether these transcription factors contributed to the activation of HBXIP promoter mediated by TNF-α, we constructed a series of mutant-type HBXIP promoters  at site 705 tyrosine translocates into the nucleus and functions as a transcription factor to activate the expressions of target genes , 37. AccAs we know, the feedback regulation is frequently occurred in cancers , 11. Ourin vitro and in vivo.Next, we evaluated whether TNF-α could promote the growth of breast cancer through HBXIP. MTT and colony formation assays revealed that knockdown of HBXIP significantly blocked the TNF-α-enhanced proliferation in MDA-MB-468 cells Figure . MoreoveThe links between inflammation and cancer become a hot topic for cancer . HoweverGiven that TNF-α could promote the development of head/neck squamaous cancer and gastric tumor , 11, we via NF-κB and/or p38 signaling, leading to the up-regulation of HBXIP in breast cancer cells. TNF-α can also induce the expression of many oncoproteins through NF-κB or p38 signaling, such as JAG1, Fascin, VEGF and MMP2/MMP9, to promote the development of cancers [in vitro and in vivo. Thus, we conclude that TNF-α/TNFR1 activates the NF-κB (and/or p38)/STAT3/HBXIP signaling, leading to the development of cancer. Therapeutically, the oncoprotein HBXIP may serve as a target for breast cancer.Next, we try to explore the mechanism by which TNF-α up-regulates HBXIP in breast cancer. It has been reported that STAT3 acts as an important transcription factor in cancers , 40. The cancers , 38, 44.In summary, we present a model that inflammatory factor TNF-α contributes to the carcinogenesis through activating the oncoprotein HBXIP in breast cancer. In this model, TNF-α as a tumor promoter stimulates TNFR1, followed by NF-κB and/or p38/MAPK signaling leads to the activation of transcriptional factor STAT3, which results in the up-regulation of oncoprotein HBXIP. Strikingly, HBXIP up-regulates TNFR1 expression, forming a positive feedback loop of TNFR1/NF-κB (and/or p38)/STAT3/HBXIP/TNFR1 in breast cancer. Our finding provides new insights into the mechanism by which TNF-α stimulates HBXIP in the link between inflammation and cancer in breast cancer.Twenty-four cases of clinical breast tumor tissues and their corresponding peritumor tissues were obtained from Tianjin First Center Hospital  after surgical resection. Written consent approving the use of samples for research purposes was obtained from patients. The information of patients with breast cancer is presented in 2 at 37°C.The reagents used in this study were SB202190 , PDTC , TNF-α . Breast cancer cell lines MDA-MB-468 and SK-BR3 were cultured in DMEM medium , 10% fetal calf serum (FCS), 100 U/ml penicillin, and 100 μg/ml streptomycin in humidified 5% COChIP assays were performed in MDA-MB-468 cells according to the manufacturer's protocol  as reported previously . The proImmunohistochemistry staining of samples were performed as previously reported . Anti-Ki7 per ml with sterile phosphate-buffered saline. Three groups of 4-week-old female BALB/c athymic nude mice   were subcutaneously injected at the mouse shoulder with 0.1 ml of the cell suspensions. Tumor volume was measured 8 days after injection and then every 4 days. Tumor volume (V) = (L× W2) × 0.5 was calculated by measuring the length (L) and width (W) with calipers. After 30 days, mice were sacrificed and the tumors were excised and measured for further research.Nude mice were nursed and treated according to the guidelines established by the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The animal transplantation was conducted according to the Declaration of Helsinki. Tumor transplantation was performed in nude mice. In brief, mock group with MDA-MB-468 cells was added into the control solvent  and the test group with MDA-MB-468 cells was treated with 20 ng/ml TNF-α coupled with silencing of HBXIP or si-control every two days for 5 times. Cells were harvested and re-suspended at 2× 10Adherent cells were seeded into 24-well plates to make sure the cell confluence up to 60-80%, and then were cotransfected with the plasmids containing wild type or mutant of HBXIP promoter or pGL3-Basic as a negative control, and the pRL-TK plasmid  which was used as internal normalization. Cell extracts were harvested after 24 hours and lysed by using lysis buffer . Dual luciferase reporter gene assays were implemented using the Dual-Luciferase Reporter Assay System  as the manufacture's procedures. All experiments were repeated at least 3 times.PGL3-Basic, pCMV-tag2B, pGL3-HBXIP, pRL-TK  and pCMV-HBXIP, pcDNA3.0, pcDNA3.0-HBXIP were kept in our lab. The mutants of STAT3, STAT5a, and AP1 in HBXIP promoter were cloned into the plasmid pGL3-basic. All primers and siRNA sequence were listed in Total RNA was extracted from the cells (or tissues) using Trizol  according to the manufacturer′s procedures. Reverse transcription was performed using poly (A)-tailed total RNA and reverse transcription primer with ImPro-II Reverse Transcriptase , according to the manufacturer′s protocols. RT-PCR was applied as previously reported . The qRTβ-actin antibody , rabbit anti-STAT3 antibody , rabbit anti-Phospho-STAT3 antibody  [Western blot analysis was used as described previously . Primaryny, USA) , rabbit ny, USA)  and mousny, USA) , rabbit Cells were seeded on acid-treated glass cover slips, then cells were fixed 20 minutes with ice-cold 4% paraformaldehyde, washed three times with phosphate-buffered saline (PBS), 5 minutes per time on the table concentrator. Then, cells were permeabilized with 0.1% Triton X-100 in PBS for 20 minutes. After washing three times with PBS, samples were blocked in PBS containing 5% BSA for 1 hour, which then were incubated with primary antibodies for 2 hours at room temperature. After washed three times with PBS, the cells were incubated with fluorophore-conjugated secondary antibody  and executed for DAPI staining. Washing again, slides were mounted with 90% glycerol and observed under fluorescence microscope . The cells were observed in three randomly selected fields.MDA-MB-468 cells were seeded onto 96-well plates (1000 cells/well) for 24 hours before transfection or other treatments. Then 3--2,5 diphenyltetrazolium bromide (MTT) assays were used to assess cell proliferation every day from the first day until the third day or fourth day after transfection.Five-ethynyl-2’-deoxyuridine (EdU) incorporation assays were carried out using the Cell-Light TM EdU imaging detecting kit according to the manufacturer's instructions .5 cells per dish and maintained in serum free DMEM for 48h and then cells were harvested and stained with propidium iodide or Annexin-V.The flow cytometry analysis protocol was used as described previously . MDA-MB-For clonogenicity analysis, 48 hours after transfection, 1000 viable transfected cells were placed in 6-well plates and maintained in complete medium for 2 weeks. Colonies were fixed with methanol and stained with crystal violet.t test for independent groups and was assumed for p<0.05 (*), p<0.01 (**), p<0.001 (***) and not significant (ns). The expression levels of TNF-α and TNFR1 in breast cancer tissues and their corresponding peritumor tissues were analyzed using the Wilcoxon signed-rank test. Pearson's correlation coefficient was used to determine the correlation between HBXIP and TNF-α or TNFR1 mRNA levels in breast cancer tissues.Each experiment was repeated at least 3 times. Statistical significance was assessed by comparing mean values (± sd) using a Student's


The term cytokine storm has become a popular descriptor of the dramatic harmful consequences of the rapid release of polypeptide mediators, or cytokines, that generate inflammatory responses. This occurs throughout the body in both non-infectious and infectious disease states, including the central nervous system. In infectious disease it has become a useful concept through which to appreciate that most infectious disease is not caused directly by a pathogen, but by an overexuberant innate immune response by the host to its presence. It is less widely known that in addition to these roles in disease pathogenesis these same cytokines are also the basis of innate immunity, and in lower concentrations have many essential physiological roles. Here we update this field, including what can be learned through the history of how these interlinking three aspects of biology and disease came to be appreciated. We argue that understanding cytokine storms in their various degrees of acuteness, severity and persistence is essential in order to grasp the pathophysiology of many diseases, and thus the basis of newer therapeutic approaches to treating them. This particularly applies to the neurodegenerative diseases. As previously discussed , 2, the Some 50 years ago the idea of unsuspected secreted polypeptides, mediators akin to hormones but arising throughout the body from cells with other primary functions rather than from specialized secretory organs, and communicating with many other cells, was quite new. The word cytokine became a generic term for these molecules once immunologists realized that classifying them according to their cell of origin, such as lymphocytes releasing lymphokines  and monoChondrosia reniformis [Acropora spp. [Asterias forbesi [When a molecule present in mammals proves also to be found in earlier, particularly very early, life forms it is generally taken to denote that it has been conserved in the biological repertoire because it is essential to life. Cytokines can be good examples. For instance, although TNF is large in the mammalian literature it is also found in the marine sponge, niformis , and theora spp. . Remarka forbesi . Among i forbesi , so conc forbesi , 21, as  forbesi , IL-8 [2 forbesi , IL-12 [ forbesi  and IL-1 forbesi .The earliest published record relevant to this story seems to have been the observation by Maegraith in his 1948 monograph  that theAlthough cytokines now loom large in immunology, awareness of them emerged from curiosity about the useful and harmful (endotoxic) effects of bacterial endotoxin. Originally found, as noted above, in cell walls of gram-negative bacteria, endotoxin proved to be a form of lipopolysaccharide (LPS). The term was used interchangeably with LPS for many years, the later taking precedence in recent times. Endotoxin/LPS influences larger life forms in what can seem a bewildering number of ways. Intriguing early phenomena included the in vivo tolerance to its effects observed after a second and subsequent injections . AnotherMycobacterium bovis termed Bacillus Calmette-Guérin (BCG), which greatly sensitizes mice to LPS [Further complexity arose from the long history of experimental anti-tumor activity  of bactee to LPS . Combinie to LPS , to the e to LPS  proved te to LPS . As far e to LPS . These tWork on the identity of endogenous pyrogen  As we have recently reviewed in this context , from thWhile the principles governing cytokines in innate immunity and disease pathogenesis were being elucidated it was not yet appreciated that TNF and IL-1 are crucial for physiological homeostasis . IL-1 isAs has been reviewed , in the This proposal could not, of course, be properly tested until peptides such as cytokines became available in recombinant form, and in sufficient quantity for in vivo use. Since TNF was first recognized for its tumor killing properties, ambitions were high in the late 1980s that rTNF  would beMycobacterium spp. [Brucella abortus [Salmonella spp. [Listeria monocytogenes [Leishmania spp. [Toxoplasma gondii [Thus developed the view that infectious disease is caused not directly by the invading pathogen, but by the host’s excessive innate immunity to it. The concept eventually spread across the board to include both innate immunity and disease pathogenesis in, for example, infection with ium spp. , 70, Bru abortus , 72, Sallla spp. , 74, Lisytogenes , 76, Leinia spp. , 78, Toxa gondii , 80, anda gondii , 82. Thia gondii , is now Many reports link high circulating levels of TNF and IL-1, arguably the original hallmarks of a cytokine storm, with the biochemical details of the pathogenesis of clinical markers of critically ill patients. A random illustration is hypoalbuminemia, an independent marker of poor outcomes in severely ill patients with various diagnoses , as receModerate, but persistent, cytokine storms are typical of the chronic neurodegenerative states, including post-stroke, post-traumatic brain injury, and Alzheimer’s disease (AD). In the weather analogy, cytokines are what water is to life. Light falls of rain, like low levels of TNF and IL-1, keep physiology ticking over and organisms alive. In moderate amounts rain improves outcome, as does self-limiting innate immunity, but in acute excess, or unrelenting moderate amounts, both rain and cytokines can kill. Both the acute and unrelenting patterns are valid cytokine storms. Acute systemic cytokine excesses, ie those outside the central nervous system (CNS), typically arise from the effects from bacterial or viral PAMPs, and are, if not acutely fatal, generally transient. In contrast, when excess cytokine is generated within the brain in sufficient quantities, as distinct from entering from outside, the usual type of cytokine storm is an unrelenting moderately raised activity that leads to non-resolving inflammation.This non-resolving pattern of inflammation in the brain is consistent with the consequences of injecting LPS, the prototype TNF inducer, and a TLR4 agonist, in order to generate models of neurodegenerative disease in rodents . FollowiSince brain function determines subtleties such as personality, behavior, executive function, mood, willpower, learning and memory, we can expect the effects of brain TNF excess to be much more nuanced than the same change in the rest of the body. This is indeed what happens. For example, mice without certain TNF receptors do not become aggressive . The ori++ homeostasis, and thus the ionic signaling cascades on which normal function of these cells depends [As we have noted , physiol depends . Likewis depends .As we have recently reviewed , high brMuch relevant material on TNF and the brain that space considerations preclude here is contained in our 2010 review . This teLikewise, developments in cytokine biology have meant that Aβ, for decades almost universally argued to be central to understanding AD, has lost momentum as the key to this disease as well as its proposed importance in post-stroke syndromes  and trauMore recently a technically impressive study  has idenBorrelia duttonii, the cause of African East Coast Relapsing Fever, as a consequence of treating patients with penicillin [The obvious inference from the literature reviewed here is that patients suffering from cytokine storms should be treated by neutralize the offending cytokines, mainly TNF. It was, however, established quite early in mice  and babonicillin . Treatmenicillin . There inicillin .Chronic inflammation is quite another story. Some 5 years ago Karl Nathan reviewed the therapeutic challenge represented by resolving compared to non-resolving inflammatory diseases . SpecifiA useful starting point to discussing this topic is the 1986 report  that TNFNevertheless, while this literature provides an excellent rationale for the therapeutic use of specific anti-TNF biological agents, there may be an opportunity, particularly regarding IL-1, that is still relatively neglected. There is ample evidence that in certain circumstances IL-1, induced by TNF , can itsTNF and IL-1 received roughly equal prominence in the earlier literature, but neutralizing the effects of TNF has largely dominated the therapeutics literature. In part this may be because specific anti-TNF biologicals have been freely available laboratory and clinical tools, since the mid 1980s and mid 1990s respectively. Two specific anti-IL-1β biologicals, canakinumab  and anotIn conclusion, we note that these recent implications of cytokine storms for understanding encephalopathies usefully allow disease pathogenesis to be appreciated as a single entity through bridging the gap between TNF in systemic disease and the brain, as well as encompassing infectious and non-infectious disease on both sides of the blood-brain barrier. This promises new therapeutic perspectives for important cerebral disorders, particularly chronic neurodegenerative states, as exemplified by new data linking cerebral TNF and extra-cellular glutamate referred to earlier in this text. As we have summarized , cerebra


Trypanosoma brucei and T. congolense is characterized by parasitemia waves coupled with the production of systemic levels of TNF. This cytokine is known to control T. brucei growth, but also to contribute to tissue damage, shortening the survival time of infected mice. Using a dominant-negative version of TNF to discriminate between the effects of the membrane-form versus the soluble form of TNF, we show that the second form is involved in neither parasite control nor induction of liver injury. Therefore, soluble TNF is likely not a major contributor to disease outcome. We propose that membrane-bound TNF is responsible for both T. brucei control and host pathology.Infection of C57Bl/6 mice by pleomorphic African trypanosomes  Both the membrane-bound (mTNF) and the soluble form of TNF (sTNF) require homotrimerization to be functional2.Tumor necrosis factor alpha (TNF) is a transmembrane protein that can be released from the myeloid cells surface as a soluble form by a metalloprotease, the tumor necrosis factor-α-converting enzyme (TACE)3, which triggers the classical activation of myeloid cells (M1). Together with other defense molecules such as nitric oxide or reactive oxygen species , these activated myeloid cells produce TNF as a major effector to control the growth of these extracellular parasites6. Yet, this inflammatory response can induce severe tissue pathogenicity, with TNF levels being correlated to disease severity in the host. In T. brucei infected TNF−/− mice (lacking both sTNF and mTNF) parasitemia is higher than in wild-type (WT mice), but the immunopathology is reduced6. During T. congolense infections, the parasitemia is uncontrolled in TNF −/− mice, and survival is drastically reduced6.In different African trypanosome infection models, control of the protozoan parasite burden in mammalian hosts is mediated by interferon-gamma (IFN-γ)T. brucei and T. congolense growth in mice are not yet known.However, the respective roles of mTNF and sTNF in controlling T. brucei growth7. Accordingly, a T. brucei cell line rendered defective in cAMP synthesis following transgenic expression of a constitutive dominant-negative adenylate cyclase (ESAG4 DNc) develops parasitemia levels 100 times lower than control (CTRL) parasites. Such attenuation of ESAG4 DNc parasite growth is fully reverted in TNF −/− mice7.African trypanosomes can limit the parasite-controlling function of TNF by inducing the production of cyclic AMP (cAMP) in myeloid cells such as macrophages and inflammatory monocyte-derived cells, thereby reducing their capacity to synthesize TNF and restrict T. brucei infection, TNF is posited to control parasite growth in the host by its direct toxicity after uptake into trypanosomes9, although this toxic effect could not be reproduced in axenic culture for either T. brucei or T. congolense10. The activity of recombinant sTNF was not dependent on the TNF receptor binding site of TNF homotrimer, but relied on the TNF lectin-like activity. This activity is mediated by the so-called TNF TIP domain located opposite of the TNF-R binding site of the molecule8. Given the lytic effects of recombinant sTNF on T. brucei, it was proposed that sTNF is the main effector of trypanosome control10.During T. congolense and is thought to control T. congolense growth through induction of nitric oxide6. Therefore, the T. congolense model is an attractive complement to our investigations using T. brucei.However, sTNF is not directly toxic to 11. XPro1595 sequesters the native sTNF into inactive heterotrimers and prevents its signaling through TNF receptors, while it preserves the signaling of mTNF, mostly through TNFR212. These properties make it a useful reagent to assess the specific effects of sTNF independent of mTNF13. Here, we used this tool to investigate the role of sTNF during the early infection by T. brucei or T. congolense parasites, since this is the stage where the absence of TNF appears to exhibit the most drastic effects.To further address this sTNF hypothesis, we used the biologic XPro1595, a dominant-negative variant of sTNF that is modified at key amino acids involved in the interaction with the TNF receptors TNFR1 and TNFR2T. brucei infected mice are a thousand-fold more sensitive to lipopolysaccharide (LPS) challenge than non-infected mice4. To validate the efficacy of XPro1595 treatment, which blocks sTNF activity, we tested the sensitivity of C57Bl/6 mice infected with CTRL T. brucei with or without treatment with XPro1595. The infected mice that did not receive XPro1595 treatment all died within 48 h of the LPS challenge, whereas all treated infected mice survived  sTNF activity is efficiently blocked in vivo by XPro1595, and (iii) a soluble TNF inhibitor protects against a lethal endotoxin challenge13.Due to the induction of TNF expression, ved Fig. . This co8, which is present in XPro1595. As such, we tested the ability of two forms of sTNF to kill T. brucei parasites in vitro, in comparison with human TNF (hTNF) and murine TNF (muTNF). No effect of either of these TNF molecules was observed on T. brucei, whereas both hTNF and muTNF killed L929 cells  parasitemia, respectively. As reported, host survival increased three-fold in mice infected by ESAG4 DNc parasite compared to the CTRL infected mice7. In contrast with the full reversal of the parasitemia and survival phenotype observed in TNF −/− mice7, treatment with XPro1595 did not affect the parasitemia in mice infected with CTRL or ESAG4 DNc parasites  with CTRL or ESAG4 DNc ion Fig. .Figure 314, an increase of serum alanine aminotransferase (ALT) and aspartate transaminase (AST) activity, both markers of liver damage with AST also reflecting damage to other organs, was observed at day 7 post-infection in T. brucei CTRL-infected mice  and CD11b+CD11c+ inflammatory dendritic cells (IDC), coupled with a decrease of the macrophage (CD11b+Ly6C−) population. Neither the accumulation of IM or IDC, nor the decrease of macrophage numbers was affected upon treatment of infected mice with XPro1595  increased in acute/trypanosusceptible infection by CTRL T. brucei. The mechanism behind this slight increase in survival is unclear, we speculate it may be through increased TNFR2 signaling, which is not blocked by XPro1595 and can be protective during T. brucei infection21.However, contrary to what was observed in other models of infection or inflammationT. brucei infection, hereby delaying the mortality of trypanosusceptible mice.Though ALT levels were not affected upon treatment with XPro1595, we speculate that the slightly decreased AST level in the later stage of infection may reflect lower systemic inflammation of the host. Considering that AST can reflect brain damage, we cannot exclude that blockade of TNF by XPro1595 treatment reduce blood brain barrier damage occurring during T. brucei in mice21 and the fact that the systemic TNF response could be decreased without affecting parasitemia control during African trypanosome infection22. Our data contradict previous in vitro studies reporting a direct killing effect of sTNF on T. brucei through cytokine uptake by the parasite9. Significantly, we used the same methodology and strain of parasite as employed in the previous studies, the only experimental difference appears to be the source of sTNF. These previous studies also proposed that the lectin-like domain of TNF, located opposite of the TNF-R binding site, is involved in T. brucei killing. This hypothesis was supported by the observation that in vivo treatment of T. brucei infected mice with antibodies neutralizing the TNF TIP domain lectin-like activity resulted in increased parasitemia9. Although this domain is present in the XPro1595 recombinant protein11, no effect on trypanosome growth in mice was observed here following treatment with up to 50 mg/kg XPro1595, which is over the cytokine concentration used in the previous toxicity studies9.Altogether, these data indicate that sTNF does not play a direct role in the control of African trypanosome parasitemia and although AST levels tend to decrease in XPro1595 treated mice, sTNF does not appear to play a major role in early stage infection induced pathogenicity. The absence of an effect of sTNF on parasite growth could have been expected given the presence of high systemic TNF levels during the entire course of infection by 10, we also could not reproduce the trypanolytic in vitro effect of sTNF on T.brucei. As is, it is difficult to speculate why we, and others, failed to reproduce these results. However, if the trypanolytic effect is so sensitive to any change, it raises the question on how broadly it could represent or be significant in an actual physiological response. Moreover, T. congolense has been shown to be insensitive to a direct in vitro lytic effect of recombinant sTNF6. Therefore, we are bound to conclude that the trypanolytic effect of sTNF does not occur in vivo nor in vitro. Moreover, in a more general context, TNF-dependent control of the early parasite load observed in TNF −/− mice does not likely rely on sTNF. Rather, mTNF could contribute to reduce the in vivo fitness of the trypanosome, for example by increased production of reactive nitrogen or oxygen species, facilitating parasite killing by myeloid cells24.As others previouslyin vitro and in vivo. It is tempting to speculate instead that the control of early parasitemia may be ascribed to mTNF in both trypanosusceptible and trypanotolerant African trypanosome infection mouse models.Collectively, our data clearly reject the hypothesis of a trypanolytic effect of sTNF both Experiments, maintenance and care of C57Bl/6 mice (Janvier) complied with guidelines of the European Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes (CETS n°123) and were approved by the Ethical Committee for Animal Experiments of the Université Libre de Bruxelles, Brussels, Belgium (laboratory accreditation number LA2500482).4 pleomorphic parasites 7, T. congolense variant antigenic type 1325. Infection parameters  were monitored as previously described7.The mice were inoculated by intraperitoneal (ip) syringe injection of 1026. Recombinant murine and human TNF  were from Peprotech .The dominant-negative TNF (XPro1595) was obtained from Xencor, Inc. It was delivered ip at the specified concentration, or at 10 mg/kg when not specified, one day before the infection and twice a week until day 50 post infection. Injections started one day before the infection as sTNF has been detected at the site of injection one hour after infection4. Briefly, mice were infected intraperitoneally (ip) with CTRL T. brucei and treated, or not, with XPro1595 (10 mg/kg) twice per week. After 14 days of infection, mice were injected ip with 5 µg of LPS and their survival was monitored for one week.LPS challenge was assessed as previously described14. Alanine aminotransferase (ALT) and aspartate transaminase (AST) levels were measured following the manufacturer’s instructions (Boehringer Mannheim).Liver mononuclear non-parenchymal cells were isolated and analyzed by FACS as previously described2 humid atmosphere. T. brucei parasites for in vitro assays were purified from mice and cultured as in8. TNF lysis tests were performed as in8, using a unique 50 ng/ml TNF concentration. FACS analysis was performed using FlowJo 10. Statistical analyses were performed in Graphpad Prism, sample size is indicated in the figure legends. Results are expressed as mean ± SEM . Statistics were assessed by Log-rank test for survival curves, by Kruskall-Wallis one-way analysis of variance followed by Dunn’s post-test or two-way ANOVA followed by Dunnett corrected post-test.L929 mouse fibroblasts cells were cultured in RPMI supplemented by 10% heat-inactivated fetal calf serum, L-glutamine (0.3 μg/ml), streptomycin (0.1 mg/ml), penicillin (100 U/ml) and kept at 37 °C in a 5% COThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.