Here, we tested the hypothesis that systemic upregulation of TNF-α induces neuroinflammation and behavioral changes relevant to depression. We report that a single intraperitoneal injection of TNF-α in mice increased serum and brain levels of the proinflammatory mediators TNF-α, IL-6, and MCP-1, in a dose- and time-dependent manner, but not IL-1β. Protein levels of the anti-inflammatory cytokine IL-10 increased in serum but not in the brain. The transient release of immune molecules was followed by glial cell activation as indicated by increased astrocyte activation in bioluminescent Gfap-luc mice and elevated immunoreactivity against the microglial marker Iba1 in the dentate gyrus of TNF-α-challenged mice. Additionally, TNF-α-injected mice were evaluated in a panel of behavioral tests commonly used to study sickness and depressive-like behavior in rodents. Our behavioral data imply that systemic administration of TNF-α induces a strong sickness response characterized by reduced locomotor activity, decreased fluid intake, and body weight loss. Depressive-like behavior could not be separated from sickness at any of the time points studied. Together, these results demonstrate that peripheral TNF-α affects the central nervous system at a neuroimmune and behavioral level.Clinical observations indicate that activation of the TNF- Clinical depression is a chronic, disabling psychiatric condition that affects over 120 million people worldwide , cancer α (TNF-α) is one of the key cytokines involved in the pathogenesis of inflammation-associated depression. Recent meta-analyses confirmed that circulating levels of TNF-α are significantly higher in depressed patients compared with healthy control subjects ).Astrocyte activation in 10-week-old male Gfap-luc mice was quantified before (baseline) and at 2 h, 6 h, 24 h, 48 h, 72 h, and 96 h after i.p. administration of either 0, 63, or 250 eviously , 35. Briμg/kg TNF-α (n = 8 per group) and tissue was collected 24 h later. Immunohistochemical staining of Iba1 protein in the dentate gyrus of the hippocampus was performed using a rabbit polyclonal anti-Iba1 primary antibody  and a fluorescent Alexa 555 goat anti-rabbit secondary antibody , as previously described [10-week-old male FVB mice were injected i.p. with vehicle or 250 escribed .All behavioral tests were performed on separate groups of 10-week-old male NMRI mice. The OFT and FST setups were custom-made and were described in detail previously .μg/kg TNF-α (n = 10 per group) and repeatedly tested at 2 h, 6 h, 24 h, and 48 h after administration. Two of the 10 mice that received 1000 μg/kg TNF-α died during the first 24 h after injection. Therefore, it was decided to take 250 μg/kg TNF-α as the highest test dose for all experiments and to repeat the OFT with a vehicle and 125 μg/kg TNF-α group.Two independent OFTs were performed in this study. In the first OFT, mice were injected with 0, 63, 250, or 1000 n = 10 per group) were injected with 0, 63, 125, or 250 μg/kg TNF-α and tested at 2 h, 6 h, 24 h, and 48 h after administration.In the FST, mice  fitted with two 250 mL drinking bottles and ad libitum access to food. Each bottle contained either filtered tap water or a 5% sucrose solution. The location of the bottles on the cage was randomized during every exposure session with half of the animals receiving sucrose on the left and half on the right. The SPT protocol lasted for 5 days and consisted of a familiarization and a test phase. The familiarization phase started on day 1 by exposing all mice to one water- and one 5% sucrose-filled bottle (W/S) for 24 h. On day 2, the animals had free access to two water-filled bottles (W/W) until 4:00 p.m., after which they were fluid-deprived overnight. The test phase started on day 3 by injecting mice i.p. with 0, 63, 125, or 250 μg/kg TNF-α (n = 10 per group). To test the effects of TNF-α at 2 h, 6 h, 24 h, and 48 h, the animals were presented with W/S during a 1 h exposure period at these time points. Mice were fluid-deprived in between exposure periods. In order to avoid a protracted deprivation period between the 24 h and 48 h time points, mice were given access to W/W from 4:00 to 5:00 p.m. on day 4.The sucrose preference test (SPT) started by single-housing the animals in individually ventilated cages . At 6 h after TNF-α, mice from the 6 h time point group were exposed to W/S for a 1 h period. Animals from the 24 h time point were allowed to drink W/W for 1 h at 6 h after TNF-α in order to avoid a protracted deprivation period between TNF-α administration and the 24 h time point. At 24 h, mice from the 24 h group were presented with W/S during a 1 h exposure period.In the SPT study using a within-subject design it became clear that exposing the thirsty animals to W/S at 2 h affected the total volume intake at 6 h (less thirsty). To exclude that the effects of TNF-In both SPT studies, the amount drunk by a mouse was determined by subtracting the weight of a bottle at the start of an exposure period and at the end (taking fluid density as 1 g/mL). Total fluid intake was calculated as the total change in volume from both bottles combined. A fluid intake that was greater than the mean +2x standard deviation was considered to be an invalid measure that probably resulted from leaking bottles. Invalid measures were replaced by the group mean of the relevant solution (water or sucrose). This occurred for less than 4% of all bottle measurements. Sucrose preference was calculated as the percentage of consumed sucrose solution of the total fluid intake.ε) was used for repeated measures analysis [ε and the significance of the F-ratio is evaluated with the new degrees of freedom, resulting in a more conservative statistical test. To account for the skewness of the data distribution, bioluminescence measurements and cytokine concentrations were log-transformed prior to analysis. ANOVAs and rmANOVAs were considered statistically significant if P < 0.05. When appropriate, post hoc comparisons were made by using an independent samples t-test with a Bonferroni-corrected P value. For consistency between the analysis and the visualization of bioluminescence measurements and cytokine concentrations, the group means and its standard error of the mean (SEM) were back-transformed and visually presented on a logarithmic scale. All other data are expressed as mean ± SEM on a linear scale.SPSS Statistics software version 20 (IBM Inc.) was used for data analysis. Analysis of variance (ANOVA) or repeated measures ANOVA (rmANOVA) was performed to determine the statistical significance of differences between treatment groups. To correct for potential violation of the sphericity assumption, a Greenhouse-Geisser correction epsilon  = 13.4, P < 0.001; TNF-α: F = 15.7, P < 0.001; MCP-1: F = 7.2, P < 0.001), a main effect of time and dose on serum levels of IL-10  = 5.3, P < 0.01; dose: F = 4.8, P < 0.01), and a main effect of dose on serum levels of IL-1β  = 3.2, P < 0.05) and IFN-γ  = 5.8, P < 0.01). Post hoc analysis demonstrated that serum levels of IL-6, TNF-α, and MCP-1 peaked at 2 h after systemic injection of TNF-α and then gradually waned over time  = 6.4, P < 0.001; TNF-α: F = 70.2, P < 0.001; MCP-1: F = 15.4, P < 0.001) and a main effect of dose on IFN-γ levels  = 4.0, P < 0.05). No significant effect of time or dose could be detected on brain levels of IL-1β or IL-10. Post hoc analysis revealed that brain levels of IL-6 and TNF-α peaked at 2 h and had dissipated by 6 h  = 5.8; P < 0.001; ε = 0.53) for photons emitted per second in the brain ROI. Post hoc analysis demonstrated that, at 6 h after administration, a strong bioluminescent signal was present in the brain of TNF-α-injected mice  = 7.3, P < 0.05) (In order to confirm TNF-pression –39. Pair < 0.05) .μg/kg TNF-α died within 24 h after administration. These subjects were removed from the analyses, resulting in a group size of n = 8 for this dose. rmANOVA showed a time × dose interaction for change in body weight  = 24.9; P < 0.001; ε = 0.95). Post hoc analysis demonstrated that there was a dose-dependent weight reduction at 24 h and 48 h after TNF-α administration  = 10.2; P < 0.001; ε = 0.70). At 2 h after systemic application, TNF-α reduced locomotor activity in a dose-dependent manner  = 6.0; P < 0.05; ε = 0.74) and a main effect of time  = 14.2; P < 0.001; ε = 0.66) and dose  = 7.0; P < 0.05; ε = 0.66) for distance travelled in the OFT. Post hoc analysis revealed that the weight of mice injected with 125 μg/kg TNF-α was reduced at 24 h, but not anymore at 48 h  = 20.4; P < 0.001; ε = 0.77) and immobility time  = 38.0; P < 0.001; ε = 0.75). Post hoc analysis showed that compared to the 2 h time point all animals swam less and remained immobile longer at 6 h, 24 h, and 48 h after TNF-α  = 26.0; P < 0.001; ε = 0.98) and dose  = 4.0; P < 0.05; ε = 0.98) for total volume intake. Moreover, there was an effect of time  = 23.5; P < 0.001; ε = 0.96) and a trend for dose  = 2.6; P = 0.07; ε = 0.96) for sucrose preference. At 2 h and 6 h after administration, animals that were injected with 250 μg/kg TNF-α drank significantly less than vehicle-treated controls, while mice that received 125 μg/kg TNF-α only showed reduced fluid intake at 6 h  = 5.7, P < 0.05) and dose  = 9.0, P < 0.001) for total volume intake and a main effect of dose  = 3.3, P < 0.05), but not time, for sucrose preference. Post hoc analysis revealed that using naive animals for each time point stabilized total volume intake in mice injected with vehicle , and the cell type studied [α on IFN-γ levels in vivo are poorly described in literature. Our study did not detect a robust effect of TNF-α administration on IFN-γ levels in either serum or brain.Severalerferons –49. Howe studied . Effectsα influences these processes in several ways. For example, TNF-α increases the permeability of the BBB [α stimulates the release of the chemokine MCP-1, which increases BBB permeability even further and subsequently drives the infiltration of leukocytes into the brain [α, IL-6, and MCP-1 transiently increased in response to peripheral TNF-α administration. As we did not assess the integrity of the BBB, it is not clear whether these immune mediators entered the brain through a leaky BBB and/or if they were actively produced and released locally in the brain. It is possible that a fraction of the measured brain cytokine levels originated in the periphery. However, several studies described that bolus injected TNF-α is rapidly cleared and has a short half-life of up to 20 minutes [α administration [Cytokines from the periphery can pass the BBB through various mechanisms and access the brain . TNF-α i the BBB , therebyhe brain . Accordi minutes –56. Thesstration  indicateα caused a robust release of MCP-1 both in serum and in whole brain tissue. As MCP-1 is preferentially expressed in the hippocampus and other neuroanatomical regions linked to depressive symptoms [Besides elevated concentrations of proinflammatory cytokines, depressed patients frequently display increased circulating levels of MCP-1 , 58. Thisymptoms , it may α was further characterized using a transgenic mouse line that expresses luciferase under the transcriptional control of the Gfap promoter. GFAP is an intermediate filament protein that is predominantly expressed by astrocytes, and its expression is upregulated when astrocytes are activated [α caused a strong dose- and time-dependent activation of astrocytes. This TNF-α-induced astrocyte activation occurred after the peak release of proinflammatory cytokines and lasted for 2 days, thereby suggesting that the brain sequelae to a peripheral immune challenge may propagate in absence of the initial stimulus.The neuroinflammatory response to peripheral TNF-ctivated . These Gα injection increased Iba1 immunoreactivity in the hippocampus of FVB wild-type mice. This indicates that, in addition to astrocytes, microglia also show signs of activation following peripheral TNF-α administration.Although quantification of glial cell activation using bioluminescence imaging offers numerous advantages, this technique does not allow for spatial discrimination of specific brain regions. However, previous work has shown that neuroinflammatory responses to a peripheral immune challenge are brain region specific –66. To cα and IL-1β, but brain levels of these cytokines were not elevated at the time point at which we observed microglial activation. This may partly be explained by the fact that we quantified cytokine levels in the whole brain and not in specific brain regions. Moreover, assessing protein levels of cytokines in the brain is hampered by the limited sensitivity of available quantification techniques. This problem could be overcome by quantifying transcript expression using quantitative PCR, which is more sensitive approach compared to measuring protein levels of immune mediators in brain tissue. Cytokine as well as chemokine activity, however, is not only limited by gene expression, but also regulated at the posttranscriptional and posttranslational level [α administration [Activated microglia are known to release proinflammatory cytokines, particularly TNF-al level , 70, 71.al level . Irrespestration .α administration. After confirming that peripheral injection of TNF-α induces a central inflammatory response, we evaluated the time course of TNF-α-induced behavioral changes across a panel of assays commonly used to study sickness and depressive-like behavior in rodents. Our behavioral data demonstrate that TNF-α dose dependently induces sickness during the first 24 h after systemic administration. This could be seen as a decrease in body weight, reduced exploration in the OFT, and suppressed drinking in the SPT. In contrast to i.c.v. administration [α did not affect measures of behavioral despair in the FST. Moreover, mild signs of anhedonia observed in the SPT overlapped with the time course of sickness and can therefore be considered biologically irrelevant. One limitation in our study is the within-subject design for the individual behavioral paradigms. This approach allowed reduction of animal numbers but also led to habituation of the mice to some of the experimental paradigms. Such habituation effects were observed in vehicle-injected control animals upon retesting in the FST  and in the SPT . To rule out that effects of TNF-α were missed because of habituation during retesting, the SPT study was repeated using separate groups of naive animals for the 6 h and 24 h time points. From this experiment it also became clear that peripheral TNF-α administration induced sickness, but not anhedonia. Taken together, the behavioral data indicate that acute systemic injection of TNF-α is not a reliable model to induce depressive-like behavior in mice. Based on the strong but short-lasting effects of TNF-α on neuroinflammation and behavior, it may be possible that prolonged or intermittent administration of TNF-α, leading to chronic upregulation of cytokines, offers a more valid approach to study depressive-like behavior in rodents. Such chronic TNF-α administration would mimic the human situation where inflammation-associated depression is believed to develop on a background of sustained, low-grade inflammation.To our knowledge, no study has systematically assessed the time course of sickness and depressive-like behavior following systemic TNF-stration , 31, perα induced a strong but temporal release of immune mediators in the circulation and the brain. This release of inflammatory factors was followed by glial cell activation, as measured by astrocyte activation in the Gfap-luc mouse and increased Iba1 immunoreactivity in the hippocampus of FVB wild-type mice. Additionally, systemic administration of TNF-α led to a strong sickness response and mild signs of anhedonia. Due to the overlapping time course of these behavioral states it was not possible to unambiguously distinguish depressive-like behavior from sickness. Taken together, these results demonstrate that TNF-α in the periphery affects the central nervous system by inducing neuroinflammatory processes and behavioral changes.The present set of experiments using a variety of techniques and readouts showed that systemically administered TNF-


The concentration of TNF-α in plasma and of glutamine in plasma and the small intestine was measured. The fractional synthesis rate (FSR) of protein and the mRNA and protein expression levels of ASCT2 in the small intestine were assessed. The level of TNF-α was highest in the TNF-α group and the glutamine concentration was elevated to a greater extent in the TNF-α group than in the other two groups. However, the FSR of protein in the small intestine was significantly higher in the Gln group compared with that in the TNF-α group. The mRNA and protein expression levels of ASCT2 in the experimental groups were significantly higher that those in the control group, but did not differ significantly between the Gln and TNF-α groups. These results indicate that TNF-α may attenuate glutamine-stimulated protein synthesis in the small intestine in the early stage of sepsis in rats. The mechanism may be that TNF-α inhibits the function of the glutamine transporter in the uptake the glutamine into target cells for protein synthesis. This inhibition may occur at or following protein translation.The objective of this study was to examine whether and how TNF-α affects glutamine-enhanced protein synthesis and the expression of the amino acid transporter ASCT2 in the small intestine at the mRNA and protein levels. A total of 30 male Sprague-Dawley rats were randomly assigned into three groups, namely the total parenteral nutrition , glutamine-treated (Gln), and glutamine- and tumor necrosis factor-α (TNF-α)-treated (TNF-α) groups. At 30 min prior to examination, all rats were mainlined with [L- Sepsis is the leading cause of mortality in intensive care units around the world . The cor+-dependent neutral amino acid transport system Ala-, Ser- and Cys- preferring (ASC), which has established a transporter superfamily : 50 g sucrose, 20 g casein-vitamin free, 15 g corn starch, 5 g powdered cellulose, 5 g corn oil, 3.5 g AIN-76A mineral mix, 1 g AIN-76 vitamin mix, 0.3 g DL-methionine, 0.2 g choline bitartrate, and 0.001 g ethoxyquin. The protocol and animal use were approved by the Animal Research Committee of Nanjing University . All procedures were carried out in accordance with the principles of the Guide for the Care and Use of Laboratory Animals .A total of 30 male Sprague-Dawley (SD) rats, weighing 120–180 g , were used. For 7 days prior to the experiments, the rats were housed in a temperature-, humidity-, and light-controlled environment on a 12 h light/12 h dark cycle and were allowed standard rat chow and water et al  and saline was infused at an initial rate of 5 ml/100 g body weight per hour.Following an overnight fast, the rats were anesthetized with an intraperitoneal injection of ketamine (100 mg/kg body weight). The total parenteral nutrition (TPN) model was constructed, and a rotary transfusion apparatus was used for TPN infusion as described by Eizaguirre et al . ImmediaAs the aim of this study was to investigate whether TNF-α inhibits the regulatory effect of glutamine on protein synthesis in the small intestine, it was first tested whether glutamine stimulated protein synthesis in the small intestine and then whether this stimulation was attenuated by TNF-α was examined. Thus, after 24 h of catheterization, the rats were randomly divided into the following three groups, each containing 10 rats: TPN (control), Gln (treated with glutamine) and TNF-α (treated with glutamine and TNF-α). The rats in the control group received TPN; the Gln group was given glutamine-enriched TPN, and the TNF-α group was received glutamine-enriched TPN followed by intravenously infused TNF-α for the last 24 h of the experiment. The parenteral nutrition solution was infused via the infusion pump at a constant rate of 1.5–2 ml/h. The TPN solutions were isonitrogenous and isoenergetic, containing 3.8 kJ/ml as a 60% carbohydrate and 40% lipid solution, representing ~502 kJ/kg/day and 0.75 g nitrogen/kg/day.®, Fresenius Kabi AB, Uppsala, Sweden). The detailed compositions of the two solutions are shown in The basic formulations included amino acids , glucose and lipids . The TPN mixture contained no glutamine, whereas the glutamine-enriched TPN mixture contained 0.15 g nitrogen per liter and was supplied by Dipeptiven .The TPN mixture solutions were freshly prepared aseptically in a laminar flow hood each day. Nutrients were continuously infused for 3 days. Recombinant human TNF-α  was diluted in 0.9% saline and infused intravenously for the final 24 h at a rate of 5 μg/kg/h, in accordance with a previous study . Thirty The rats were sacrificed by decapitation after 72 h of TPN. Blood was collected into lithium heparin tubes and centrifuged at 3,000 × g at 4°C for 10 min. The plasma was decanted and stored at −70°C until analysis. The small intestine was rapidly dissected, and the dissected small intestine was plunged into an ice-water slurry (0°C–4°C), at which time it was assumed that all metabolic processes including protein synthesis had ceased or were negligible. Samples of the small intestine were then weighed, flash-frozen in liquid nitrogen, and subsequently stored at −70°C until analysis.The plasma level of TNF-α was analyzed with a sandwich enzyme-linked immunosorbent assay from PeproTech EC Ltd. and performed according to the manufacturer’s instructions.The glutamine concentration in plasma and tissue from the small intestine, extracted from a homogenate, were measured using high-performance liquid chromatography, as described previously .et al . The protein pellets were washed twice in 10–12 ml 0.2 mol/l perchloric acid followed by centrifugation  to remove contaminating free leucine. The pellets were then digested in 10 ml 0.3 mol/l NaOH at 37°C for 1 h, re-precipitating with 4 mol/l perchloric acid to further disrupt any cellular compartments that still contained free leucine. The pellets were then washed eight times with 0.2 mol/l perchloric acid and hydrolyzed in 3.0 ml 6 mol/l HCl for 24 h at 105°C. The hydrolysates were vacuum-dried. Dried residues were then suspended in 3.0 ml sodium citrate buffer , and samples were stored at −4°C until processed for measurement of the enrichment of protein-bound leucine (EB) , the supernatant was decanted for subsequent neutralization and measurement of the enrichment of free amino acids (Eine (EB) .The fractional synthesis rate (FSR) of protein, defined as the percentage of tissue protein renewed by synthesis each day, was calculated by the following equation :B is the enrichment of 15N of protein-bound leucine, EF is the enrichment of tissue-free leucine, and t is the period from injection of the isotope to the immersion of the tissue into iced water. The enrichment of 15N of protein-bound and tissue-free leucine was detected by mass spectrometry .ETotal RNA was extracted from each 0.5 ml purified sample using TRIzol , and stored in 1 ml dehydrated alcohol at −80°C. The specific PCR primers and fluorescently-labeled probes that were used are listed in Protein samples (5 μg) were electrophoretically separated on SDS polyacrylamide gel and transferred to a polyvinylidene difluoride membrane that was blocked with blocking solution (5% w/v skimmed milk) at room temperature for 2 h. The membrane was then incubated overnight with the goat anti-mouse polyclonal antibody to ASCT2  diluted in Tris-buffered saline with 0.1% Tween 20 (TBST). Then, the membrane was incubated with donkey anti-sheep IgG-HRP antibody  at room temperature for 1 h and was treated with the enhanced chemiluminescence western blot (ECL-WB) assay . Semiquantitative assessment of bands was performed densitometrically using Quantity One software . The detection and assessment procedure were repeated at least three times.Data are presented as the mean ± standard error of the mean. One way analysis of variance followed by the Student-Newman-Keuls test was used to assess the means among multi-groups while the Dunnett’s T3 test was used to assess pairwise multiple comparisons. Statistical Package for Social Sciences  version 11.0 was used for data analysis. Statistical significance was accepted at the P<0.01 level.The plasma levels of TNF-α in the three groups are presented in The plasma levels of glutamine are also presented in The concentration of glutamine in the small intestine is shown in As shown in As shown in The protein expression of ASCT2 was identified in all groups . Among tSepsis has become a major worldwide health problem due to the continual increase of its incidence, as well as its high mortality rate –29. TherIn the present study, the changes in rats induced by an intravenous injection of TNF-α were investigated. The results showed the plasma TNF-α rose to a high level and was similar to that observed in sepsis . The preTherefore, the high concentration of extracellular glutamine fails to be utilized well under infection conditions. As an amino acid, the process of transmembrane transport depends on the normal structure and function of glutamine transporters. The first glutamine transporter was isolated more than 40 years ago, and large numbers of transporters have now been isolated –40. ASCTThe present study also demonstrated that TNF-α had a modest promoting effect on the expression of ASCT2 mRNA, which indicates the suppressive effect of TNF-α did not occur at the transcription level. There are two major methods by which protein function is regulated: one is the alteration of expression while the other is conformational change at the functional site. The present study revealed that although TNF-α suppressed the expression of the ASCT2 glutamine transporter protein, the magnitude of the reduction was not significant, which indicated that the suppression of transporter function may be accompanied by structural changes to the transporter. However, the molecular mechanism of the effect of TNF-α on glutamine utilization remains unclear.In conclusion, the present study suggests that TNF-α attenuates the glutamine-induced stimulation of protein synthesis in the small intestine. The mechanism may be that TNF-α inhibited the function of glutamine transporters in the uptake the glutamine into target cells for protein synthesis. This inhibition may occur at or after protein translation. However, additional studies on whether TNF-α affects the structure of glutamine transporters and on how TNF-α affects the structure of glutamine transporters are required.


Bacillus anthracis is the etiological agent of anthrax. Lethal toxin (LT) produced by B. anthracis is a well-known key virulence factor for anthrax because of its strong cytotoxic activity. However, little is known about the role of B. anthracis genomic DNA (BAG) in anthrax pathogenesis.B. anthracis spore infection in mouse macrophage cell lines (RAW264.7 cells and J774A.1) and BALB/c mice. Infection of RAW264.7 cells with B. anthracis spores induced TNF-α expression in a multiplicity of infection (MOI)-dependent manner, and this enhancement was attenuated by the toll-like receptor (TLR) 9 inhibitor oligodeoxynucleotide (ODN)2088. BAG led to TNF-α expression in a dose- and time-dependent manner when applied to RAW264.7 cells. TNF-α expression induced by BAG was reduced by either pretreatment with TLR9 inhibitors (ODN2088 and chloroquine (CQ)) or transfection with TLR9 siRNA. Furthermore, BAG-induced TNF-α production in TLR9+/+ macrophages was completely abrogated in TLR9−/− macrophages. BAG enhanced the phosphorylation of mitogen-activated protein kinases (MAPK), and BAG-induced TNF-α expression was attenuated by pretreatment with MAPK inhibitors. A reporter gene assay and confocal microscopy demonstrated that BAG increased NF-κB activation, which is responsible for TNF-α expression. Treatment with BAG alone showed no cytotoxic activity on the macrophage cell line J774A.1, whereas LT-mediated cytotoxicity was enhanced by treatment with BAG or TNF-α. Enhanced LT-induced lethality was also confirmed by BAG administration in mice. Furthermore, LT plus BAG-mediated lethality was significantly recovered by administration of Infliximab, an anti-TNF-α monoclonal antibody.We examined the effect of BAG on TNF-α production and LT-mediated cytotoxicity during B. anthracis DNA may contribute to anthrax pathogenesis by enhancing LT activity via TLR9-mediated TNF-α production.Our results suggest that The online version of this article (doi:10.1186/s12866-014-0300-9) contains supplementary material, which is available to authorized users. Bacillus anthracis is a Gram-positive, spore-forming bacterium that causes anthrax [ anthrax . Intenti anthrax . When an anthrax ,2. Once  anthrax . The bin anthrax ,3.Once the pathogen invades the host, the innate immune response is the first line of defense. Innate immune cells such as macrophages and dendritic cells elicit inflammatory responses to counteract microbial infections through pattern-recognition receptors (PRR) that recognize conserved microbial structures known as pathogen-associated molecular patterns (PAMP) . The besB. anthracis–mediated pathogenesis. Once the host is infected, B. anthracis spores are engulfed by macrophages and induce production of TNF-α and IL-6 [B. anthracis have also been reported to be recognized by macrophages and stimulate innate immune responses [B. anthracis, induces TNF-α production in bone marrow–derived macrophages (BMDM) in a TLR4-dependent manner [d-glutamic acid (PGA) capsules in B. anthracis elicit IL-1β production in PMA-differentiated THP-1 macrophages [B. anthracis peptidoglycan (PGN), which is a major component of bacterial cell walls, stimulates TNF-α production by human monocytes [B. anthracis in innate immunity and pathogenesis remains unclear.Macrophages have been crucially implicated in and IL-6 . Some coesponses -13. It wrophages . Furtheronocytes . Bacterionocytes . Howeverin vitro and in BALB/c mice. Our results might suggest the importance of BAG in anthrax pathogenesis.In this study, we investigated the effect of highly purified BAG on TNF-α production. Furthermore, we examined the role of BAG on LT-mediated cytotoxicity of macrophages B. anthracis spores increases TLR9 mRNA expression, we infected RAW264.7 cells with B. anthracis spores (MOI of 10) for various time periods and then examined TLR9 mRNA expression using real-time RT-PCR  and 63.05% (P = 0.019), respectively, as compared with that of samples infected with B. anthracis spores only  and 57.2% (P = 0.0004), respectively, as compared with results from BAG or CpG-ODN treatment in the presence of the control siRNA  as compared with samples treated with LT only.LT is a key factor in anthrax pathogenesis, and constituents of ty of LT ,13. TherP = 9.06 × 10−7) as compared with LT treatment alone. In addition, we further examined whether pretreatment of low concentrations of TNF-α can enhance the cytotoxicity of LT. Pretreatment with 7.8, 15.6, or 31.2 ng/ml TNF-α significantly enhanced the cytotoxic activity of LT, although cytotoxicity was lower than that of high concentrations of TNF-α treatment  that received PBS or 20 μg BAG alone survived, but only 50% (6/12) of the mice that received LT survived  as compared with those given LT alone (6/12). To verify whether the increase of mice lethality was due to the BAG-induced TNF-α, 1 mg of Infliximab  was administered into each mouse before injecting BAG with LT. Consequently, the decreased survival due to the injection of BAG with LT was significantly recovered up to 75% (6/8) by injection of Infliximab (P = 0.0061) compared to BAG + LT group. All mice  that received Infliximab alone were alive. These results suggest that BAG produced in blood of infected mice during anthrax progress can enhance the LT-induced death by increasing TNF-α production.Next, to determine the influence of BAG on LT-induced lethality d Figure B. SurvivB. anthracis spore infection are mediated by various types of PRR including TLRs and NOD-like receptors [B. anthracis spores induced not only transcriptional upregulation of tlr9 but also production of TNF-α in murine macrophage RAW264.7 cells. We showed that TLR9 is involved in spore-induced TNF-α production by pretreatment of RAW264.7 cells with both the endosomal acidification inhibitor CQ and the TLR9 inhibitor ODN2088. Purified BAG, a putative TLR9 ligand containing CpG motifs, led to production of TNF-α in a dose-dependent manner in both RAW264.7 cells and BMDM, and this production was mediated by MAPK and NF-кB signaling pathways via TLR9. LT-mediated macrophage cytotoxicity was enhanced by treatment with BAG and exogenous TNF-α. This effect was confirmed in vivo using mice. Compared with the intravenous administration of BAG that had no lethal effect, co-injection of BAG with LT increased LT-mediated lethality in mice and this effect was significantly reversed by administration of Infliximab, an anti-TNF-α monoclonal antibody. In addition, detection of BAG in the sera of anthrax spore–infected mice is consistent with a possible role for BAG-induced TNF-α in the lethality in mice. Based on these observations, we postulated that BAG and other virulence factors during anthrax infection are recognized by TLR9 and their corresponding receptors, which induce TNF-α production via MAPK and NF-кB signaling pathways. TNF-α induced by BAG and other virulence factors enhances LT-mediated cytotoxicity, which results in enhanced lethality in mice , whereas LT slowly induces caspase-1–independent apoptosis in LT-resistant macrophages  through cleavage of MEKs [Nalp1b  by inhibiting p38 kinase but not through caspase-1 activation [LT is a major virulence factor for anthrax pathogenesis , and it d Nalp1c . Nalp1b ty to LT . LT rapi of MEKs . However of MEKs ,16,31. Itivation . ConversB. anthracis LT alone or LT together with PGN, PGA, or LPS [The inflammatory response is beneficial to the host, as it helps the host eliminate pathogens. However, excessive immune responses can be deleterious to the host because they can result in tissue damage or organ dysfunction including sepsis . TNF-α i, or LPS . Indeed,, or LPS . In addi, or LPS . As thosB. anthracis has been known to reach 107-108 organisms per milliliter of blood and induce high amount of cytokines such as TNF-α, IL-6, and IL-1β in primary macrophages and an experimental animal model [Recently, a number of studies have shown that LT could cause increase of vascular permeability and following vascular leakage, thereby contributing to shock and death of animals -41. Bactal model . Thus, BB. anthracis, TNF-α may also be involved in spore-induced sepsis in anthrax pathogenesis. Delayed death was observed in B. anthracis spore–infected mice after treatment with a TNF-α antibody [B. anthracis spores, the amount of BAG circulating in the blood was <0.89 μg/ml, but it may increase as anthrax infection develops in mice, resulting in TNF-α production. In our study, the maximal concentration of BAG in in vitro culture supernatants reached to 2.5 μg/ml when bacterial density was 8.1 × 105 cfu/ml (data not shown). During anthrax infection in animal models, it was reported that the concentration of B. anthracis in blood may rise up to 107-108 cfu/ml [B. anthracis such as PGN, anthrolysin O, and PGA induced considerable amount of TNF-α in host immune cells [B. anthracis could contribute to pathogenesis of anthrax. Therefore, we propose that the presence of BAG circulating in the blood may enhance the toxic effects of LT by increasing the TNF-α level.Our results showed that BAG led to significant increase in TNF-α secretion. Therefore, TNF-α that is produced as a result of the presence of both BAG and LT may enhance LT-mediated macrophage death. In the case of antibody . In our 8 cfu/ml . Based one cells ,13,24. TB. anthracis might enhance LT-mediated macrophage cytotoxicity and eventually increases the lethality of mice.The current study demonstrates that BAG during anthrax infection is recognized by TLR9 and this recognition stimulates TNF-α production via MAPK and NF-κB pathways in mouse macrophages. During infection, secreted TNF-α due to BAG-mediated TLR9 activation as well as activation by other components of CpG ODN2395, TLR9 inhibitors ODN2088, and CQ were purchased from InvivoGen . Recombinant TNF-α was purchased from Abcam . All MAPK inhibitors were purchased from Calbiochem . MAPK antibodies against ERK, phospho-ERK (p-ERK), p38, phospho-p38 (p-p38), JNK, and phospho-JNK (p-JNK) were from Cell Signaling Technology . Dulbecco’s modified Eagle’s medium (DMEM), RPMI-1640, fetal bovine serum (FBS), and antibiotics for cell culture were purchased from Invitrogen .2. BMDMs were prepared as described previously [2. BMDM were trypsinized and seeded at 24 well plates for experiments.The mouse macrophage cell lines RAW264.7 (TIB-71) and J774A.1 (TIB-67) were obtained from the American Type Culture Collection . The cells were cultured in DMEM supplemented with 10% FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin at 37°C in a humidified incubator with 5% COeviously . In brieB. anthracis ATCC 14578 (pXO1+ pXO2+) or B. anthracis H9401 (pXO1+ pXO2+) was streaked and incubated on blood agar plates overnight at 37°C. Spores were prepared as described [Encapsulated toxigenic escribed  and stor5 cells/ml) were seeded onto a 24-well plate in complete DMEM without antibiotics and grown overnight. The cells were washed twice with serum-free DMEM and then infected with the indicated MOI of B. anthracis ATCC 14578 spores for 45 min. Unphagocytosed spores were removed by washing with complete DMEM five times, and the cells were further cultured for the indicated times.For spore infection experiments, RAW264.7 cells . According to this assay, 10 μg/ml DNA contained <0.1 EU/ml.escribed  and storescribed . EndotoxB. anthracis ATCC 14578 spores, RAW264.7 cells were pretreated with 0, 2.5, 5, or 10 μM ODN2088 for 1 h, followed by infection with B. anthracis spores (MOI of 10) for 5 h. Then, culture supernatants were harvested by centrifugation at 12,000 × g for 10 min.To determine whether TLR9 is involved in TNF-α production by To investigate whether BAG-induced TNF-α production was also mediated by TLR9, RAW264.7 cells were pretreated with 0, 1, 10, or 100 nM ODN2088 or 0, 0.1, 1, or 10 μΜ CQ, followed by stimulation with 10 μg/ml BAG for an additional 24 h. Secreted TNF-α was measured using an ELISA kit .5 cells/ml) were plated in a 6-well plate, cultured overnight, and stimulated with various concentrations of BAG for the indicated times. Total RNA was isolated from RAW264.7 cells with TRIzol reagent (Invitrogen) according to the manufacturer’s instructions. cDNA was synthesized from 5 μg total RNA using random hexamers and reverse transcriptase . Real-time semi-quantitative PCR was conducted using the ABI Prism® Sequence Detection System 7500  and Power SYBR® Green PCR Master Mix (Applied Biosystems) under the following conditions: denaturation at 95°C for 1 min and amplification by cycling 40 times at 95°C for 15 s, 60°C for 15 s, and 72°C for 34 s. To determine TLR9 and TNF-α mRNA levels, the copy number for each was normalized to that of ribosomal protein L19 (L19) using the 2−ΔΔCt method, and then the value was compared to that of the untreated control group. The PCR primer sequences included TLR9 forward primer, 5′-ACTTCGTCCACCTGTCCAAC-3′, and reverse primer, 5′-TCATGTGGCAAGAGAAGTGC-3′; TNF-α forward primer, 5′-TCCCAGGTTCTCTTCAAGGGA-3′, and reverse primer, 5′-GGTGAGGAGCACGTAGTCGG-3′; L19 forward primer, 5′-CCAAGAAGATTGACCGCCATA-3′, and reverse primer, 5′-CAGCTTGTGGATGTGCTCCAT-3′. Real-time RT-PCR was performed in triplicate for each RNA sample. The mean ± SD of the relative TLR9 and TNF-α copy number was expressed as the fold induction and was compared with that of the untreated control group.RAW 264.7 cells (3 × 104 cells/ml) were plated in a 96-well plate for 16 h and then transiently transfected with 200 nM TLR9 siRNA or control siRNA using Oligofectamine (Invitrogen) according to the manufacturer’s instructions. Four hours after transfection, the medium was replaced with complete DMEM containing 10% heat-inactivated FBS, 100 U/ml penicillin, and 100 μg/ml streptomycin. After 36 h, cells were stimulated with 10 μg/ml BAG or 1 μM CpG2395 for an additional 24 h. Then, culture supernatants were collected for TNF-α analysis with ELISA.The siRNA that targets mouse TLR9  and the non-targeting siRNA (D-001810-01-05) were purchased from Dharmacon . In brief, RAW264.7 cells  at room temperature for 1 h and then incubated with rabbit anti-MAPK (1:1000) at 4°C overnight. After washing three times with TBST (TBS with 0.5% Tween 20), the membrane was incubated with HRP-conjugated goat anti–rabbit IgG secondary antibody (1:3000) in blocking buffer at room temperature for 1 h. Next, the membrane was washed three times with TBST, and the immunoreactive bands were detected with ECL Western blotting reagents (Invitrogen) and X-ray film .RAW264.7 cells were stimulated with 10 μg/ml BAG for 0, 15, 30, or 60 min. Then, the cells were lysed with NP40 Cell Lysis Buffer (Invitrogen) on ice for 30 min. The cell lysates were collected by centrifugation at 13,000 × 5 cells/ml in 96-well plates and cultured overnight in the above medium. For experiments using various inhibitors, the cells were pre-incubated with the inhibitor for 1 h, followed by stimulation with BAG for an additional 24 h. BMDM were plated at 1 × 106 cells/ml in 24-well plates followed by stimulation with BAG , CpG2395 (1 μM), or PAM3CSK4 (1 μg/ml) for 24 h. At the end of the incubation period, the culture medium was collected, and the TNF-α level was analyzed using an ELISA kit (BioLegend).RAW264.7 cells were plated at 5 × 105 cells/ml) were plated in 12-well plates. The cells were transfected with 1 μg pNF-κB-Luc  together with 0.1 μg pRL-TK Renilla luciferase plasmid (Promega) using LipofectAMINE and PLUS reagent (Invitrogen) according to the manufacturer’s instructions. Twenty-four hours after transfection, cells were stimulated with 0, 0.1, or 10 μg/ml BAG for a further 8 h. Cells were then lysed with reporter lysis buffer (Promega), and cell lysates were assayed for firefly and Renilla luciferase activity with the Dual Luciferase Reporter Assay System (Promega) in a Victor 1420 Multilabel counter .RAW 264.7 cells (5 × 105/ml) were seeded on a chamber slide  and then treated with 1 μg/ml BAG and 1 μg/ml LPS for 1 h or with B. anthracis ATCC 14578 spores (MOI of 30) for 45 min at 37°C. After incubation for the indicated times, the cells were washed with cold PBS  and fixed with cold methanol for 10 min. Samples were subsequently blocked with 15% goat serum for 1 h at room temperature and washed with PBST (PBS with 0.5% Tween 20). Cells were stained with anti-NF-κB p65 primary antibody  for 2 h at room temperature. Cells were then stained with Alexa Fluor 488–conjugated anti-mouse secondary antibody  for 1 h at room temperature and washed three times with PBST. Nuclei were stained with DAPI (Invitrogen). After being washed with PBST, samples were mounted and analyzed using an Olympus FV1000 confocal microscope .RAW264.7 cells , 0.1, 1, and 10 μg/ml. PA and LF were added to the cells at final concentrations of 0.5 μg/ml and 0.05 μg/ml, respectively. The mixtures were incubated for 1 h at 37°C before they were added to J774A.1 cells. Then, the medium was removed from the J774A.1 cell monolayers, and 100 μl of the BAG/PA/LF mixture was added to the cells for 4 h.To evaluate the effect of BAG and TNF-α on LT cytotoxicity, we performed LT-mediated cytotoxicity experiments with J774A.1 cells. Monolayers of J774A.1 cells in DMEM containing 10% FBS were cultured at 37°C in 96-well plates  at 1.5 × 102, 100 μl MTT  was added to each well at a final concentration of 0.5 mg/ml. After additional incubation at 37°C for 1 h, the J774A.1 cells were lysed by adding 100 μl extraction buffer . Absorbance was then measured at 570 nm with an ELISA reader . For each assay, controls consisted of six wells with LT only and six wells with culture medium only. Each sample was tested in duplicate and averaged for analysis.To examine the effect of TNF-α on LT-mediated cytotoxicity, the cells were pretreated with 0, 250, 500, or 1000 ng/ml TNF-α for 24 h, followed by treatment with PA and LF at final concentrations of 0.5 μg/ml and 0.05 μg/ml, respectively. After a 4-h incubation at 37°C in 5% CO50 of B. anthracis H9401 spores or PBS as a control by tail vein injection. After 24 h, mice were sacrificed, and sera were isolated. For removal of bacilli, sera were filtered through 0.2-μm sterile syringe filters. Genomic DNA was isolated from sera using DNeasy Blood and Tissue Kit (QIAGEN), and PCR was performed using B. anthracis 16S rRNA gene–specific primers . Serially diluted DNA (1.56 pg-5 μg) isolated from B. anthracis 14578 was spiked into the normal mouse blood as a standard and DNA purification procedures were performed as described above including standards. Isolated genomic DNA was used as a template, and PCR conditions were as follows: 95°C for 2 min; 30 cycles of 95°C for 30 s, 60°C for 40 s, and 72°C for 60 s; 72°C for 10 min. PCR products were separated on 1.2% agarose gels, and the signal intensity of specific bands was quantified using densitometry software . The standard curve equation was derived by analyzing signal intensities of the PCR products using known quantities of B. anthracis genomic DNA. Final DNA quantities were calculated by interpolating the signal intensity of the tested samples. Sequences of specific PCR-amplified bands were confirmed by sequencing.All animal studies were performed under the approval of the Institutional Animal Care and Use Committee of the Korea National Institute of Health. Five-week-old BALB/c female mice (Orient Bio Inc.) were housed in a specific pathogen-free environment and six-week-old mice were infected with 5× LDn = 8-13 mice per treatment). To block the effect of BAG-induced TNF-α, Infliximab  was used. Before injection of LT + BAG, 1 mg of Infliximab was injected into each mouse via intraperitoneal route. Mice were then monitored for survival for 14 days.Five-week-old BALB/c female mice were housed in a specific pathogen-free environment  and six-week-old mice were injected with LT (PA 50 μg + LF 20 μg) and/or 20 μg BAG by tail vein injection . For other measures, the mean values ± SD were determined for each treatment group in the individual experiment. Treatment groups were compared with the appropriate control, and statistical significance was calculated with the two-tailed Student’s


Predicted and clinically observed times to escape immune responses agree well, and we show that the mutational pathways to escape depend on the viral sequence background due to epistatic interactions. The ability to predict escape pathways and the duration over which control is maintained by specific immune responses open the door to rational design of immunotherapeutic strategies that might enable long-term control of HIV infection. Our approach enables intra-host evolution of a human pathogen to be predicted in a probabilistic framework.Human immunodeficiency virus (HIV) evolves within infected persons to escape being destroyed by the host immune system, thereby preventing effective immune control of infection. Here, we combine methods from evolutionary dynamics and statistical physics to simulate  HIV evolves within infected persons to escape being destroyed by the immune system. Here, Barton et al. combine evolutionary dynamics and statistical physics to simulate this process, successfully predicting the relative rate and location of escape mutations in viral sequences for a cohort of HIV-infected persons. HIV evolves to accumulate mutations that enable the virus to escape host immunity24576910Ideally, vaccine-induced immune responses should be directed towards combinations of epitopes where escape mutations are highly deleterious in diverse sequence backgrounds, thus minimizing the probability of escape and allowing long-term control of infection. Indeed, prior studies have observed connections between epitope targeting and disease progressionRecently, we proposed a computational model to translate sequence data of HIV polyproteins into estimates of how the frequency of different HIV strains across the host population depends on genetic sequence1015Following simple evolutionary models, fitter viruses are expected to be more prevalent, at least over very long time scales 18Although human T-cell responses lead to the selection of escape mutants, these responses are extraordinarily diverse22riposte, are the key inputs to our inference procedure, and thus our landscape describes the collective mutational pathways that HIV uses to evade host immunity. Because of the great diversity of human immune responses, specific sets of correlated mutations observed at the population level, which inform our inference procedure, cannot be uniquely assigned to individual HLA molecules aloneOf course, in individual hosts the virus evolves to evade host immunity and this is an important driver forcing HIV to explore sequence space. Compensatory mutations can arise in conjunction with deleterious escape mutations, and therefore these combinations of mutations are observed more frequently than by chance in the circulating virus population. Similarly, combinations of mutations that are especially deleterious may be observed less frequently than by chance. These correlations, which reflect the host–pathogen in vitro replicative capacity for a library of HIV strains generated by introducing mutations into these subunit proteins of an NL4-3 reference strain10Theoretical and computational studies suggest that, for the reasons noted above, the rank order of the inferred prevalence of HIV strains is statistically similar to the rank order of intrinsic fitnessin vivo non-equilibrium viral evolution in response to T-cell-mediated immune pressure in a cohort of 17 persons during acute HIV infection. These simulations yield predictions for both the relative time necessary for specific CD8+ T-cell epitope escape mutants to dominate the virus population in the host as well as the specific residues at which escape mutations are most likely to arise. We illustrate the potential effects of the viral sequence background on escape through some examples. Explicit simulation of dynamical escape trajectories takes into account the contribution of multiple pathways to escape, and we contrast the enhanced predictive power of the dynamic simulations with static measures of fitness. Our results suggest that by combining stochastic evolutionary dynamics with the fitness landscape of a human virus and knowledge of the immune response, its evolution in individual hosts is predictable.Here, we first infer the fitness/prevalence landscape of HIV polyproteins. We then combine the inferred fitness landscape with a simple model from population genetics, and incorporate knowledge of the host immune response to investigate how fitness constraints influence P(z) of an HIV protein sequence z={z1, z2, …, zN} isIn our model, the prevalence/fitness N is the length of the sequence and Q is a normalizing factor; zi denotes the amino acid at each residue i. Following the language of statistical physics, our proxy for fitness is a quantity referred to as energy (E). The energy depends on the mutability of individual residues (quantified by the h parameters) and the entire sequence of the viral protein with explicit account for synergistic (or antagonistic) interactions between mutations in different residues, quantified by the J parameters. Sequences with high energies are estimated to be relatively unfit, and vice versa.Here, Using equation 1027+ T-cell responses to autologous virus had been performed and time to escape had been experimentally defined230In the infected individuals that we studied, a comprehensive analysis of acute-/early-phase CD8180–188 (TL9) occurred after 122 days in patient CH185, but in patient CH159, who targeted this same epitope restricted by the same class I molecule, escape mutations were not observed even up to 1,103 days after the response to this epitope was first detected. Our calculations show  targeted by patients CAP239 and CH198. In CAP239 escape occurred in just days, even though TW10 was considered to be a protective epitopeS=0.19, in the bottom 31% of epitope entropies), making the rapid escape appear puzzling. However, the average fitness cost for mutational escape  for this epitope, which includes the effects of the sequence background, is very low . This is true in part because the T/F virus in patient CAP239 contained the mutations H219Q and I223V, compared with the consensus sequence . Thus, we predict that escape should occur more slowly in patient CH198, and only through the T242N mutation, in agreement with the clinical data.As another example of the effects of differences in background viral sequence on escape times, consider the Gag epitope TSTLQEQVAWsee ref. , which asee ref. , contribractions . This rer=0.39, P=1 × 10−3, n=65, see S) of residues in the reactive 8–12-amino-acid (aa) epitope  mutations available is also significantly correlated with time to escape , hinting at the potential importance of multiple escape paths . Mapping from energy values to differences in fitness was estimated using measurements of HIV replication in vitro obtained from a separate studyWe simulated the evolution of the virus population in response to CD8tWF) that elapsed before escape mutants comprised >50% of the total virus population. The values of tWF can be interpreted as relative rates for the evolution of escape mutants for each epitope. The values of tWF are strongly correlated with the true escape times observed in the patients  and the incurred intrinsic fitness costs are independent effects. The larger the intrinsic fitness costs incurred by making a mutation, the greater must be the strength of the immune response directed towards the corresponding epitope in order for the virus to evolve an escape mutation at that residue. Immunodominance information alone provides no knowledge about which regions of the virus should be targeted by vaccine-induced immune responses to minimize the rate of escape due to large fitness costs. Given the same strength of the immune response directed towards two epitopes, escape will be faster in the epitope for which the fitness cost of evolving a mutation (given the sequence background) is lower. The intrinsic fitness cost can be estimated more accurately using our methods compared with past efforts using entropy.M), was determined for 53 epitopestWF%M). This further improves our ability to predict escape times for cases where immunodominance information is known  markedly improves upon both the static fitness cost  and epitope entropy , even when rapidly escaping epitopes are excluded. Overall, tWF displays similar predictive power to %M , suggesting that both viral and host factors strongly influence the rate of escape. Encouragingly, we found that simulations combining our inferred fitness landscape with knowledge of immunodominance patterns  capture much of the predictive power of both variables summed individually (pseudo-R2=0.56). This result is consistent with our argument above that the intrinsic fitness cost of escape mutations and the corresponding selective advantage due to immune evasion are independent effects whose balance determines the kinetics of escape. These results also hold in patient-stratified CPH models, which incorporate patient-specific baseline escape rates  model, which properly accounts for whether or not escape was observed for each epitope during the time of observation . Here, wpe rates . OverallFollowing the hypothesis that escape mutations should preferentially appear at residues where the fitness cost of mutation is minimized, the same methods described above can also be used to predict the residues where escape mutations are most likely to emerge. For each targeted epitope, we ordered each residue in the epitope according to how often an escape mutation was observed at that residue in simulations of evolutionary dynamics (from high to low). We then counted the frequency of escape mutations observed at each residue in the clinical data at the time that escape mutants first comprised ≥50% of the virus population. + T-cell responses, as well as the location of emerging escape mutations, is predictable in silico, given knowledge of the epitopes targeted by CD8+ T cells and the infecting virus's sequence. Collectively, our results emphasize the importance of viral factors in the kinetics and location of escape from T-cell-mediated immune control in early HIV infection when virus set point is being established, and reveal predictable constraints on HIV evolution.Our results show that the relative time to escape from HIV-specific CD8Recent work has also highlighted the role of viral fitness in HIV transmission, observing a significant bias towards the transmission of fitter viruses over less fit variants+ T-cell recognition would help to improve the results we have shown here by identifying the specific mutations that effectively confer escape. Such differing effects of mutations within reactive epitopes is another reason that considering multiple escape pathways is important: the existence of several escape pathways with low intrinsic fitness costs could allow the virus to select for escape mutants with higher effective fitness through decreased recognition by the host immune system. More realistic simulations should also include a time-varying fitness penalty for viruses without escape mutations, to take into account the dynamic growth and contraction of epitope-specific CTL (cytotoxic T lymphocyte) clones. Despite these simplifications, our results show good agreement, and significant enhancement over Shannon entropy alone, with relative rates of escape in vivo as well as the identity of residues where escape mutations arise. Future refinements are expected to further improve the ability to predict HIV evolution in patients.We note that the evolutionary dynamics considered here incorporate several simplifying assumptions. First, we treat the effective population size as constant, a reasonable assumption in the chronic phase when viral load is fairly stable. Variable population sizes may lead to a better description of escape in the acute phase, when viral load is dynamic, but the appropriate relationship between viral load and model-specific effective population size is unclear. Second, we have conservatively assumed that any non-synonymous mutation within a targeted epitope confers escape. It is not certain that all mutations impair T-cell recognition  identified in acute HIV-1 infection (Fiebig stages I–IV) recruited under the CHAVI 001 and CAPRISA studies at sites in the United States, Malawi and South Africawww.hiv.lanl.gov; accessed 6th October 2014). The MSA were then processed to remove insertions relative to the HXB2 reference sequence (GenBank accession code K03455). To improve sequence quality, sequences labelled as ‘problematic' in the sequence database were not downloaded, and sequences with gaps or ambiguous amino acids present at >5% of residues were removed from the MSA. The remaining ambiguous amino acids were imputed with simple mean imputation. For details on the number of sequences obtained for each protein/clade, see We downloaded multiple sequence alignments (MSA) of HIV-1 clade B and clade C protein sequences from the Los Alamos National Laboratory HIV sequence database  present at residue i in the sequence. We refer to possible values of the zi as states. Our goal will be to infer a model that accurately describes the distribution of HIV sequences circulating in the population, of which the sequences in the MSA are a sample. To describe this distribution we focus on the lowest moments: the frequency of each state at each residue, and the frequency of each pair of states at each pair of residues. These are given byEach sequence in the MSA can be represented as a vector of variables k is an index running from 1 to B used to label each sequence in the MSA, and B is the total number of sequences in the MSA. The function δ is the Kronecker δ function,Here k in the MSA by a factor wk. We set wk equal to one divided by the total number of sequences in the MSA obtained from the same individual from whom the sequence labelled k was extracted. In this way, the total weight of the sequences from each individual is equal. The normalizing factor W in equation W=∑kwk. Following standard terminology in statistical physics, we refer to the pi*(a) and pij* given in equation To prevent multiple sequences obtained from the same individual from biasing the sequence distribution, we weight the contribution of each sequence labelled z isThere are, in principle, a vast family of probabilistic models that could reproduce the correlations observed in equation E(z) is referred to as the energy of the sequence z, andHere N.is a normalizing factor ensuring that the probabilities of all sequences sum to one. The sum in equation hi(a), Jij in equation The parameters hi(a), Jij parameters from the measured correlations is referred to as the inverse Potts problem. Its solution is given by the parameters that maximize the log-likelihood functionare equal to their counterparts estimated from the MSA, given in equation Q, which requires summing over a number of terms that grows exponentially with the length of the protein N.However, no analytical solution exists for systems of nontrivial size, and the likelihood cannot be directly maximized numerically due to the presence of L2-regularized likelihood,To obtain a fast and accurate solution to the inverse Potts problem, we applied an extension of the selective cluster expansion method, described in ref. Γ of the full system, where numerical approaches are feasible. For example, for a two-site subset Γ={1,2} we would compute the set of fields h1(a), h2(a) and couplings J12 that maximize the likelihood of the model restricted to just the sites 1 and 2, constrained to reproduce the correlations for those sites p1*(a), p2*(a) and p12*; sites {3, 4, …, N} outside of Γ are ignored in this calculation.restricted to small subsets Γ, an approximate solution of the hi(a), Jij for the full system can be constructed27hi(a), Jij for the Potts model, which accurately recover the measured correlations, without overfitting the model  to 2/W. Rather than using the full set of 21 states (20 aa and 1 gap state) at each residue, we used a compressed representation of the states at each residue, as described below.Using the parameters inferred for many different subsets see also . SelectiN(q-1) (N(q-1)+1)/2, where N is the length of the protein sequence and q is the number of states, assuming that this number is the same for each residue. Choosing q=21 for the 20 possible amino acids plus 1 gap state, we would require more than two million variables to parameterize the Potts model for a protein of length 100, a typical length scale for HIV proteins.Even with the use of sophisticated algorithms, solving the inverse Potts problem remains a challenging computational task. This task is complicated by the large number of parameters in the model, equal to qi at each residue i is as follows. First, we order the amino acids at residue i according to how frequently they are observed in the MSA, such thatFortunately, it is not necessary to include all possible amino acids at each residue in the model explicitly to obtain a useful characterization of the sequence distribution. We adaptively adjusted the number of states allowed at each residue based on the frequencies with which different amino acids are observed there in the MSA. Our procedure for choosing the number of states The Shannon entropy of the distribution of amino acids at this residue can be written aspi*(a) must sum to one when summed over all states a. Then, we set qi equal to the smallest integer q, such thatas the qi such that the reduced representation captures at least 90% of the full entropy of the distribution of amino acids at that residue. The qi-1 most frequently observed amino acids at that residue each map to particular Potts states. All the remaining, infrequently observed amino acids map to a single aggregate state.That is, we choose a number of states Our choice of the number of states to model at each residue is adaptive, compressing the amino acid alphabet heavily at residues where little variation is observed, but allowing for a larger number of states when many different amino acids are present at nontrivial frequencies. The particular choice of cutoff given in equation In our study, we included all epitopes identified in ref. ex vivo IFN-γ ELISPOT assays using overlapping 18 mers matched to the transmitted/founder strain. In 7/71 cases optimal 8–11 mers could not be identified, and hence we used the LANL ELF tool  to search for known HLA-matched epitopes from the LANL CTL database. If no matches were found in the database, we used NetMHC version 3.4 to identify likely epitopes within the reactive 18 mer (ref. 50≤500 nM). Where possible we used empirically determined HLA-specific cutoffsS, ΔE and tWF values across these likely epitopes and used these averages for escape time prediction. The selected epitopes are summarized in Attempts to identify the optimal epitopes were made in ref. mer ref. . We analIn total, the distribution of the 71 epitopes we considered among HIV proteins is as follows. We analysed 24 epitopes from Gag: 4 epitopes from p17, 16 from p24, 3 from p7 and 1 from p6. From Pol, we analysed 5 epitopes: 1 from protease, 3 from reverse transcriptase and 1 from integrase. Our study includes 7 epitopes from the regulatory proteins: 3 from Tat and 4 from Rev. We analysed 12 epitopes from the gp41 subunit protein of Env and 23 epitopes from the accessory proteins: 4 from Vif, 1 from Vpr and 18 from Nef. See Limited numbers of sample sequences and long delays between sampling times make reliable inference of escape times difficult. To combat this issue, we used a mathematical method developed to infer the kinetics of viral escape from T-cell pressuref(t) is the fraction of escape mutants in the population over time, f0 is the initial fraction of escape mutants, and ɛ is a parameter that expresses the rate of growth of the escape mutants relative to the rest of the virus population. The parameters f0 and ɛ appearing in equation n={n1, n2, …, nT} collected at times t={t1, t2, …, tT}, the likelihood of observing a number of escape mutants k={k1, k2, …, kT} assuming that the true fraction of escape mutants in the population obeys equation 33Here, f0 and ɛ, and then used these parameters in equation For each epitope we thus obtained maximum likelihood estimates of r=0.92, P=3.8 × 10−30, n=71), but the maximum likelihood approach tends to yield shorter escape times in cases where escape is rapid. This method was used to estimate the escape time for both conventional escapes (through mutations within an epitope) and escapes occurring through putative antigen-processing mutations.which we refer to as the maximum likelihood escape time. The threshold of 50% escape mutants in the population was chosen to reflect previously used definitions of escape timein vitro tests of viral replicative capacity for multiple closely related HIV strains, which found a strong correlation between differences in energy and replicative capacity10The difference in energy between sequences can be used to quantify their expected difference in fitness. This assertion is supported by z for the viral protein in which that epitope is located. In case the T/F sequence was not available, we used the most common sequence in the virus population at the earliest time point when sequencing data were available. We then generate the set of all sequences {z′} that differ from z by a single non-synonymous mutation in the targeted T-cell epitope, and compute the average difference in energy between this set of sequences and z:For each targeted epitope, we began with the transmitted/founder (T/F) sequence z′} that differ by a single nucleotide mutation from z allows us to estimate the fitness cost of the shortest mutational path to escape. More involved escape trajectories are effectively taken into account when we simulate the evolution of the virus population, as described below.This Boltzmann-like average emphasizes the contribution of the escape mutation with the lowest fitness cost. Focusing only on sequences {in vivo, we coupled the inferred Potts model to a Wright–Fisher-like evolutionary model. We assume a fixed population size of N=104 viruses in the population, in line with estimates of the effective population size of HIV for intra-host evolutionTo simulate the evolution of virus populations z survives with probabilityThe starting population of sequences then evolves in discrete time steps, with rounds of selection, replication and mutation. In the selection step, each sequence P=0, for sequences that are much less fit  than the rest of the population, and P=1, for sequences that are much fitter than the population average. Using experimental measurements of viral replicative capacities and sequence energies for a set of Gag mutantsN by random resampling with replacement from the survivors. Following the replication step, each sequence mutates with rate μ=3 × 10−5 per base, in line with known HIV mutation ratesρ=1 × 10−5 per base, following recent estimates of HIV recombination rates45b=10, a value chosen to be larger than the largest ΔE (average cost of escape) so that escape confers a selective advantage for all epitopes. To quantify the ease of escape at each epitope we computed the number of generations to escape (≥50% of escape mutants in the population), averaged over 103 simulations. The predicted order of escape is not sensitive to the precise values of β and b, provided that the latter is larger than the largest ΔE. Choosing b=9 or b=11, for example, leads to virtually identical values for the correlation between the escape generation tWF and the escape time for all epitopes , but larger values of b lead to shorter average escape times <tWF> across all epitopes .This form of the survival probability smoothly interpolates between b applied to viruses lacking escape mutations is small enough so that all mutations within the targeted epitope are deleterious even including the fitness benefit of escape, then clearly escape would be observed only after extremely long periods of time. Indeed, we expect that in some real cases the fitness cost of escape mutations in an epitope can be high enough that no selective advantage is gained through escape, and thus escape mutants never come to dominate the population. In the present work, our goal is to predict the relative ease of generating escape mutations in each targeted epitope; thus we have chosen b large enough that escape is preferred in all the epitopes we considered. The average escape generations computed through the simulation described above should therefore be interpreted as relative rates for the evolution of escape mutants for each epitope.We note that, if the fitness penalty 37–44 targeted by patient CH77, which has the highest ΔE (=6.6) of all epitopes we considered, is never observed. With b=10, the mean escape generation in the Wright–Fisher simulation is 52.9, also the largest among all epitopes. As b approaches ΔE the value of tWF begins to increase sharply as escape no longer confers a large selective advantage . Selecting b≥9 avoids this threshold effect for epitopes with the highest ΔE.As an example, escape at the Gag epitope ASRELERFM) of each CD8+ T-cell response influences the rate of escape. More vigorous immune responses increase the selective pressure for escape, and thus escape occurs more rapidly at epitopes where the vertical immunodominance is higher. We can incorporate this factor into the Wright–Fisher simulation by increasing the fitness penalty b for viruses without escape mutations in proportion with the strength of the immune response directed towards each epitope: b=(1−%M) bmin+%M bmax. To avoid extremely long escape times for epitopes with the highest ΔE, we took bmin=9 and bmax=2 bmin. We then computed the average escape time tWF for the set of epitopes for which vertical immunodominance measurements are available, incorporating this immunodominance-dependent b. For these epitopes, incorporating the effects of immunodominance does not result in significant changes in the Pearson correlation with the inferred escape times , but the rank correlation is substantially improved due to better ordering of epitopes with intermediate predicted escape times . As before, provided that bmin is large enough to avoid threshold effects for epitopes with the largest values of ΔE, our results are not sensitive to the precise values of the parameters .As shown above and in ref. For 11 epitopes, the sample of the virus population at the time that the T-cell response towards that epitope was first detected already contains one or more escape mutants. These cases represent instances where either testing of T-cell responses was performed too late to detect the response before escape began, or where assays performed at earlier times had insufficient sensitivity to detect T-cell responses before escape occurred. This uncertainty in the exact timing of the T-cell response is large in proportion to the estimated escape time for epitopes where escape occurs rapidly. Because we are unable to infer the precise time that the T-cell response was initiated (and the composition of the virus population at exactly that time), we have used the available sequence data at the time the T-cell response was first detected as the basis of our evolutionary simulations.t=−2.27, P=0.035, n=71, two sample t-test). To a lesser extent, these epitopes also tend to be more immunodominantly targeted . These epitopes thus represent a select sample where the fitness cost of escape is unusually low and where rapid escape is successfully predicted, arguing against their exclusion. Alternatively, reverting observed escape mutations in the sequence data and the time the T-cell response was first detected and using these reverted sequences as a starting point for evolutionary simulations also recover rapid escape times for these epitopes, but overall correlation is lowered in this case because of the inaccurate estimation of the true time that the T-cell response was initiated for these epitopes  or occurred through putative antigen-processing (AgP) mutation outside the epitope (n=3) were treated as censored events. Incorporating vertical immunodominance in a multivariate model considerably improves the model fit for epitope entropy and the fitness cost of escape . We repeated the same analysis for patient-stratified CPH models, which include variable escape rates for each patient. Although the predictive power is weaker in this case, the overall results here are similar to those described above .Supplementary Figures 1-7, Supplementary Tables 1-5, Supplementary ReferencesList of targeted epitopes organized by patient, including information about the estimated time to escape, epitope entropy (S), estimated fitness cost of escape mutations (ΔE), time to escape in simulated evolution (tWF), and vertical immunodominance . Users of this data in a subsequent paper should also cite the primary experimental source ).


Heart failure (HF) poses a heavy burden on patients, their families and society. The syndrome of HF comes in two types: with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF). The latter is on the increase and predominantly present in women, especially the older ones. There is an urgent need for mortality-reducing drugs in HFpEF, a disease affecting around 5 % of those aged 65 years and over. HFpEF develops in patients with risk factors and comorbidities such as obesity, hypertension, diabetes, COPD, but also preeclampsia. These conditions are likely to drive microvascular disease with involvement of the coronary microvasculature, which may eventually evolve into HFpEF. Currently, the diagnosis of HFPEF relies mainly on echocardiography. There are no biomarkers that can help diagnose female microvascular disease or facilitate the diagnosis of (early stages of) HFpEF. Recently a Dutch consortium was initiated, Queen of Hearts, with support from the Netherlands Heart Foundation, with the aim to discover and validate biomarkers for diastolic dysfunction and HFpEF in women. These biomarkers come from innovative blood-derived sources such as extracellular vesicles and circulating cells. Within the Queen of Hearts consortium, we will pursue female biomarkers that have the potential for further evolution in assays with point of care capabilities. As a spin-off, the consortium will gain knowledge on gender-specific pathology of HFpEF, possibly opening up novel treatment options. Cardiovascular disease (CVD) is the number one killer among women worldwide. Controversially, women have been underrepresented in most clinical trials on cardiovascular disease. The most striking differences in the prevalence of CVD between men and women are visualised in the syndrome of heart failure (HF).Among older patients in developed countries, HF probably represents the greatest health burden with an estimated 23 million people with HF worldwide, with nearly 50 % of them women . HF is aThe diagnosis of HF is based on suggestive symptoms and signs and structural or functional abnormalities on echocardiography , 5. PatiHFpEF is associated with diastolic left ventricular dysfunction that involves reduced left ventricular relaxation and increased left ventricular stiffness with a relatively normal ejection fraction of 50 % or more . A consiNatriuretic peptides are useful biomarkers with added diagnostic value on top of clinical assessment , althougThe prevalence of HFpEF is around 3–5 % in the open population aged 65 years and over, and 4–6 % in men and 8–10 % in women for individuals of 80 years and older [Comorbidities have been strongly associated with HF but differ between HFrEF and HFpEF. In a predominantly male HF cohort it was shown that there was a higher non-cardiac comorbidity burden in HFpEF patients compared with HFrEF patients .Differences in prevalence of comorbidities also exist between men and women with HFpEF. Women are generally older and have a higher likelihood of having diabetes, systemic hypertension and systolic blood pressure with a similar body mass index to men .Diabetes is one of the main risk factors for developing HFpEF. This is evident in Western society but even more eminent in Asian people. The prevalence of diabetes is rapidly rising which is likely to push up the incidence of HFpEF in the coming decades. Astounding prevalence rates of diastolic dysfunction between 27 and 70 % have been reported in the growing diabetics . As alreHypertension is a major risk factor for HF as it increases cardiac workload. Hypertension is more prevalent in HFpEF than in HFrEF . SystemiPreeclampsia is the occurrence of both hypertension and proteinuria during pregnancy and is associated with the development of hypertension, diabetes type 2 and obesity later in life. Although the exact pathophysiology of preeclampsia is unclear, it is already known that it can result in endothelial dysfunction during the pregnancy . PreeclaPreeclampsia can already be associated with diastolic dysfunction during pregnancy, but also 1 year after pregnancy. In a small cohort of women with preterm preeclampsia, diastolic dysfunction persisted up to 1 year postpartum in 51 % of the women . In the Diabetes, hypertension and preeclampsia have in common that they accelerate systemic and coronary microvascular dysfunction, which is highly associated with the development of HFpEF. This relation seems to be stronger in women. In contrast, HFrEF is typically myocardial infarction with eccentric remodelling as a result . A dilatHFpEF seems thus to be a different phenotype within the HF spectrum. According to a recently postulated paradigm, HFpEF develops from a ‘systemic proinflammatory state’ induced by certain comorbidities .Comorbidities such as diabetes, obesity, COPD and preeclampsia can induce a systemic inflammatory state and subsequent systemic microvascular endothelial dysfunction . The prohttp://www.queen-of-hearts.eu) hypothesises that comorbidities such as diabetes, COPD, and preeclampsia share a common pathway to the development of HFpEF from which gender-specific biomarkers in women can be deduced. By using follow-up echocardiographic data in women who have experienced preeclampsia, we hope to detect novel biomarkers that could be helpful in clinical practice for individual risk stratification and diagnostic and prognostic prediction.The Queen of Hearts program  it is clear that diabetes and hypertension are on the increase worldwide, also affecting low income countries. Asia and Africa are overtaking the Western world and as a result cardiovascular diseases, including heart failure, are on the increase in these parts of the world.From the WHO statistics report 2012 (In contrast to the wealth of data regarding HFpEF in Western populations, data are limited in Asian populations. The true prevalence of HFpEF in Asian communities is unknown, and may vary in different regions of Asia. In Singapore, the proportion of HF patients with HFpEF has been estimated at 22 % in a single-centre study —this is Next to these initiatives, Singapore has a comparable health standard to the Netherlands and is inhabited by three Asian ethnicities. The Queen of Hearts program aims to strongly collaborate with this Asian country. Initiatives are being taken for exchange of people and validation of new markers in Singaporean cohorts.The number of older women with a history of hypertension and diabetes will increase worldwide and thus, early risk stratification and early and accurate diagnosis of HFpEF with the help of novel blood markers could help to identify high-risk patients, and provide the opportunity for novel treatment options.The Queen of Hearts program anticipates to discover novel proteins and miRNAs that may improve risk stratification, early diagnosis and provide novel treatment targets for HFpEF, especially in well-established high-risk groups such as women with diabetes or a history of preeclampsia. We will collaborate with international cohorts of heart failure and preeclampsia in Singapore and Asia.The consortium will improve the knowledge on gender-specific pathology of HFpEF.


Tumor necrosis factor-α (TNF-α) is a proinflammation cytokine secreted by various cells. Understanding its secretive pathway is important to understand the biological functions of TNF-α and diseases associated with TNF-α. TNF-α is one of the first proteins known be modified by lysine fatty acylation (e.g. myristoylation). We previously demonstrated that SIRT6, a member of the mammalian sirtuin family of enzymes, can remove the fatty acyl modification on TNF-α and promote its secretion. However, the mechanistic details about how lysine fatty acylation regulates TNF-α secretion have been unknown. Here we present experimental data supporting that lysine fatty acylation promotes lysosomal targeting of TNF-α. The result is an important first step toward understanding the biological functions of lysine fatty acylation. TNF-α is a proinflammation cytokine produced by various types of cells, including macrophages, lymphocytes, dendritic cells, and neurons12426There are two protein forms of TNF-α, membrane TNF-α  and secreted TNF-α sTNF-α, 17 kD)91011131516 kD910111184+)-dependent deacylaseBecause TNF-α has important immune functions, there has been significant research interest in the regulation of TNF-α. TNF-α is known to be regulated by many protein posttranslational modifications. In addition to proteolytic cleavage by TACE, proteases SPPL2a and SPPL2b were found to cleave the intramembrane part of mTNF-α and promote the release of the intracellular domain to induce the expression of pro-inflammatory cytokine interleukin-1220Previously we demonstrated that lysine fatty acylation downregulated TNF-α secretion (the production of sTNF-α)In order to find out how lysine fatty acylation affects TNF-α secretion, we overexpressed wildtype (WT) TNF-α with a C-terminal green fluorescent protein (GFP), termed TNF-α_WT_GFP, in WT and Sirt6 KO MEF cells and examined the cellular localization of TNF-α. Confocal imaging results  showed tWe next set out to find out the identities of the TNF-α containing vesicles in both WT and Sirt6 KO MEF cells. We examined the co-localization of TNF-α with protein markers of various organelles involved in protein secretionThe different localizations of TNF-α in WT and Sirt6 KO cells could be caused by the different levels of lysine fatty acylation on TNF-α or by differences in the secretory pathway. In order to find out whether the different localizations of TNF-α in WT and Sirt6 KO MEF cells were caused by differences in TNF-α lysine fatty acylation, we further examined the localization of the TNF-α mutant, TNF-α_KR, in which both Lys19 and Lys20 were mutated to Arg . TNF-α_KTo further confirm the results obtained with confocal imaging, we also employed lysosome fractionation to determine the lysosomal localization of TNF-α WT and KR in WT and Sirt6 KO MEF cells. Consistent with the imaging data, more TNF-α WT was found in the lysosomal fraction of Sirt6 KO cells than WT cells . In contAlthough no TNF-α was observed in the lysosomes in Sirt6 WT cells , we wondThese results suggest that unacylated TNF-α can also be targeted to the lysosome, but they are degraded very quickly. If TNF-α molecules are targeted to the lysosome and degraded, we would predict that E64 would not affect the amount of TNF-α secreted. However, it is also possible that the TNF-α molecules are targeted to lysosome as part of the normal secretory pathway . In that case, we would predict that E64 would decrease the amount of sTNF-α secreted. To differentiate the two possibilities, we measured the sTNF-α secretion in WT and Sirt6 KO MEF cells with or without E64 treatment . The secAll the above experiments were carried out with over-expressed TNF-α. To make sure endogenous TNF-α are also regulated similarly by SIRT6 and lysine fatty acylation, we examined the localization of endogenous TNF-α in THP-1 cells with control or Sirt6 knockdown . SimilarUnderstanding the mechanism that regulates cytokine secretion is essential to understand and treat cytokine-associated diseases. There are canonical and non-canonical secretion pathways for various cytokines1011101127The data presented here demonstrate that lysine fatty acylation helps to target TNF-α to the lysosomes for degradation and at the same time decrease its localization in the recycling endosome , which eThe observation that TNF-α can only be observed in the lysosomes of Sirt6 KO cells may be caused by a combination of increased lysosomal targeting and resistance to lysosomal degradation. Given that lysine fatty acylation decreases TNF-α secretion, we are certain that lysosome targeting is increased by lysine fatty acylation . However, we cannot rule out that lysine fatty acylation also increases resistance to lysosomal degradation. Whether TNF-α degradation by lysosomal proteases is affected by lysine fatty acylation awaits further studies.+ level decreases in hypoxiaInterestingly, it was previously reported that hypoxia reduced TNF-α secretion and enhanced co-localization of TNF-α with LAMP1It is not clear at this point whether lysine fatty acylation is a general mechanism for lysosomal targeting or not as only one defatty-acylation substrate for SIRT6 has been reported thus far. This question will be addressed when more defatty-acylation substrates of SIRT6 or other sirtuins are discovered in the future.Escherichia coli 0111:B4 (LPS) were purchased from Sigma-Aldrich . Anti-Flag antibody conjugated with horseradish peroxidase (A8592) was purchased from Sigma-Aldrich. Rat monoclonal anti-mouse LAMP1 antibody (1D4B) was purchased from BD Biosciences . Goat anti-rat IgG conjugated with Alexa Fluor 647 was purchased from Life Technologies . Rabbit monoclonal anti-Rab11 antibody (D4F5), rabbit monoclonal anti-Rab7 antibody (D95F2), rabbit monoclonal anti-EEA1 antibody (C45B10), rabbit monoclonal anti-SIRT6 antibody (D8D12), goat anti-rabbit IgG conjugated with Alexa Fluor 647, and ProLong Gold antifade reagent with DAPI were purchased from Cell Signaling Technology . Anti-human TNF-α antibody (MAB610) was purchased from R&D systems . Human TNF-α ELISA kit was purchased from eBioscience. ECL plus western blotting detection reagent and Lysosome enrichment kit were purchased from Thermo Fisher Scientific Inc. SIRT6 shRNA lentiviral plasmid (pLKO.1-puro vector) was purchased from Sigma, the sequence of shRNA was: TRCN0000378253 (ccggcagtacgtccgagacacagtcctcgaggactgtgctcggacgtactgtttttg). Plasmid of pEGFP-N1 vector was a kind gift from Professor Barbara A. Baird.Palmitic acid, E64, phorbol 12-myristate 13-acetate (PMA) and lipopolysaccharides from For construction of the expression vector for wild-type human TNF-α protein (TNF-α_WT) or the double lysine mutant  with N-terminal Flag or C-terminal EGFP tag, full-length TNF-α_WT or TNF-α_KR cDNATHP-1 cells were maintained in Roswell Park Memorial Institute Medium (RPMI) supplemented with 10% heat-inactivated fetal bovine serum (Life Technologies). WT and Sirt6 KO MEF cellsSIRT6 shRNA plasmid, pCMV-dR8.2, and pMD2.G were co-transfected into HEK 293T cells. After 24 hr, the medium was collected to infect the THP-1 cells. After infection for 6 hr, the medium was changed to fresh medium and the cells were cultured for 72 hr. Then 1.5 μg/mL of puromycin was added into the cells for at least three generations to select stable SIRT6 knockdown cells.3) for 1 hr at room temperature and then incubated with primary antibody  solution (100 μL in Saponin-BSA solution) at 4 °C for 12 hr. After washing 4 times with 1 mL Saponin-BSA solution, cells were incubated with the secondary antibody (goat anti-rabbit or anti-rat IgG conjugated with Alexa Fluor 647) solution (100 μL in Saponin-BSA solution) for 2 hr at room temperature. After washing 4 times with 1 mL Saponin-BSA solution, cells were incubated with 250 μL ProLong Gold antifade reagent with DAPI for confocal imaging experiments. For quantification of colocalization, Mander’s overlap coefficients in images were determined using ImageJ coloalization plugin Intensity Correlation AnalysisAfter WT and Sirt6 KO MEF cells were transfected with TNF-α_WT_GFP or TNF-α_KR_GFP for 12 hr, cells were incubated with fresh medium containing 50 μM palmitic acid or 50 μM E64 or DMSO (as control) for 6 hr. Then cells were washed with 1 mL 1× PBS buffer and fixed with 1 mL 4% paraformaldehyde (PFA) for 15 min at room temperature. After washing cells 3 times with 1 mL 1× PBS buffer, cells were permeabilized with 1 mL Saponin-BSA solution (0.1% Saponin with 3% BSA in 1× PBS buffer with 0.02% NaN3) for 1 hr at room temperature. The cells were then incubated with the primary antibodies  in Saponin-BSA solution at 4 °C for 12 hr. After washing 3 times with 1 mL Saponin-BSA solution, the cells were incubated with the secondary antibodies (goat anti-rabbit or anti-rat IgG conjugated with Alexa Fluor 647) solution in Saponin-BSA solution for 1 hr at room temperature. After washing 3 times with 1 mL Saponin-BSA solution, confocal imaging experiments were performed. For quantification of co-localization, Mander’s overlap coefficients in images were determined using ImageJ coloc 2 plugin with manual setting of thresholds.THP-1 cells (with control shRNA or SIRT6 shRNA) were treated with PMA (200 ng/mL) for 24 hr. Then cells were cultured in fresh RMPI medium with palmitic acid (50 μM) and LPS (1 μg/mL) for 4 hr. After washing with 1 mL 1× PBS buffer, the cells were fixed with 1 mL 4% PFA for 15 min at room temperature. The cells were then washed twice with 1× PBS buffer and permeabilized with 1 mL Saponin-BSA solution .After transient transfection of TNF-α_WT or TNF-α_KR, shown as error bars). Differences were examined by Student’s t test between two groups. P value below 0.05 was attributed to significance and represented by one asterisk. P value below 0.01 was attributed to most significance and represented by double asterisks.Data were expressed as mean ± SD .


Inflammation and inflammatory mediators may affect glucose and lipid metabolism through a number of pathways. However, effects and mechanisms seem to vary dramatically in different individuals and settings. In obese adults with metabolic syndrome and impaired glucose-tolerance, inflammatory mediators from fat tissue including tumour necrosis factor-alpha (TNF-α) further reduce glucose tolerance. In such patients, treatment with TNF-α-blocking agents has been shown to ameliorate glucose tolerance. However, in non-obese healthy adults, vigorous exercise may induce an inflammatory response including release of TNF-α. Glucose tolerance increases during and after the exercise, and TNF-α blocking agents may inhibit this beneficial effect. Furthermore, the positive effects of exercise on cholesterol and triglyceride levels have also been diminished by TNF-α blocking agents.In children with juvenile idiopathic arthritis (JIA) treatment with TNF-α blocking agents is increasingly common as the treatment is efficient and well tolerated. The possible harmful effects of this treatment on glucose and lipid metabolism, however, give rise to concern.The aim of the study was to investigate the glucose and lipid metabolism in children with JIA treated with TNF-α blocking agents.Fifty-two children with JIA were included in the study. All were treated with immunosuppressive agents: 30 were treated with methotrexate (MTX) in monotherapy, 13 were treated with MTX and a TNF-α blocking agent and 9 were treated with a TNF-α blocking agent in monotherapy. None were treated with oral glucocorticoids. All treatments had been given at least 3 months at the time of blood sampling.1C (an indicator of the average blood glucose in the 8 weeks prior to testing), fasting blood glucose, total cholesterol, high density lipoprotein (HDL), low density lipoprotein (LDL) and triglyceride.All participants had a fasting blood sample drawn and subsequently the following parameters were assessed: HgbATable 1The data were tested and found to be normally distributed. Thus, unpaired t-test was used to compare mean-values.Mean HgbA1C was significantly lower children treated with a TNF-α blocking agent compared to children treated with MTX as monotherapy (p = 0.01). The difference was significant in both children treated with TNF-blocker as monotherapy (p = 0.05) or in combination with MTX (p = 0.03). No other significant differences were detected.1C. The reduction, however, is small and barely of clinical significance. No effects could be detected on fasting blood glucose or cholesterol levels. The results are reassuring with regard to the concerns for harmful long term side effects of this treatment.Children with JIA treated with a TNF-α blocking agent have significantly lower HgbANone declared.