Failure of circulating monocytes for adequate cytokine production is a trait of sepsis-induced immunosuppression; however, its duration and association with final outcome are poorly understood.We conducted a substudy of a large randomised clinical trial. Peripheral blood mononuclear cells (PBMCs) were isolated within the first 24 h from the onset of systemic inflammatory response syndrome in 95 patients with microbiologically confirmed or clinically suspected gram-negative infections. Isolation was repeated on days 3, 7 and 10. PBMCs were stimulated for cytokine production. The study endpoints were the differences between survivors and non-survivors, the persistence of immunosuppression, and determination of admission clinical signs that can lead to early identification of the likelihood of immunosuppression.p = 0.046). This trait persisted as long as day 10. Logistic regression analysis showed that cardiovascular failure on admission was the only independent predictor of defective TNF-α production on day 3.PBMCs of survivors produced significantly greater concentrations of tumour necrosis factor-α (TNF-α), interleukin (IL)-6, IL-8, IL-10, interferon-γ and granulocyte-macrophage colony-stimulating factor after day 3. Using ROC analysis, we found that TNF-α production less than 250 pg/ml after lipopolysaccharide stimulation on day 3 could discriminate patients from healthy control subjects; this was associated with a 5.18 OR of having an unfavourable outcome  contains supplementary material, which is available to authorized users. Sepsis is the leading cause of death in critically ill patients. Mortality can reach up to 70% of patients with septic shock and up to 80% of patients with multiple organ dysfunction . This grsepsis-induced immunosuppression. In their study, Boomer et al. explored the function of splenocytes isolated from early cadavers of severe sepsis and compared them with those from brain-dead subjects with multiple injuries. A clear-cut down-regulation of both innate and adaptive immune responses was found, leading to the assumption that sepsis-induced immunosuppression is the driver towards unfavourable outcome [Early patient death may result from exaggerated pro-inflammatory responses . However outcome .How is cytokine production associated with final outcome?How long does down-regulation of sepsis last?Which clinical signs on admission can help discriminate the patient who is entering into the immunosuppression stage?The findings of the study by Boomer et al.  generateA proper answer to these questions can lead to appropriate application of drugs with the aim of reversing immunosuppression of sepsis. In the present study, we used serial stimulation of circulating mononuclear cells during patient follow-up to seek answers to these questions.A prospective, randomised clinical trial  was conducted during the period from July 2007 to April 2011 in six departments in Greece to assess the efficacy and safety of intravenous clarithromycin over placebo for patients with microbiologically documented gram-negative infections or with infections at high clinical risk of being of gram-negative origin [3; (b) diagnosis of HIV infection; and (c) corticosteroid treatment over the last 30 days in doses greater than 0.4 mg/kg prednisone or equivalent.Inclusion criteria for this subgroup of patients were (a) age 18 years or older; (b) written informed consent provided by first-degree relatives; (c) presence of at least two signs of the systemic inflammatory response syndrome (SIRS); (d) presence of acute pyelonephritis, acute intra-abdominal infection or primary bacteraemia caused by gram-negative bacteria; and (e) start of blood sampling within the first 24 h from the onset of signs of SIRS. Exclusion criteria were (a) neutropenia due to causes other than SIRS, defined as an absolute number of neutrophils less than 1000/mmDefinitions of acute pyelonephritis, acute intra-abdominal infections, primary gram-negative bacteraemia and organ failure have been published . The fol6 cells/ml at 37 °C in a 5% CO2 atmosphere in RPMI 1640 medium enriched with 2 mM of l-glutamine, 100 U/ml penicillin G, 100 mg/ml gentamicin and 10 mM pyruvate with or without 10 ng/ml lipopolysaccharide (LPS) derived from Escherichia coli O55:B5 , and 5 μg/ml Pam3Cys-SKKK . After incubation for 24 or 48 h, plates were centrifuged at 800 × g for 7 minutes, and the supernatants were collected and stored at −70 °C until assayed. Cytokines were measured with a Bio-Plex Pro Human Cytokine Panel on a Bio-Rad Luminex 200 suspension array system . The measurements were carried out according to the manufacturer’s instructions. All samples were measured in duplicate. Every plate had its own standard curve built from data also measured in duplicate. The cytokines used were tumour necrosis factor-α (TNF-α), interleukin (IL)-6 and IL-8 with supernatants coming from the 24-h incubation of the plates, as well as IL-4, IL-10, IL-12, interferon-γ (IFN-γ) and granulocyte-macrophage colony-stimulating factor (GM-CSF) using supernatants coming from the 48-h incubation of the plates. The lower limit of detection was 0.5 pg/ml  and repeated on days 3, 7 and 10. PBMCs were separated after gradient centrifugation of the blood over Ficoll-Hypaque density gradient . After three washings in ice-cold PBS, pH 7.2 (Biochrom), PBMCs were counted using a Neubauer plate with trypan blue exclusion of dead cells. PBMCs were then stimulated in duplicate at a density of 5 × 10U test. ROC curve analysis was done to identify TNF-α production on day 3 that could significantly differentiate between patients and healthy control subjects. Using this ROC curve, a cut-off cytokine concentration with more than 80% sensitivity for this discrimination was defined. Patients above this cut-off were considered to have adequate TNF-α production by circulating PBMCs on day 3; those below this cut-off were considered to have defective TNF-α production by circulating PBMCs on day 3. ORs and 95% CIs for death between patients with adequate and defective TNF-α production on day 3 were determined using Mantel-Haenszel statistics. In order to identify which patients’ characteristics at admission might be predictive of defective TNF-α production on day 3, admission characteristics of patients with adequate and defective TNF-α production on day 3 were compared. Comparisons were done with Student’s t test for quantitative variables and the chi-square test for qualitative variables. A step-wise forward logistic regression analysis was conducted with defective TNF-α production on day 3 as the dependent variable; admission characteristics with differences at a p value below 0.10 were entered into the equation as independent variables; and ORs and 95% CIs were determined. Any p value after adjustment for multiple comparisons according to the method of Bonferroni was considered significant.Results were expressed as mean ± SE. Patients were divided into survivors and non-survivors on the basis of their 28-day outcome. Comparison of each cytokine between healthy control subjects and patients as well as between survivors and non-survivors was done using the Mann-Whitney From among the total enrolled patients in the clinical trial, 95 patients participated in the substudy. The study flowchart is presented in Fig. 3Cys stimulation was shown for a lower number of cytokines, mainly TNF-α and IL-10 . The OR for death with defective production of TNF-α by PBMCs after stimulation with LPS was 5.18 ; this was also shown after survival analysis (Fig. Next, we designed a ROC curve to discriminate TNF-α production by PBMCs after LPS stimulation on day 3 between patients and healthy volunteers. Generated ROC curves for TNF-α production after LPS stimulation provided a statistically significant AUC Fig. . Co-ordisis Fig. .p values less than 0.100 were found for APACHE II score, sex, acute coagulopathy and CV failure. Sex, acute coagulopathy and CV failure were entered into the equation of the step-wise logistic regression analysis as independent variables. APACHE II was not entered into the equation, because blood pressure is one of the APACHE II components. This analysis revealed that CV failure was the only risk factor associated with defective production of TNF-α on day 3 (Table These findings drive the concept that defective production of TNF-α by PBMCs on day 3 may be a marker of sepsis-induced immunosuppression and generate a key question: Can clinical signs of a patient on admission guide the prediction of the level of TNF-α production by PBMCs on day 3? To answer this question, univariate analysis was performed using demographic and clinical characteristics of patients with TNF-α production greater than 250 pg/ml and TNF-α production less than or equal to 250 pg/ml from circulating PBMCs after LPS stimulation on day 3 (Table 3Cys is restored by day 7. Defective TNF-α production after stimulation with LPS is associated with unfavourable outcome. CV failure on the first day is a predictive sign of the persistence of defective TNF-α production on day 3.The present study shows that defective cytokine production from circulating PBMCs following a severe gram-negative infection is a major trait of sepsis-induced immunosuppression. Defective TNF-α production after stimulation with LPS is the most susceptible trait of this process and remains as late as day 10, whereas defective TNF-α production induced by stimulation with PamAlthough traditionally conceived of as a regulator of an exaggerated immune response, it is now broadly accepted that immunosuppression may exist right from the onset of sepsis . This isPseudomonas aeruginosa and Staphylococcus aureus. By flow cytometric analysis, they showed that the intracellular levels of IL-6 and TNF-α in patients with sepsis were significantly lower than those of healthy control subjects. Analysis of a subset of 15 patients after 7 days showed an increase of intracellular cytokine levels. However, they studied fewer patients than we did; they did not report differences between survivors and non-survivors on day 7; and their population comprised patients with infections of mixed aetiology, including the respiratory tract. The same group of researchers [TNF, IL-6 and IL-8, was decreased on day 7 among non-survivors compared with the first 48 h from organ dysfunction. This finding was compatible with our observation of persistence of sepsis-induced immunosuppression among non-survivors.In a recent publication, Santos et al.  stimulatearchers  focused 3Cys, which is a purified ligand for TLR1 and TLR2. Owing to the interaction of TLR4 becoming tolerant to LPS with the other TLRs making them tolerant to their ligands, it becomes evident that survivors of our study escaped from tolerance to both TLR2 and TLR4 as early as 48 h after the first blood sampling.It should be emphasised that the enrolled studied population had sepsis originating from gram-negative bacteria Table . AlthougTwo main limitations of our study should be underscored. The first limitation is related to the inability to isolate the exact pathogen in all enrolled pathogens, raising concerns regarding whether the results may be generalisable to all patients with severe gram-negative infections. However, the infections to be studied were selected because they are caused mainly by gram-negative bacteria , whereasOur results encourage former attempts with the use of immunostimulation to improve sepsis outcomes. Recombinant human IFN-γ was the most common approach, originating as an idea in the early 1980s . LeentjeThe present study emphasises the association between cytokine stimulation of circulating PBMCs and final outcome. Sustained decreased cytokine production is linked with great risk for unfavourable outcome, and it is a characteristic of sepsis-induced immunosuppression. Defective production of TNF-α lasts as long as day 10, and it is associated with greatest risk for unfavourable outcome. CV failure on admission is a major indicator of the likelihood for defective TNF-α responses over the time course of sepsis. These findings can help in the design of future trials on immunostimulation for the management of severe sepsis.


Virus-like-particles (VLPs) are attractive nanoparticulate scaffolds for broad applications in material/biological sciences and medicine. Prior their functionalization, specific adaptations have to be carried out. These adjustments frequently lead to disordered particles, but the particle integrity is an essential factor for the VLP suitability. Therefore, major requirements for particle stabilization exist. The objective of this study was to evaluate novel stabilizing elements for functionalized chimeric hepatitis B virus core antigen virus-like particles (HBcAg-VLP), with beneficial characteristics for vaccine development, imaging or delivery.The effects of a carboxy-terminal polyhistidine-peptide and an intradimer disulfide-bridge on the stability of preclinically approved chimeric HBcAg-VLPs were assessed. We purified recombinant chimeric HBcAg-VLPs bearing different modified C-termini and compared their physical and chemical particle stability by quantitative protein-biochemical and biophysical techniques. We observed lower chemical resistance of T = 3- compared to T = 4-VLP (triangulation number) capsids and profound impairment of accessibility of hexahistidine-peptides in assembled VLPs. Histidines attached to the C-terminus were associated with superior mechanical and/or chemical particle stability depending on the number of histidine moieties. A molecular modeling approach based on cryo-electron microscopy and biolayer interferometry revealed the underlying structural mechanism for the strengthening of the integrity of VLPs. Interactions triggering capsid stabilization occur on a highly conserved residue on the basis of HBcAg-monomers as well as on hexahistidine-peptides of adjacent monomers. This new stabilization mechanism appears to mimic an evolutionary conserved stabilization concept for hepadnavirus core proteins.These findings establish the genetically simply transferable C-terminal polyhistidine-peptide as a general stabilizing element for chimeric HBcAg-VLPs to increase their suitability.The online version of this article (10.1186/s12951-018-0363-0) contains supplementary material, which is available to authorized users. Virus-like-particles (VLPs) are self-assembled, proteinaceous nanoparticles derived from naturally occurring viruses . In lastOne of the best characterized model VLP is based on the wildtype hepatitis B virus core antigen (HBcAg WT) , 10. TheVirus-like-particles per se are robust structures –19, and Aim of this study was to investigate the stabilizing effect of a C-terminal, histidine-peptide extension, as well as a conserved intradimer disulfide-bridge on the stability of chemically and physically stressed T = 3 and T = 4 chimeric HBcAg-VLPs. Protein-biochemical and -biophysical methods were employed to allow comparison and semi-quantification of the stability. To investigate this, we used a preclinically evaluated chimeric HBcAg-VLP which is decorated by the extracellular loop of splice variant 2 of claudin 18 (CLDN18.2). Proteins of the claudin family are essential transmembrane components of tight junctions. CLDN18.2 is a highly selective tumor-associated antigen and is significantly expressed in different solid cancer entities  and in normal tissues strictly restricted to gastric mucosa . TherefoIn this paper we demonstrate that chimeric HBcAg-VLPs are stabilized against diverse stress conditions by addition of a C-terminal polyhistidine-peptide and/or a disulfide-bridge. These findings may be of high importance for future HBcAg-based nanostructural design.E. coli and purified both under native and under capsid dissociating conditions , on the stability of a chimeric HBcAg-VLP based vaccine candidate for cancer immunotherapy . For thions Fig. b. Preparons Fig. b. Those ons Fig. b, c. Theons Fig. d. The imThe particulate structure of the purified VLPs was verified independently by approved techniques, namely DLS, NAGE, dot blot analysis and negative staining TEM (transmission electron microscopy) analysis Table  16, 39––39–41. UTo evaluate the influence of the disulfide-dependent HBcAg-multimerization on the stability of chimeric VLPs, we analyzed their occurrence, localization and reducibility in the 6His and ΔHis constructs. Disulfide-dependent chimeric HBcAg-multimers could be separated and detected by non-reducing SDS-PAGE conditions Fig. b. We ideIn summary, these results indicate that both constructs form highly pure particles which can be further analyzed for their capsid stability.ΔHis- and 6His-VLPs samples were subjected to various chemical stress conditions and were analyzed for their capsid integrity by three independent approaches, NAGE, DLS, and dot blot. Dot blot data relied mainly on the detection of the spotted VLPs by the mAb3120. This antibody detects a discontinuous, conformational epitope, located on the basis of two monomers assembled in a dimeric trimer VLP-structure , 43. RedWhen the chimeric VLPs were treated with urea, a chaotropic, nonionic protein denaturant, the particle stability was affected in a urea-concentration dependent manner. 6His-VLPs were significantly more resistant than ΔHis-VLPs. The stability of both icosahedral particle symmetries was diminished in the presence of DTT. Furthermore, the capsid stability of T = 3 particles of both constructs dissociate at lower urea concentrations than T = 4 particles. Similar results were obtained by dot blot analysis using mAb3120 specific for assembled capsids  at concentrations between 0.01 and 0.1% on the stability of 6His and ΔHis capsids was analyzed Fig. b. Until We next examined the VLP stability of 6His and ΔHis over a broad pH range. At acidic ranges, both constructs were similarly stable Fig. c and parOverall, all analyzed chemical stress parameters have an impact on the particle stability of chimeric VLPs and DTT treated samples showing a lower stability in urea- and SDS-tests. In general T = 3 particles show a minor stability compared to T = 4 particles . As delineated above the most significant effects on the chemical and physical particle stability of 6His and ΔHis were observed in urea and temperature stress tests Figs. . TherefoThe temperature stress test were closely examined, samples were exposed to different temperatures above 76 °C in steps of 1 °C. Again, a sequential stability of the four VLP constructs could be observed: 12His, 6His > 3His > ΔHis. No significant difference between T = 4 and T = 3 stability was observed . The additional 6His2nd/ΔHis2nd constructs deviate from 6His/ΔHis but varied in the epitope sequence and furthermore in the epitope insertion site. Here, a novel epitope was inserted, resulting in particles with different behavior (e.g. higher electro mobility) in contrast to the previous used chimeric VLPs. In four out of six stress tests a similar stability of both constructs were observed (data not shown). Remarkably, similar results as for the original 6His and ΔHis constructs were obtained in urea and pH stress test  exerts its stabilizing influence also on other HBcAg-VLPs, and therefore show a transferability of the peptide-mediated stabilizing effect, all stress-tests were repeated with a second chimeric VLP pair  or disassembled (VLP + 8 M urea) chimeric particles were performed, using two specific antibodies in a biolayer interferometry (FortéBIO Octet Red) experiment Fig. . As expeIn order to obtain possible 3D structures of the C-terminal hexahistidine-peptide and hereby assess potential stabilizing interactions at sub macromolecular level, we performed MC (Monte Carlo) and MD (Molecular Dynamics) simulations in combination with a density map derived from 6His-HBcAg cryo-electron microscopy images. From 1800 single particles extracted from cryo-electron microscopical images of VLP, a 3D reconstruction with a final resolution of 7 Å was produced. The published crystal structure of the HBcAg dimer fitted well into the obtained density map Fig. a.Fig. 7SFrom free MD simulations Fig. d, it becIn summary, we developed a molecular simulated model for the hexahistidine-peptide-dependent particle stabilizing effect based on cryo-electron microscopy data, which suggested stabilization via two stages. An interaction of proximal peptide-histidines with the HBcAg luminal side mainly via his47 may occur, additionally adjacent hexahistidine-peptides within one dimer may interact.Employing protein-biochemical and -biophysical methods, we have demonstrated that a C-terminal modification consisting of a short linker peptide (GGS) and a polyhistidine-peptide substantially increases the proportion of assembled chimeric HBcAg-VLPs when subjected to various chemical or physical stress conditions. The stabilizing effect was proportional to the length of the polyhistidine-peptide . Interestingly, T = 3 particles demonstrated a minor chemical resistance compared to T = 4 particles. By utilizing MD simulations which were deduced from cryo-electron microscopy data and supported by biolayer interferometry, we were able to develop a theoretical model where the hexahistidine-peptide interacts on the basis of the HBcAg-mono/dimer and exert stabilizing effects. In addition, a naturally occurring and experimentally confirmed disulfide-bond (cys61) was identified as an additional, independent stabilizing element for chimeric HBcAg-VLPs.The exact localization of the C-terminal linker-hexahistidine-peptide could not be determined yet. The crystal structure of the HBcAg WT (terminates at position 149) revealed that the amino acid visualized last is thr142, which resides on the basis of the dimer and inside of the VLP . From thWe want to deduce the relevance of the identified stabilization mode to related WT core particles. First, we analyzed the sequence similarity between the full lengths WT C-terminus and the utilized C-terminal linker-polyhistidine-peptide Fig. a. The ovSecond, we combined HBcAg substitution analysis with phylogenic analysis of different hepadnavirus lineages. Here, we identified his47 together with gly111; trp125 and pro138 as absolutely conserved core residues between hepadnaviridae from men to fish, highlighted in blue in Fig. With the possibility to separate chimeric T = 3- from T = 4-particles by semi-quantitative native agarose gel method, we were able to quantify T = 3/T = 4 symmetry ratios and discover that T = 3-capsids are less chemical stress resistant. A previous report indicates an equal stability for the smaller T = 3- and the larger T = 4-capsids of the HBcAg WT, adr subtype 1–140 aa) in SDS- and pH-tests, but no quantifications were carried out . Böttche0 aa in Sm of the HBcAgΔ was reduced [It is widely accepted that a N- or C-terminal hexahistidine-tag may improve thermal stability without being hazardous to the structure of native proteins , 66–68.  reduced . We confTaken together VLPs are produced for various applications, and enhanced chimeric HBcAg-VLP-integrity is a constitutive factor that influences their suitability. The improvement of VLP-stability is of high interest for pharmacology. Studies by Ulrich as well as Lu noted problems which needed to be solved for the use of chimeric HBcAg-VLPs in human vaccines: one major problem was to receive intact particles that can withstand long-term storage , 74. Add2nd  and 6His2nd coding sequences were ordered as synthetic genes (Geneart) and cloned into the pET-21a expression vector (Novagen). The HBcAg 6His construct consists of the HBcAgΔ  which displays a 10mer-surface epitope of the highly selective tumor-associated cell lineage marker claudin-18 isoform 2 with additional flanking linkers (G4SG4), onto the MIR of HBcAg [E. coli BL21-Gold (DE3) cells (Agilent Technologies) pre-cultured in LB-medium at 37 °C, were used for expression. After overnight cultivation, a large scale expression in TB-medium was inoculated. All media were supplemented with 100 µg/mL ampicillin. Chimeric HBcAg-VLP expression was induced at an OD595 = 2.0–2.5 by the addition of 1 mM IPTG for 4 h at 37 °C. VLPs were purified from E. coli lysates by heat treatment at 42 °C overnight, followed by precipitation of VLPs in the soluble fraction with 0.2–0.25 M ammonium sulfate (AMS) for 2 h at 4 °C and slow stirring. After centrifugation, the AMS-precipitate was resuspended in PBS-Tween-buffer  by stirring overnight. For purification under native conditions, resuspended VLPs were loaded onto a HiLoad 26/60 Superdex 200 pg SEC-column , equilibrated with PBS. Chimeric HBcAg-VLPs were eluted in the void volume and stored until further usage at 4 °C. For purification under capsid dissociating conditions, resolubilized VLPs were disassembled with 3–3.5 M urea in PBS  at 4 °C overnight. The preparations were loaded onto a HiLoad 26/60 Superdex 200 pg SEC-column equilibrated with 3–3.5 M urea in PBS-buffer and the fractionated HBcAg dimer-pool was collected. The reassembly into VLPs was initiated by overnight dialysis at RT into 50 mM Tris–HCl, 800 mM NaCl (pH 7.0) buffer. Reassembled VLPs were further purified and concentrated by precipitation with 0.5 M AMS, followed by centrifugation, resuspension in PBS-Tween-buffer and dialysis into PBS-buffer. VLPs were stored at 4 °C until further use. All samples were subsequently analyzed by Bis–Tris-NuPAGE (Invitrogen) or Tricine-SDS-PAGE [http://www.cgl.ucsf.edu/chimera/). For all calculations, at least five different positions on the grid were documented and evaluated with ImageJ 1.45e software [Chimeric HBcAg ΔHis , 3His, 12His, ΔHisof HBcAg . E. coliSDS-PAGE . The proSDS-PAGE . When nosoftware .All stress tests were performed with VLPs purified under native (data not shown) and dissociating conditions and showed similar results. The VLP concentrations were kept constant and assays were carried out in buffer with low ionic strength to minimize concentration and/or reassembly effects , 52, 70.2 ≥ 0.98 for 6His and ΔHis-VLPs (0–8.5 µg protein). The inter-assay variance showed a maximum deviation of ± 3% (n = 3). For comparative dot blot analysis, 0.27 µg  or 0.54 µg (ΔHis) of VLP samples were dotted onto a nitrocellulose membrane , followed by membrane blocking, washing and incubation for 1 h at RT with a HBcAg particle-specific, 1:10,000 diluted monoclonal antibody  [Native agarose gel electrophoresis (NAGE) was performed using standardized 2.6% agarose gels poured in custom made equipment ensuring a homogeneous, smooth, plane surface and an equal thickness of the gels. 7 µg of each chimeric VLP sample were loaded per well and separated for 13 ± 1 h at 50 V in TAE buffer at 4 °C. Gel staining was done with fresh PageBlue colloidal Coomassie staining solution (Thermo Fisher Scientific) for 30 min, followed by overnight destaining with distilled water until a homogeneous background was obtained. The linearity between protein band intensity/protein quantity was calculated at R, Japan) . Bound a, Japan) , 79. SecDensitometric analysis of stained native agarose gels was done with four (6His) or three (ΔHis) independent VLP purifications and at least 4–10 stained agarose gels were quantified for each stress test. The pH-test was performed with only two independent purifications for both constructs. Semi-quantitative analysis of colloidal Coomassie stained protein band intensities was performed using a high-resolution image scanning system (Microtek ScanMaker i800) and the evaluation software ImageQuant TL version 7.0 . To ensure comparability, the complete NAGE procedure  was standardized. Quantification was only done with gels showing a clear separation of T = 3-/T = 4-symmetries and a uniform background staining. Signal intensity  minus background was calculated for each protein band and normalized against untreated reference samples on the same NAGE  sensors on a FortéBIO Octet Red system . 4.5 µg/mL of assembled VLPs (6His or ΔHis) and 20 µg/mL of denatured (8 M urea) VLPs were used for loading routine (200 µL per sensor). Loading was terminated manually when all sensors reached the same response-rate. Sensors were blocked after loading with 5% BSA in PBS-buffer for 300 s. The His6-peptide antibody was used at concentrations of 5, 7.5 and 10 nM. The HBcAg particle-specific antibody mAb3120 was used at a concentration of 50 nM. Data were processed and analyzed using the Octet Data Analysis 7.0 software. Program: baseline: 1000 rpm for 180 s; load: 1000 rpm for 600–1200 s; baseline: 1000 rpm for 180 s; blocking: 1000 rpm for 300 s; association: 1000 rpm for 600 s; dissociation: 1000 rpm for 600 s.For the simulation of the chimeric VLP C-terminus including the hexahistidine-peptide, a 7 Å density map derived from cryo-electron microscopy images of 6His-VLPs was used as described previously , 80. TheAdditional file 1: Table S1. Quality control of chimeric VLPs. Table S2. Mass spectrometric analysis (MALDI-TOF-MS) of tryptic fragments derived from PAGE-purified 6His (bold) and ΔHis (underlined) VLPs. Figure S1. Graphical evaluation of densitometric analysis of the monomeric protein bands. Monomer protein bands intensities of 0 mM DTT were set to zero (background) and max was set to 100 % (complete reduction) see Fig. Figure S2. Effects of chemical or physical stress on VLP stability analyzed by dynamic light scattering (DLS). Chimeric 6His-VLPs (6His) and ΔHis-VLPs (ΔHis) were A) chemically or B) physically stressed in the absence (black and light grey) or presence  of 100mM DTT and subsequently analyzed with DLS. The mean hydrodynamic diameters of all stressed VLP samples are analyzed and only measurements with adequate scattering intensities were plotted into the graphs. Standard deviations are indicated by error bars. Figure S3. Graphical representation of T=3/T=4-symmetry-ratios of different stress tests. T=3/T=3-symmetry-ratios of chemically (A) or physically (B) stress test were calculated from normalized densitometric measurements Figs. Additional file 2: Movie S1. Simulated, internal localization of the hexahistidine-peptide in 6His-VLP. Capsid density map (gray) and fitted crystal structure of a HBcAg dimer . Penta-dimer structure in colored ribbons, green distal monomers and rainbow colors proximal monomers from which the C-terminal ends (red) are simulated (compare Fig.


Journal peer review lies at the heart of academic quality control. This article explores the journal peer review process and seeks to examine how the reviewing process might itself contribute to papers, leading them to be more highly cited and to achieve greater recognition. Our work builds on previous observations and views expressed in the literature about (a) the role of actors involved in the research and publication process that suggest that peer review is inherent in the research process and (b) on the contribution reviewers themselves might make to the content and increased citation of papers. Using data from the journal peer review process of a single journal in the Social Sciences field , we examine the effects of peer review on papers submitted to that journal including the effect upon citation, a novel step in the study of the outcome of peer review. Our detailed analysis suggests, contrary to initial assumptions, that it is not the time taken to revise papers but the actual number of revisions that leads to greater recognition for papers in terms of citation impact. Our study provides evidence, albeit limited to the case of a single journal, that the peer review process may constitute a form of knowledge production and is not the simple correction of errors contained in submitted papers. This article explores journal peer review and seeks to examine how the reviewing process might itself contribute to the citedness of papers. Our paper employs a range of bibliometric data, including that on the time taken by the journal from the moment a paper is received to the point in time when the journal decides finally to accept or reject the paper, the count of submissions of papers, and initial editorial decisions about a paper on receipt by a journal. We relate these measures to the citedness of the published papers. Our work builds on previous observations about (a) the role of actors involved in the research and publication process which has argued that peer review is inherent in the research process  see for example  and the capacity of the process, in terms of the time and resources to be allocated to reviewing. While this is a useful perspective, and raises the important issue of journal size—in terms of the amount of space journals have to publish papers—the actual internal processes are not investigated. A related work which complements the system level perspective adopted by the authors discussed above is the work on the seasonality of paper submission by Shalvi et al.  who obseWe now examine the important work that has looked more closely at features internal to the process of journal peer review, beginning with the factors that affect the judgement of quality. We then move to a discussion of how the peer review process influences the content and quality of the papers submitted. Questions on how content and quality are influenced sit within the context of decisions about judgement, and, as we have indicated above, ultimately journal editors must decide whether to accept a paper or reject it. However, within the process of peer review, the issue of how content and citation are affected is important.The literature on the topic of judgement in peer review is diverse and very extensive. Much has been made of the subjectivity of peer review, and this has been supported by studies which have shown that peer reviewer’s assessments of the same paper may often disagree. For an earlier by but important work on the sources of bias in peer review see Bakanic et al. , and forAs the peer review process involves two main types of actor, the editor of the journal  and the referees themselves who are normally two in number—but this can vary—various writers have focused upon agreement between the judgements of these two groups. The work of Hargens and Herting  examinedLooking more at the way in which reviewing take place and the interaction between authors, editors and reviewers, there is awareness that double-blinding, signing and other techniques of managing the review process  have fewer citations. We also consider that number of authors might affect the reviewing process in a number of ways. Following Bakanic et al. , a largeThe variable we chose to measure citation impact—i.e. the interest of the paper to later writers—needed to allow comparison of papers published at different times. The choices open to us to create a variable that would allow us to analyse the variable for citation impact using multi-variate methods were twofold. Firstly, one approach might have been to take the basic count divided by elapsed time (citations plus 1/time between publication and census date on which the citation counts were measured) and employ a Tobit model that addresses the issue of the left censoring of the citations (as published papers cannot have fewer than zero citations).The alternative was to use the transformation of the kind employed by other writers , URL, Keywords, Volume, Vol published year, Issue, Scopus citation Link, Scopus citation count. Other data were then obtained from another part of the publisher’s database for each paper: First submission date, First submission year, Original Decision, Final Submission Date, Final Submission Year, Manuscript Type, Manuscript Status, Decision, Days between Original Submission and Original Decision, Total Days in Review from Original Decision to Final Decision, the number of submissions, number of reviewers in addition to editor involved in the review of the paper. This data was inspected and a data file containing the following fields was prepared for review and checking.Number of Authors;Start Page;End Page;Issue;Citation Count.Using the DOI from the publisher’s data, the following fields of data were then cross-checked with Scopus.The initial data set obtained from the publishers records comprised 598 publications shown below in Table Around 100 papers were submitted for most of the years, although in the last year for which data was collected, only 57 papers were submitted. Of the 598 papers submitted in the period, 199 were accepted over the period 2010–2015, while 60 were rejected. The rejection rate for the whole period is around 10% but this proportion changes from year to year with the rejection rate being very low for the first three years, and then rising for the remaining three years. The column Grand Total is the sum of the papers in the previous five columns. The rejection rate is calculated as the number of papers rejected, divided by the Grand Total. Papers which are in revision (Major or Minor) or which are in the Reject and Submit Category are in this data set still in the process of revision and resubmission and were not included in the analyses of either turnaround time or of the relationship between the reviewing process and ultimate outcome.The analysis therefore focused upon papers where a formal decision to accept had been reached by the editor of the journal at the point the data was downloaded, which was in March 2017. These papers are noted in the first column of data in the first table. To help identify the set, these annual paper numbers and the grand total are in bold.The analysis of the link between aspects of the reviewing process and citation was carried out on a small subset of the 199 papers, comprising 152 papers. This smaller set of papers excluded two types of papers from the analysis which we thought might make our analysis unreliable: (a) those submitted in 2015, of which there were 23, as the citation counts of such papers was likely to be very low and possibly unrepresentative; (b) a further 44 papers were excluded from the set of 199 accepted papers as no DOI was logged in the publisher’s data, and no citation count could be considered accurate as there was doubt over the identity of the paper.We reviewed the data set for the period 2010–2015 and report the information on the major variables of note, which are those concerned with the reviewing process, and with citation impact.As can be seen in the following Fig. Before we examined the effect of the reviewing process on subsequent citation, we were concerned to examine the original decisions made by the editor of the journal to see to what extent these have on the subsequent submission process and need for resubmission. We expected to see some consistency in our data on the question of whether papers that were considered to need more work at first review did indeed receive more work as measured by the number of revisions which they underwent. We investigated whether papers that were subject to a requirement for major revisions, or a revise and resubmit judgement, needed more resubmissions than papers that were in the categories “accept” or “minor revision”. Within our data set there was information concerning the original decision made about papers received on initial submission. There were four categories of initial decision. The first of these was “Accept”, the second “Minor Revision”, the third “Major Revision”, and the third “Reject and Resubmit”. The data from the journal for all papers subsequently accepted (and therefore published) is given below.The evidence of the frequency counts from table suggests that the initial “original decision” about the paper and its need for further work is not a random event and that there is an association between the original decision on the need for re-work of the paper and what subsequently happens in terms of revisions.p value is 0.000.Analysis of the data in Table Two interpretations of this statistical association may be considered. On the one hand, the initial judgement may be considered to be one of quality assessment where papers that have more interest and better execution are deemed likely to need less amendment and subsequently, because the changes required are less onerous, fewer resubmissions are required. On the other hand, this link between initial decision and the number of subsequent revisions can be seen to be a simple matter of cause and effect without any reference to the quality of the paper as such: papers requiring more work are given an initial assessment that more revisions are required, and, in the course of amending the manuscript, this greater level of work needs more resubmissions to revise the paper to meet the original criteria for acceptance. Our test is not able to distinguish between these different explanations, but our view is that the data is an indicator of the reviewing effort. We therefore created a variable “Revision Effort” by combining the accept and minor revision papers into one set of low effort papers and the major revision and revise and resubmit papers into another set of high effort papers.We present two models of the effect of reviewing on citation impact. One model uses an OLS regression with the dependent variable being the ArcSinh transformation of the citation impact. The other model applies a Tobit regression. The following tables provide the results of the statistical tests. Tables Both models suggest that the reviewing process is related to citation impact. In both models the extent to which a paper is reviewed is related to its citation impact; however, the turnaround time is also significant in the models but it is negatively associated with citation impact. Thus, the longer a paper is in review, the more likely it is to have lower citation rate, all other relevant factors considered.Of the other variables that we considered could affect the citation impact of papers , it is the count of keywords and the presence of key topic keywords which are positively associated with citation impact of the paper. While neither model has a high predictive power, both models do perform similarly in that in each, the same variables affect the dependent variable, citation impact.We must first note a number of caveats about our work: these are mainly limitations and qualifications of our findings that relate to the general context in which we have carried out the work, but there are some specific points about the analysis.Initially, we should note that our study is significantly context bound and is therefore likely to be of limited applicability to other fields, and we do not make the claim that our findings are widely applicable. The area in which we have carried out our research, which is the field of management and business, is very different from other areas of scientific enquiry. As Bjork and Solomon  note in system of peer reviewing in the journal we have chosen. But this is in fact an unavoidable constraint arising from the need to consider papers which had citations (and been accepted). Consequently, papers which are still in review, of which there were a number in the journal, were excluded from the analysis.We should also note that our analysis has not considered all the papers that have been received by the journal in the period studied. Clearly, the information we were seeking to use for our analysis concerned citation counts of papers and we therefore excluded papers that were still in the reviewing system, some of which are likely to have been in review for a considerable period. This therefore limits our description of the We believe however, that empirical studies, even if they are limited in scope as ours has been, are valuable and have the potential to enlarge the debate about peer review, a debate which will only benefit from the engagement of more of the key actors  and the availability, and analysis, of more evidence.Our analysis has demonstrated that, in the limited case we have chosen of the behaviour of reviewers, editors and authors in a single journal, and over a relatively short period in the history of that journal, there is a positive effect of our variable for reviewer effort—the count of reviews—upon the citation impact of a paper. By citation impact we mean the recognition and visibility that a paper achieves. We do not equate citation simply with quality. We also note a negative effect between the length of time a paper has spent in review and the citation impact, all other things considered. So it is not the amount of time a paper spends in review (the turnaround time) which matters to its subsequent citation but the engagement with reviewers that has an effect. We also note positive effects of the count of keywords, and the presence within the paper of discussion of key topics—as evidenced by the presence of relevant keywords.The effect of the count of reviews—the intensity and effort expended on reviewing a paper—on the subsequent citation impact suggests the credibility of the view emerging in the literature that peer reviewing has a constructive and not merely judgemental function in relation to the papers submitted.Let us briefly consider though, a sceptical line of argument which could be made against the case we have outlined. That sceptical line of argument is that peer review is simple error removal. If the reviewing process was essentially a judgemental activity by which errors are corrected , the reviewing effort variable would either have no statistical relationship with the outcome variable or, and this is more likely, it would have a negative relationship in that papers that needed more work at first review (a higher reviewer input) would have less recognition and general interest ultimately as such papers contained more errors to begin with. The analysis of our data, however, shows a positive connection between the effort of reviewing and the outcome variable. Under the assumption that peer review is merely error checking, this association would be a paradox which one could state thus: manuscripts at review that were thought to be in more need of correction were, at publication found to be of greater interest.As we have argued above, the knowledge production interpretation of the relationship appears more likely than the error correction interpretation. In this interpretation, our variable of review effort is a measure of the input to the manuscript from the reviewing process far more than we consider it to be an assessment of the extent of citation, and we have little evidence to draw the conclusion that the reviewing process misjudges the quality of manuscripts. Rather, as we have said above, we favour the interpretation based on our construction of the reviewing effort variable that it is the reviewing effort applied to the paper  that may in some way be responsible for an enhancement of the attractiveness and interest of manuscripts.How exactly peer reviewing performs as a knowledge production process, and is distinct from authorial creativity and knowledge generation, is clearly, on the basis of our study open to question. The literature suggests a number of dimensions to which we believe our evidence may point. Firstly, and perhaps closest in form to judgmental peer review itself is a dimension concerned with the removal of errors in the submitted paper’s text that subsequently strengthens the argument, and thereby leads to higher levels of citation. Other literature cited above suggests that variety, in terms of the number of reviewers and in terms of the disciplines of the reviewers involved, may play a role in manuscript development. While increasing the number of reviewers of different types increases the chance that errors will be picked up, we consider that diversity is likely to add conceptual dimensions to the submitted work, either developing concepts or arguments already present in the work or introducing new and complementary or even contrasting ones.We consider that there might well be three main dimensions to the improvement of the paper. Firstly, as we have suggested above, increasing review may reduce the number of errors present in the text. Secondly, an increasing number of reviews may help to realize the quality already inherent in the paper with the reviewing and resubmission process being nothing more than a mechanism for the realization of the manuscripts’ latent qualities. Thirdly, increasing review might add substantively to the manuscript with the enhancement in the recognition of the paper coming from the addition of new concepts, and their elaboration and incorporation.If now we return briefly to the issue of how long papers are in review before they are published (the turnaround time), we note that in our analysis, the amount of time papers spend in review is negatively associated with the citation impact of the paper. So while the number of times papers are revised (review effort) appears to lead to higher recognition for the paper (a higher citation count), a variable that might be thought to be positively linked to the number of revisions of a paper is negatively associated with the paper’s level of recognition. Our explanation of this relationship is that the time taken—we have referred to this as turnaround time—is better seen as a delay that may result from difficulties that the submitted work presents to the reviewing process. The delay might be caused by problems identifying suitable reviewers, or when authors have difficulty in dealing with the comments of reviewers. Our conclusion is therefore that time in review, contrary to our original assumption, is not a measure of or indicator of knowledge generation.In the literature review we discussed the differences in peer reviewing practices that prevailed between different subject fields. We might speculate that in fields where papers have fewer resubmissions (and the quantity of rework is less), the effect of revising the paper upon citation will be less. A suggestion for further research is to examine the reviewing practice in other journals and other fields where different writing and contribution practices apply.sui generis, but our work suggests that some form of knowledge production is taking place and we have suggested a simple typology. We suggest that reviewers should be credited with the contribution they make and that authors of papers be invited more formally to outline how they believe their work has benefited from peer review. Our suggestions tend towards what is already practice in open peer-review and post-publication peer review contexts.We have not established that peer review is a knowledge production process


None of these studies, however, have been conducted as close as possible to physiologic conditions, and so the relationship between the size distribution of TNF-antagonist complexes and the antagonists' biological activity or adverse effects remains elusive. Here, we characterized the binding stoichiometry and sizes of soluble TNF-antagonist complexes for adalimumab, infliximab, and etanercept that were formed in human serum and in phosphate-buffered saline (PBS). Fluorescence-detected sedimentation velocity analytical ultracentrifugation analyses revealed that adalimumab and infliximab formed a range of complexes with TNF, with the major complexes consisting of 3 molcules of the respective antagonist and one or 2 molcules of TNF. Considerably greater amounts of high-molecular-weight complexes were detected for infliximab in human serum. The emergence of peaks with higher sedimentation coefficients than the adalimumab monomer as a function of added human serum albumin (HSA) concentration in PBS suggested weak reversible interactions between HSA and immunoglobulins. Etanerept exclusively formed 1:1 complexes with TNF in PBS, and a small amount of complexes with higher stoichiometry was detected in human serum. Consistent with these biophysical characterizations, a reporter assay showed that adalimumab and infliximab, but not etanercept, exerted FcγRIIa- and FcγRIIIa-mediated cell signaling in the presence of TNF and that infliximab exhibited higher potency than adalimumab. This study shows that assessing distribution profiles in serum will contribute to a more comprehensive understanding of the  Since tumor necrosis factor (TNF) was established as one of the key mediators in the pathogenesis of several immune-mediated inflammatory diseases, the therapeutic effects of various TNF antagonists, including adalimumab (Ada), infliximab (Inf), and etanercept (Eta), have been demonstrated. Adalimumab is a human IgG1 monoclonal antibody (mAb),Adalimumab, infliximab, and etanercept are effective in treating rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis, and psoriasis.Common side effects associated with TNF antagonists are usually mild and well tolerated, and the incidence and severity varies slightly between adalimumab, infliximab, and etanercept.Several hypotheses have been posited in an attempt to explain reported differences in the effectiveness of the 3 TNF antagonists in the treatment of various diseases. In addition to possible differences in pharmacokinetics and tissue distribution, the most likely explanation is related to differences in their ability to form complexes with TNF.d) value of ∼4.6 pM.Each agent has been shown to have a strong intrinsic binding affinity for soluble TNF. The affinities, as determined by a surface plasmon resonance (SPR), were 30.4 pM for adalimumab, 27.3 pM for infliximab, and 11.8 pM for etanercept.in vitro. A number of complexes with molecular weights (MWs) in the 600–5,800 kDa range formed between adalimumab and TNF. However, these larger complexes were found to be transient, and upon overnight incubation, a single stable complex with an MW of ∼598 kDa, consisting of 3 adalimumab and 3 TNF molecules, was identified.Size distributions and stoichiometry of complexes formed between TNF and 3 different antagonists have been characterized in vivo environment to explain differences in the clinical efficacy of different TNF antagonists. Size-exclusion chromatography (SEC) coupled with light scattering (LS) or refractive index (RI) detectors and dynamic light scattering (DLS) techniques that were used in these studies require relatively simple solutions where only the molecule of interest and its interaction partner are present. Additionally, analysis is often restricted by the small number of amenable solvents, which are usually limited to general solvents such as phosphate buffers. Nevertheless, Demeule et al. showed that different complexes between a recombinant humanized mAb and its antigen can form in serum and phosphate-buffered saline (PBS).The above-mentioned studies have clearly revealed that different TNF antagonists form various types of complexes. These findings, however, cannot be easily extrapolated to the c(s) distribution modeling with SEDFIT,The present study aimed to reveal binding characteristics of adalimumab, infliximab, and etanercept to recombinant human TNF under near-physiologic concentrations and solution environment conditions. The sedimentation velocity analytical ultracentrifugation (SV AUC) with absorbance (UV) detection conducted at the micromolar range showed that infliximab formed the largest complexes, followed by adalimumab, and the smallest complexes were detected with etanercept, which is consistent with previously reported findings. The next target drug concentration (25 nM) was chosen based on actual serum concentrations measured in patients.E. coli was shown to be present in its trimeric form in previous crystallographic20,ws of 3.9 S and an estimated MW of 52 kDa, consistent with the calculated molecular weight of a trimer . Similar results were obtained in the control experiments performed with recombinant human TNF prepared using an E. coli expression system . Furthermore, FDS-SV with fluorescently labeled baculovirus-expressed TNF confirmed that it exists primarily as a stable, soluble trimer over a 2.5–25 nM concentration range in both PBS  and human serum . Accordingly, we used this concentration for trimeric TNF throughout the remainder of the manuscript.Recombinant human TNF purified from yeast or c(s) distributions. The peak sedimentation coefficient 20,ws and estimated MW were 6.9 S and 144 kDa for adalimumab and 7.4 S and 141 kDa for infliximab, consistent with the monomeric form of the respective antibody. In the case of etanercept, a major peak was at 5.0 S with an estimated MW of 119 kDa, which is less than the 150 kDa expected for monomeric etanercept.20,ws of 14.6 S (c(s) distribution of etanercept-TNF complexes were detected in response to overnight incubation  distribution. Mixing adalimumab and TNF at a molar ratio of 10:1 induced an ∼10% reduction in uncomplexed adalimumab. When the mixing ratio was 2:1 adalimumab to TNF, the level of uncomplexed adalimumab was less than 50%. In the presence of an equimolar concentration of TNF, no free monomeric adalimumab was observed and the major adalimumab:TNF complexes were detected at 8.2 S, 11.9 S, 14.4 S, and 17.4 S  distributions of mixtures of the different antagonists and TNF, the weight-average sedimentation coefficients were calculated for each experimental condition (To compare the sizes of various complexes detected in the ondition . The res1(TNF)1, (Ada)2(TNF)1, (Ada)3(TNF)1, and (Ada)3(TNF)2. Three other acceptable models, for which the rmsd values do not exceed the critical value, are shown in Table S2.To evaluate the stoichiometric ratio of adalimumab to TNF in the 4 major complexes, SV data were further analyzed in SEDPHAT using the hybrid local continuous distribution and global discrete species model. To define parameter values for discrete species, each of the 4 above-mentioned sedimentation coefficients was combined with calculated MWs, using 148 kDa for adalimumab and 55 kDa for TNF, according to values specified in the product monograph1(TNF)1, (Ada)2(TNF)1, (Ada)2(TNF)2, and (Ada)3(TNF)2 complexes were detected in experiments with 2–4 µM adalimumab and 2 µM TNF, corroborating the peak assignment determined by SV analysis  distributions of the 2:1 molar mixtures of adalimumab:TNF, the largest major complex was detected at 18.2 S, whereas with ratios of 1:1 and 1:2 molar this peak was absent, and the 17.0 S-peak was observed instead. As discussed above, the 17.0 S peak is likely to represent (Ada)3(TNF)2, whereas the 18.2 S peak corresponds to a larger complex. These results suggest that a molar excess of TNF over adalimumab results in the formation of smaller complexes.In the 25 nM equimolar mixture of adalimumab and TNF, the area under the 11.9 S-peak was larger compared with the 2:1 adalimumab:TNF mixture. In addition, an 8.2 S-peak only appeared in the equimolar mixture and was not detected when adalimumab and TNF were mixed at a 2:1 molar ratio. Similar results were obtained in experiments performed with 50 nM  and 100 nM of adalimumab . When adalimumab and TNF were mixed at molar ratios of 2:1, 1:1, and 1:2, major peaks were detected at 11.9 S and 14.4 S. Again, a smaller peak at 8.4 S was detected only in the presence of equimolar or excess amounts of TNF. In the 2, Ab2Ag, Ab2Ag2, Ab2Ag3, Ab3Ag, Ab3Ag2, Ag3Ag3, and Ab3Ag4 were expressed as functions of molar fractions of each of the components in the solution. Together with the total Ab and Ag concentration constraints, the derived equations were used to determine the best-fit populations of each AbAg complex in the mixtures containing 25 nM Ab and 2.5–100 nM Ag by nonlinear regression analysis. All 9 dissociation constant values were assumed to be equal to that derived from the ITC analysis of direct titration of TNF into an adalimumab-Fab solution. The raw heat signals were integrated to generate a plot of ΔH kcal/mol of injected TNF versus the molar ratio of TNF/Fab, as displayed in the middle panel of To verify that the observed changes in complex sizes were due to changes in the relative concentrations of adalimumab and TNF, numerical simulations using the Solver add-in for Microsoft Excel were performed.3Ag4 complex, the simulations predicted dependence similar to that observed in AUC, indicating that at equimolar and higher concentrations of TNF, populations of complexes containing 3 molcules of adalimumab decreased, accompanied by an increase in the amount of complexes composed of one or 2 adalimumab molecules  distribution, indicating that infliximab exists primarily in its monomeric form, in agreement with the UV-SV result. At a 10-fold molar excess of infliximab over TNF, a few peaks were detected, with the major peak at 13.9 S. In the mixtures containing infliximab:TNF at molar ratios of 2:1 and 1:1, the major peak was at 15.2 S with an apparent MW of ∼500 kDa, which suggests that this peak may correspond to (Inf)3(TNF)1 or (Inf)3(TNF)2. Similar to adalimumab, at equimolar infliximab:TNF, peaks with smaller sedimentation coefficients of 8.0 S and 12.6 S were observed. It should be noted that for each infliximab:TNF mixing ratio, the weight-average sedimentation coefficients were higher than those for adalimumab:TNF mixture 1, (Inf)2(TNF)1, (Inf)3(TNF)1, and (Inf)3(TNF)2 complexes provided the best fit for the experimental databased on rmsd. Statistically indistinguishable fits with rmsd values less than the critical value were achieved using 5 other models, which are summarized in Table S3. Thus, the 8.0 S peak likely represents (Inf)1(TNF)1 and the 12.6 S peak corresponds to (Inf)2(TNF)1, which is highly consistent with the MS results Fig. S5; .20,ws of 5.3 S and an estimated molecular weight of 114 kDa, which was tentatively attributed to monomeric etanercept, similar to the UV-SV result. Increasing amounts of added TNF resulted in an increased 7.0 S peak area with an apparent MW of 163 kDa. Given an estimated MW of 114 kDa for the etanercept monomer and 55 kDa for the trimeric form of TNF, the 7.0 S peak probably corresponds to an (Eta)1(TNF)1 complex, consistent with previously published studies.Finally, we performed FDS-SV on 25 nM etanercept alone and in the presence of varying concentrations of TNF . In the 20,ws of ∼4.0 S. For 25 nM antagonists suspended in human serum, at an optimized gain setting, approximately half of the total fluorescent signal was due to albumin. Similar contributions from albumin were reported for 10 nM anti-IgE IgG in human serum,Human serum is a highly complex medium that contains a substantial amount of proteins, with human serum albumin (HSA) being the most abundant, followed by immunoglobulin (IgG). HSA is a highly soluble and stable protein that has a molecular weight of 66.5 kDa and is present in serum at a concentration of ∼40 mg/mL.c(s) analysis of SV data acquired for 25 nM of adalimumab, infliximab, and etanercept in the presence of various amounts of TNF in human serum yielded frictional ratio values between 2.5 and 3.5, which were higher than those observed in PBS. It has been reported that larger frictional ratio values cause peak broadening.c(s) distributions from analysis of data acquired in human serum exhibited broader peaks than in PBS.In the previous attempts to apply FDS-SV to proteins in serum, imperfections of the fit arising from non-ideality or other unknown origin were encountered.20,ws of ∼7.6 S, corresponding to a monomer, is seen in the c(s) distribution obtained for adalimumab  peaks were not baseline- resolved, which could potentially compromise the accuracy of quantification for individual adalimumab-TNF complexes  distribution of adalimumab-TNF mixtures. At a 10:1 adalimumab:TNF molar ratio, the adalimumab monomer fraction decreased by more than 10%. Higher concentrations of TNF resulted in a greater decrease, with less than one-third of the adalimumab present in an uncomplexed form at adalimumab:TNF molar ratio of 2:1. Similar to the results obtained in PBS, at equimolar concentrations in serum, no free monomeric adalimumab was present, and approximately a third of the HSA-corrected fluorescence signal corresponded to the 14.8 S peak, whereas higher-order complexes contributed to approximately half of the total signal intensity. In the SV experiments performed with 50 nM and 100 nM adalimumab, at a 2-fold molar excess of TNF over adalimumab, the area under the 11.6 S peak was higher, whereas the amount of higher order complexes was lower . The tendency toward forming smaller complexes when TNF was in molar excess in serum was similar to that obtained in PBS. Even though the peak positions of the major complexes were similar, weight-average 20,ws values from the resulting c(s) distributions in human serum, excluding the albumin peak, were larger than those in PBS  distribution of 25 nM infliximab in serum was markedly broader than that in PBS and slightly shifted toward larger values, with a 20,ws value of 8.8 S compared with 7.1 S  distributions 1(TNF)1 complex. The 8.1 S-peak probably corresponds to Eta-TNF complexes of higher stoichiometry. In contrast to adalimumab and infliximab, at an equimolar ratio approximately one-fifth of the etanercept remained uncomplexed. The peak with the largest sedimnetaion coefficient was located at 10.3 S, indicating that, with the exception of the HSA-related peak, there were no substantial differences in the c(s) profiles of etanercept in PBS and human serum.The FDS-SV analysis of etanercept in human serum was complicated by the apparent co-sedimentation of the etanercept and HSA-bilirubin complex, causing an overlap of the corresponding peaks in ibutions . To estiTo further exclude the possibility that fluorescent labeling of antagonists may affect their interaction with TNF, we performed experiments with the same mixing concentrations using 25 nM unlabeled antagonists and 2.5–25 nM labeled TNF . Within the limitations imposed by the differences in the labeling efficacy, both in PBS and human serum, distributions of antagonists-TNF complexes exhibited essentially similar patterns irrespective of whether fluorescent probe was introduced into antagonist or TNF. The results confirm that labeling does not influence the interaction. In addition, the effects of incubation times on the distribution of the formed complexes were evaluated. Overnight incubation at 37°C caused the loss of fluorescent signal in mixtures formulated in PBS, presumably due to non-specific adsorption of antagonists-TNF to vial surfaces. In the case of human serum, it appears that overnight incubation induces a decrease in the weight-average sedimentation coefficient. This effect is more pronounced in adalimumab than in infliximab mixtures, whereas it is negligible in etanercept mixtures, consistent with the UV-SV results.The distribution of adalimumab and etanercept complexes with TNF was similar in PBS and in human serum. In contrast, a significantly broader distribution, with a considerable amount of larger complexes, was observed for infliximab in human serum compared with PBS  and 2B. c(s) distribution of 25 nM adalimumab in PBS without HSA c, indicating that non-labeled adalimumab competes with the labeled adalimumab for binding to HSA, which confirms that their HSA-binding abilities are similar.As described above, a single peak with a sedimentation coefficient of 6.8 S, corresponding to monomeric adalimumab, was present in the hout HSA . Upon adhout HSA , presuma20,w)cc, other than the bilirubin-HSA peak at 4.1 S, produced a main peak at 7.6 S, which was attributed to monomeric adalimumab, and no larger peaks were detected. To explain the differences in the 0 S peak . These r20,w)c. We performed a cell-based reporter assay using Jurkat/FcγRIIa/NFAT-Luc and Jurkat/FcγRIIIa/NFAT-Luc cells.We performed biophysical characterization of TNF-antagonist complexes for 3 antagonists, adalimumab, infliximab, and etanercept, comparing complexes formed in human serum with those formed in PBS using AUC FDS-SV. We demonstrated that complex size varies among the 3 antagonists, providing a rationale for differences in the induction of downstream signaling pathways via antagonist complex interaction with FcγRs.20,ws values of 6.8 S and 7.5 S, respectively. Weight-average sedimentation coefficients calculated for solutions containing adalimumab and TNF at the same concentration were higher in human serum than in PBS c2(TNF)1 and (Ada)3(TNF)1 complexes, respectively (1(TNF)1 and (Ada)3(TNF)2 complexes were identified.Despite differences in the weight-average sedimentation coefficients, the ectively . In addi20,ws of 8.2 S, which was attributed to an (Ada)1(TNF)1 complex, was revealed (1(TNF)1 complexes are detected with a corresponding decrease in the abundance of complexes with higher stoichiometries.Similar findings regarding the wide distribution pattern of adalimumab-TNF complexes have been previously reported. Data obtained using SEC revealed that adalimumab:TNF complexes span a wide range of molecular weights: 600–5,800 kDarevealed . Thus, w3(TNF)1 complex to which the 14.5 S-peak in the c(s) distribution was assigned was absent. Instead, a 408,047 Da band corresponding to (Ada)2(TNF)2 was detected. As discussed above, the slight difference in complexes detected by AUC and MS is likely attributable to differences in concentration and molar ratios at which measurements were performed.Native MS revealed the presence of adalimumab-TNF complexes with 1:1, 2:1, 2:2, and 3:2 stoichiometries, consistent with the AUC results . HoweverThe importance of determining the binding profiles of small molecule drug candidates to serum protein components, and incorporating these analyses into drug development protocols has been emphasized.c(s) distribution in human serum was wider than that in PBS, it was narrower than in PBS containing HSA. These observations imply that HSA may bind weakly to adalimumab-TNF complexes when present at over 1000-fold molar excess, whereas in human serum this low affinity interaction is considerably reduced. In SV experiments with adalimumab in HSA solutions, the formation of putative adalimumab-HSA complexes was abolished in the presence of human immunoglobulin. We therefore suggest that endogenous immunoglobulin in human serum is primarily responsible for the diminished adalimumab-HSA interaction.No separate peaks could be discerned in the sedimentation coefficient distribution pattern of adalimumab-TNF complexes in PBS solutions containing 40 mg/mL HSA, and it was considerably broader than that in the absence of HSA . Interestingly, even though the The formation of complexes between fluorescently labeled protein OPG at picomolar concentrations and serum albumin as low as 1 mg/mL has been recently observed.c(s) distribution of infliximab-TNF complexes in PBS was similar to adalimumab with regard to complex stoichiometry, but with larger weight-average sedimentation coefficients (3(TNF)1 complex was detected when TNF was in molar excess over infliximab, the additional formation of complexes with lower stoichiometries was observed in our study. The c(s) profile of infliximab-TNF complexes in human serum was markedly different than in PBS, showing a continuous distribution of complexes with no distinct peaks  distribution of adalimumab and infliximab in human serum and concluded that the soluble antagonist-TNF complexes were smaller than 100 nm. Nevertheless, our cell-reporter assay clearly showed that these complexes were able to induce considerable FcγR activation, suggesting a potential role in immunogenicity and manifestation of adverse effects.Self-aggregation is one of the major concerns with regard to the clinical safety of biological therapeutics.In conclusion, the results reported here provide clear evidence that FDS-SV is an extremely valuable technique for the prediction of therapeutic action and in-depth understanding of the binding of target protein drugs to their respective antigens directly in human serum at physiologically relevant concentrations. It is anticipated that incorporating FDS-AUC in the drug development process will positively contribute to optimal design of pharmaceuticals with enhanced efficacy and minimized immunogenicity.E coli was purchased from R&D Systems Inc. FDS-SV experiments performed with adalimumab and TNF prepared using a baculovirus and an E coli expression system yielded essentially the same results  and therefore, unless otherwise stated, baculovirus-expressed TNF was used in all experiments.Adalimumab (Humira®) was purchased from Eisai, infliximab (Remicade®) was obtained from Mitsubishi Tanabe Pharma, and etanercept (Enbrel®) was received from Takeda Pharmaceutical Company Limited. Recombinant human soluble TNF was produced recombinantly using a silkworm baculovirus expression system by ProCube® Biotechnology Center at the Sysmex Research and Development Center in Kobe. Recombinant human TNF purified from Two μM respective antagonist and 2 μM TNF were mixed in PBS pH 7.4 (Gibco) and incubated at 20°C for 2 hours or at 37°C overnight before AUC. Sedimentation velocity (SV) experiments with the resulting mixtures and with 2 μM antagonists in the absence of TNF were performed using an Optima XL-I analytical ultracentrifuge equipped with absorbance optics (Beckman Coulter) at rotor speed of 42,000 rpm. Scans were acquired at a wavelength of 280 nm with 30-μm radial increments. Experiments with 2 μM TNF were conducted at a rotor speed of 60,000 rpm using absorbance detection at 230 nm.DLS was conducted at 25°C using ZetaSizer Nano . Adalimumab, infliximab, and etanercept were diluted to 3.3 μM using PBS, filtered by 0.22 μm-pore size syringe filter (Millipore), and mixtures containing the respective antagonist:TNF at molar ratios of 1:0, 1:0.3, and 1:1 were prepared.Labeling of target proteins with a fluorescent probe that can be excited using 488-nm laser light and which emission wavelengths are greater than 505 nm is necessary for FDS measurements. We chose Alexa Fluor® 488 fluorescent dye (Molecular Probes®), which meets these requirements, and is also relatively insensitive to pH, and highly resistant to photobleaching. In addition, it was shown to be stable during AUC experiments conducted in serum.Twenty-five nM respective labeled antagonist and 0–25 nM TNF were mixed in PBS or human serum  and incubated at 20°C for 2 hours before FDS-SV AUC. Similar mixtures containing 25 nM unlabeled antagonist and 2.5–25 nM labeled TNF were incubated at 20°C for 2 hours or at 37°C overnight before FDS-SV AUC. Additionally, mixtures containing 50 nM and 100 nM labeled adalimumab were analyzed. The experiments were performed using fluorescence optics  at a rotor speed of 42,000 rpm. To prevent non-specific adsorption of protein to centerpiece walls and windows, 0.1 mg/mL lysozyme was added to PBS.c(s) distribution model.20,ws using density and viscosity of the respective buffer solutions measured using an Anton Paar density meter DMA4500 and viscometer Lovis 2000ME, respectively. To evaluate the fractional amount of antagonist-TNF complexes, the signal originating from the HSA-bilirubin complex as measured in SV experiments performed with human serum alone was subtracted from the total fluorescent signal obtained by integrating the area under the c(s) distribution of antagonist-TNF mixtures.The acquired data were analyzed with SEDFIT (version 14.6e) using the continuous i HDMS mass spectrometer (Waters Corporation) in positive ionization mode at 1.33 kV with a 150 V sampling cone voltage and source offset voltage, 0 V trap and transfer collision energy, and 5 mL/min trap gas flow. The spectra were calibrated using 1 mg/mL cesium iodide and analyzed using MassLynx software (Waters Corporation).TNF, adalimumab, and infliximab were buffer-exchanged into 150 mM ammonium acetate, pH 6.8 by passing the proteins through a Bio-Spin 6 column (Bio-Rad). The buffer-exchanged TNF (2 μM trimer) and respective antagonist  were mixed, incubated at 25°C for 20 min, and analyzed by nanoflow electrospray ionization MS using gold-coated glass capillaries made in-house . Spectra were recorded on a SYNAPT G2-SITC experiments were performed with a Fab fragment and full-length adalimumab at 25°C using an iTC200  or PEAQ-ITC  instruments. Prior to each experiment, TNF, adalimumab-Fab, and full-length adalimumab were extensively dialyzed against PBS containing 0.1% polysorbate 80. For adalimumab-Fab binding to TNF, 9.8 μM adalimumab-Fab was placed into the cell and 39.8 μM trimeric TNF was loaded into the syringe. For the experiments with full-length adalimumab, 1.7 μM adalimumab was placed into the cell and the 27.5 μM TNF was loaded into the syringe. Titrations consisted of a preliminary 1-μL injection, followed by 2-μL injections with a 120 s interval between 2 successive injections. The integration of thermograms was performed using NITPIC software2, luciferase activities were measured by using ONE-Glo Luciferase Assay System (Promega).Stable cell lines Jurkat/FcγRIIa/NFAT-Luc and Jurkat/FcγRIIIa/NFAT-Luc expressing FcγRIIa or FcγRIIIa, respectively, were established as described in.ref.


Candida species are commensals but some develop biofilms in prosthetic materials and host surfaces that may represent up to 30% of deaths related to infections, particularly in immunosuppressed patients. Tumor necrosis factor (TNF) exhibits a plethora of functions in host defense mechanisms whereas excessive release of TNF in inflammation promotes tissue damage. Cytokines released in an inflammatory milieu may influence the development of microorganisms either by promoting their growth or displaying antimicrobial activity. In protozoa, TNF may affect growth by coupling through a lectin-like domain, distinct from TNF receptors. TNF was also shown to interact with bacteria via a mechanism that does not involve classical TNF receptors. Using an in vitro C. albicans biofilm model, we show that TNF dose-dependently prevents biofilm development that is blocked by incubating TNF with N,N’-diacetylchitobiose, a major carbohydrate component of C. albicans cell wall. This finding represents a relevant and hitherto unknown mechanism that adds to the understanding of why TNF blockade is associated with opportunistic C. albicans infections. In biofilm development, features related to the microorganism and to the extracellular polymeric substances that compose its environment are relevant2.Biofilms are the major state that microorganisms utilize in their struggle to thrive since antimicrobials act against their planktonic, free-floating, state. Development of aggregates formed by the microorganism and an extracellular coat of secreted components and even host parts leads to biofilm formationCandida albicans account for a major part of those opportunistic infections with a mortality rate that can reach 40% of affected individuals, with obvious health and economic impacts3.Patients subjected to immunosuppression as well as those exposed to implanted devices and indwelling catheters are particularly affected by life-threatening systemic infections. Organization in biofilms after adherence to those devices offers to microorganisms an alternative to evade host defense. 6. Thus, targeting TNF has become an alternative to treat autoimmune diseases, including rheumatoid arthritis and inflammatory bowel disease. Considering that TNF has a major protective role against microorganisms, anti-TNF treatment would render patients prone to develop life-threatening infections4.There is extensive knowledge on the role of cytokines in human defense against microbes. Usually, cytokines are synthesized and released by host cells after being triggered by diverse stimuli. Analogous to other cytokines, TNF acts on mammalian cells via coupling to specific membrane or soluble receptors, leading to cell activation. Excessive TNF release during inflammation is associated to pain development, cell infiltration, hypotension, hepatotoxicity, and structural damage to tissues7. We investigated whether TNF could alter C. albicans in vitro growth. Our data, showing a previously unrecognized TNF interference with C. albicans biofilm formation, unravels a protective role of TNF against systemic, life-threatening opportunistic infections. In addition to the importance in the pathogenesis of Candida infections, this finding does also offer an alternative to prevent yeast biofilm formation.To the best of our knowledge, TNF was never shown to directly provoke changes in fungal development without the participation of mammalian cells. However, there is evidence that TNF induces trypanosome lysis through a TNF receptor independent mechanismC. albicans  significantly and dose-dependently inhibited the metabolic activity of the yeast biofilm, measured by the 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)- 2H-tetrazolium-5-carboxanilide (XTT) assay . As a control of antifungal activity, amphotericin B significantly impaired C. albicans planktonic growth (data not shown).Adding increasing concentrations of TNF to the growing biofilm of two strong biofilm producer strains of say Fig. . The assity Fig. , suggestC. albicans TNF-treated biofilms is shown in Fig. C. albicans growing biofilm led to a decreased filamentation after 24 h of incubation. The appearance, as well as the filamentation index, after 6 h of incubation  of ion Fig.  is similion Fig. . On the hae Fig. . Increashae Fig. .Figure 2Candida species have not been shown to display classic TNF receptors, we wondered whether TNF inhibitors would alter that TNF effect on Candida biofilms. Preincubation of TNF with the humanized monoclonal anti-TNF antibody adalimumab inhibited TNF effect on growing biofilms, whereas adding adalimumab without TNF actually increased biofilm metabolic activity, when compared to TNF-untreated biofilm , and D-mannose are major carbohydrates in Candida cell wall. Polymers of GlcNAc are called chitin and aggregate to glucans (glucose polymers) strengthening the fungus cell wall. A chitobiose is formed by GlcNAc molecules and provides linkage of asparagine to glucan residues in the cell wall. Those carbohydrates are very relevant to yeast metabolism. For instance, the structure of the septum during hyphae formation is formed by chitin. In addition, bioses rather than monosaccharides, as competitors, are considered more relevant to demonstrate a role of carbohydrates in yeast physiology13. Preincubation of TNF with N,N′-diacetylchitobiose abrogated the cytokine inhibition of biofilm formation, restoring yeast filamentation, a step where carbohydrates participate in Candida growth. This was a clear and significant effect shown whether using the XTT assay or biofilm morphological analysis  TNF or Thr105 to Glu110 of human(h) TNF altered that lectin-like activityC. albicans and Aspergillus fumigatus planktonic and biofilm growth, activating various genes, rendering those fungi less susceptible to antimicrobials17.TNF is one of the cytokines operating both in innate and adaptive immune mechanisms and our results were similar regardless of using mTNF or hTNF. Other cytokines may also operate similarly. Interleukin(IL)-17 was recently shown to promote in vitro biofilm model, as it may not reflect in vivo systems. However, patients exposed to treatment with etanercept were reported to have increased susceptibility to oral candidiasis18 and it was recently shown that TNF protected mice from disseminated C. tropicalis infection19. An in depth approach to possible mechanisms to explain TNF effect on yeast metabolism and biofilm structure is also needed to better evaluate the clinical relevance of the present data. Institutionalized patients as well as those needing implanted devices and prosthetic material are seriously affected by systemic, resistant, multigerm infections, commonly due to biofilm formation in implanted materials. Lining of implant materials with bioactive components is a current prophylactic strategy to prevent infections, rendering the development of compounds reproducing TNF domain with that lectin activity to halt C. albicans biofilm growth plausible.Limitations of our study include the use of an C. albicans strains from the collection of the Specialized Medical Mycology Center .ATCC 10.2.31 and CEMM 01.05.006TNF solutions were prepared at the time of use using RPMI 1640 supplemented with L-glutamine. Recombinant rat TNF-α (mTNF) and human Tumor Necrosis Factor-α (hTNF) were purchased from R&D Systems and Sigma-Aldrich, São Paulo, Brazil, respectively. Other reagents were purchased from Sigma-Aldrich do Brasil Ltda., SP, Brazil. Adalimumab and Etanercept were from Abbvie Farmaceutica and Laboratórios Pfizer Ltda., São Paulo, Brazil, respectively.C. albicans to TNF was determined using the broth microdilution method . Amphotericin B was a drug control.Susceptibility of 3CFU mL−1 in RPMI 1640 supplemented or not with TNF solutions, added at the start of the experiment, were incubated for 48 h, at 37 °C to evaluate TNF effect in growing biofilms. In order to investigate TNF effect on mature (preformed) biofilms, fungal suspensions containing approximately 0.25 × 103CFU mL−1 in RPMI 1640 were incubated for 48 h, at 37 °C, medium was then aspirated, nonadherent cells removed by thoroughly washing with sterile buffered PBS and wells were then filled with RPMI supplemented or not with TNF solutions and incubated for an additional period of 48 h, at 37 °C. Biofim formation was calculated using XTT - 2H-Tetrazolium-5-Carboxanilide) assay8, performed after either 48 h (growing biofilm) or 96 h (preformed biofilm) of incubation. XTT was added and samples were incubated at 36 °C for 5 h using an orbital shaker. After staining, plates were read at 492 nm.TNF solutions were prepared using buffered RPMI 1640. Biofilms were grown on 96-well microtitre plates. Fungal suspensions containing approximately 0.25 × 10After 6, 24 or 48 h incubation, plates were stained with lactophenol cotton blue dye and photographed under an inverted light microscope (Fisher Scientific™). The number of filamentous (hyphae) and yeast (blastoconidia) in 10 high power fields  was calculated to obtain a filamentation index: Filamentation = number hyphae/total cell counts (hyphae and blastoconidia) ×100.Biofilms were fixed with Karnovsky solution, dehydrated in graded ethanol, dried in hexamethyldisilazane, coated with gold and observed in an Inspect S50™ scanning electron microscope.Data are means ± SEM of at least 3 replicates for each treatment, analyzed using one-way ANOVA, followed by Tukey’s test.