Tnfr-deficient mice following exposure to soluble TNF ex vivo. Most TNF-induced effects were exclusively mediated by TNFR1, including induced glomerular expression of adhesion molecules, chemokines, complement factors and pro-apoptotic molecules. However, TNFR2 contributed to TNFR1-dependent mRNA expression of inflammatory mediators in glomeruli when exposed to low TNF concentrations. Chemokine secretion was absent in TNF-stimulated Tnfr1-deficient glomeruli, but also significantly decreased in glomeruli lacking TNFR2. In vivo, TNF-induced glomerular leukocyte infiltration was abrogated in Tnfr1-deficient mice, whereas Tnfr2-deficiency decreased mononuclear phagocytes infiltrates, but not neutrophils. These data demonstrate that activation of intrinsic glomerular cells by soluble TNF requires TNFR1, whereas TNFR2 is not essential, but augments TNFR1-dependent effects. Previously described TNFR2-dependent glomerular inflammation may therefore require TNFR2 activation by membrane-bound, but not soluble TNF.TNF is an important mediator of glomerulonephritis. The two TNF-receptors TNFR1 and TNFR2 contribute differently to glomerular inflammation in vivo, but specific mechanisms of TNFR-mediated inflammatory responses in glomeruli are unknown. We investigated their expression and function in murine kidneys, isolated glomeruli ex vivo, and glomerular cells in vitro. In normal kidney TNFR1 and TNFR2 were preferentially expressed in glomeruli. Expression of both TNFRs and TNF-induced upregulation of TNFR2 mRNA was confirmed in murine glomerular endothelial and mesangial cell lines. In vivo, TNF exposure rapidly induced glomerular accumulation of leukocytes. To examine TNFR-specific inflammatory responses in intrinsic glomerular cells but not infiltrating leukocytes we performed microarray gene expression profiling on intact glomeruli isolated from wildtype and  Cytokines produced by intrinsic renal cells and infiltrating leukocytes are central mediators of inflammatory kidney diseases. Tumor necrosis factor-α (TNF) is involved in glomerular inflammation and scarring The functional role of TNF in GN has been demonstrated in animal models. Systemic administration of TNF induced glomerular damage in rabbits Tnfr1-deficiency delayed the onset of GN, whereas deficiency of intrinsic renal cell-expressed TNFR2 abrogated glomerular injury following immune complex deposition Biological activities of TNF are mediated through two functionally distinct TNF receptors (TNFR), TNFR1 (CD120a) and TNFR2 (CD120b). An increased glomerular expression of TNFR1 and TNFR2 has been suggested by animal and human biopsy studies of GN Tnfr-deficient mice demonstrated that soluble TNF mediates most inflammatory effects in glomeruli via TNFR1-dependent mRNA induction of proinflammatory mediators, including adhesion molecules and chemokines. TNFR2 contributed to TNFR1-dependent effects after stimulation with low TNF concentrations. In addition, we identified a posttranscriptional role of TNFR2 in enhancing chemokine secretion. Consistent with these in vitro results, we demonstrated a predominant role of TNFR1 in mediating soluble TNF-induced glomerular leukocyte accumulation in vivo, with a selective contribution of TNFR2 to the accumulation of glomerular mononuclear phagocytes. Together, these data suggest that activation of intrinsic glomerular cells by soluble TNF requires TNFR1, whereas TNFR2 augments TNFR1-dependent effects. In contrast, no exclusively TNFR2-dependent inflammatory pathways were identified in soluble TNF-stimulated glomeruli. These data suggest that mice deficient for renal cell-expressed TNFR2 are protected from GN in vivo With TNF receptor-targeted interventions becoming a potential therapeutic strategy to reduce glomerular inflammation, mechanisms of TNFR-specific inflammatory responses in glomeruli remain to be elucidated. Here, we characterize TNFR1- and TNFR2-dependent glomerular inflammatory pathways in murine kidneys in vivo, in isolated glomeruli ex vivo, and in murine mesangial cells in vitro. Microarray gene expression profiling on intact glomeruli isolated from wildtype and Tnfr1−/−, B6.129-tm1MakTnfrsf1a) and 2  were originally purchased from the Jackson Laboratory  and bred in our Central Animal Facility  under specific pathogen-free conditions. Tnfr1 and 2 double-deficient mice  and C57BL/6J wildtype controls were generated by cross-breeding of single Tnfr-deficient mice. Renal leukocyte infiltration was induced in 6 to 8 week old male mice by intraperitoneal injection of 5 µg recombinant murine TNF . Perfused kidneys were harvested eight hours after TNF injection. Untreated male mice were used for paramagnetic isolation of glomeruli subsequently analyzed in ex vivo experiments. All experimental procedures involving mice were approved by the Ethical Committee of the Regierung von Oberbayern (AZ 55.2-1-54-2532.3-8-12).Mouse strains with targeted deletion of TNFR1  through the heart with a constant pressure of 40 mmHg using a custom-made, pressure-controlled syringe. Kidneys were digested with 1 mg/ml collagenase A  in 1 ml Hanks` balanced salt solution  at 37°C for 30 minutes. The digested tissue was passed through a 100 µm cell strainer . The cell suspension containing glomeruli with trapped paramagnetic Dynabeads was placed into a magnetic particle concentrator (Invitrogen) for 7 minutes. The first supernatant was pipetted into a separate tube and stored on ice. The remaining tissue was resuspended in 10 ml PBS, and placed again into the magnetic particle concentrator for 5 minutes. The supernatant was removed and remaining paramagnetically labelled glomeruli were resuspended in 4 ml PBS and passed through a 100 µm cell strainer. This suspension was placed again in the magnetic particle separator for 5 minutes, the supernatant discarded, and the remaining tissue was washed three times with 10 ml PBS. Finally, purified glomeruli were resuspended in 1.5 ml of PBS. All procedures were performed on ice except the collagenase digestion at 37°C. Samples of all obtained fractions were viewed microscopically. The final paramagnetically labelled fraction contained highly purified glomeruli with a yield of approximately 10,000 glomeruli per mouse kidney and a purity >97% (intact glomeruli versus tubular fragments or single cells). As previously described, isolated glomeruli were lacking the Bowman`s capsule Isolation of glomeruli from mouse kidneys was essentially performed as described by Takemoto and coworkers A murine glomerular endothelial cell line was originally derived from tsA58 immorto mice Tnfr-deficient mice following paramagnetic isolation of glomeruli as previously described Primary murine mesangial cells (pMC) were prepared from C57BL/6J wildtype and After isolation 5,000 intact glomeruli were resuspended in 3 ml RPMI 1640 supplemented with 15% FCS, 15 mM HEPES buffer (Invitrogen), 0.66 U/ml insulin and penicillin-streptomycin, and were incubated in 6 well plates at 37°C for 24 hours. After serum starving for further 24 hours glomeruli were stimulated with indicated concentrations of murine TNF in RPMI 1640 for 12 or 24 hours.Tnfr-deficient mice were serum starved and subsequently stimulated with 50 ng/ml TNF. After 12 hours total RNA was prepared using RNeasy Mini Kit . Integrity of each RNA sample was tested by denaturing agarose gel electrophoresis. Three independent preparations of 2 µg total RNA per genotype was used for biotin-labeled complementary RNA probe synthesis and hybridization of Affymetrix Mouse Genome 430 2.0 arrays according to the Affymetrix Expression Analysis Technical Manual. Triplicate arrays per genotype were scanned and analyzed using the Affymetrix GeneChip Operating Software (GCOS1.0). The complete data set was deposited into the Gene Expression Omnibus (GEO) database .For microarray experiments 5,000 glomeruli isolated from male C57BL/6J wildtype and http://rmaexpress.bmbolstad.com/). For probe set annotation the default Mouse 430_2.cdf was used. Following normalized RMA, significance analysis of microarrays (SAM) was applied using the Microsoft Excel plugin of SAM, version 1.21 Tnfr1,2−/−, Tnfr1−/− and Tnfr2−/− glomeruli compared to wildtype. A q-value <5% was used to identify probe sets that detected transcipts with different expression levels in the analyzed groups. If the same gene was identified by multiple probe sets, fold-changes to wildtype were calculated from the averaged signal intensities of the respective probe sets. Genes with an at least 1.5-fold difference in mRNA abundance were considered as differentially expressed between groups.For microarray analysis robust multichip average (RMA) http://david.abcc.ncifcrf.gov) The Database for Annotation, Visualization and Integrated Discovery . Complementary DNA was generated from 500 ng of total RNA by reverse transcription (RT) using random priming and MMLV reverse transcriptase (Invitrogen).2O and RT− controls were negative in all runs.TaqMan real-time PCR was performed on an AB Prism 7000 analyser  using heat-activated TaqDNA polymerase . For murine nephrin (NM_019459), VCAM-1 (NM_011693), P-selectin (NM_011347), and GAPDH (NM_008084) the following oligonucleotide primers (300 nmol/L) and hydrolysis probes (100 nmol/L) were used : murine nephrin, sense primer 5′-ACCCTCCAGTTAACTTGTCTTTGG-3′, antisense primer 5′-ATGCAGCGG AGCCTTTGA-3′, fluorescence labelled probe (FAM) 5′-TCCAGCCTCTCTCC-3′; murine VCAM-1, sense primer 5′-AACCCAAACAGAGGCAGAGTGTAC-3′, antisense primer 5′-GACCCAGATGGTGGTTTCCTT-3′, fluorescence labelled probe (FAM) 5′-TGTCAACGTTGCCCC-3′, murine P-selectin, sense primer 5′-ATGAA CCCTGTTTTAAACGAAAGC-3′, antisense primer 5′-CTTGGTTGCTGCAGGAC ATG-3′, fluorescence labelled probe (FAM) 5′-ACACAGCCTCCTGCC-3′, and murine Gapdh, sense primer 5′-CATGGCCTTCCGTGTTCCTA-3′, antisense primer 5′-ATGCCTGCTTCACCACCTTCT-3′, fluorescence labelled probe (VIC) 5′-CCCAA TGTGTCCGTCGTGGATCTGA-3′: For murine FXYD2 domain-containing ion transport regulator 2 , TNFR1 (NM_011609), TNFR2 (NM_011610), CCL2/MCP-1 (NM_011333), CCL3/MIP-1α (NM_011337), CCL5/RANTES (NM_013653), ICAM-1 (NM_010493), E-selectin (NM_011693), and 18S rRNA commercially available pre-developed TaqMan reagents (Applied Biosystems) were used. For some target genes amplification and PCR product detection was performed using SYBR Green PCR Master Mix reagents. The SYBR Green forward (f) and reverse (r) oligonucleotide primer sequences were as follows: murine Rab6B, 5′- GGTTGCCTGGTAGGTGTTGT-3′ (f), 5′- GCTGCGAAAATTC AAGTTGG-3′ (r); and 18S rRNA, 5′-GCAATTATTCCCCATGAACG-3′ (f), 5′-AGG GCCTCACTAAACCATCC-3′ (r). The expression of candidate genes was normalized to reference genes GAPDH or 18S rRNA. The mRNA expression was analyzed by standard curve quantification. All measurements were performed in duplicates. Controls consisting of bidistilled HTnfr1- and Tnfr2-deficient mice, respectively.Protein expression of renal TNFR1 and TNFR2 was detected on 4 µm cryostat sections of OCT-embedded tissue stained with polyclonal goat anti-mouse TNFR1 and polyclonal goat anti-mouse TNFR2 antibodies . Specificity of the antisera was shown by negative staining of tissue sections from 2) with a monoclonal hamster anti-mouse TNFR1 antibody , followed by a biotin-labeled mouse anti-hamster IgG cocktail (BD Biosciences). Cells were then stained with phycoerythrin (PE)-conjugated streptavidin (BD Biosciences). Hamster IgG1 was used as the appropriate isotype control. TNFR2 expression was analyzed with a PE-conjugated monoclonal hamster anti-mouse TNFR2 antibody . PE-conjugated hamster IgG1 was used as isotype control.To detect TNFR1 surface expression glomerular endothelial or mesangial cells were incubated in fluorescence-activated cell sorting (FACS) buffer  following the manufacturer’s protocols.Single-cell suspensions from glomerular tissue preparations of individual kidneys were prepared as previously described U test. When more than 2 experimental groups were compared, Kruskal-Wallis test was carried out, followed by pairwise Mann-Whitney U tests. Significance was assigned to P<0.05.Numerical results of each experimental group were expressed as mean±SD or SE as indicated, and were compared using two-sided non-parametric Mann-Whitney Glomeruli were isolated from 6 to 8 week old wildtype mice after perfusion with paramagnetic Dynabeads . The firCompartment-specific quantitative PCR revealed a constitutive expression of TNFR1 and TNFR2 in normal mouse glomeruli, with substantially lower transcript levels in tubulointerstitial tissue . ConsistIn vitro, glomerular endothelial  and mesaTogether, these data demonstrate a constitutive glomerular expression of both TNFRs in normal mouse kidneys, which can be induced by proinflammatory stimuli, with TNFR2 being more readily upregulated than TNFR1.+ leukocytes, mainly Ly6C+ neutrophils and F4/80+ mononuclear phagocytes, as revealed by compartment-specific flow cytometry  seen for the proinflammatory chemokine CCL2/MCP-1 . This waytometry . These dTnfr-deficient mice with soluble TNF ex vivo. Global gene expression levels were determined on Affymetrix Mouse Genome 430 2.0 gene chips. Representative genes differentially expressed in TNF-stimulated Tnfr1,2−/−, Tnfr1−/− and Tnfr2−/− glomeruli compared to wildtype are listed in Tnfr1,2−/−, Tnfr1−/−, and Tnfr2−/− glomeruli compared to wildtype is shown in To examine TNFR-specific inflammatory responses in intrinsic glomerular cells but not infiltrating leukocytes we stimulated intact glomeruli isolated from wildtype and Tnfr1,2−/− glomeruli, i.e. glomeruli without TNF-signaling capacity, we detected 290 differentially expressed genes compared to wildtype glomeruli following TNF stimulaton, with 219 genes being down-regulated, and 71 genes with up-reglulated expression  2, and proteolytic enzymes like MMP 3, 9, and 13 were down-regulated in Tnfr1-deficient glomeruli. Many TNFR1-induced proinflammatory responses are mediated through NF-κB signaling pathways, and expression of various components and regulators of the NF-κB signaling cascade was reduced in Tnfr1−/− glomeruli. Besides proinflammatory genes, several mediators of apoptosis (including Fas and caspase 12) were expressed TNFR1-dependently.Comparing TNF-stimulated wildtype and enotypes . Of thesype mice . TNFR1-dTnfr1−/−, only 5 genes were found to be differentially expressed in Tnfr2−/− glomeruli, all of which were down-regulated compared to wildtype  gene ontology categories associated with the differentially regulated genes in Tnfr1,2−/− glomeruli. Many of the 10 most enriched gene ontology categories in Tnfr1,2−/− glomeruli were immune-related, including chemokine activity and chemokine receptor binding  isolated from wildtype and olecules . We coulolecules , althougolecules . These dTnfr1−/− and Tnfr1,2−/− glomeruli . C: mRNA expression of the glomerular marker gene nephrin and D: FXYD2, the γ-subunit of the tubular Na,K-ATPase, in glomerular and tubulointerstitial tissue from wildtype mice as analyzed by quantitative real-time PCR. Values were normalized to rRNA expression used as reference gene. ∗ p<0.05.(TIF)Click here for additional data file.Table S1Tnfr1,2−/−, Tnfr1−/− and Tnfr2−/− glomeruli compared to wildtype as identified by microarray profiling.Representative genes differentially expressed in TNF-stimulated (PDF)Click here for additional data file.Table S2Tnfr1,2−/− glomeruli compared to wildtype as identified by microarray profiling.Differentially expressed genes in TNF-stimulated (PDF)Click here for additional data file.Table S3Tnfr1−/− glomeruli compared to wildtype as identified by microarray profiling.Differentially expressed genes in TNF-stimulated (PDF)Click here for additional data file.Table S4Tnfr2−/− glomeruli compared to wildtype as identified by microarray profiling.Differentially expressed genes in TNF-stimulated (PDF)Click here for additional data file.Table S5Tnfr1,2−/− glomeruli compared to wildtype as identified by DAVID.Enriched functional groups of differentially expressed genes in TNF-stimulated (PDF)Click here for additional data file.Table S6Tnfr1−/− glomeruli compared to wildtype as identified by DAVID.Enriched functional groups of differentially expressed genes in TNF-stimulated (PDF)Click here for additional data file.


Tumour necrosis factor (TNF) is a pleiotropic cytokine with dual roles in cancer biology including prostate cancer (PCa). On the one hand, there is evidence that it stimulates tumour angiogenesis, is involved in the initiation of PCa from an androgen-dependent to a castrate resistant state, plays a role in epithelial to mesenchymal plasticity, and may contribute to the aberrant regulation of eicosanoid pathways. On the other hand, TNF has also been reported to inhibit neovascularisation, induce apoptosis of PCa cells, and stimulate antitumour immunity. Much of the confusion surrounding its seemingly paradoxical roles in cancer biology stems from the dependence of its effects on the biological model within which TNF is investigated. This paper will address some of these issues and also discuss the therapeutic implications. True to its name, TNF is cytotoxic to tumour cells under certain conditions; however, it also fuels tumour-promoting inflammation and angiogenesis. This paper will discuss the function of TNF in cancer biology, with special emphasis on PCa.Cytokines are soluble, low molecular weight proteins that mediate cell-cell communication. They are mainly produced by immune cells and stromal cells  and act in concert to regulate biological responses such as cell activation, proliferation, differentiation, migration, and cytotoxicity. Cytokines have an integral role in tumour induction and progression. They can facilitate the generation and maintenance of robust antitumour immune responses, but they can also contribute to chronic inflammation and promote tumour formation, growth and metastasis . WhetherκB (IκB) kinase (IKK) is recruited to complex 1 and is activated via RIP-dependent mechanisms. The activated IKK phosphorylates IκB triggers its ubiquitination then degradation in the proteasome; hence allowing nuclear-factor- (NF-)κB to translocate from the cytoplasm to the nucleus to promote transcription of target antiapoptotic genes such as B-cell lymphoma extra-large (Bcl-xL), A20, cellular inhibitor of apoptosis protein (cIAP-)1 and 2 anthracene (DMBA) and 12-O-tetradecanoyl-phorbol-13-acetate (TPA) were applied to the skin of mice, TNF−/− mice were significantly more resistant to tumour induction than wild-type animals [−/− mice had a lower incidence of total tumours, and where these did develop, there was a delay in onset as compared to those in wild-type mice. Histologically, tumours from wild-type mice contained a heavy infiltrate of inflammatory neutrophils and eosinophils, whereas only a mild infiltrate was seen in tumours from TNF−/− mice [−/− mice contained lower levels of myeloperoxidase (MPO), a constituent of neutrophil granules which contribute to carcinogenesis via the generation of DNA-damaging reactive oxygen species and hypochlorous acid [TNF exhibits both tumour-promoting and tumour-inhibitory properties, depending on the experimental context within which the conclusions are made . There i animals . TNF−/− −/− mice . In addious acid . These rin vitro chemotaxis of bovine adrenal capillary endothelial cells and induces the formation of branching capillary-tube-like structures, but these effects are inhibited at high TNF doses (500 ng/mL) [In vivo, TNF also induces capillary blood vessel formation in the rat cornea and the developing chick chorioallantoic membrane at very low concentrations  [TNF also has a role in neovascularisation, which may have implications for tumour angiogenesis. At low concentrations (0.5–50 ng/mL), TNF induces 0 ng/mL) . Lower l0 ng/mL) . In vivo, resp.) . Moreove, resp.) . Therefoin vivo [The ability of this cytokine to suppress the proliferation of endothelial cells under some conditions renders it as a potential antitumour angiogenesis agent, if strategically delivered to the tumour site. Indeed, a fusion protein composed of mouse TNF and a high affinity antibody fragment to the extradomain B (ED-B) domain of fibronectin, a marker of angiogenesis, induced significant antitumour activity against subcutaneously grown F9 embryonal teratocarcinoma, an effect attributed by the authors to targeting of the tumour vasculature ). The acin vivo .in vitro migration and invasion of LNCaP cells through increasing the expression of several glycosyl- and sulfo-transferase genes that are involved in the synthesis of mucin-type selectin ligands [in vitro invasion of PC-3 cells; this increased invasion was accompanied by an increase in their expression of matrix metalloproteinase (MMP-)9 and fibronectin, and a decrease in E-Cadherin [κB ligand (RANKL), a suppressor of osteoclastogenesis, as compared to LNCaP-C4-2B cells without TNF [in vitro mineralisation of MC3T3-E1 osteoblast-like cells, suggesting that TNF within the tumour microenvironment could play a role in bone remodelling and promote osteoblastic activity [While TNF has been shown to promote tumour progression through its role in chronic inflammation, it is also important to note that TNF may also directly endow tumour cells with greater metastatic potential. As tumour cells begin to metastasise, they invade and migrate towards endothelial cells in order to enter the bloodstream. This process involves dynamic interactions between selectins expressed on stromal cells for example, endothelial cells, and selectin ligands on cancer cells . TNF has ligands . As a reCadherin . These eCadherin . TherefoCadherin , it is whout TNF . Moreovehout TNF  induced activity .in vitro and in vivo [γ) and cytotoxic factors  by macrophages and NK cells and enhancing the proliferation of T cells alone or synergistically with IL-6 [β-mediated inhibition [−/− but not wild-type mice failed to recruit NK cells to the peritoneum, the site where a variety of tumour cells including RM1 murine PCa, have been injected [Despite abundant evidence to suggest that TNF has a role which favours tumour progression, this cytokine was originally identified as a factor that strongly induced tumour necrosis, hence its name. In PCa, TNF has been shown to dose-dependently induce apoptosis of LNCaP cells , 36 and  in vivo , 48. How in vivo  suggests in vivo . While T in vivo , TNF sti in vivo . This is in vivo . TNF mayith IL-6 , 37. TNFhibition . Mice dehibition . In addiinjected . TNF alsinjected . These rinjected .Eicosanoids, the oxidative metabolites of the essential fatty acid arachidonic acid, are a focus of increasing interest due to a growing body of evidence across broad disciplines that these products may contribute to the development and progression of PCa , 56. The2 enzymes, cPLA2-α (Group IVA PLA2). cPLA2-α is expressed in PCa and pharmacological inhibition of this enzyme results in reduction of tumour size in a PCa xenograft model [Given this complexity and lack of knowledge, a key strategy has been to identify and target factors that regulate the pathway, rather than individual biosynthetic enzymes, in the hope that blockade of these regulators will have a broader benefit. Arachidonic acid flux to the eicosanoid pathways in PCa tissues, as in other tissues, appears to be mediated primarily by one of the 20 known mammalian phospholipase Aft model . This inft model .2  [κB signalling pathways.A second important regulatory feature of the eicosanoid pathway is that, in addition to a cell-type and tissue-specific complement of constitutively present biosynthetic pathway enzymes that define the range of eicosanoid products made under normal physiological conditions, key pathway enzymes have duplicated genes whose expression is responsive to stress signals such as cytokine activation. Induction of these genes results in a rapid increase in the capacity of the eicosanoid pathways to metabolise arachidonic acid. Several of these genes, notably a secreted phospholipase ALA2-IIA)  and cyclLA2-IIA) , are abeThere is growing evidence that inappropriate expression of inflammatory cytokines including TNF may contribute to the aberrant regulation of eicosanoid pathways in PCa. TNF transiently induces steady-state COX-2 protein and mRNA levels over constitutively high basal levels  in normal prostate epithelial cells . This inInduction of cytokine gene expression, including TNF, can be demonstrated in cultured PC-3 cells following addition of arachidonic acid . This inin vivo appear warranted in an effort to determine the relative importance of this cytokine over other factors such as stimulators of the HER/HER2 pathway [In combination, these data provide evidence that TNF may contribute to PCa growth by directly and indirectly modulating pathways that stimulate proliferation and reduce apoptosis of cancer cells. Studies aimed at blockade of TNF  pathway  that mayin vitro versus in vivo) can greatly influence the type of activity seen to be exerted by TNF. By virtue of its dual role in tumour biology, it may appear at face value that TNF would have limited therapeutic utility against cancers because its protumour properties would nullify its antitumour effects. However, here we propose two TNF-centred therapeutic approaches which are rational for the treatment of PCa. The first approach is to neutralise TNF in patients with androgen-sensitive non-metastatic disease. Since TNF is central in chronic inflammation (important in tumour initiation and progression), drives epithelial to mesenchymal plasticity (facilitating metastasis), is involved in the progression of prostate tumours from an androgen-sensitive to CRPC, and may contribute to the aberrant regulation of eicosanoid pathways (stimulate proliferation and reduce apoptosis), then blockade of TNF could keep PCa progression in check. In addition, since elevated serum levels of TNF correlate with increased likelihood of cancer-related cachexia and coagulopathy, neutralisation of TNF may also have palliative effects. A recent pilot study provided anecdotal evidence that neutralisation of TNF may benefit some patients with advanced PCa [Much of the confusion relating to the function of TNF in cancer can be attributed to the dependence of its effects on the biological context within which the cytokine has been investigated. Variables such as cytokine dose, target cell type, hormone sensitivity of the cell type, and the complexity of the system , and melanoma in-transit metastases confined to the limb . In thispatients . Tumour patients . The ratpatients . In addipatients , granulopatients , and IL-patients  or a compatients  which ha


Here we characterized the effect of this variant on TNF promoter function. A transcriptional reporter, encoding the -238A variant of the TNF promoter, resulted in 2.2 to 2.8 times greater transcriptional activation versus the ‘G’ variant in murine macrophages when stimulated with pro-inflammatory stimuli. Bioinformatic analysis predicted a putative binding site for thyroid hormone receptor (TR) for the -238A but not the -238G allele. Overexpression of TR-α induced promoter expression 1.8-fold in the presence of the ‘A’ allele only. TR-α expression both potentiated and sensitized the -238A response to LPS or a titanium particulate stimulus, whilst siRNA knockdown of either THRA or THRB impaired transcriptional activation for the -238A variant only. This effect was independent of receptor-ligand binding of triiodothyronine. Immunohistochemical analysis of osteolysis interface membranes from patients undergoing revision surgery confirmed expression of TR-α within osteoclast nuclei at the resorption surface. The ‘A’ allele at rs361525 confers increased transcriptional activation of the TNF promoter and influences susceptibility to several arthritic conditions. This effect is modulated, at least in part, by binding of TR, which both sensitizes and potentiates transcriptional activation of the ‘A’ variant independent of its endogenous ligand.Tumor necrosis factor (TNF) is a powerful pro-inflammatory cytokine and immuno-regulatory molecule, and modulates susceptibility to musculoskeletal diseases. Several meta-analyses and replicated association studies have implicated the minor ‘A’ variant within the TNF promoter single nucleotide polymorphism (SNP) rs361525 ( Protein extract were isolated from human coronary artery smooth muscle cells, as described previously [Electric Mobility Shift Assay was performed according to the method of Lee et al . Complemeviously  and analFigure S1The nucleotide sequence of the promoter region of the TNF gene used in this study has been verified by sequencing.Polymorphic residues that have been previously been studied by Bayley at all as part of the same haplotype are highlighted . The -238 A/G variant that was the focus of this work is highlighted in red.(TIFF)Click here for additional data file.Figure S2Reproducible knockdown of Thra and Thrb was achieved (n=3) in Raw 264.7 cells, as verified by gene specific Taqman qRT-PCR (ABI).Mean expression values (relative to beta-actin levels) were used and expressed as normalized to control treated (siNC) samples. Experimental conditions were identical to those, used in experiments that are presented in (TIFF)Click here for additional data file.


The importance of TNF-alpha in arthritis is well documented. It may be that TNF-alpha is also markedly involved in muscle inflammation (myositis). An animal model where this can be investigated is needed. A newly developed rabbit myositis model involving pronounced muscle overuse and local injections of substances having proinflammatory effects was therefore used in the present study. The aim was to investigate the patterns of TNF-alpha expression in the developing myositis and to evaluate the usefulness of this myositis model for further TNF-alpha research. Human rheumatoid arthritis (RA) synovial tissue was examined as a reference. TNF-alpha immunoexpression and TNF-alpha mRNA, visualized via in situ hybridization, were detected in cells in the inflammatory infiltrates of the affected muscle (soleus muscle). Coexistence of TNF-alpha and CD68 immunoreactions was noted, suggesting that the TNF-alpha reactive cells are macrophages. Expression of TNF-alpha mRNA was also noted in muscle fibers and blood vessel walls in areas with inflammation. These findings demonstrate that TNF-alpha is highly involved in the myositis process. The model can be used in further studies evaluating the importance of TNF-alpha in developing myositis. Tumour necrosis factor alpha  is one of the most frequently studied pro-inflammatory cytokines. It drives the activation and recruitment of inflammatory cells, amplifies the production of other pro-inflammatory cytokines, and activates nuclear transcription factors, thereby promoting and maintaining the inflammatory response . TNF-alp A very large number of studies have been performed investigating the importance of TNF-alpha in arthritis, especially RA. TNF-alpha has also attracted interest in recent years for its possible role in skeletal muscle damage. Increased protein degradation, as well as decreased body weight and food consumption, was demonstrated when TNF-alpha was administered to rats via a catheter into the external jugular vein . Crush iRemarkably little data is available on the role of TNF-alpha in situations where there is a pronounced infiltration of inflammatory cells in the muscle tissue, that is, myositis. From studies in tissue other than muscle, it is known that macrophages and other immunoactive cells such as monocytes, mast cells, and neutrophils are responsible for TNF-alpha production –16. DataOur laboratory has developed a rabbit model of marked muscle (m. triceps surae) and tendon overuse that, when combined with injections of substances eliciting pro-inflammatory effects, results in significant myositis . This moTwenty-eight adult female New Zealand white rabbits were used for the studies. The animals had an average weight of 4 kg and ranged in age from 6 to 9 months. The animals were kept in ordinary cages allowing good freedom of movement. Six of the animals corresponded to control nonexercised animals (subgroup 1) and 22 were assigned to an exercise protocol leading to marked overuse of the triceps surae muscle (subgroups 2–5). In order to increase the muscle affection, including the degree of inflammation, the muscle overuse was combined with paratendinous injection treatment (cf. the following). As a control for this, five of the exercised animals (subgroup 2) were given control substance (NaCl) just outside the tendon of the triceps surae muscle . In essence, these five animals did not develop myositis . For the purpose of achieving muscle inflammation, 17 of the exercised animals (subgroups 3–5) were in parallel to being subjected to marked overuse, given local injections of pro-inflammatory substance  outside the Achilles tendon. Substance P was given as this neuropeptide has well-known pro-inflammatory effects  and the For clarification of all the various animal groups, see The animals were exposed to an exercise procedure designed to cause marked overuse of the triceps surae muscle and the associated tendon (the Achilles tendon). The procedure is performed according to previously described procedures , with so An apparatus (kicking machine) was used to achieve passive repetitive flexions and extensions of the right ankle joint; a pneumatic piston attached to the right foot produced the movements. During the plantar flexion, an active contraction was furthermore induced by electrical stimulation via surface electrodes  placed 2 cm apart over the triceps surae muscle of the right leg. The stimulation was synchronized with the plantar flexion movement of the piston by a microswitch, which trigged the stimulator unit . An impulse of 0.2 ms duration was delivered 85 ms after the initiation of the plantar flexion at an amplitude of 35–50 V. The movement frequency was 150 repetitions per minute. The left leg was not attached to the kicking machine. The pelvis was strapped down and there were no ankle movements on the left side. One day after the final exercise session, the animals were sacrificed by an overdose of Pentobarbital. For further details about the apparatus and the exercise protocol, see , 28–30.n = 5), (b) Substance P (10-8 μmol/mL) and Captopril (Sigma)  both in distilled water (volume: 1 mL) and Dl-Thiorphan (N-[(RS)-2-Benzyl-3-mercaptopropanoyl]-glycine) (Sigma)  , (c) Captopril (Sigma)  + DL-Thiorphan (Sigma)  , and (d) Captopril (Sigma) alone  . For further details, see Injections were given into the loose connective tissue around the Achilles tendon, that is, in the paratenon region. The injections were given directly after each of the 2-hour exercise periods. The substances injected were (a) NaCl   and as recently reported , it has μm thick sections were cut using a cryostat. The sections were mounted on slides precoated with chrome-alum gelatine and were then processed for immunohistochemistry. Other sections were processed for morphology or in situ hybridization.After the animals were sacrificed, the right triceps surae muscle was dissected out and immediately transported on ice to the laboratory. Samples conforming to the soleus muscle part (5–8 × 10 mm) were dissected out and fixed by immersion overnight at 4°C in an ice-cold solution of 4% formaldehyde in 0.1 M phosphate buffer (pH 7.0). The samples were thereafter thoroughly washed in Tyrode's solution containing 10% sucrose at 4°C overnight, mounted on thin cardboard in OCT embedding medium , frozen in propane chilled with liquid nitrogen, and stored at −80°C. Series of 5 As a reference, human RA synovial tissue was analyzed. The tissue was fixed and further processed in the same way as were the rabbit specimens (cf. the following).One section from all specimens was stained in Harris Haematoxylin solution for 2 min. These sections were then rinsed in distilled water, dipped in 0.1% acetic acid for a few seconds, and then washed in running water. Counterstaining was achieved by immersion in eosin for 1 min. The sections were dehydrated in ethanol and mounted in Permount.Sections of all specimens were processed for immunohistochemistry. The sections were pretreated with acid potassium for 2 min, a procedure found to enhance specific immunofluorescence reactions . ThereafTo clarify the TNF-alpha immunoreaction pattern in relation to that of white blood cells, double stainings were made. As it is frequently emphasized that macrophages , 16 show Alexa FluorO donkey antigoat was used as secondary antibody for TNF-alpha immunolabelling, and TRITC (tetramethylrhodamine isothiocyanate-) conjugated rabbit antimouse antibody was used for stainings for CD68 and T-cell/neutrophil marker. For detailed information about the staining procedures for TNF-alpha, see above. When doing double stainings for CD68 and T-cell/neutrophil marker, 5% normal rabbit serum , diluted in 0.1% BSA  in PBS, was used as normal serum, and TRITC-conjugated rabbit antimouse antibody , diluted 1 : 40 in 0.1% BSA in PBS, as the secondary antibody.E. coli-derived recombinant human TNF-alpha. The TNF-alpha-specific IgG was purified by human TNF-alpha affinity chromatography. It is described to be specific via having the ability to neutralize the biological activity of recombinant human TNF-alpha. Of note, the TNF-alpha amino acid sequence homology between species is reported to be highly conserved and TNF-alpha DNA sequence comparison shows an overall high sequence homology between various species (including rabbit) -H2O) and mounted onto Super Frost Plus slides . The protocol that thereafter followed was that previously used in our laboratory for detection of mRNA for other substances ..μm thick An alkaline phosphatase-(AP) labelled anti-DIG antibody  was used for detection. The sections were finally mounted in Pertex mounting medium.β-actin antisense probe (GD5000-OP) was used, comparisons being made with sense β-actin probe .The corresponding sense DIG-hyperlabeled ssDNA probe was used as a negative control. As a positive control probe, a The study protocol was approved by the local ethical committee at Umeå University.Myositis was observed in subgroups 3–5, and these are now collectively being referred to as the “myositis group” (cf. above). The most noteworthy feature was the presence of an inflammatory infiltrate , althougIt was considered relevant to examine a reference tissue regarding the demonstration of TNF-alpha. Human RA synovial tissue was therefore analysed to provide reference information for the particular TNF-alpha antibody used, as it a well-known fact that there is a marked TNF-alpha expression in the inflammatory infiltrates in the synovial tissue of patients with RA . We observed that mononuclear-like cells of the human synovial tissue exhibited immunoreactions when incubated with the TNF-alpha antiserum Figures  and 3. TPronounced TNF-alpha immunoreactions were observed in cells of the inflammatory infiltrates Figures  and 5 in Fibroblasts in the connective tissue did also to some extent display TNF-alpha immunoreactions, however the reactions were very faint. No specific TNF-alpha reactions were noted for blood vessel walls, the nerve fascicles, muscle spindles, and the muscle fibers (not shown).In order to clarify the patterns of cellular reactions for TNF-alpha in the inflammatory infiltrates, double stainings for TNF-alpha/CD-68 and TNF-alpha/T cells and neutrophil marker were performed. It was found that CD68 coexisted with TNFalpha in cells in the inflammatory infiltrates . On the Reactions for TNF-alpha mRNA were revealed for white blood cells of the inflammatory infiltrates in the myositis specimens . ReactioIt is well known that TNF-alpha is highly involved in arthritis, notably in RA. Accordingly, in our reference studies in the present investigation we found that TNF-alpha was expressed in mononuclear-like cells in the RA synovial tissue. Detection of TNF-alpha reactions was thus clarified from the methodological point of view, and verifications were obtained via preabsorption stainings. With this as a basis, studies on TNF-alpha in myositis were performed.A unique model for the production of myositis in rabbit musculature (the soleus muscle) was utilized. The main finding was that cells in the inflammatory infiltrates in the myositis muscles were found to express TNF-alpha at both at the mRNA and protein levels. Colocalization between TNF-alpha and CD68 was noted for these cells. Expression of TNF-alpha in macrophages has previously been noted in other situations e.g., , 16), in, in16]),From a methodological point of view, it was clear that muscle fibers, blood vessel walls, and fibroblasts exhibited TNF-alpha mRNA but that no reactivity  or very weak reactivity (fibroblasts) was noted at the protein level. The production level in these locations is therefore likely to be low, which precluded clear detection with our immunohistochemical methods. It is also possible that our in situ hybridization method detects very small quantities of TNF-alpha mRNA. Nevertheless, it has previously been shown that TNF-alpha can be expressed not only in inflammatory cells but also in injured muscle fibers and fibroblasts in response to muscle injury (crush-injury)  as well The patterns of morphologic appearances of the inflammatory infiltrates and other morphologic changes seen resembled the appearances that can be seen in the muscle tissue in inflammatory myopathies . NeverthAs noted previously, TNF-alpha immunoreactions in inflammatory cells invading muscles affected by myositis have previously only been documented in biopsies from patients with inflammatory myopathies , 18 and Whether or not the TNF-alpha produced by the cells of the inflammatory infiltrates is entirely responsible for pro-inflammatory and damaging effects remains open to speculation. It is well known that TNF-alpha administration can have pro-inflammatory and detrimental effects, for example, leading to various catabolic changes as seen in studies on cultured skeletal muscle cells , 43, 44.Although the use of other animal models have shown inflammation in muscle tissue in response to various forms of exercise , 52, theA noteworthy aspect with the currently used model is that marked overuse is applied in the procedures. Nevertheless, a limitation of the present study is that the relative contributions of the exercise protocol and the injections of the proinflammatory substances to the observed myositis are unclear. Forthcoming studies will clarify this issue. In any case, the model will, as it is currently used, provide the opportunity to evaluate the effects of interference with TNF-alpha actions in myositis development. An animal model in which the importance of TNF-alpha for myositis development can be followed has previously been lacking. Using a newly established rabbit model of myositis development, a marked TNF-alpha expression has here been shown for the cells of the inflammatory infiltrates within damaged muscle. There was thus a clear evidence of local TNF-alpha production via infiltrated inflammatory cells, presumably leading to secondary inflammation-modifying effects. Using in situ hybridization, it was also seen that TNF-alpha mRNA was detected for muscle fibers and blood vessel walls in regions of inflammatory infiltrates. The current model can be used for further studies on the importance of TNF-alpha in the development of myositis and to document the expression patterns of other cytokines and signal substances in this condition.


To evaluate safety and efficacy in young adults with JIA treated by TNF and non anti-TNF inhibitors.36 patients  with JIA treated with anti-TNF  followed at Transition Clinic of Florence from January 2008 to December 2010 were enrolled in a observational, single-centre, retrospective study. 12 pts  failed to respond or did not tolerate the first therapy and switched to a second one. Moreover, 9 patients has received non anti-TNF drug . In all, 53 treatments  were performed. Safety assessments were based on adverse events (AEs) report, divided in moderate AES (if infective events or injection site reactions have been occurred) and severe AES (including infusion reactions). Efficacy was assessed using the PedACR30/50/70 criteria.Of the 36 patients treated with TNF-blockers, Ped ACR30/50/70 response was reached by 78%/67%/58% after 24 weeks, 72%/68%/60% after 48 weeks and 87%/87%/73% after 96 weeks of treatment. Of the 9 patients treated with abatacept or tocilizumab, PedACR30/50/70 response was reached by 89%/78%/67% after 24 weeks, 80%/80%/80% after 48 weeks and 75%/75%/75% after 96 weeks. 108 AEs (both moderate and serious) occurred in 26 patients (59%) treated with anti-TNF. Among non anti-TNF agents, 36 AEs occurred in 7 patients (78%), no one was serious and any patient leaved the biological treatment due to AEs.In our study, anti-TNF agents were well tolerated and provided clinically significant efficacy in young adults with JIA. In the refractory systemic form of JIA, tocilizumab seemed to be effective and safe.


Cytotoxic T-lymphocytes (CTLs) recognize viral protein fragments displayed by major histocompatibility complex molecules on the surface of virally infected cells and generate an anti-viral response that can kill the infected cells. Virus variants whose protein fragments are not efficiently presented on infected cells or whose fragments are presented but not recognized by CTLs therefore have a competitive advantage and spread rapidly through the population. We present a method that allows a more robust estimation of these escape rates from serially sampled sequence data. The proposed method accounts for competition between multiple escapes by explicitly modeling the accumulation of escape mutations and the stochastic effects of rare multiple mutants. Applying our method to serially sampled HIV sequence data, we estimate rates of HIV escape that are substantially larger than those previously reported. The method can be extended to complex escapes that require compensatory mutations. We expect our method to be applicable in other contexts such as cancer evolution where time series data is also available. During the first few months of HIV infection, the HIV genome typically undergoes a series of rapid amino acid substitutions that reduce immune pressure by cytotoxic T-lymphocytes (CTLs); this process is referred to as CTL escape . The subde novo mutation than recombination with another rare single mutation.With a single escape mutation and dense, deeply sampled data, the escape rate can simply be estimated by fitting a logistic curve to the time course of the mutation’s frequency , 6. The Here, we develop a strategy for inference that allows one to obtain robust escape rate estimates from the scarce data typical of studies of CTL escape. The inference is based on explicit modeling of the process of mutation accumulation in the founder sequence. Thereby, we exploit constraints imposed by the underlying dynamics of mutation and selection in the high dimensional space of possible genotypes.a priori. Instead, we observe a sequence of dominant genotypes and try to infer the evolutionary scenario that most likely gave rise to this sequence of genotypes. While we model only these genotypes, many minor variants certainly exist. But only those dominant variants that are likely to give rise to the future populations need to be modeled accurately. Later in infection, the viral population is very diverse and cannot be analyzed using our method.Despite the large number of possible genomes that can be formed from different combinations of escape mutations, we typically observe one or two dominant genotypes at a time – at least during the first few months of the infection. Furthermore, these genotypes dominate only transiently and are quickly displaced by genotypes with an even greater number of escape mutations; see Figure Given a data set from early infection, it is typically straightforward to define a series of dominant genotypes that likely have arisen through step-wise accumulation of mutations. Note that most likely all escape mutations constantly arise in different combinations, but typically only one combination rises quickly enough to dominate the population. This dominant genotype is then in most cases the source for the next dominant genotype. Later in infection, however, recombination is sufficiently frequent that no dominant genotype exists and mutations can spread simultaneously.In Ganusov et al. , a frameWe will first define a model of the dynamics of escape mutations. This model serves a twofold purpose: it defines the parameters we would like to estimate from the data and provides us with a computational tool to investigate how the accuracy of the inference depends on sampling depth and frequency, as well as how sensitively it depends on the values of parameters such as mutation rates or the population size. We reanalyze existing CTL escape data and find that accounting for multi-locus effects in a finite population results in higher estimates of the escape rates.L epitopes that can be either be mutant or wild-type. Assuming that the escape at multiple epitopes has additive effects, εj, the growth rate (birth rate minus death rate) of a genotype is given byg = {s1, …, Ls} specifies the genotype. Here, is = 0 corresponds to a wild-type epitope at locus i, whereas is = 1 signifies escape at that epitope. F0(t) accounts for a genotype independent modulation of the growth rate. The latter could, for example, be due to variable numbers of target cells (F0(t) controls the total population size, while the differences between genotypes are accounted for by i are the escape rates that we would like to estimate from the data and should be interpreted as the net effect of avoided killing and the possible fitness costs associated with the mutation; see e.g., Ganusov et al.  of each of the 2L possible genotypes. In each generation, the expected changes of the n due to mutation, selection, and recombination are calculated. The population of the next generation is then sampled from the expected genotype frequencies γ = n/N. The size of the population, N, can be set at will each generation. In this way, up to 15 epitopes can be simulated for 1000 generations within seconds to minutes.We implemented our model as a computer simulation in Python using the population genetic library FFPopSim . The simA typical realization of the population dynamics is shown in Figure L), we will restrict the inference to a chain of genotypes, each containing one additional mutation compared to its predecessor.Of the many possible genotypes that are present at any moment, only a small fraction is likely to be observed in a small sample and to be relevant in the future. Simulations and data suggest that the dominant genotypes accumulate mutations one by one – this greatly simplifies the task of estimating escape rates from the data. Instead of considering the dynamics of all possible genotypes  copies. If the actual frequencies of those genotypes at different times are γ, the probability of obtaining the sample at it is given by the multinomial distributionSuppose we have obtained sequence samples of size g, t) would be unique functions of the model parameters we want to estimate. In that case we could use Bayes’ theorem, choose suitable priors, and determine the posterior distribution of the parameter values. However, both the model and the actual viral dynamics are stochastic, and “replaying” the history would result in different trajectories. Furthermore, most of the 2L possible genotypes remain unobserved. This leaves us with the choice of either some type of approximate Bayesian computation that compares repeated simulations of the model with appropriate summary statistics  stochasticity can be accounted for by an appropriate choice of the seed time  = 0 for t < τj. The growth rate F of genotype j is the sum of the escape rates εk of the epitopes k = 1, …, j and the density regulating part F0(t); compare to equation  = N−1. If seed times are chosen appropriately, this model provides a very accurate description of the frequency dynamics of the dominant genotypes in the full stochastic model; see Figure After crossing the deterministic threshold, the population frequencies of the dominant genotypes evolve according toγ˙jt=Fgj,jg carrying mutations i = 1, …, j arises with rate μN(t)γj − 1(t) from the genotype jg − 1 carrying only j − 1 mutations. This means it is unlikely that genotype j arises early while γj − 1(t) is still very small. However, once the previous genotype j − 1 is common, genotype j is produced frequently. The distribution of the time at which the first copy of genotype j arises is given by the product of the rate of production and the probability that it has not yet been produced. The latter is the negative exponential of the integral of the production rate up to this point. Hence, the distribution of the seed time τj, given the trajectory of the previous genotype γj − 1, is given byAt face value, the deterministic model has two parameters per epitope – one escape rate and one seed time. The seed times, however, are quite strongly constrained by basic facts of the evolutionary dynamics. The genotype j(t) are uniquely specified by {τk, εk}k = 1, …, L, we can write the posterior probability of the parameters ask, τk}k = 1…L and U(εj) is our prior on the escape rates. We employ a Laplace prior U(ε) = exp(−Φε) parameterized by Φ favoring small escape rates. The prior regularizes the search for the minimum and results in conservative estimates of escape rates.Since the γFinding the set of escape rates and seed times that maximizes the posterior probability can be difficult due to multiple maxima and ridges in the high dimensional search space, and uncertainty remains. To ensure that the global optimum will be reliably discovered, we exploit the sequential nature of the dynamics and use the fact that earlier escapes strongly affect the timing of the later ones, but not vice versa. Thus adding genotypes with an increasing number of mutations one at a time results in a reasonable initial guess on top of which a global true multi-locus search can be performed.1 = 0 by a simple one dimensional minimization. This assumes that single mutants are already present in the population, consistent with the large viral population size present by the time a patient has been identified as HIV-1 infected  at fixed {εk, τk} for k < j. This step is illustrated in Figure Add additional epitopes successively by mapping the entire two-dimensional posterior distribution Refine the estimates through local optimization via gradient descent, Monte Carlo methods, or local exhaustive search. The resulting parameters and trajectories are shown for one example in Figure Generate posterior distributions by Markov chain Monte Carlo (MCMC).We have implemented such a search in Python, and the computationally expensive calculation of the posterior probability is implemented in C. The code infers parameters as follows:6 steps.This procedure is described in more detail in the Section “Materials and Methods.” Fitting five epitopes takes on the order of a minute on one 2011 desktop machine (Apple iMac i7 2.93 GHz). Generating the local posterior distribution by MCMC takes roughly 20 min for 10To evaluate the accuracy and reliability of our inference scheme, we performed true multi-locus stochastic simulations using FFPopSim  and sampled genotypes from the simulation at a small number of time points. Time points and sample sizes were chosen to mimic patient data. We then inferred parameters from this “toy” data set and compared the result to the actual values. When interpreting these comparisons, it is important to distinguish two sources of error. First, limited sample size and sampling frequency will incur errors due to inaccurate estimates of the actual genotype frequencies from the sample. The second source of uncertainty is an inappropriate choice of model or model parameters. Such inappropriate model choices might include wrong estimates of the population size or mutation rates, the presence or absence of recombination, or time variable CTL activity.j = 0.5, 0.4, 0.25, 0.15, 0.08 per day and sample the population on days it = 0, 20, 40, 60, 120, 250. An example of such samples is shown in Figure We generate data assuming escape rates εWith more frequent and deeper sampling, inferring the model parameters is expected to become simpler. Indeed, as soon as each genotype is sampled more than once at intermediate frequency, one can estimate its growth advantage simply from its rate of increase. This is the rationale behind previous studies such as , 6. In mFigure Nμ is larger than it really is, our inference method will favor seeding subsequent genotypes too early, which in turn results in erroneously small estimates of escape rates. We varied N and μ and observed the expected effect on the estimates as shown in Figure N, since the effect of a larger population size is partly canceled by the longer time necessary to amplify the novel mutation to macroscopic numbers. However, even the dependence on μ is rather weak, and changing μ 10-fold only changes estimates of escape rates by ±50%. The underlying reason is that the seed times depend primarily on the logarithm of Nμ⋅Q(τj|γj-1(t)) (see equation (Nμγj − 1(t) ≈ 1. Because γj-1(t) is growing exponentially, the position of the peak changes only logarithmically with the prefactor Nμ. Changes in μ also affect the dynamics through the initial rise in frequency of novel genotypes due to recurrent mutations; see equation , which equation .Another factor that affects seed times is recombination. HIV recombines via template switching following the coinfection of one target cell by several virus particles . In chroThe time intervals between successive samples are sometimes too large to observe the accumulation of single mutations, so the dominant genotype at one time point differs by more than one mutation from the previous. This can arise for two reasons. First, one or several unobserved genotypes may have transiently been at high frequency but been out-competed by later genotypes before the next sample was taken. Second, one escape might have required more than one mutation, for example because single mutants are not viable and a compensatory mutation is needed . Both scUnobserved, but individually beneficial, intermediate genotypes can be included by assuming they all have the same escape rate and were seeded one from the other. There is not sufficient information to estimate more than an average escape rate for all of them. For a given set of sampled frequencies, the estimated escape rates increase as more and more intermediates are assumed. Such unobserved intermediates are common in the data from infected individuals analyzed below.k, where k is the number of mutations needed. In other cases, the rates to multiple hits can be calculated using branching process approximations , the estimates of escape rates of the first epitope ε1 are around 0.9, while later escapes are almost not sensitive to the choice of T.Figure −4 and show the results in Figure −5 per day, but escape rates tend to be lower.While the posterior distributions of escape rates of subsequent escape rates are quite broad, they nevertheless suggest that escape rates can be substantially higher than previously estimated , 6. FurtWe have suggested a way to infer viral escape rates from time series data sparsely sampled from the evolutionary dynamics of asexual or rarely sexual populations such as HIV. We exploit the sequential manner in which escape mutations accumulate, which allows us to constrain the times at which new escape mutations arose. These constraints regularize the inference to a large extent, but additional stability is gained by prioritizing small escape rates through an exponential prior.Escape rates of single escape mutations have so far been estimated by comparing the time series data to a model that assumes logistic growth of the mutation with a constant rate. This approach has been used to analyze the intra-patient dynamics of recombinant HIV , drug reA related method to estimate CTL escape rates has been proposed by Leviyang , who modWhile previous methods neglect interactions between epitopes altogether – equivalent to assuming very rapid recombination – our method ignores recombination during the inference. By comparison with simulations that include recombination, we have shown that neglecting recombination can result in overestimation of the escape rates by roughly 30% at plausible recombination rates of 1% , 14. We 6 or larger. Early in infection, it is plausible to assume that the relevant size is N = 107 , we estimate that the escape rates of the first 4–6 escapes are on the order of 0.3-0.4 per day. The estimates at large population sizes are fairly insensitive to the prior for population sizes of 10 N = 107 , 29, 41.The timing of escape has been shown to depend on epitope entropy and immunodominance . HoweverThe proposed method to analyze multi-locus time series of adaptive evolution could be useful in many context where the genotypic compositions of large populations of viruses or cells can be monitored over time. Whenever mutations occur rapidly enough that they compete which each other, this competition has to be accounted for in the analysis. Outside of virus evolution, possible applications include the development of cancer and microbial evolution experiments.ijk of genotypes jg at time points it to infer escape rates of individual mutations. The procedure used to obtain successive genotype counts from sequence data sampled from patients is outlined in the text. As input data, our analysis scripts expect a white-space delimited text file with a format shown in Table Our fitting method uses counts ijk = k were obtained from simulated trajectories (generated by FFPopSim) by binomial sampling (with size in) at specified time points it. Trajectory generation and sampling are implemented in the file model_fit/ctlutils.py at http://git.tuebingen.mpg.de/ctlfit; see below.To test our method, artificial data http://www.hiv.lanl.gov/content/sequence/HIV/USER_ALIGNMENTS/Salazar.html  per day due to mutations from the founder and decreases by μν1 due to further mutations to additional escapes. Combined with the logistic growth, the dynamics of ν1 is described by1(0) = 0. Note the difference between the allele frequency ν, which refers to a particular escape mutation, and γ, which corresponds to frequencies of particular multi-epitope genotypes. The above ODE has the solution1 is determined by maximizing the likelihood  using fom scipy .j of subsequent escape mutants jg is determined by maximizing the seed time prior Q(τj|γj − 1) defined in equation . Note that the interpretation of genotype counts depends on how many epitopes are modeled. For example, if we model epitopes 1, …, j out of a total of L epitopes, counts for genotype j are Given the initial estimates for the first escape, we now add subsequent escapes to the multi-epitope model, which is formulated in terms of genotype counts If the added escape is unique, i.e., no other escape mutation has the exact same temporal pattern, we calculate the likelihood on a 21 × 31 grid of escape rates and seed times; comp. Figure If multiple epitopes exhibit the same temporal pattern, we add them all at once, constrain their escape rates to be equal, and assume they emerged in the order listed in the genotype matrix. Since we now have to optimize one joint escape rate and multiple seed times, we do not map the likelihood surface exhaustively but rather perform a greedy search. We examine next-neighbor moves with steps δτ = ± 1 day and δε = ± 0.02 per day, moves which change all seed times by δτ, and 20 moves in which all seed times and escape rates are changed by δτ and δε with random sign; the step that maximizes the likelihood is accepted. This is repeated until no favorable move is found and further repeated with δε = ±0.01 and ±0.001 per day.We then iterate sequentially over every epitope and optimize its seed time and escape rate as described above, but with all other epitopes part of the multi-epitope model. This typically leads to rather small adjustments and converges rapidly.To determine the posterior distribution of the escape rates, we attempt to change all seed times and escape rates by δτ = ± 1 day and δε = ± 0.01 per day with random sign. The move is accepted with probability min), where Δ is the difference in log-likelihood before and after the change. We sample this Markov chain every 1000 moves and thereby map the posterior distribution of seed times and escape rates.http://git.tuebingen.mpg.de/ctlfit.All source code and scripts are available at make and the Makefile provided in the src directory. Prerequisites for building are python2.7, scipy, numpy, swig, and a gcc compiler.The part of our method that is implemented in C and the python bindings can be built using fit_escapes.py with Python. Parameters can be set via command line arguments:--input specifies the file with the genotype counts. Other parameters can be modified in a similar manner. Running the script with the option --help prints a list of all parameters. The estimated escape rates and seed times as well as the sampled posterior distribution will be saved in the directory fit_escapes_output, unless otherwise specified.Given a text file with genotype counts specified as shown in Table The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Studies of cerebral ischemia and other neuroinflammatory states have demonstrated a strong association between new vessel formation and microglial recruitment and activation, raising the possibility that microglia may be involved in promoting angiogenesis. As endothelial cell proliferation is a fundamental early step in angiogenesis, the aim of this study was to test this hypothesis by examining the influence of microglial secreted factors on brain endothelial cell (BEC) proliferation using BrdU incorporation.Primary cultures of mouse BEC, microglia and astrocytes were used in this study. Proliferation of BEC was examined by BrdU incorporation. ELISA was used to quantify TNF and TGF-β1 levels within cell culture supernatants.Microglia regulated BEC proliferation in a biphasic manner; microglia conditioned medium (MG-CM) from resting microglia inhibited, while that from activated microglia promoted BEC proliferation. A screen of microglial cytokines revealed that BEC proliferation was inhibited by TGF-β1, but promoted by TNF. ELISA showed that TNF and TGF-β1 were both present in MG-CM, and that while TGF-β1 dominated in resting MG-CM, TNF levels were massively increased in activated MG-CM, shifting the balance in favor of TNF. Antibody-blocking studies revealed that the influence of MG-CM to inhibit or promote BEC proliferation was largely attributable to the cytokines TGF-β1 and TNF, respectively.This data suggests that microglial activation state might be an important determinant of cerebral angiogenesis; inhibiting BEC proliferation and neovascularization in the normal central nervous system (CNS), but stimulating the growth of new capillaries under neuroinflammatory conditions. Angiogenesis occurs in the central nervous system (CNS) not just during development , but alsIn the normal adult CNS, brain endothelial cells (BEC) occupy an angiostatic state, and have the impermeable, tight-barrier characteristics of mature cerebral endothelium . During The studies described have been reviewed and approved by The Scripps Research Institute Institutional Animal Care and Use Committee. All animals were maintained under pathogen-free conditions in the closed breeding colony of The Scripps Research Institute (TSRI).Pure cultures of mouse brain endothelial cells (BEC) were prepared as previously described . BrieflyMixed glial cultures were prepared from 0-2 day old C57Bl/6 mouse pups, as previously described , and maiMicroglia were shaken off mixed glial cultures and plated in 6-well plates overnight. Media was then changed to serum-free DMEM containing N1-supplement, L-glutamine and penicillin/streptomycin . Microglia were left unstimulated or activated with 1 μg/ml lipopolysaccharide  to produce resting and activated MG-CM respectively. After 3 days, media was collected and filtered through a 0.22 μm filter before use in BEC studies. Astrocyte conditioned media (A-CM) was prepared in a similar manner.3 U/ml, PBL Biomedical Labs), IFN-γ , IL-6 , TNF , and TGF-β1 . BEC were cultured for 16 hours in the presence of BrdU , then fixed in acid/alcohol and processed for BrdU immunocytochemistry according to the manufacturer's instructions. BEC proliferation was assessed by quantifying the number of BrdU-positive cells as a percentage of the total number of cells (Hoechst staining), and the results expressed as the mean ± SEM of four experiments. Statistical significance was assessed by using Student's t test, in which p < 0.05 was defined as statistically significant.Glass coverslips were coated with collagen I  for 2 hours, then washed, and BEC plated and cultured until cells reached ~50% confluence. For investigation of the effect of MG-CM, BEC were cultured in 67% ECGM and 33% MG-CM. Control cultures were maintained in 67% ECGM and 33% N1-supplemented media. In the function-blocking experiments, the anti-TNF and anti-TGF-β1 blocking antibodies and control antibodies  were used at 2 μg/ml. For the investigation of the influence of cytokines on BEC proliferation, BEC were cultured in ECGM with factors added across a concentration range, with the maximum indicated: IFN-α  according to the manufacturer's instructions. Results represent the mean ± SEM of 3 experiments, with each sample examined in triplicate within each experiment.Microglia were purified from mixed glial cultures of postnatal mice, and grown in serum free media for 3 days, in the absence or presence of the activating agent lipopolysaccharide (LPS), to produce microglia-conditioned media (MG-CM) from resting (unstimulated) or activated microglia, respectively. The influence of resting or activated MG-CM on BEC proliferation was examined using BrdU incorporation. As shown in Figure Microglia produce a large number of cytokines, whose production is heavily-dependent on the state of microglial activation . To idenWe have demonstrated that BEC proliferation rate is regulated by MG-CM in a biphasic manner; inhibited by resting MG-CM, and promoted by activated MG-CM. Furthermore, we have shown that BEC proliferation is inhibited by TGF-β1, but promoted by TNF. While it is known from previous studies that microglia can produce these two cytokines ,23, we nTo address whether the influence of MG-CM on BEC proliferation is mediated by TNF or TGF-β1, we examined the influence of resting or activated MG-CM on BEC proliferation in the presence of function-blocking antibodies directed against TNF or TGF-β1. As shown in Figure Cerebral angiogenesis occurs in the adult CNS in a number of conditions, including cerebral ischemia , neoplasMicroglia, as the primary immune effector cells in the CNS, play important roles in the surveillance and response to pathological insults. As the gatekeeper of the BBB, microglia are well positioned to mount a rapid aggressive response to noxious stimuli that enter the CNS. Under normal conditions, microglia occupy a resting "on surveillance" phenotype, but after stimulation, switch to an activated highly migratory, mitogenic phenotype, with upregulated production of inflammatory cytokines such as TNF and IFN-γ ,24. WithOur studies suggest that the positive and negative influences of microglia on BEC proliferation is mediated, at least in part, by the antagonistic cytokines TNF and TGF-β1, respectively. The influence of these cytokines on angiogenic events is still a matter of debate, with different studies demonstrating either pro- or anti-angiogenic effects, depending on the source of endothelial cells and the concentration of cytokine employed. TNF has been shown to both promote -32 and iIn summary, our data provides evidence that microglia, the principal immune effector cells in the CNS, can regulate BEC proliferation in a biphasic manner by altering the balance of TNF and TGF-β1. As BEC proliferation is an early stage of the angiogenic process, and new vessel formation leads to increased cerebral blood flow  and clinOur data demonstrate that microglia regulate BEC proliferation in a biphasic manner; microglia conditioned medium (MG-CM) from resting microglia inhibit, while that from activated microglia promote BEC proliferation. BEC proliferation was also inhibited by TGF-β1, but promoted by TNF. ELISA showed that TNF and TGF-β1 are both present in MG-CM, and that while TGF-β1 dominated in resting MG-CM, TNF levels were massively increased in activated MG-CM, shifting the balance in favor of TNF. Antibody-blocking studies revealed that the influence of MG-CM to inhibit or promote BEC proliferation was largely attributable to the cytokines TGF-β1 and TNF, respectively. Taken together, this data suggests that microglial activation state might be an important determinant of BEC proliferation, an early event in cerebral angiogenesis.The authors declare that they have no competing interests.JVW prepared the cell cultures, carried out the biochemical analysis and contributed to drafting the manuscript. LL participated in the design of the study and also assisted in manuscript preparation. RM conceived of the study, performed the proliferation studies, and helped to draft the manuscript. All authors read and approved the final manuscript.


Synovial fibroblasts are key players in the pathogenesis of Rheumatoid Arthritis (RA) and potentially attractive treatment targets. Upon activation within the joint's inflammatory milieu, they gain a transformed phenotype and produce pro-inflammatory cytokines (mainly IL-6) and tissue destructive enzymes .Synovial fibroblasts were isolated via enzymatic processing from synovial tissues obtained from patients with RA or Osteoarthritis (OA). Synovial fibroblasts (passages 2-4) were stimulated with TNF-α (10 ng/ml) only on day 1. The expression of TNF-α-target genes was measured by qPCR in time course experiments .Human macrophages (Mϕ) generated in vitro (blood derived CD14+ cells stimulated for 48 h with M-CSF) were used in similar time course experiments as controls.TNF-a, IL-1β, IL-6 and IL-8) that was restrained back to the baseline within a few hours (3-24 hours depending on the gene). In stark contrast, synovial fibroblasts displayed a remarkably more sustained response to TNF-α. IL-6 mRNA expression was induced within a few hours by TNF-α, and induction increased continuously for 72-96 h despite the absence of any further exogenous TNF-α stimulation. The levels of IL-6 mRNA induced by TNF-α in synovial fibroblasts were substantially higher compared to human Mϕ, suggesting that within the joint microenvironment, synovial fibroblasts and not Mϕ are the main source of IL-6. By adding the supernatants from 96 h TNF-α-stimulated fibroblast cultures on unstimulated synovial fibroblasts, a similar robust induction of IL-6 mRNA was observed, suggesting that there is a TNF-α-induced soluble factor that mediates the sustained response. A similar pattern of sustained expression was observed for other TNF-α-target genes including IL-1β, IL-8 and MMPs. Interestingly, there was no difference between OA- and RA-derived synovial fibroblasts in their response to TNF-α.In Mϕ it was observed a rapid (within 1-3 hours) induction of TNF-α-target genes (including In contrast to human Mϕ, synovial fibroblasts display a sustained inflammatory and tissue destructive response to TNF-α. Our observations suggest that synovial fibroblasts may lack the homeostatic mechanisms that control and terminate the effects of TNF-α on human Mϕ . To supp


Additional NC (n = 5) and AD patients (n = 10) from the ADNI cohort were examined. Although mean ventricular volume was significantly greater in the NPH group than all others, the range of values overlapped those of the AD group. Individuals with NPH could be better distinguished when ventricular volume and total cortical thickness were considered in combination. This pilot study suggests that volumetric MRI measurements hold promise for improving NPH differential diagnosis.Current radiologic diagnosis of normal pressure hydrocephalus (NPH) requires a subjective judgment of whether lateral ventricular enlargement is disproportionate to cerebral atrophy based on visual inspection of brain images. We investigated whether quantitative measurements of lateral ventricular volume and total cortical thickness  could be used to more objectively distinguish NPH from normal controls (NC), Alzheimer's (AD), and Parkinson's disease (PD). Volumetric MRIs were obtained prospectively from patients with NPH ( Normal pressure hydrocephalus (NPH) is a chronic neurologic disorder in adults characterized by impairments of gait, urination and cognition in association with enlargement of the cerebral ventricles. Brain imaging is integral to the diagnosis of NPH  and has The most characteristic physical change in the brains of NPH patients is ventricular enlargement. Radiographic identification of NPH is made by visual inspection of X-ray computed tomographic (CT) or magnetic resonance imaging (MRI) scans of the brain. Diagnosis can be challenging because ventricular enlargement also occurs as a consequence of aging, cerebrovascular disorders, neurodegenerative diseases, and other forms of hydrocephalus. To distinguish NPH from these other conditions, a determination must be made that the ventricular enlargement is not wholly attributable to cerebral atrophy or macroscopic obstruction to cerebrospinal fluid circulation. Most clinicians use other signs of atrophy such as sulcal widening to judge whether the extent of ventricular enlargement is greater than expected from cerebral atrophy alone. Even when carried out by skilled readers, this assessment is subjective and prone to error. Although various imaging signs such as a rounded ventricular horns, expansion of the Sylvian fissure, thinning of the corpus callosum, and upward displacement of the superior parietal lobule may help to distinguish NPH from cerebral atrophy, these signs are not universally present or specific to NPH. Two-dimensional methods for quantifying ventricular enlargement such as the Evan's index  address In the past decade, several techniques have emerged for quantitatively measuring cerebral atrophy. Voxel-based morphometry (VBM) has been recently applied in a study that found NPH patients had overall preservation of the cortex with volume loss in periventricular regions . This teIn this study, 5 NPH participants were recruited from the Weill Cornell Medical College (WCMC) Memory Disorders Program and were identified by a neurologist or neurosurgeon as having Probable NPH by International Consensus (IC) criteria . This inhttp://www.loni.ucla.edu/ADNI). In addition, five PD participants were recruited from the WCMC Movement Disorders Clinic having been diagnosed according to the United Kingdom Parkinson's Disease Society Brain Bank criteria [Five normal control (NC) and 10 AD participants, matched for age and gender, were chosen at random from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset  , sagittal 3D BRAVO MRI sequences were performed on a 3T GE Signa scanner located at the WCMC Citigroup Biomedical Imaging Center. ADNI images (AD and older NC) were acquired from a straight sagittal 3D MPRAGE sequence.http://surfer.nmr.mgh.harvard.edu) that provides automated global and regional measures of cortical thickness. FreeSurfer performs gyral-based cortical parcellation using an algorithm that incorporates probable locations of regions of interest and the potential interparticipant variance based on the sample used. The atlas-generated ROIs were highly accurate compared to manual ROIs using intraclass correlation and mean distance maps [Measurement of cortical thickness, based on the perpendicular distance from the pial surface to the gray/white matter juncture, is a validated measure of cerebral atrophy. FreeSurfer –15 is annce maps . Corticance maps . FreeSurnce maps  but sensnce maps , 19.To control for head size, total intracranial volume was derived from the MRIs by an automated routine, using FreeSurfer.The Evans Index is the ratio of the maximal frontal horn ventricular width to the transverse diameter of the inner table of the skull. A ratio of 0.3 or greater signifies ventriculomegaly .We used a semiautomated algorithm for measuring ventricular volume, implemented in the program Brain Ventricular Quantification (BVQ ). BVQ coT-tests were used to compare the younger and older NC groups on the outcome measures, and Chi-Square was used to determine if gender distribution differed across groups. Group differences in TICV were calculated with ANOVA. Group and pairwise comparisons of imaging outcomes were calculated using nonparametric methods . For the purposes of this exploratory analysis, no adjustments were made for multiple comparisons.F = 5.131, P = .006, and post hoc testing showed that NPH participants had significantly larger TICV than all other groups (P < .05), and PD participants had significantly smaller TICV than all other groups (P < .05).The younger and older NC participants did not significantly differ from each other on any outcome variable. For the purposes of subsequent analyses, the NC participants were all treated as one group. Chi-square results showed no significant differences between groups in gender distribution. One-way ANOVA was significant for group differences in TICV, P = .000), VentVol/TICV (P = .000), TCortTh (P = .042), and MTempTh (P = .002). Pairwise Kruskal-Wallis Tests showed that NPH participants had significantly larger VentVol and VentVol/TICV compared to all other groups with P < .05 for all comparisons. AD participants had significantly larger VentVol and VentVol/TICV compared to NC and PD participants at P < .01. NPH participants had lower TCortTh compared to PD participants (P = .028). AD participants had lower TCortTh compared to NC (P = .021) and PD (P = .037) participants. NPH participants had lower MTempTh compared to NC (P = .022) participants, and AD participants had lower MTempTh compared to all other groups at P < .05. NC and PD participants did not significantly differ from each other on any outcome. See All 5 of the NPH participants scored an Evan's Index above the cutoff for ventricular enlargement. Five of the 10 AD participants and 1 of the 5 older NC participants also scored above this cutoff. Kruskal-Wallis Tests across all groups showed significant differences in VentVol  imaging with amyloid tracers detects amyloid plaques in the brain, which are characteristic of AD . This teFuture follow-up studies with larger samples can use parametric statistics such as logistic regression to further establish the separation of NPH from other groups based on ventricular volume and cortical thickness. In addition, future studies can examine cortical thickness and/or volume changes in specific structures that are vulnerable to AD to further improve the differential diagnosis of NPH relative to AD. Hippocampal volume, however, may not be as useful in differentiating the two diseases because it can be reduced in NPH due to compression by the expanded temporal horn of the lateral ventricle. Cortical structures, being farther removed from the ventricles, might better contribute to the differential diagnosis of NPH. In this study, middle temporal thickness was not as effective as total cortical thickness in distinguishing groups, since NPH patients showed thinning of this area. Further research is needed to identify specific areas of the cortex that can contribute to the differential diagnosis of NPH.Another possible confounder to this analytic strategy in practice is the simultaneous occurrence of NPH and AD or PD in the same patient. Such cases were excluded from the present analysis but are often encountered in clinical practice. Further studies will be required to determine if patients with dual diagnoses can be identified by these methods.While these preliminary findings require replication in larger numbers of subjects, these results highlight the promise of combining quantitative measures of cortical thickness and ventricular volume as potential brain imaging markers for the differential diagnosis of NPH.