A 70-year-old woman underwent a live unrelated, ABO-incompatible renal transplant for end-stage renal disease. One year after transplantation, protocol biopsy TSA HDAC clinical trial revealed pathological changes indicative of the histological subtype of ‘early lesions of PTLD’ according to the World Health Organization classification, while the patient showed no clinical signs or symptoms. The patient was finally diagnosed with EBV-positive PTLD by in situ hybridization for EBER (EBV-encoded RNA), and was successfully treated based on the reduction

of immunosuppression. Protocol biopsy within the first post-transplant year is the only diagnostic measure to detect asymptomatic early PTLD, which allows for early intervention and leads to better outcomes. Post-transplant lymphoproliferative disorder (PTLD)

is a neoplastic complication with a potentially fatal outcome that develops as a consequence of immunosuppression, and is generally associated with Epstein-Barr virus (EBV) infection.[1] The reported incidence of PTLD in renal transplant recipients is lower (1–3%) than that for other types of allograft (1–30%); however, it is 20 times higher than in the general population.[2, 3] We report a 70-year-old woman who underwent a live unrelated (spouse), ABO-incompatible renal transplant for end-stage renal disease secondary to nephrosclerosis. She had received maintenance immunosuppression with the tacrolimus extended-release capsule (TACER, 7 mg/day), mycophenolate buy ABT-263 mofetil (MMF, 1000 mg/day), and methylprednisolone (4 mg/day). Her postoperative course had been uncomplicated and Phloretin rejection-free, with serum creatinine levels of around 0.6 mg/dL, except for pathological calcineurin-inhibitor (CNI) nephrotoxicity diagnosed on 2 month protocol allograft

biopsy. CNI nephrotoxicity had been well controlled and had no impact on her renal function after the reduction of TACER to 6 mg/day. One year after transplantation, protocol biopsy revealed pathological changes including tubular atrophy and interstitial enlargement with the massive infiltration of mononuclear plasmacytic cells, and the Banff ’09 lesion scores (i2, t0-1, g0, v0, ci1, ct1, cg0, cv0, ptc0, mm0, ah0, aah0, c4d0) of the biopsy specimen showed no histological signs of cellular rejection. Infiltrating plasmacytic cells consisted of predominant CD20-positive B cells located in the centre of lesions with nodular formation and dispersed CD3-positive T cells around the B-cell nodules (Fig. 1A–E). These findings were indicative of the histological subtype of ‘early lesions of PTLD’ according to the latest World Health Organization (WHO) classification from 2008,[4] while the patient showed no clinical signs and had no abnormal findings on palpation of the lymph nodes, blood test, urinalysis, and image inspection including CT.

T lymphocytes and B lymphocytes specific for other antigens are not activated in the current model. CD4+ regulatory T lymphocytes.  Innate (or natural) regulatory T lymphocytes (iTregs), representing

CD4+CD25+ T lymphocytes, mTOR inhibitor are modelled as a distinct population of thymic-derived cells, distinguished from the aforementioned aTregs by not requiring further differentiation to express regulatory activity [52]. Once activated via presentation of autoantigen on MHC class II molecules (MHCII antigen), regulatory T lymphocytes exhibit both cell contact-mediated and cytokine-mediated immunosuppressive activity [46,53,54]. CD8+ T lymphocytes.  CD8+ T lymphocytes in the model are initially activated by MHCI-antigen in the PLN, with help provided Small molecule library by activated CD4+ T lymphocytes [55–58]. Acquired cytotoxic effector activity includes both cell contact- and cytokine-mediated mechanisms [59,60]. B lymphocytes.  B lymphocytes in the model interact with DCs, natural killer (NK) cells and T lymphocytes. They differentiate (in the PLN), present antigen to CD4+ and CD8+ T lymphocytes and produce cytokines and autoantibodies [61–63]. Autoantibodies form immune complexes, influencing antigen uptake

[26,64]. NK cells.  On the recommendation of the scientific advisory board, NK cells were included in the model based on a high degree of scientific interest and investigation [65–68]. Because the data characterizing NK cells in type 1 diabetes and their relative role in disease are sparse relative to other cell types, the use of the NK cell module is optional (i.e. it can be omitted from the virtual mouse simulations). Inclusion of the NK cell module may be used to explore specific hypotheses on the role of NK cells in disease. Arachidonate 15-lipoxygenase Activation of NK cells in the model is mediated by DCs and B lymphocytes and is regulated further by cytokines and co-stimulatory molecules [69–74]. Effector activities include cytokine synthesis and killing of immature

DCs and β cells [75,76]. Blood glucose.  The level of blood glucose in the model is regulated by insulin-dependent and insulin-independent mechanisms, based on deviations of insulin and glucose from their basal levels [77,78]. Dietary glucose intake is assumed to be constant and implicitly accounted for in the basal glucose level. Gut and gut-associated lymphoid tissue.  The gut and gut-associated lymphoid tissue (GALT) were built to investigate the role of local immune activity on the efficacy of oral insulin therapy. The gut tissue in the model is simplified to include only DCs. The GALT includes all the biological components present in the modelled PLN. Following the design phase, the components of the model were represented mathematically. As illustrated in Fig.

Samples for intracellular staining were additionally fixed and permeabilized using BD Cytofix/Cytoperm Fixation/Permeabilisation Kit (BD Biosciences) according to the manufacturer’s instructions. FACS acquisition was performed on LSR-II (Becton-Dickinson) and results were analysed using FlowJo software (TreeStar

Inc, Ashland, OR). RNA was isolated using an RNeasy Micro Kit (Qiagen, Hilden, Germany). Complementary DNA synthesis was carried out with an iScript Kit (Bio-Rad, Munich, Germany) and quantitative PCR was performed Selleck Doxorubicin using the following primers: S100A12: forward primer 5′-CAC ATT CCT GTG CAT TGA GG-3′, reverse primer 5′-TGC AAG CTC CTT TGT AAG CA-3′; S100A8: forward primer 5′-TGT CTC TTG TCA GCT GTC TTT CA-3′, reverse primer 5′-CCT GTA GAC GGC ATG GAA AT-3′; S100A9: forward primer 5′-GGA ATT CAA AGA GCT GGT GC-3′, reverse primer 5′-TCA GCA TGA TGA ACT CCT CG-3′; cyclophilin A: forward primer 5′-ATG CTC AAC CCC ACC GTG T-3′, reverse primer 5′-TCT GCT GTC TTT GGG ACC TTG TC-3′. Reactions were performed in triplicate using iQ SYBR Green Supermix (Bio-Rad) and normalized to endogenous cyclophilin A mRNA level using the ΔΔCt method. Lysates from FACS sorted CD14+ HLA-DR−/low MDSC and CD14+ HLA-DR+ monocytes were denatured

at 95° for 5 min and subjected to SDS–PAGE. The gel was blotted onto nitrocellulose Smad inhibitor membrane followed by incubation with anti-S100A12 antibody (Abcam, Cambridge, UK) or a control anti-glyceraldehyde 3-phosphate dehydrogenase antibody

(Sigma, St Louis, MO). Binding of the antibodies was visualized using horseradish peroxidase-conjugated rabbit anti-mouse IgG (Abcam). Western blot imaging and quantitative analysis were performed using FluorChem HD2 Multiplex Fluorescent Imaging System (Cell Biosciences Inc., Santa Clara, CA). All the statistical analyses were based on two-tailed Student’s t-test. All P-values < 0·05 were considered to be significant. Differential gene expression analysis was performed to identify genes expressed in CD14+ HLA-DR−/low new MDSC but not in CD14+ HLA-DR+ monocytes. Using PIQOR Immunology Microarrays (Miltenyi), we found that S100A12 was 40-fold more strongly expressed in MDSC than in monocytes (GEO database accession no. GSE32001). Real time PCR was performed on FACS-sorted MDSC (CD14+ HLA-DR−/low) and monocytes (CD14+ HLA-DR+) from peripheral blood to confirm these results. Higher S100A12 expression was seen in MDSC than in monocytes (Fig. 1a). S100 is a family of proteins including 21 calcium-binding proteins.11 Among them, S100A8, S100A9 and S100A12 are closely related. We focused on these three proteins because monoclonal antibodies for FACS and Western blotting were available for them. First, we analysed the expression of S100A8 and S100A9 genes in the PBMC of healthy donors. Both S100A8 and S100A9 were about 10-fold to 15-fold more expressed in MDSC than in monocytes (Fig.

6A). Alternatively, differential TRAIL expression could result from stochastic cell

activation, and only continuous BAY 73-4506 or additional triggering allows for optimal TRAIL expression of the whole pDC population. In support of this, unmanipulated CAL-1 cells also displayed a broad spectrum of TRAIL expression at 4 h post CpG activation and 6 h post Imiquimod triggering, when the cells were not fully activated yet (Fig. 2B and Supporting Information Fig. 6B). As TRAIL expression in pDCs results both from type I IFN-R signaling and from TLR signaling (Fig. 1; [13]), we addressed whether these two signaling pathways act separately and/or cooperate to induce optimal TRAIL expression. CpG triggering — that elicits both TLR signaling and IFN-R signaling — results in lower selleck screening library TRAIL levels in CAL-1-NAB2E51K cells than in CAL-1-NAB2, or CAL-1-EV cells (Fig. 5; top panel). To dissect the contribution of TLR signaling versus IFN-R signaling, we activated CAL-1 cell variants with CpG, while blocking

type I IFN-R signaling with the vaccinia virus-encoded type I IFN decoy receptor B18R [28-30]. Blocking type I IFN-R signaling resulted in reduced TRAIL levels in CAL-1-EV and CAL-1-NAB2 cells (Fig. 5, middle panel) that were comparable to suboptimal activation conditions (i.e., at 4 h post CpG activation, Fig. 3C). Remarkably, addition of B18R completely abolished TRAIL expression in CpG-activated CAL-1-NAB2E51K cells (Fig. 5, middle panel), indicating that both TLR signaling through PI3K/NAB2 and type I IFN-R signaling contribute to optimal TRAIL expression. Of note, all three cell variants expressed high levels of TRAIL when stimulated solely via type I IFN-R with recombinant IFN-β (Fig. 5; bottom panel). Together,

these data imply that (1) NAB2-dependent TRAIL induction occurs downstream of TLR engagement, independently of type I IFN-R signaling, and that (2) the remaining TRAIL expression upon CpG stimulation in CAL-1-NAB2E51K cells possibly resulted from type I IFN-R signaling. Here, we have identified NAB2 as a novel transcriptional regulator of TRAIL in pDCs. We show that NAB2-mediated TRAIL expression is dependent on TLR-mediated PI3K signaling, and independent of type I IFN-R signaling. In addition, our results reveal that TRAIL induction in pDCs can occur at least via Calpain two independent signaling pathways: (i) downstream of TLR signaling and at least in part mediated by NAB2, and (ii) downstream of type I IFN-R signaling, independently of NAB2. As both pathways must be blocked to completely abolish TRAIL induction in pDCs (Fig. 5), our data show that these two signaling pathways independently induce TRAIL, and suggest that they act in concert to achieve full TRAIL expression. Recent data have indicated that TRAIL induction upon TLR7 triggering can occur independently of type I IFN stimulation [13, 31].

[1, 4, 5] Sequencing of PCR products is a very powerful method for the correct typing of dermatophytes but, unfortunately,

it is not convenient for the processing large numbers of samples.[13, 14] Real-time PCR proved valuable in the identification of dermatophytes because of its high sensitivity and rapidity, but it is costly.[10, 11] This study aimed at evaluating a MX PCR technique based on the amplification of the CHSI gene and the ITS region which are the most widely used targets in the Alisertib molecular diagnosis of dermatophytic onychomycosis in humans.[8, 17, 21, 23] On the other hand, MX PCR was shown to be a powerful tool for the characterisation of dermatophytes when DNA extracted from clinical specimens is used.[1, 6, 7, 9, 17] We were only interested in T. rubrum and T. mentagrophytes complex because they are the most frequent among the species isolated in our region.[14, 23] In addition, previous reports on PCR assays that allow distinguishing TR and TM are very few.[9] In this study, MX PCR was applied to a collection of culture samples (standards and controls) of dermatophytes and non-dermatophytes fungi, and to nail specimens obtained from patients with dermatophytic onychomycosis previously confirmed by mycological examination. The analysis of our results showed that the specificity of the

technique was excellent as none of the non-dermatophytic fungal specimens and none of the uninfected nails yielded positive results in MX PCR. Our results Ulixertinib ic50 are in agreement with most previously studies.[4, 6, 7, 11, 16, 17, 25] As far as sensitivity is considered, MX PCR may be considered very satisfactory as 100% of controls and 97% of nail specimens yielded positive results. Sensitivity values reported in previous studies using different PCR methods and primers ranged between 51% and 94.8%.[1, 4, 6, 7, 11, 17, 19, 21] On the other hand, our results showed the PCR to be more sensitive than mycological examination (97% vs. 81.1%). This finding is in accordance with most previously reported studies.[5, 7, 9, 13, 19, 20, 25] In contrast, in some reports, results of PCR and mycological examination were nearly similar

in terms of sensitivity.[3, 12, 15] The threshold of DNA detection in MX PCR was 50 pg of DNA per reaction. This value is similar to that reported in Candida and Aspergillus almost systemic infections.[15, 16] In contrast, it is much higher than the threshold reported in MX PCR for the detection of other non-dermatophyte fungi.[19] The limited existing genomic data on dermatophytes and the close ITS gene sequence similarity between related dermatophyte species (e.g. T. rubrum and T. violaceum on one hand, and T. mentagrophytes, T. schoenleinii and T. tonsurans on the other hand) impede designing specific primers for all known dermatophyte species. Indeed, ITS region primer pair TR was found to cross-react with T. violaceum, and TM with T. tonsurans, T. equinum and T. schoenleinii.

45 Silmitasertib mouse examined the outcomes in patients with CKD referred late to a nephrologists.

The analysis did not distinguish between the cause of CKD nor conduct sub group analyses for diabetes. Overall, 20 studies (total sample size 12 749) examined the effect of late referral met inclusion. The definition of late referral varied from 1 month to 6 months. There was a significantly increased overall mortality in the late referral group compared with the early referral group (relative risk 1.99 95% CI: 1.6–2.39) and a significantly longer duration of hospital stay. However, the mean serum creatinine and creatinine clearance at time of referral were not significantly different between the groups. Cass et al.,46 investigated the association between area level measures of socioeconomic disadvantage Selleckchem MLN8237 and the proportion of ESKD patients who were referred late for renal replacement therapy. The analysis, which utilized the ANZDATA database, considered the timing of referral to a nephrologists and the postcode of residence at the start of treatment. Late referral was defined as those who required dialysis within 3 months of referral. The analysis was restricted to capital cities and excluded overseas visitors and those where ESKD was caused by disease with very short course. The ABS Statistical Sub-Division (SSD) level socioeconomic data from the 1996 census was used for the assessment. Of the total of 3334 patients (April 1995 – December 1998),

889 (26.7%) were found to have been referred late with a high variability between

SSDs. There was a significant correlation between late referral and disadvantage (r = 0.36, P = 0.01), with a higher proportion of late referral being associated Calpain with the more disadvantaged regions. Areas with higher incidence of ESKD in population terms were also areas where a higher proportion of patients were referred late. Issues of access, availability and quality of care are all potentially relevant to late referral. Disadvantaged areas had both an increased population burden of ESKD and a greater risk of delayed access to specialist renal services which is then associated with a poorer outcome. The study concludes that despite an overall improvement in the prevention and care of chronic diseases, with regard to chronic renal failure, there is a failure to address the needs of general practitioners and the public especially in disadvantaged areas. Of interest, late referral was found not to be related to geographical access to dialysis units.46 Overland et al. analysed information on the number of diabetic individuals and number of services for selected Medicare item codes by NSW postcodes using the Health Insurance Commission data file.47 The analysis was conducted for the 1996 calendar year and indicated that people at most disadvantage were less likely to be under the care of a GP (OR 0.41 0.40–0.41) or consultant physician (0.50 0.48–0.53) despite this group having the highest prevalence of diabetes.

In development of the vertebrate hindbrain, segmentation of the neuroepithelium into rhombomeres is an early developmental step which provides a framework for correct neural connectivity [108] and rhombomere boundaries are associated with CSPG expression [109]. Within the cranial mesenchyme the correct rhombomeric projection of sensory trigeminal and facial/acoustic ganglia axons is thought to depend on such CSPG boundaries [110]. Additionally, commissural projections of vestibular nuclei neurones are regulated by CSPGs, where CS moieties have been shown to control guidance of pioneer axons, fasciculation and timing of axon arrival at the contralateral target [111]. In the visual

system CS-GAGs are implicated in extrinsic regulation of the divergence of retinal axons at the optic chiasm

selleck screening library midline (a developmental step which imparts binocular vision) [112] as well as repelling axons to confer retinal cell topography [113–115]. CSPGs in the developing CNS also act to modulate the properties of other guidance cues. The transmembrane protein semaphorin 5A (Sema5A) exerts proteoglycan-dependent signalling. Chondroitin sulphate/heparin sulphate-GAGs bind to thrombospondin repeats within Sema5a, switching it from an attractive to a repellent molecule to guide formation of the fasciculus retroflexus, a diencephalon fibre tract associated with limbic Selumetinib molecular weight function [116]. During postnatal development, the composition of the ECM gradually matures as neuronal circuitry approaches its adult form. Stabilization of connectivity is prefixed by a ‘critical period’ in which circuits are sensitive to experience-dependent plasticity. Ocular dominance plasticity is a classic system in which this has received much attention. Monocular deprivation during the critical period, but not in the

adult, causes cortical neurones to shift in coding preference to the nondeprived eye [117,118]. Studying the mechanisms by which the critical period is initiated and terminated is informative to approaches aiming to reactivate plasticity to promote repair following injury. The rate at which fast-spiking parvalbumin positive cortical interneurones mature (a process delayed by dark-rearing from birth) and release Enzalutamide supplier the neurotransmitter GABA is known to contribute to the onset of the critical period. The ECM also undergoes significant changes as the critical period closes. PNN formation coincides with critical period termination and attenuating PNN structure results in persistent ocular dominance plasticity in Ctrl1−/− mice [38]. Accordingly, as the critical period closes there is an upregulation of Ctrl1, aggrecan and HA [119]. CSPG expression is also associated with closure of the critical period [120]. Indeed dark rearing from birth, which extends the critical period, is associated with delayed expression of PNN CSPGs [121].

In the 12 studies[28, 31-33, 35, 37, 39, 41, 43-46] reporting the association of statin use and AKI requiring RRT, the incidence of AKI requiring RRT ranged from 0.049%[46] to 9%[28] (Table 1). The pooled incidence of AKI requiring RRT for all 12 studies was 0.94%. The pooled incidence of AKI requiring RRT among statin user and nonstatin user were 1.31% and 0.76%, respectively (Table 2). Two studies[34, 40] were not included in the calculation of the pooled incidence because the numbers of RRT events signaling pathway were not reported. For the same reason, we used the number of RRT events in the PSM cohort

instead of the source population in one study.[45] Among all the 24 studies, only three RCTs described adverse effects of statin therapy. One study[28] adopted a clinically significant elevation or serum creatinine kinase and alanine aminotransferase within the first five postoperative days as safety outcomes. The incidence of these adverse events was the same in the statin and the

placebo group in this study (10% vs. 10%). The other two RCTs[25, 27] merely reported no observed significant side-effects in the statin group, and the incidence was not specified. The 21 studies with use of statins and risk of postoperative AKI included a total of 106 586 cases and 869 889 controls (Table 1). When the results from all 21 studies[24-30, 32, 34-38, 40-47] were combined, the use of statins was associated with a significant Unoprostone protective effect for perioperative Palbociclib AKI (pooled OR 0.87, 95% CI 0.79–0.95, I2 = 58.8%) (Fig. 2A). If multiple effect sizes of different methodological quality were reported in the same study, only the one with the highest quality was included in this analysis of the 21 studies. In general, the propensity score matching (PSM) adjusted effect size was viewed as of the highest quality, the crude effect size of the lowest quality, and the multivariate adjusted effect size in between. In each study, the variables adjusted for in the multivariate

models and the variables used to calculate the propensity score, if available, were listed in the Appendix 1 (Table App2). To determine other sources of heterogeneity, we performed several sensitivity analyses (Table 3). First, we examined the impact of selection of studies of different methodological quality. We excluded RCTs from analysis, and the pooled summary effect estimate of the remaining 19 observational studies was still significant and was very similar (pooled OR, 0.87; 95% CI 0.79–0.96, I2 = 67.0%). We combined crude OR reported in 14 studies[29, 30, 32, 34, 35, 37, 38, 40-44, 46, 47] and an insignificant effect of statins on perioperative AKI was shown (pooled OR, 1.02; 95% CI 0.84–1.23, I2 = 90.6%). However, after pooling of the 13 studies[30, 34-38, 40-43, 45-47] with PSM or multivariate adjusted effect sizes, use of statins was associated with a significant protective effect (pooled OR, 0.

It has also recently been observed that Guillain–Barré syndrome and multiple sclerosis patients with a lower capacity to produce ROS develop a more severe and chronic disease 18, 19. So far it has not been possible to study the genetic impact of NCF1 polymorphism in RA as the human genetic region is very complex due to several duplications. Nevertheless, polymorphisms in NCF4 gene, coding for another subunit (p40phox) of the same NOX2 complex, have been associated with Crohn’s disease and RA 20–22. Our data suggest that macrophages ROS

production dampens autoimmune disease manifestations and T-cell activation is mediated via macrophages. Macrophages form a heterogeneous population and have been shown to play a proinflammatory role in the arthritic joints in CIA 23, 24 and in RA. In RA Staurosporine affected joints, infiltrating activated macrophages produce proteinases, pro- and anti-inflammatory cytokines and chemokines that stimulate fibroblasts and osteoclasts to degrade the cartilage and bone Doxorubicin (reviewed in 25). Cell-to-cell contact between activated T cells and macrophages in the synovium regulate cytokine production by macrophages 26. The antigen-presenting capacity of synovial macrophages has been assumed, due to their expression of MHC class II and costimulatory

markers, but not directly shown so far. Nevertheless, in vitro derived macrophages Lck were shown to be able to present autoantigens to T cells. This

has been shown also for CII and its peptides in the murine system 7, 27–29 and in the human system 30. It is widely believed that macrophages cannot prime T cells but that they further activate already stimulated T cells in an antigen dependent fashion. The question whether macrophages can prime T cells themselves has been assessed by injecting antigen-pulsed macrophages in mice: depending on the source of macrophages and their activation status, different T-cell populations were stimulated. If these APC were in vitro differentiated macrophages or cloned macrophages, they could prime CD8+ T cells 31, 32. Others showed that when a macrophage cell line was stimulated with IFN-γ and pulsed with antigen, these cells could induce a Th1 response, but macrophages pulsed with antigen only selectively elicited a Th2 response 33, 34. However, in order to assess the capacity of macrophages to prime T cells in vivo, an animal model is required where the relevance of macrophages and the importance of MHC class II were established. In the murine CIA model that we used here both requirements were fulfilled. RA seems to be driven by an inflammatory attack on peripheral, cartilaginous joints: joint-specific or cross-reactive antigens in the joints are recognized by antibodies and by MHC class II restricted T cells that are likely to mediate the inflammatory process 35–37.

The isolated granulocytes were 95% pure and contained 1–3% CD3+ T cells. Granulocytes (2 × 106/ml) were stimulated with

PMA/ionomycin or Toll-like receptor (TLR) ligands [10–100 µg/ml zymosan, 1–10 µg/ml poly I:C, 0·1–1 µg/ml lipopolysaccharide BIBW2992 (LPS) or 1 mm CpG] or cytokine cocktails (10 ng/ml IL-1β+ 20 ng/ml IL-23, 4 ng/ml TGF-β+ 10 ng/ml IL-6 + 20 ng/ml IL-23 or 25 ng/ml IL-17) for 24 h in 24-well plates. Cell pellets were collected for RNA extraction. RNA was extracted from 2 × 106 granulocytes by using RNeasy Kit (Qiagen) as described by the manufacturer. RNA was reverse-transcribed to cDNA using MultiScribe RT (Applied Biosystems, Streetsville, Ontario, Canada). cDNA was then amplified using TaqMan Universal PCR Master Mix (Applied Biosystems). Primers for IL-17 (product number: Hs99999082_m1),

IL-22 (Hs00220924_m1) and β-actin (Hs99999903_m1) genes were purchased from GS-1101 order Applied Biosystems. The fold increase in signal relative to the controls was determined with the change in cycling threshold (ΔCTsample − ΔCTcontrol) and was calculated as follows: R = 2 − (Ctsample − Ctcontrol), where R is relative expression and Ct is cycle threshold. β-actin was used as an endogenous control. Statistical analysis was performed using Prism software (GraphPad) version 2.7.2. Two-tailed P-values were calculated using Wilcoxon test, Fisher’s exact test and non-parametric one-way analysis of variance (anova), as indicated in various figure legends. Because the data are not distributed normally, the non-parametric

Kruskal–Wallis test with Dunn’s post-test was performed. The receiver operating characteristic (ROC) cut-off values were generated using sensitivity and specificity values with GraphPad prism software. The area under the curve of a ROC curve is related closely to the Mann–Whitney or Wilcoxon’s rank test, which test whether positives are ranked higher than the negatives. As the data are not distributed normally (non-Gaussian), a non-parametric Fisher’s exact test was used to generate a ROC curve to create a cut-off in order to identify TB patients based on the presence of IL-17, IL-22 and IFN-γ-positive CD4+ cells compared to the healthy controls. The circulating levels of IFN-γ-, IL-17- and IL-22-expressing Arachidonate 15-lipoxygenase CD4+ T cells in whole blood were determined by intracellular cytokine assay. The frequencies of IFN-γ-, IL-17- and IL-22-producing CD4+ T cells were found to be lower in active TB patients compared to healthy controls and latent TB subjects (Fig. 1). The gating strategy employed for the identification of IL-17-, IL-22- and IFN-γ-expressing cells is shown (Fig. S1). Due to high variability, the data were analysed using cut-off values. The ROC curve was used to generate the cut-off values maximizing the sensitivity and specificity for predicting the true positives and true negatives within the healthy, latent TB and active TB patient group.