Supplemental Digital Content is available in the text. Delayed graft function (DGF) affects over 25% of deceased donor kidney transplants (DDKTs) and is associated with increased cost, worsened graft outcomes, and mortality. While approaches to preventing DGF have focused on minimizing cold ischemia, donor factors such as acute tubular injury can influence risk. There are currently no pharmacologic therapies to modify DGF risk or promote repair, in part due to our incomplete understanding of the biology of preimplantation tubular injury.We collected intraoperative, preimplantation kidney biopsies from 11 high-risk deceased donors and 10 living donors and followed transplant recipients for graft function. We performed quantitative high-dimensional histopathologic analysis using imaging mass cytometry to determine the cellular signatures that distinguished deceased and living donor biopsies as well as deceased donor biopsies which either did or did not progress to DGF.P < 0.0001) and increased macrophage infiltration (P = 0.0037) in high-risk DDKT compared with living donor biopsies. For those high-risk DDKTs that developed postimplant DGF (n = 6), quantitative imaging mass cytometry analysis showed a trend toward reduced tubular cells (P = 0.02) and increased stromal cells (P = 0.04) versus those that did not (n = 5). Notably, these differences were not identified by conventional histopathologic evaluation.We noted decreased tubular cells (The current study identifies donor tubular cell loss as a precursor of DGF pathogenesis and highlights an area for further investigation and potential therapeutic intervention. To increase the depth of information gained from a single biopsy, several groups have applied single-cell transcriptional sequencing methods to study ischemic and inflammatory transplant injury.22 However, single-cell transcriptomic techniques exclude spatial information and therefore cannot define regional heterogeneity or individual cellular interactions. Additionally, protease dissociation steps may preferentially release particular cell compartments and thus bias data toward certain cell types.24 Therefore, quantitative cellular analysis of intact biopsy sections could more accurately measure changes in individual cell subsets.DGF is due primarily to ischemia-reperfusion injury (IRI),26 The tissue is labeled with a cocktail of antibodies specific for antigens of interest, each conjugated to a unique heavy-metal isotope, followed by sequential pulsed laser ablation of 1-μm2 regions. Each resulting aerosolized plume undergoes time-of-flight mass spectrometry analysis to determine the presence and abundance of each antibody, and data are reconstructed to form a quantitative map of each protein with 1-μm resolution. This approach vastly increases the dimensionality and throughput of protein expression data compared with serial immunofluorescence while preserving spatial information.27 Our group has previously developed and validated an antibody panel defining the cellular diversity of reference human kidney tissue and an automated kidney-specific data analysis pipeline.28Imaging mass cytometry (IMC) is a highly multiplexed tissue imaging technology that allows for simultaneous detection of up to 42 protein markers on an individual formalin-fixed, paraffin-embedded biopsy section.In this study, we extended our validated IMC pipeline to investigate cellular changes preceding DGF. We obtained preimplantation biopsies from high-risk deceased donor kidney transplants (DDKTs) and used preimplantation biopsies from living donor kidney transplants (LDKTs) as healthy controls. Biopsies were interrogated for signs of injury and differences in tubular, stromal, endothelial, and immune populations between living and deceased donors and between those that subsequently developed immediate graft function (IGF) versus DGF. These data represent the first use of IMC to study human kidney disease and provide a better understanding of the cellular basis for a predisposition of transplanted kidneys to developing DGF.28Kidney transplant recipients were recruited from June 2018 through May 2019 under a protocol approved by the Yale University institutional review board (Protocol 2000022915). Informed consent was obtained for all patients before enrollment. For each participant, an 18-gauge core or wedge biopsy was collected intraoperatively during organ preparation and processed as previously described.Eighteen LDKT recipients and their corresponding donors were enrolled, with biopsies collected after approximately 5 minutes of cold ischemia. Fifteen samples were sectioned, with 3 biopsies excluded after processing due to inadequate size.16 . A DGF risk cutoff of 25% was selected as it was associated with significantly decreased graft survival in that study.16 Forty-four DDKT recipients were assessed for eligibility, and 23 were excluded due to low DGF risk (20/44), simultaneous liver or dual kidney transplant (2/44), or duplicate donor with another patient (1/44). Of 21 eligible recipients, 16 consented to participate, of whom 7 developed DGF defined by dialysis requirement within 1 week of transplantation , similar to the group’s calculated DGF risk of 39.0% ± 10.6%.16Adult DDKT recipients were identified for DGF risk >25% based on recipient and donor data using the calculator developed by Irish et al5 The total analyzed tissue area was 10.26 mm2. Eleven DDKT samples (6/11 with subsequent DGF) were analyzed, with clinical data shown for both donors (Table 2 (DGF: 6.98 mm2).Based on our prior experience that ~10% of IMC regions would not yield analyzable data, 11 LDKT and 13 DDKT biopsies were selected at random for cortical ablation and labeling, with a goal of 10 per group. After hybridization, 3 samples 1 LDKT, 2 DDKT) were excluded due to large areas with unrecorded or nonspecific data. Ten LDKT biopsies were analyzed, with donor data shown  blinded to tissue identity reviewed 1 H&E and 1 Trichrome stained section from each biopsy, with histopathologic scoring shown Table . Each bi28 In brief, one 5-μm thick section from each biopsy within 1–3 sections of the scored H&E and Trichrome slides was labeled using a cocktail of metal-conjugated antibodies . Biopsies were then ablated and imaged via the Nd:YAG 213 nm laser in the Hyperion Imaging System (Fluidigm Corporation) with 1-μm pixel size, with each aerosolized plume analyzed in a mass cytometer. Pixel classification was performed on resulting antibody images in ilastik (version 1.3.2)31 to define pixels as belonging to nuclei; membranes of tubular, endothelial, or interstitial cells; or background. Cell segmentation was performed in CellProfiler (version 3.1.8)32 using size-gated nuclei to define a cell and the surrounding membrane pixels to define cell borders. Finally, cell phenotyping was performed in histoCAT (version 1.76),33 an open-source program designed for analysis of mass cytometry data, using the Phenograph algorithm,34 with each cell cluster manually annotated for phenotypic identity by canonical protein expression.Antibody validation and IMC immunolabeling and data analysis were performed as previously described.For each biopsy, multiple regions were selected to span the depth of renal cortex beginning 400 μm from the renal capsule with a width of 400–600 μm and cortico-medullary depth of ~1000 μm per region. Slides were labeled on consecutive days in batches of 6–8 samples and then imaged over the course of approximately 1 month, with sample order randomized.28Validation of the analysis pipeline on injured tissue by manual cell counting was performed in representative regions from 4 deceased donor samples (3/4 DGF) as previously described.For patient data, continuous variables were compared by the Welch’s t-test, binary variables by the Fisher’s exact test, and HLA mismatches by the chi-square test. Histopathologic scoring was compared using the chi-square test. For comparison of the 4 repeated LDKT samples to the reference data set and for validation comparing manually counted and calculated cell populations, the Wilcoxon matched-pairs signed-rank test was used. Quantitative IMC data were compared by the Welch’s t-test. Twelve cell populations were compared across samples, and a Bonferroni-corrected significance cutoff of 0.004 was therefore used, with additional trends shown using a cutoff of 0.05. Reported values represent means with standard deviations.28 showed no significant differences in any cell phenotype  as well as no differences in paired comparisons of the 4 biopsies included in both studies .Most LDKT biopsies had mild histopathologic changes Table A and qua28 automated quantitation successfully identified 96.0% ± 1.8% of overall cell number as defined by manual counting, with no significant difference in the proportion of any cell phenotype . In particular, 99.5% ± 4.6% of tubular cells were correctly identified by our automated pipeline when compared with manual adjudication, suggesting that differences in protein expression between reference and injured tissue did not impair detection or classification of renal cell populations.Before analysis of DDKT samples, we validated the use of our data analysis methodology on injured tissue. Four DDKT biopsies were subjected to IMC imaging followed by manual identification of over 8,000 cells in representative regions using 8 protein markers defining cell phenotype. This was compared with cell identification and quantitation using the automated pipeline. Consistent with our previous experience,P = 0.08) (Table P = 0.003), with a trend toward reduction in multiple tubular segments, including PT   and total immune cells (P = 0.001) and, unexpectedly, a higher proportion of endothelial cells (P = 0.002) compared with LDKT.As compared with LDKT kidneys, DDKT samples showed a trend toward more significant tubular injury by histopathologic scoring, with 7/11 graded as moderate, although this did not reach statistical significance (P < 0.0001), specifically in the PT (P < 0.0001) . In the interstitium, DDKT biopsies had significantly more macrophages (P = 0.0037) and a trend toward increased total immune cells (P = 0.007), whereas the endothelial cell numbers were equivalent in the 2 groups when corrected for area. Without a compensatory rise in interstitial populations, this tubular cell loss drove a significant decrease in total nuclei per mm2 in the DDKT group . Overall, these data indicate that high-risk DDKT biopsies showed a significant pretransplant decrease in tubular cell mass and overall cell number with significantly increased macrophage infiltration. Notably, there appeared to be large variance present in the deceased donor population  and, surprisingly, had significantly lower panel reactive antibody values compared with the IGF patients   Table . DGF rec35 IMC, conversely, revealed a comparative reduction in area and labeling intensity of canonical tubular markers as well as a reduction in overall tubular cell number in the DGF samples compared with the IGF group (P = 0.02)  , which represent a combination of smooth muscle, fibroblasts, pericytes, and other interstitial cell types  but in only 1/10 LDKT tissues. These cells comprised 1.66%–10.09% of total PT cells in the biopsies in which they were identified. Qualitative analysis of these cells revealed lower expression of the canonical PT protein megalin as well as thickened basement membranes by collagen IV labeling  and alternatively activated (M2) macrophages in promoting tubular injury versus repair, and incorporating antibodies to differentiate functionally distinct macrophage subsets may further subdivide the donor macrophage population and better inform our interpretation. Additionally, biopsies were procured before graft implantation, and therefore our analysis can only investigate preimplantation injury without contribution from recipient macrophages or reperfusion.In performing unsupervised, quantitative cell clustering analysis, we identified a significant reduction in tubular cells and increased macrophages in high-risk deceased donor kidneys compared with living donors, consistent with the trend toward increased histopathologic tubular injury scores in the DDKT samples. This is in line with mouse models of IRI from our group and others showing that macrophages play a significant role in mediating ischemic tubular injury as well as subsequent reparative or maladaptive responses to that injury.55 We further uncovered a small population of megalin-low, vimentin-positive PT cells almost exclusively in deceased donor tissue that was surrounded by macrophage-rich infiltrates and coexpressed markers of injury and proliferation, KIM-1 and Ki67. Interestingly, KIM-1 expression has been shown to be protective in murine models of IRI and renal transplant,56 and this population may represent surviving PT cells that serve as a source for tubular repair. Murine models suggest that the renal tubule does not have a fixed stem cell population and that regeneration after injury is triggered by stochastic dedifferentiation and division of injured, KIM-1-positive tubular cells.46 High-dimensional single-cell analysis techniques can play a role in isolating these cells and continue to clarify their viability as a therapeutic target for graft repair.Of the factors that could be quantitatively assessed by IMC, we found that tubular cell dropout in high-risk deceased donors was most significant in the PT, the segment most prominently damaged by ischemia.35 This demonstrates that IMC is a more sensitive platform to quantitatively assess renal tubular injury and could provide a key supplement to conventional histopathologic scoring techniques.We identified a trend toward further reduction in tubular cell number in DDKT biopsies that progressed to DGF as compared to other high-risk samples, suggesting that preimplantation tubular cell death may be an important driver of DGF among transplants with similar clinical characteristics. After implantation, tubules that already exhibit dropout may have impaired capability to mount a proliferative response, leading to persistent inflammation and ongoing injury. Because the analyzed DGF cohort was composed of predominantly DCD kidneys, it is a reasonable possibility that ischemia from circulatory death underlies this further decrease in tubular cell number and greater propensity to development of DGF. Additionally, it is noteworthy that this distinction in tubular cell mass between DGF and IGF samples was not apparent using standard histopathologic evaluation for tubular injury, consistent with previous studies of procurement biopsy scoring.5 as well as recent data arguing that high-risk kidneys still offer excellent long-term outcomes,17 we do not advocate this analysis as justification to discard additional organs. In fact, it should be noted that all grafts included in this study ultimately developed adequate function. However, with turnaround time comparable to a standard immunostaining protocol, IMC labeling of procurement biopsies could be available within 24 hours and thus be used to identify organs that otherwise might not have been known to confer higher risk for enrollment in future trials and to help tailor monitoring and treatment strategies. Additionally, quantitative tissue interrogation by IMC could be implemented to study marginal organs to assess for intact tubular and interstitial cell populations in the hopes of salvaging kidneys for transplant, as this may provide a better correlate with postimplant function than clinical degree of donor kidney injury.57With nearly 20% of recovered organs discarded before transplantation58 Subsequent studies can also more robustly define diverse functional immune subsets and analyze spatial data to better understand the immune interface with the tubular epithelium. Finally, in contrast to the focus on PT injury in IRI, the DGF group unexpectedly had most marked tubular cell loss in the CT/CD. Decreased expression of aquaporin-2 has been shown in some animal models of ischemia,60 and distal tubular segments may be an underappreciated player in human ischemic injury.61 Our work suggests that CT/CD dropout may be a driver in human disease, which beckons further exploration. Additional expansion of our antibody panel to differentiate principal and intercalated cells and to target neutrophil gelatinase-associated lipocalin, a marker of epithelial injury produced in distal tubular segments,63 can delineate which cell type is predominantly affected and better define the biological significance of this finding.While we identified donor tubular cell differences predisposing to DGF that were not previously detected with conventional histopathologic analysis, our antibody panel did not permit further elucidation of the precise molecular drivers of this tubular loss. Future efforts can incorporate antibody targets for cellular death and stress pathways implicated in DGF and IRI, including apoptosis, autophagy, and necroptosis and regulated necrosis.Limitations of this study include the small patient cohort, which restricted our ability to perform additional multivariate analysis. Technical concerns, including the loss of ~10% of immunolabeled regions as well as the size of our available antibody panel, remain a challenge, and further optimization will be needed to expand the yield of IMC-based analysis in the human kidney. Additionally, due to the relatively short posttransplant follow-up time and absence of postimplantation biopsies, we were unable to characterize the evolution of donor tubular injury in the recipient or the effect of the cellular changes noted on graft outcomes beyond DGF. Prospective study of a larger cohort should be performed for validation and to assess the long-term implications of donor tubular cell loss on graft outcomes.In summary, we applied quantitative high-dimensional imaging and analysis to preimplantation biopsies from living donors and high-risk deceased donors to investigate ischemic injury preceding DGF. We demonstrated reduced overall tubular and PT cell mass and increased macrophage infiltration in high-risk deceased donor biopsies, and we found a trend toward even greater tubular cell loss, particularly in the CT/CD, in the subgroup that went on to develop DGF as compared to IGF. Additionally, we found a rare population of vimentin-positive, KIM-1-positive, Ki67-positive dedifferentiated PT cells enriched in deceased donor samples as a target for further inquiry of the renal response to donor ischemic injury. This study highlights the potential of IMC to interrogate mechanisms of cell injury and survival and to uncover novel therapeutic targets for both DGF and acute tubular injury.We thank Drs Ruth Montgomery and Denis Schapiro for helpful discussion and guidance regarding IMC and data analysis. We acknowledge Shelly Ren and the Yale CyTOF core for technical assistance. We thank Ricarda Tomlin, Dr Sanjay Kulkarni, and the Yale Section of Transplantation Surgery and Immunology for assistance with biopsy acquisition.


Recently, the therapeutic importance of the anti-rheumatic drug, leflunomide, has been increased after the involvement of leflunomide in treating other autoimmune diseases and its promising role in retarding human malignancies. Few studies have focused on the safety in human or animals without clear outlining of the pathologic features on target organs. One clinical study related leflunomide with significant pulmonary complications in predisposed individuals. The current study examined the dose-dependent lung injury produced by leflunomide in healthy mice. Albino mice were allocated into four different groups. Group (1): Vehicle control group, Group (2–4): mice received leflunomide , respectively, for 8 weeks and then lungs were dissected from the mice for histopathological examination and fibrosis evaluation (Masson’s trichrome staining and α-smooth muscle actin immunohistochemistry). Enzyme linked immunosorbent assay was used to assess the vimentin and other inflammatory factors in the lung homogenate whereas Western blot analysis was employed to assess α-smooth muscle actin, vimentin and collagen 1. Results indicated that leflunomide induced dose-dependent pulmonary injury and the high dose and increased the vimentin, inflammatory markers (NLRP3 and interlukin-1β). Histologic examination showed distorted architecture, marked inflammatory cells infiltrate and increase collagen content. The findings were supported by Western blotting and the immunohistochemical study which showed greater pulmonary α-smooth muscle actin and vimentin content. In conclusion, the current results highlighted that leflunomide produced dose-dependent pulmonary toxicities that requires further investigation of the nature of injury. Leflunomide fulfills the desired goals of disease modifying antirheumatic drugs (DMARDs): it reduces the diseases signs and symptoms; it inhibits structural damage and improves the physical function . Most of the affected patients’ presentation occurs within first three months of leflunomide regimen. Patients suffering from pre-existing ILDs are mainly at risk from this complication and avoidance of leflunomide in this population is considered as a must .The innate immune system involves many pattern recognition receptors. There are five distinct classes of these receptors including the toll-like receptors (TLRs) and oligomerization domain (NOD)-like receptors (NLRs) . ActivatThe cytoskeleton consists of microfilaments, microtubules and intermediate filaments . VimentiThe current study examined the dose-dependent lung injury induced by leflunomide and physio-pathogenetic mechanism underlying the lung damage. Hence, our study explored the effect of leflunomide on the pulmonary level of vimentin and NLRP3 as a possible mechanism may underlie the drug induced injury. As rheumatoid arthritis can affect the lung and results in interstitial pneumonitis , we havead libitum throughout the study period.Forty male albino mice were housed in groups of five in hygienic conditions at the animal facility of the Faculty of Pharmacy in Suez Canal University after approval from the institutional research ethics committee (reference number 201603A7b). The mice were maintained normal dark/light cycle and temperature range 25–35°C. Food pellets and water were provided The purchased mice were acclimatized for about 10°days and then allocated into 4 different groups, 10 mice in each of the experimental groups. Mice were gavaged with leflunomide  which was prepared in suspension form using 1% carboxymethylcellulose (CMC) solution . Group . 12°ml/kAfter completion of this 8°weeks experiment, the animals were anesthetized with ketamine and sacrificed. A thoracotomy was performed to dissect the lungs; the right lobes were frozen at 80°C and the left lobes were perfused by fixation in a fixation solution . The perfused lungs were excised and processed for light microscopic study.Frozen lungs were weighed and homogenized as 10% in phosphate-buffered saline (PBS) using a Teflon homogenizer. The homogenates were centrifuged to remove solid materials and obtain the clear lysates which were quickly frozen for subsequent use in different assays. Samples from the lung lysates were analyzed by sandwich enzyme immunoassay kits for vimentin (MBS450175), NLRP3 (MBS7606270), IL-1β (MBS726898) from MyBiosource. The readings were taken by an ELISA reader .The formalin fixed lung lobes were washed, dehydrated and embedded in heated paraffin wax. After this step, 5°μm lung specimens were stained with hematoxylin and eosin (H&E) for demonstration of lung injury caused by leflunomide. Lung injury was assessed semiquantatively according to Ashcroft histological index, scoring index which grades from 0–7  and MassLung injury was assessed semiquantatively according to Ashcroft histological index, scoring index which grades from 0–8 (Grade 0): Normal lung (grade 1): minimal alveolar or bronchiolar wall fibrous thickening of (grade 3): moderate alveolar or bronchiolar wall thickening with no noticeable damage of lung architecture (grade 5): greater fibrosis with definite loss of the architecture and development of fibrous bands or small masses of fibrous tissues (grade 7): honeycomb lung and (grade 8): total fibrous obliteration of the lung tissue.In every microscopic field, the complete zone of the microscope image was outlined and the investigator first took the decision whether the parenchyma was fibrotic or normal. In case of normal tissue predominates in the zone, it was assigned a zero score whereas in case that fibrotic tissues predominates the investigator took the decision on the level of fibrosis that predominates in the zone. The investigator tried to assign each field by one of the odd numbered grades. If the investigator faced difficulty in taking a decision for the degree of scoring, the zone was given the intervening even score  between two odd numbers .2O2 prepared in phosphate-buffered saline for 30 min for blocking endogenous peroxidases. Next, the sections were incubated in a humid chamber with primary antibodies overnight. Antibodies were: rabbit polyclonal anti-vimentin antibody, dilution 1:100  and rabbit anti-α-smooth muscle actin . The sections were rinsed three times in phosphate-buffered saline and then incubated with secondary antibody (goat anti-rabbit peroxidase-conjugated streptavidin) for 1 h at room temperature. The washing step was repeated again three times. Visualization of immunoreactivity was done by adding 3,3′-diaminobenzidine as a chromogen then counterstaining was performed using Mayer’s hematoxylin. In another section, the primary antibody was replaced by phosphate buffer solution, while other steps of the procedure were the same; this served as a negative control section.About 3–4 µm cut sections from the lung blocks were fixed to positive charge adhesive microscope glass slides. Sections were subjected to deparaffinization and then were treated with 0.2% HThe % of area covered by the blue color in Masson’s trichrome staining or immunostaining area for vimentin and α-SMA were estimated by ImageJ . The areLung tissues were homogenized in RIPA buffer . Total protein from the frozen lung tissues was extracted using the BCA protein extraction kit and processed according to a common method described previously . 50°µg pp value <0.05.Quantitative data were tabulated and checked for the goodness of fit by K-S test. Data showed goof fitness were presented as mean ± standard deviation of the mean (SDM). However, the ordinal data related to Ashcroft scoring for lung sections were presented in box-plots and analyzed by Kruskal-Wallis ANOVA and Dunn’s test. All comparisons were set at Histopathological assessment of lung injury based on routine H&E staining demonstrated that the vehicle group showed normal lung architecture with thin alveolar septa, normal bronchioles and normal blood vessels. The alveoli are lined by flattened pneumocyte type I cells with flat nuclei. Bronchioles were lined by intact folded respiratory epithelium with thin spiral layer of smooth muscle around it .(A) shows disorganized lung architecture with mononuclear inflammatory cell infiltrate. Panel (B) shows areas of diffuse alveolar damage, pulmonary interstitium inflammatory cells infiltrate with interstitial alveolar hemorrhage. Panel (C) shows bronchiolar lumen with cellular debris with marked surrounding inflammatory cell aggregates. Smooth muscle around it was thickened and heavily infiltrated with inflammatory cell. Panel (D) shows congested pulmonary blood vessels showing increased wall thickness with foci of inflammatory infiltrate around it.(C) shows bronchioles are lined by hyperplastic epithelium. The lumen is shown containing cellular epithelial debris and inflammatory cells aggregate. Panel (D) An image shows markedly congested pulmonary blood vessels with marked perivascular cuffing by mononuclear inflammatory cellular infiltrate, increased wall thickness.Masson’s trichrome staining indicated minimal amount of collagen fibers in the vehicle control group; collagen was found in pulmonary interstitium surrounding blood vessels and bronchioles . LeflunoImmunohistochemical staining for α-SMA in lung specimens showed mild cytoplasmic expression in the vehicle control group that was found in the perivascular and peribronchiolar muscle coat and found in scattered few mesenchymal cells in interalveolar septa . LeflunoImmunohistochemical staining of vimentin in lung sections revealed weak expression of vimentin in the vehicle group . ModeratOne-way ANOVA highlighted a difference among the study groups  in lung vimentin, NLRP3 and IL-1β. ELISA assays indicated significant increases in these three markers in mice treated with 5 or 10 mg/kg of leflunomide . ImportaThe currents study highlighted for the first time the lung injury and fibrosis in healthy mice administered leflunomide. This was proved by histopathologic manifestations recorded in H&E stained lung sections and gradual and then significant rise in Ashcroft score. In addition, lung specimens showed dose-dependent increases in fibrotic changes as seen in Masson’s trichrome staining and α-SMA immunostaining. Furthermore, the study aimed to explore some mechanisms behind this toxic effect and found that vimentin induced NLRP3 plays a role in such toxicity and led to increased pulmonary IL-β.Regarding leflunomide pulmonary toxicity, two systematic reviews published different results. One systematic review established a relationship between the presence or deteriorating ILDs and leflunomide. Bilateral ground-glass opacities (GGO) plus diffused destructed alveoli were apparent in radiologic and histopathologic investigations due to the use of leflunomide . Other sOur study demonstrated that use of leflunomide for 8°weeks in has induced histopathologically evident lung injury in the form of distorted architecture, marked inflammatory cells infiltrate and increase collagen content in the lung tissues especially in groups given high doses of leflunomide; this was in agreement with some previous reports . In ordeActivation of the NLRP3 inflammasome requires TLRs signaling to activate NF-kB and upregulate NLRP3 and pro-IL-1β . Indeed,Pseudomonas aeruginosa Staphylococcus aureus and Mycobacterium tuberculosis (Similarly, activation of the NLRP3 inflammasome takes place as a reaction toward a broad spectrum of infectious organisms known to cause ALI. These infectious organisms include bacterial pathogens like rculosis , the infrculosis  and fiberculosis .It was shown that vimentin is essential for activating the NLRP3 inflammasome. A direct protein–protein interactions between NLRP3 and vimentin was documented; this provides new insights toward pulmonary inflammation and/or fibrosis .The wide variety of pathogens which are recognized as activators for the NLRP3 inflammasome proposes that NLRP3 senses microbes by an indirect way through searching for danger-associated molecular forms that are produced/released from the host subsequent to cell or tissue damage . For insin vitro study reported declined levels of active caspase-1 and IL-1β were detected in Vim−/− and vimentin-knockdown macrophages (In previous study inspected the action of vimentin in ALI using different models in which NLRP3 inflammasome activation is required. They demonstrated that key pathophysiological circumstances in ALI like inflammatory reactions, IL-1β production, epithelial barrier permeability, remodeling and fibrosis are diminished in the lungs of Vim−/−mice when they were confronted with bleomycin, lipopolysaccharide or subjected to asbestosis . In agrerophages . Moreoverophages .One of the strength points for our study is that it is limited to evaluating the dose-related effect of leflunomide in healthy mice, limiting as much as possible the presence of other confounding factors or factors that could modify the observed effects. In conclusion, the present study highlighted the dose-dependent lung injury prompted by leflunomide in mice and provided an evidence for such toxicity that may be of clinical importance if appropriate clinical data will be available. Until this time, the current study suggests that rheumatic patients on leflunomide therapy should monitor their pulmonary function and consult their doctors if they progressively suffer from any respiratory symptoms. In addition, the findings of the present study highlighted a novel role for vimentin-induced NLRP3 inflammasome in the lung injury produced by leflunomide in mice. This point opens avenues for new therapeutic tools that may retard drug-induced ALI by inhibiting this pathway.Further studies are warranted to confirm the current findings by concurrent administration of an NLRP3 antagonist or siRNA and look whether they may alleviate pulmonary inflammation induced by leflunomide.


DMD expression and LGG survival outcomes. High DMD expression was significantly associated with poor survival in LGG with a difference in median overall survival between high and low DMD groups of over 7 years (P = < 0.0001). In a multivariate model, DMD expression remained significant (P = 0.02) and was an independent prognostic marker for LGG. The effect of DMD expression on overall survival was only apparent in isocitrate dehydrogenase (IDH) mutant cases where non-1p/19q co-deleted LGG patients could be further stratified into high/low DMD groups. Patients in the high DMD group had a median overall survival time almost halve that of the low DMD group. The expression of the individual DMD gene products Dp71, Dp71ab and Dp427m were also significantly associated with overall survival in LGG which have differential biological effects relevant to the pathogenesis of LGG. Differential gene expression and pathway analysis identifies dysregulated biological processes relating to ribosome biogenesis, synaptic signalling, neurodevelopment, morphogenesis and immune pathways. Genes spanning almost the entirety of chromosome 1p are upregulated in patients with high overall DMD, Dp71 and Dp427m expression which worsens survival outcomes for these patients. We confirmed dystrophin protein is variably expressed in LGG tumour tissue by immunohistochemistry and, overall, demonstrate that DMD expression has potential utility as an independent prognostic marker which can further stratify IDH mutant LGG to identify those at risk of poor survival. This knowledge may improve risk stratification and management of LGG.Alterations in the expression of the Duchenne muscular dystrophy (DMD) gene have been associated with the development, progression and survival outcomes of numerous cancers including tumours of the central nervous system. We undertook a detailed bioinformatic analysis of low-grade glioma (LGG) bulk RNAseq data to characterise the association between  DMD) is located within a common fragile site on the X chromosome and at > 2 Mb in size is one of the largest known human genes2. The major protein product of the gene is the 427 kDa dystrophin protein whose primary function is to maintain skeletal muscle integrity by connecting the actin cytoskeleton to the extracellular matrix3. The resulting dystrophin-associated protein complex (DAPC) is essential for muscle function. Smaller dystrophin proteins (Dp) are produced from the DMD gene through independent promoter usage and are named according to their size in kDa3. Dp71 is a ubiquitous dystrophin protein with prominent neuronal and glial expression throughout the cortex and cerebellum at all stages of development4. Dp71 is alternatively spliced to generate four major isoforms which have a large functional diversity including neuronal differentiation, adhesion, cell division, excitatory synapse organisation, nuclear scaffolding and DNA repair5.The Duchenne muscular dystrophy gene  returned one of the highest hazard ratios. In agreement with Luce et al. we posited that these findings may be attributed to Dp71 given it is the predominant DMD gene product in the brain and has known roles in proliferation, invasion and migration10. We therefore aimed to undertake the first dedicated study of the involvement of DMD and its individual gene products in LGG.Mutations in the 11, LGG comprise grade I and II astrocytomas and oligodendrogliomas. A not otherwise specified (NOS) category is also permissible for e.g. a prior diagnosis of oligoastrocytoma which is no longer considered a distinct subtype. Grade III and IV gliomas are considered high-grade, the latter includes the most aggressive type known as glioblastoma, or glioblastoma multiforme (GBM). LGGs account for approximately 11% of all primary brain tumours with a peak incidence between 35 and 44 years of age and an average mean survival time of 7 years12. In most cases LGGs progress to fatal high-grade tumours within 5–10 years13. Treatment and management is difficult due to uncertainty over progression and lack of best practice13. There is an urgent unmet scientific and clinical need to characterise the mechanisms responsible for LGG development and progression. Genotypic features and prognostic markers such as isocitrate dehydrogenase (IDH) mutation status as well as the presence/absence of the chromosomal 1p/19q co-deletion are used to aid the diagnosis and management of LGG11. Both IDH mutation and 1p/19q co-deletion are predictive of better survival but the detailed mechanisms are unknown14. Recent changes in surgical practice have improved survival rates with early resections now considered part of the overall standard of care for LGG16. However, recurrence remains common and is unpredictable with significant variability across tumour subtypes which presents a continued dilemma for treatment and management17.According to the revised 2016 WHO criteria for the classification of central nervous system tumoursDMD gene expression with overall survival in LGG. We show that DMD is an independent prognostic marker for LGG where high expression is linked to poor overall survival in IDH mutant LGG. Furthermore, we demonstrate that the expression of multiple DMD gene products, including Dp71, are also associated with LGG survival outcomes and have differential biological effects relevant to the pathogenesis of LGG.We present, for the first time, an in-depth bioinformatic analysis of the association of DMD gene expression in LGG, we analysed RNAseq data from a LGG (WHO grade II) TCGA dataset using an independent bioinformatic pipeline and cut-point approach . The median overall survival for the high DMD group was 36.79 months compared to 130.7 months for the low DMD group, a difference of over 7 years. We performed a tumour subtype analysis to determine whether the survival associations are limited to or differ between subtypes  which constituted the smallest number in the cohort (n = 66). Oligodendroglioma had the highest increased risk of poor survival, though with a wide confidence interval . Multivariate analysis including tumour subtype as a factor revealed that DMD expression remained significant whilst tumour subtype did not  anaplastic tumours and high-grade (grade IV) glioblastoma TCGA datasets did not result in any significant survival differences when stratified by high/low DMD expression  and IDH mutation/co-deletion status  and is therefore independently prognostic of these for LGG . DMD expression was not significantly associated with survival in IDH wild-type patients with both DMD high and DMD low groups having an overall median survival of approximately 20 months (P = 0.8965); this was also replicated in the CGGA dataset . Interestingly, high DMD expression appears very rare in co-deleted patients (4% [4/107] co-deleted vs 34% [55/161] non-co-deleted), this was also replicated in the CGGA cohort where only 3% (2/58) of co-deleted patients had high DMD expression compared to 31% (31/101) of non-co-deleted patients.Since both IDH mutation status and 1p/19q co-deletion are used clinically to aid prognosis we also investigated, using pairwise comparisons of Kaplan–Meier curves, the survival outcomes of high vs low ups Fig. . Low DMDDMD Fig. a. In nonDMD samples using Position RElated Data Analysis (PREDA21). Many statistically significant chromosomal regions with expression changes among neighbouring genes were identified  whilst low DMD expression maintained the more favourable median survival time (131 months) expected of this group.Given the above findings and the established link between 1p/19q co-deletion and LGG survival, we detected the differentially expressed genomic regions in high versus low ied Fig. . NotablyDMD gene in LGG tumourigenesis we undertook a detailed bioinformatic analysis of the DEGs in the TCGA LGG (grade II) cases with high versus low DMD expression. We used an integrated web application, iDEP, for data pre-processing and to identify the DEGs (DESeq2 method). The volcano and M-A plots show a subtle transcriptomic response  of the nse Fig. a. A totanse Fig. . The Seanse Fig. b. The rense Fig. c. The linse Fig. d where 1DMD expression. We used the generally applicable gene set enrichment (GAGE) method within iDEP and the genes were annotated either according to GO biological processes or with Kyoto Encyclopaedia of Genes and Genomes (KEGG)  for DEGs tool in iDEP Fig. e,f. The GG) Fig. . The mosDMD expression is significantly linked to poor survival outcomes in IDH mutant LGG. In patients with high DMD expression, ribosome pathways are downregulated and selected DEGs are enriched in processes relating to morphogenesis and development.We additionally assessed the co-expression behaviour of the top 1000 variable genes across all samples using weighted gene co-expression network analysis  and Dp427m are the most abundant in LGG tissue . Thus, high Dp71, Dp71ab and Dp427m are significantly associated with poor survival in IDH mutant LGG but do not offer any additional prognostic utility beyond that of total DMD expression.In a multivariate analysis with the significant clinicopathological variables of age at diagnosis  and IDH mutation status , none of the individual gene products remained significant  using a C-terminal dystrophin antibody which detects all dystrophin proteins. Nine out of the 24 cases were IDH mutant and 15 were IDH wild-type. We observed dystrophin expression in both the cytoplasm and nucleus of glial cells within LGG Fig. . OverallDMD expression in their tumours survive approximately 6 years less than those with low expression; this appears to be particularly relevant for non-1p/19q co-deleted patients since high DMD expression was exceptionally rare amongst 1p/19q co-deleted cases. In non-co-deleted patients, survival outcomes are already poorer than for co-deleted patients20 and we demonstrate that if high DMD expression occurs then the outcome is even worse. Interestingly, we found that 1p/19q genomic regions are co-ordinately up-regulated in LGG cases with high DMD expression which may contribute to pathogenesis. Whilst we have confirmed dystrophin protein is expressed in LGG tumour tissue, further work is needed to experimentally confirm our findings in for e.g. a large immunohistochemical cohort as well as to investigate the potential involvement of DMD gene product(s) in LGG tumourigenesis. It is unknown for example whether the DMD gene plays a driver role in the cancers it has been linked to, or whether what we (and others6) have described are passenger effects. Determining the prognostic utility of DMD gene expression and the drug target potential of specific DMD gene products may lead to improved risk stratification and the development of new therapeutic strategies for LGG. The Dp71 isoforms have not been studied in low grade glioma before and studies on DMD in cancer have not fully considered the complexity of the gene and the roles its individual gene products may play.Our results identify a new subset of IDH mutant LGG patients who have a significantly poor survival outcome. IDH mutant LGG patients with high DMD as a marker for prognosis. We think it of importance however, that in our study both cohorts confirmed that DMD expression is not significantly associated with survival in the most invasive and aggressive grade IV glioblastoma.Of note, in our CGGA replication cohort, the above findings were replicated in a different grade of tumour (high-grade III versus low-grade II). The decision by neuropathologists as to whether to score a tumour grade II or grade III is difficult and very subjective; practices may vary world-wide. Whilst both cohorts are well matched in terms of age of onset, sex and IDH mutation status, there may also be additional population-specific differences and/or mutations that might be relevant for stratifying the patients before considering . who reported in 2017 that DMD is significantly overexpressed in astrocytoma and non-significantly underexpressed in glioblastoma8. One study has linked Dp71 to high-grade glioblastoma where Dp71 expression appears decreased in glioblastoma cell lines and inversely correlated with the Ki-67 tumour proliferative index in tumour tissue22. However, the Ki-67 index is considered a poor predictor of survival in glioblastoma23 and further work is needed to determine the true effect (if any) of altered Dp71 expression on survival outcomes in high-grade glioblastoma. Our data from two independent cohorts suggests that overall DMD expression is significantly associated with survival outcomes in only the less invasive, IDH mutant, gliomas indicating a likely role for DMD in the early stages of gliomagenesis.Our findings extend those of Luce et al4. Other components of this complex such as the dystroglycans have also been linked to the progression of primary brain tumours including gliomas25. Day et al. demonstrated that dystroglycan plays a role in maintaining tumour supporting glioma stem cells in the extracellular matrix (ECM)-rich perivascular niche. Surface markers on these glioma stem cells are strong independent prognostic markers of low-grade glioma progression and survival26. Thus, considering our own data implicating ECM-linked dystrophin proteins in low-grade glioma, an area of future investigation could be whether higher levels of these structural protein(s) more effectively support the LGG tumour microenvironment.Both Dp427m and the Dp71 isoforms form DAPCs in the brainDMD expression is coupled with a significant down-regulation of ribosome pathways including biogenesis and that the DEGs are enriched in biological processes relating to development and morphogenesis. Malignant gliomas recapitulate steps in neurodevelopment to form organ-like structures27 and high DMD expression may support such a strategy. TP53 mutation and ATRX loss are characteristic of IDH mutant, non-1p/19q co-deleted LGG11. TP53 encodes the tumour suppressor p53 which is tightly linked to ribosome activity28 and ATRX encodes a chromatin remodelling protein essential for development. Thus, high DMD expression effects biological pathways relevant to the functions of known LGG biomarkers as well as overall cellular functions related to LGG tumourigenesis. Our analysis does not implicate a single DMD gene product but rather we observed an overall comparable effect of high expression on survival across both full length and short DMD gene products. There were however some differences in the DEGs and pathway analyses between the individual gene products, namely there was a more widespread transcriptomic response when Dp71ab and Dp427m were highly expressed than observed for high Dp71 or overall DMD expression. Dp427m expression was also the only gene product associated with an alteration of biological process related to the immune system.We demonstrate that high DMD expression as an independent prognostic marker for LGG and highlights a potentially important role for DMD gene product(s) in the progression of low-grade glioma. This knowledge may help reduce and manage the unpredictable nature of LGG progression and recurrence by improving risk stratification.Overall, our data identifies DMD gene . The data extracted was mRNA expression, RSEM . The replication dataset was downloaded from the Chinese Glioma Genome Atlas (CGGA), dataset ID: mRNAseq_693. The Firebrowse portal was used to extract TCGA RNAseq isoform data from the LGG mRNASeq archives  and the case IDs matched to those obtained from cBioPortal. DMD transcript IDs were matched to specific transcripts using the table browser tool from the UCSC genome browser. We note however, that the transcript expression estimates by RSEM may not be 100% accurate. Note the TCGA PanCancer Atlas 2018 dataset downloaded from cBioPortal contains anaplastic grade III cases which are identifiable by cross matching with clinical data from Firebrowse. Unless otherwise stated, only the grade II (non-anaplastic) cases were used in our analyses.The Cancer Genome Atlas (TCGA) LGG dataset and corresponding clinical data was downloaded from cBioPortal after querying for the 29) was used to dichotomise the datasets into high and low DMD  expression groups using a minimal P-value approach. The optimal cut-point value  generated by X-tile was used for survival analysis. A cut-point was generated for each gene product and used across all tumour subtypes. The cut-point used for the TCGA total DMD expression dataset was 1183.5 RSEM. The cut-points for Dp427m, Dp71, Dp71a, Dp71b and Dp71ab were 312.1, 278.6, 465.5, 0.01 and 336.2 RSEM respectfully. The cut-point for the CGGA total DMD expression dataset was 8.0 FPKM. Plots showing the variability of low versus high expression are presented in Supplementary Fig. X-tile  expression data and formatted into low and high groups using the cut-points derived from X-tile. iDEP encompasses many R packages for bioinformatic analysis of RNAseq data. DEGs were identified using the DESeq2 method within iDEP  cut-off of 0.05 and a minimum fold-change of 2). Heatmaps, volcano and M-A plots were generated in iDEP. Functional enrichment analysis of DEGs was performed in iDEP using gene ontology (GO) biological processes, enrichment trees and networks were generated in iDEP.We used the integrated web application, iDEP 0.93http://bioinformatics.psb.ugent.be/webtools/Venn/.The online Search Tool for the Retrieval of Interacting Genes (STRING) was used to identify the protein–protein interaction (PPI) network connectivity of DEGs. The minimum required interaction score was set to median confidence at 0.4. The resulting network files were imported to Cytoscape v3.8.2 and analysed using the Cytohubba plugin. The top 20 nodes were ranked by the Maximal Clique Centrality (MCC) and Density of Maximum Neighbourhood Component (DMNC) algorithms and the extended subnetwork displayed. Venn diagrams were created using an online tool at Pathway analysis for high-low comparisons was performed in iDEP using the generally applicable gene set enrichment (GAGE) method and the genes were annotated according to GO biological processes and Kyoto Encyclopaedia of Genes and Genomes (KEGG). The minimum and maximum gene set sizes were set to 15 and 2000 respectively, the pathway significance cut-off (FDR) was set to 0.2, and the top 30 pathways were retrieved. Pathway trees were created in iDEP.Weighted gene co-expression network analysis (WGCNA) was performed in iDEP. The top 1000 variable genes were included with a soft threshold of five and a minimum module size of 20 genes. Module networks of the top 20 genes in each module were created in the network tab of iDEP using an edge threshold of 0.4. Functional enrichment analysis was performed using GO biological processes in iDEP and the resulting enriched pathway tables for each module were exported. The adjusted P values were converted to -log10 and a heatmap created in GraphPad.Position RElated Data Analysis (PREDA) was conducted within iDEP using a minimum FDR of 0.01 and minimum statistic of 0.5.Haematoxylin and eosin (H&E) and unstained formalin-fixed paraffin-embedded (FFPE) sections from 24 LGG cases were obtained from University Hospital Southampton NHS Foundation Trust as part of BRAIN UK and under the extended ethical approval of the South Central—Hampshire B Research Ethics Committee . The cohort included 18 cases of astrocytoma, one case of oligodendroglioma and five cases of ‘not otherwise specified’. Nine out of the 24 cases were IDH mutant and 15 were IDH wild-type as previously determined by the providing centre. Sections were deparaffinised with xylene and rehydrated through graded alcohol. Antigen retrieval was achieved by immersing sections in pH 6.0 citrate buffer and heating in a 800 W microwave for 10 min at high power and 10 min at low power. Immunohistochemistry was performed using a C-terminal anti-dystrophin antibody (Abcam 15277) at a 1:100 dilution followed by a Tyramide SuperBoost™ kit (Invitrogen) according to manufactures’ instructions. Staining was visualised using 3,3′-diaminobenzidine tetra hydrochloride (DAB), and counterstained with haematoxylin.Dataset S1.Dataset S2.Supplementary Information.


MBL2 promoter (− 550H/L and − 221X/Y) and functional region (exon 1 A/O) polymorphisms and their association with disease susceptibility, clinical features and serum MBL among GBS patients (n = 300) and healthy controls (n = 300) in Bangladesh. The median patient age was 30 years . MBL2 polymorphisms were not significantly associated with GBS susceptibility compared to healthy controls. HL heterozygosity in GBS patients was significantly associated with mild functional disability at enrolment . The HY, YA, HA and HYA heterozygous haplotypes were more common among mildly affected  than severely affected patients with GBS. Reduced serum MBL was significantly associated with the LL, OO and no HYA variants and GBS disease severity. No significant association was observed between MBL2 polymorphisms and electrophysiological variants, recent Campylobacter jejuni infection or anti-ganglioside (GM1) antibody responses in GBS. In conclusion, MBL2 gene polymorphisms are related to reduced serum MBL and associated with the severity of GBS.Complement activation plays a critical role in the pathogenesis of Guillain-Barré syndrome (GBS), a debilitating immune-mediated neuropathy. Mannose-binding lectin (MBL) is a complement activation factor of lectin pathway which as genetic host factor may influence the susceptibility or severity of GBS. We investigated the frequency of  The global incidence of GBS is 1–2 per 100,000 person/year, though this rate varies regionally with significant heterogeneity in terms of clinical presentation and severity3. Two-third of patients have symptoms of a precedent infection4 and the most identified cause is Campylobacter jejuni5. The recent public health outbreak of Zika virus caused many patients to develop neurological symptoms like GBS6. However, the mechanisms underlying GBS have not yet been elucidated. There are two major variants of GBS: axonal  and demyelinating 1. Conclusive evidence indicates GBS after C. jejuni infection is an auto-antibody mediated disease triggered by molecular mimicry between the surface epitopes of an infection agent and peripheral nerve gangliosides, which accounts for the pathogenesis of axonal GBS7. Very few patients with a microbial infection develop GBS8, which indicates involvement of host factors triggering this autoimmunity. Genetic susceptibility could be a predisposing factor for GBS, though the genetic factors that affect the interactions between microbial and host are poorly studied.Guillain-Barré syndrome (GBS) is a rapidly progressing peripheral neuropathy and the major cause of flaccid paralysis after an acute infection10. Over the last decade, mannose-binding lectin (MBL) has been the center of substantial interest as it possesses the ability to turn on the complement pathway through enzymatic regulation and thus may influence disease susceptibility and severity11. MBL is encoded by the MBL2 gene, which contains four exons. Three single nucleotide polymorphisms (SNPs) in the structural region of the MBL2 gene, located in codons 52 (A/D), 54 (A/B), and 57 (A/C) of exon 1, and two promoter polymorphisms, located at − 550H/L and − 221X/Y, are the major determinants of serum MBL concentration and activity14. The variant B, C and D alleles, collectively referred to as the O allele, are associated with serum MBL deficiency compared to the wild-type A allele13. In addition, polymorphisms within the promoter region (− 550H/L and − 221X/Y) determine the serum MBL concentration to an extent by influencing gene expression14. Host MBL2 genetic variations are associated with susceptibility to a wide variety of infectious and autoimmune diseases15, including tuberculosis (TB)16, rheumatoid arthritis17, systemic lupus erythematosus (SLE)18 and GBS19. However, other reported MBL polymorphisms protect against infectious disease like TB20. MBL can act as double-edged sword in post-infectious diseases as GBS depending on its concentration. Serum MBL deficiency has been reported as a predisposing factor for the development of SLE21, atherosclerosis22, TB16 and coronary artery disease23. However, some studies reported elevated serum MBL was associated with increased disease susceptibility24, thus, the data remain inconclusive. These inconsistent results may be due to the relatively small study populations, different ethnic groups, and environmental influences between studies.The host complement system plays a crucial role in the pathogenetic mechanism of GBS; by mediating complement fixations through anti-ganglioside antibody that elevated serum complements and deposited on the surface of Schwann cells and nodes of Ranvier25, apoptotic gene FAS26 and toll-like receptor-427 were associated with GBS disease susceptibility, whereas human leukocyte antigen- DQB128, antigen presenting glycoprotein CD1A and CD1E29 genes have no effect on development of GBS. However, very limited data are available on MBL2 gene polymorphisms and their influence on the serum MBL levels in patients with GBS. Therefore, we determined the potential association between MBL2 gene polymorphisms and disease susceptibility, clinical subtypes, severity and level of serum MBL protein in patients with GBS.Host–pathogen interactions and environmental factors have attracted attention as concepts that may contribute to GBS. Identifying GBS susceptibility genes would represent an advancement in our understanding of the pathogenesis of the disease. Previously, we reported immune-mediated genes, including tumor necrosis factor-alphan = 300) and 34 years  for healthy controls (n = 300). Almost 225/300 (75%) patients with GBS had a history of preceding events, mostly diarrhea  or respiratory tract infections . Electrophysiological studies were performed on 240/300 patients (80%), resulting in classifications of the axonal variant for 143/240 (60%) cases ; acute inflammatory demyelinating polyradiculoneuropathy (AIDP) for 66/240 (28%) cases; unclassified GBS with unexcitable nerves or equivocal findings for 23 (9%) cases and normal for 8/240 (3%). The majority of patients 230/300 (79%) were severely affected at the time of enrolment on the basis of their Medical Research Council (MRC) sum score; 56/300 (19%) patients had dysautonomia and 44/300 (15%) required mechanical ventilation (MV), 248/300 (83%) patients with GBS regained locomotion and 18/300 (6%) died within six months of follow up.The basic demographic and clinical characteristics of all patients with GBS included in this study are summarized in Table MBL2 genotypes were compared between patients with GBS and healthy controls to assess the association with GBS susceptibility. All genotype distributions in healthy controls fitted Hardy–Weinberg equilibrium. There was no significant difference in the genotype and allele frequencies between patients with GBS and controls. In particular, genotypes and alleles of − 550 (H/L), − 221 (X/Y) and exon 1 (A/O) SNPs occurred at similar frequencies in patients with GBS and healthy controls  or mildly affected (MRC sum score between 40 and 60). The frequency of the MBL2 gene polymorphisms revealed a significant association with developing the mild form of GBS. The HYA heterozygous haplotype  was significantly more prevalent among mildly affected patients (67%) than severely affected patients (44%) , heterozygous YA  and heterozygous HA  haplotypes were significantly more prevalent in patients with the mild form of GBS  compared to the control group .We categorized the patients with GBS based on electrophysiological subtypes  and serological data to assess the associations between these features and n = 166) and healthy controls (n = 102). The serum MBL levels were slightly higher in patients with GBS than healthy controls  and no significant difference was revealed between GBS and healthy control. We compared MBL serum levels with the requirement of mechanical ventilation and development of autonomic, dysfunction in patients with GBS. However, no association was found with the elevated serum MBL and mechanical ventilation or autonomic dysfunction. The median of serum MBL (4417 ng/mL) increased among the patients who died during the course of the disease.We measured the serum concentration of MBL for a subgroup of patients with GBS n = 66 and heMBL2 genotypes among the patients with GBS and healthy controls to investigate the influence of MBL2 polymorphisms on MBL protein levels. In patients with GBS, the homozygous LL promoter genotype was associated with lower serum MBL compared to the HH homozygous genotype  and structural exon 1(A/O) region of the MBL2 gene and their association with GBS susceptibility, subtype and severity. The analysis of genetic associations in this study revealed MBL2 gene polymorphisms do not significantly contribute to GBS susceptibility. However, the serum levels of MBL were significantly associated with the HL genotype and heterozygote haplotypes  of the MBL2 gene and the disease severity of GBS in our Bangladeshi cohort.Preliminary studies of genetic variation in the 30. MBL is a key regulatory element that activates the complement system, as part of first-line defense in the pre-immune host in several autoimmune neurological disorders31. Previously, Geleijns et al. reported MBL polymorphisms contributed to the susceptibility in patients with GBS and were associated with disease severity19. The B allele of exon 1 was found to protect against developing severe form of GBS in a Dutch population19. However, this study from Bangladesh revealed no significant association between the B allele and disease severity. The probable explanation could be the inclusion of high number of severe cases (77%) in Bangladeshi cohort due to high severity32. In contrast, the heterozygous HL, HY, YA, HA and HYA haplotypes were significantly associated with developing the mild form of GBS. In addition, HL and AO genotypes were significantly associated with autonomic dysfunction that includes hypertension, hypotension, tachycardia, pupillary abnormality. However, the previous study did not examine any association between the clinical and serological subgroups, including the subtype of GBS, recent infection with C. jejuni and auto-antibodies, with MBL2 polymorphisms or serum MBL levels19. In our population, MBL2 polymorphisms had no influence on the neural damage that occurs in the axonal and demyelinating variants of GBS. More evidence for relation between MBL and disease susceptibility and severity need to be studied to confirm this finding.Complement activation has recently been reported to be involved in the induction of post-infectious immune-mediated peripheral nerve damage in patients with GBS11. Many infections lead to higher serum MBL levels and increase the disease susceptibility34. Several reports suggest that the MBL protein can also modulate disease severity including GBS, leptospirosis and dengue35. We observed significant elevation of serum MBL in severely affected patients with GBS, in accordance with the Dutch study19. Geleijns et al. reported the only study on GBS and MBL2 polymorphisms thus far, though, they did not measure serum MBL in their control population which is a limitation of their study19. MBL2 gene polymorphisms appear to influence the serum concentration of MBL as we could confirm both for HC and GBS patients. Three single point mutations in structural codons 52 (A/D), 54 (A/B) and 57 (A/C) of exon 1 of the MBL2 gene are the major determinants of MBL deficiency36. The frequencies of these variant alleles vary in different populations38. In line with previous observations19, we found polymorphism in structural region of exon 1 is associated with lower serum MBL. Individuals with AO and OO genotype had lower serum MBL than the wild-type variant AA homozygous individuals in GBS. The effect of AO and OO genotype on serum MBL in patients with GBS is consistent with the findings in healthy controls, further indicating this polymorphism affects the serum MBL level. Several reports have suggested the promoter region (− 550) variant allele  downregulates serum MBL by functionally regulating the immune system in several diseases39. This study also revealed the L allele was associated with lower serum MBL levels in patients with GBS and the healthy control group.MBL contains pattern recognition features that allow the widest range of potential microbial structures to be recognized41, including human immunodeficiency virus43, Plasmodium falciparum44, cryptosporidium45 and N. meningitidis46. In addition to these associations with infectious disease, there is also evidence of associations between reduced serum MBL and susceptibility to autoimmune diseases. Several reports suggest strong association in the case of systemic lupus erythematosus (SLE). British47, Hong Kong Chinese48, African American49 and Spanish50 patients with SLE all exhibited increased frequencies of mutant MBL alleles or reduced serum MBL levels. In our Bangladeshi population, we did not find any significant association between GBS disease susceptibility and lower levels of MBL protein. As earlier reported, elevated levels of MBL protein have been associated with increased susceptibility towards chronic rheumatic heart disease in patients previously diagnosed with rheumatic fever51. The pathogenesis of rheumatic fever and GBS are similar in terms of antecedent infections, molecular mimicry, and cross-reactive antibodies. Therefore, the higher levels of MBL in severe GBS cases might be explained by binding of MBL to damaged nerve tissue, followed by complement activation, attraction of inflammatory cells, and aggravation of tissue injury. One of the limitations of this study was the lack of determination of complement product activation (C3a and C5a) in serum of GBS patients. Further functional activity of MBL2 polymorphism and its serum levels can be confirmed by measuring the products of complement pathway.Several studies have presented evidence that MBL deficiency increases the generalized susceptibility of an individual to infectious diseasesMBL2 haplotypes and serum MBL levels may be one key determinant of the severity of weakness in patients with GBS in Bangladesh. Furthermore, both promoter and functional region polymorphisms in the MBL2 gene are associated with downregulation of serum MBL protein in both patients with GBS and healthy controls. The precise mechanistic role of MBL in GBS is not clear. Further in-depth research is required to elucidate complement activation mediated by MBL and its involvement in the lectin pathway during the pathogenesis of GBS; analysis of samples from the multi-center International GBS Outcome Study (IGOS)52 of a large population of various ethnic groups around the world may to explain the underlying mechanisms.In conclusion, 53. The study was reviewed and approved by the Ethical Committee of icddr,b, Dhaka, Bangladesh and all participants gave written informed consent prior enrolment.The study was conducted in the Laboratory of Gut-Brain Signaling, icddr,b, Dhaka, Bangladesh. The patients with GBS and healthy controls were recruited from Dhaka Medical College and Hospital (DMCH) and the National Institute of Neurosciences and Hospital (NINS), Dhaka, Bangladesh. All patients with GBS fulfilled with the National Institute of Neurological Disorders and Stroke (NINDS) criteria and were enrolled within the two weeks of onset of weakness54. GBS patients with a MRC sum score < 40 were defined as severely affected and 40–60, mildly affected55. Disease outcome was assessed using the GBS disability scale (GBS-DS)56 at six months follow-up; patients able to walk independently (GBS-DS of ≤ 2) were defined as having a good outcome and patients unable to walk independently (GBS-DS of ≥ 3), poor outcome57. Ethnicity-based healthy controls (HC) were recruited from people accompanying the patients to physical examinations at the hospitals mentioned above and pair matched with the GBS cases by age and gender. All control subjects were genetically unrelated inhabitants of Bangladesh without a prior history of other neurological complications or non-communicable diseases, and no recent history of pregnancy or surgery.Clinical and blood samples were obtained from 300 consecutive patients with GBS and 300 healthy controls for this study. The patients were followed up and clinically assessed at standard time-points  to evaluate disease outcome. Disease severity was defined using the Medical Research Council (MRC) sum score for six muscles in the upper and lower limbs on both sides; the sum score ranges from 60  to 0 (quadriplegic)Genomic DNA was isolated from whole blood for 600 subjects using QIAamp® DNA Blood Midi Kits (100)  following the manufacturer’s instructions. The extracted DNA was dissolved in 1 × TE-buffer  and stored at − 80 °C. The stock DNA was diluted to a final concentration of 10 ng/μL in Milli-Q-water and stored at − 20 °C until SNP detection.C. jejuni infection at the time of enrolment. Enzyme-linked immunosorbent assay (ELISA) was used to detect IgG, IgM and IgA antibodies against C. jejuni and IgG against the nerve GM1-ganglioside, as described previously59. Serum MBL was measured in duplicate from 166 randomly selected serum samples from patients with GBS at the time of enrolment and 102 serum samples from healthy controls by ELISA method using a human MBL oligomer ELISA kit , which specifically detects oligomerized forms of MBL protein. Serum samples were stored at − 80 °C and diluted 1:100 in the sample diluent provided in the ELISA kit during the experiment. The ELISA was performed according to the manufacturer’s instructions; absorbance values were measured at 450 nm and serum concentrations were expressed as nanograms of MBL per milliliter (ng/mL).Serological tests were performed on the 300 sera of patients with GBS to assess baseline anti-ganglioside antibodies and recent MBL2 gene were determined in 600 DNA samples extracted from patients with GBS and healthy controls. The real-time reverse transcription polymerase chain reaction (RT-qPCR) and melting curve analysis were performed in LightCycler capillaries (Roche Diagnostics). The primers, probes and the PCR amplification program were based on Geleijns et al.19. The PCR amplification program was designed to determine both promoter regions (− 550H/L and − 221X/Y) and exon 1 (A/O) SNPs using 1 × LightCycler DNA Master Hybridization Probes . The melting curve profile was determined after PCR by constant detection of emitted light 19. Approximately 20% of the samples were sequenced to confirm the MBL2 polymorphisms.Single nucleotide polymorphisms (SNPs) in the promoter regions at − 550 , − 221  and the structural region exon 1 at codon 52 , codon 54  and codon 57  of the MBL2 genotype and haplotype frequencies were determined by a simple counting method and managed using Microsoft Excel 2010  and SPSS . Fisher's exact test was used to determine the association of MBL2 polymorphisms and GBS susceptibility; odd ratios (ORs) and 95% confidence intervals (95% CI) were reported. The chi-square test was conducted to confirm the Hardy–Weinberg equilibrium among healthy controls. Serum MBL levels were presented as medians and interquartile ranges (IQR). The non-parametric Mann–Whitney U-test was used to compare the differences in the serum MBL concentrations (ng/mL) between subgroups of patients with GBS and healthy controls. The associations between serum MBL levels and MBL2 genotypes for the studied SNPs in patients with GBS and controls were analyzed using the Kruskal–Wallis test. The associations between the clinical features of GBS and genotypes were analyzed using Fisher's exact test. A probability level (P) of less than or equal 0.05 was considered as the significance criterion. To avoid type I errors in multiple comparisons, the Bonferroni adjustment was performed to correct the P-values obtained by dividing the number of comparisons; the corrected P-values are denoted as Pc60. The significance threshold for Fisher's exact test was adjusted to 0.01667 and 0.025 to obtain Pc for genotypic and allelic differences. All statistical analyses  were performed on SPSS software  and GraphPad Prism (version 5 and 9).The chi-squared test was conducted to examine the differences in demographic characteristics and potential confounders between the GBS cases and healthy controls. The Supplementary Table S1.Supplementary Table S2.


The mechanisms driving clonal heterogeneity and evolution in relapsed pediatric acute lymphoblastic leukemia (ALL) are not fully understood. We performed whole genome sequencing of samples collected at diagnosis, relapse(s) and remission from 29 Nordic patients. Somatic point mutations and large-scale structural variants were called using individually matched remission samples as controls, and allelic expression of the mutations was assessed in ALL cells using RNA-sequencing. We observed an increased burden of somatic mutations at relapse, compared to diagnosis, and at second relapse compared to first relapse. In addition to 29 known ALL driver genes, of which nine genes carried recurrent protein-coding mutations in our sample set, we identified putative non-protein coding mutations in regulatory regions of seven additional genes that have not previously been described in ALL. Cluster analysis of hundreds of somatic mutations per sample revealed three distinct evolutionary trajectories during ALL progression from diagnosis to relapse. The evolutionary trajectories provide insight into the mutational mechanisms leading relapse in ALL and could offer biomarkers for improved risk prediction in individual patients. Despite such remarkable improvements, approximately 10–20% of patients suffer from relapse or resistant disease or experience adverse drug effects during treatment2. In patients treated according to the Nordic Society for Pediatric Hematology and Oncology (NOPHO) ALL-2008 protocol the frequency of severe adverse effects was as high as 60%3. After relapse, the 5-year survival rate of the patients drops to ~ 40–70%, depending on treatment protocol and molecular subtype4. Novel biomarkers are needed to improve the prediction of which patients could benefit from intensified and novel targeted treatments. On the genetic level, pediatric ALL is grouped into subtypes characterized by large-scale chromosomal aberrations that are believed to be the disease-initiating lesions. Together, structural variations and acquisition of mutations in key genes and pathways contribute to leukemogenesis2.Acute pediatric lymphoblastic leukemia (ALL) is a malignancy with marked heterogeneity in molecular and clinical phenotypes. With protocol-based aggressive combined chemotherapy regimens the 5-year survival rates in pediatric ALL are now approaching 90%5. By harnessing the improved resolution from whole genome sequencing (WGS), a more recent study demonstrated that the clones dominating at ALL relapse likely evolve from a minor clone present at diagnosis and that relapse-associated mutations are concomitant with chemotherapy resistance6. It is well known that relapse-associated mutations in ALL are specifically selected upon by chemotherapy, exemplified by mutations in KRAS, NRAS and PTPN11, which illustrates a chemotherapy driven selection mechanism for clonal evolution7. The mutational dynamics differ between patients with early and late relapse, with earlier relapses showing an overrepresentation of mutations in DNA repair genes8. Collectively, studies on relapsed ALL have uncovered potentially actionable target mutations in tyrosine kinases9 and the RAS pathway10, which could be options for future individualized treatment. Although chromosomal rearrangements that lead to fusion genes are well known in ALL, our understanding of large-scale and medium sized structural mutations in ALL is still incomplete. Most studies on somatic point mutations have focused on protein-coding single nucleotide variants (SNVs) and small insertion/deletions (indels), while the role of somatic mutation that affect regulatory regions of the genome has not yet been explored in ALL. We therefore determined the genome-wide patterns of somatic mutations in Nordic pediatric ALL patients using WGS and RNA-sequencing (RNA-seq). Our aim was to identify the somatic mutations with the largest impact on the development of relapse in ALL, and to define the clonal evolution patterns from diagnosis to relapse based on a comprehensive set of somatic mutations.Clonal heterogeneity is common in ALL. One of the first studies using genome-wide SNP genotyping in relapsed ALL indicated that clones present at relapse were derived from clones that were ancestral relative to the diagnostic cloneTo determine the mutational patterns leading to relapse and to delineate the clonal evolution of the ALL cells due to somatic point mutations and structural aberrations, we sequenced 96 genomes from matched diagnostic, relapse and remission samples from 29 Nordic patients with pediatric ALL Table . SomaticWe identified a median of 524 somatic point mutations per patient at diagnosis range 152–1343), a median of 923 mutations at first relapse (range 68–3156), and a median of 1566 mutations (range 730–5839) at second relapse. The distribution of somatic mutations differed largely between the ALL subtypes and between individual samples within a subtype  signatures in the Catalogue Of Somatic Mutations In Cancer (COSMIC) database (v3.1)Armed with a set of six COSMIC signatures, we next investigated their contribution to the mutational load in each sample Fig. b. SBS1, SBS2 and SBS13 are attributed to activity of the AID/APOBEC family of cytidine deaminases. Four patients harbored numerous mutations of these signatures at first relapse, and two of the patients harbored them already at diagnosis  replicated 29 genes previously established to be drivers of ALL22. Nine genes were recurrently mutated at diagnosis and/or relapse in two to ten patients and 20 genes carried driver mutations in one sample only  region in at least two patients with ALL. This analysis detected potential cis-acting SNVs in regulatory regions of eight genes in 18 patients at diagnosis and/or relapse  in each gene, which suggests that they are part of the same regulatory element , active promoters H3K4me3) or enhancers (H3K4me1 and H3K27ac) or in conserved transcription factor binding sites   tool26 to further evaluate the regulatory role of the non-coding variants in the affected samples, and report allele specific expression (ASE) and outlier high expression (OHE). ASE and OHE did not provide additional support for a cis-acting role for non-coding variants in our samples (Supplementary Table Each of the seven genes with non-coding somatic mutations were expressed in the patient carrying the putative regulatory non-coding variant with log2 of FPKM (fragments per kilobase per million mapped reads) value > 1 (Supplementary Table ETV6-TSL fusion gene at diagnosis and relapse in patient ALL_827 with mixed phenotype acute leukemia (MPAL). Split reads between the ETV6 (exon 2) and TSL (exon 2) in RNA-seq data and identification of the genomic breakpoints giving rise to this fusion gene in the WGS data at chr7:27,536,526 (120 kb upstream of TSL) and in intron 2 of ETV6 on chr12:11,905,675 confirm the presence of the ETV6-TSL fusion gene at diagnosis and relapse in this patient  is another characteristic of this trajectory observed in eight patients (8/12), compared to one patient (1/11) in the founding clone trajectory and one patient (1/3) in the persistent clone trajectory. Three of the patients in the rising clone trajectory carried heterozygous deletions of one allele together with a non-silent mutation in the other allele in the KDM6A, IKZF1 and ETV6 genes.Twelve patients were assigned to the “rising clone trajectory”. A prominent feature of this trajectory is the high proportion of patients 9/12) who harbor large-scale structural aberrations resulting in expressed fusion genes (Table  who harbBCP-ALL patients in the high risk (HR/infant) treatment group and each of the T-ALL patients which typically suffer from severe ALL, relapsed within three years from diagnosis , which was available for 16 of our 29 diagnostic samplesIn each of the seven patients who experienced a second relapse Table  and for 22, we observe a rather silent mutational landscape at diagnosis in most patients, and that the mutational burden almost doubled successively at first and second relapse. In addition, we observed increasing genomic instability from diagnosis to first relapse in terms of an increased burden of CNAs and SVs, however the patients with the largest number of SNVs did not also have the largest mutational burden of CNAs.Here we have described three categories of evolutionary trajectories and the most critical genes leading to relapse in 29 Nordic pediatric ALL patients. In line with previous studiesCREBBP, NT5C2 and PRPS1) in our study as previous studies28. We and others have shown that mutations in these genes are typically acquired at relapse, but if they occur at diagnosis they remain at subsequent relapses. Reassuringly, with our limited sample set there were only small differences in the frequency of the identified relapse- and diagnosis-specific driver genes compared to other studies22. However, digital karyotypes constructed based on the WGS data allowed us to uncover previously unknown somatic CNAs in the majority of the ALL samples at diagnosis and relapse. We also detected homozygously deleted tumor suppressor genes and tumor suppressor genes with biallelic loss of gene function and a novel expressed fusion gene ETV6-TSL in a sample with mixed lineage leukemia.We did not identify any previously unknown ALL driver genes carrying protein-coding somatic mutations, perhaps due to the small size of our sample set and the recent rapid progress in identification of driver genes in pediatric ALL. We observed the same relapse-specific driver genes  at second relapse, most likely due to the homozygous deletion of the PMS2 mismatch repair gene observed in this sample. A high mutational load at relapse caused by homozygous inactivation of mismatch repair genes has also been observed in previous studies of ALL22. Mismatch repair-deficient cancers with their large number of mutations have been suggested to be recognized by the immune system31. Thus, ALL patients displaying this mutational signature may be candidates to be susceptible to immunotherapy. Four patients displayed APOBEC signature mutations (SBS2/SBS13) in line with previous findings for ALL32.By analyzing the somatic SNVs in a trinucleotide context we found that the mutations in our samples were accurately reconstructed by six of the well-characterized COSMIC mutational signaturesBesides providing insight into the mutational spectrum of ALL genomes, we examined the clonal architecture of ALL and how cell populations evolve from diagnosis to relapse. Firstly, the persistent clone trajectory seems to be rare with only three patients in our sample set, suggesting that most of the pediatric ALL patients respond at least partly to the antileukemic treatments provided at diagnosis. The most common trajectory in our sample set was the rising clone trajectory followed by 12 patients. This type of trajectory is of potential clinical importance since these patients carry subclonal mutations that may confer a selective advantage or resistance to antileukemic therapy already at diagnosis and could therefore be useful for treatment stratification. The second most common trajectory in our sample set was the founding clone trajectory followed by 11 patients. ALL genomes in this trajectory experienced a three-fold increase in driver mutations from diagnosis to relapse. This contrasts the observation in the rising clone trajectory, where the number of driver mutations did not increase from diagnosis to relapse in our sample set.33 or are being investigated as candidate drug targets34. A personalized treatment strategy could be particularly beneficial for ALL patients, for whom it is today difficult to predict which patients will respond to repeated and intensified treatment.Collectively these observations add further support for the hypothesis that rising clones are pre-resistant to antileukemic therapy, while the founding clones acquire additional mutations to emerge as a major clone at relapse. In both models relapse originates from clones or cells that resist therapy, but the contribution of specific driver mutations to relapse remains to be demonstrated. The utility of driver mutations for diagnosis and treatment stratification depends on the abundance of specific driver mutations at diagnosis in non-relapsed samples. Further studies in larger sample sets are required to investigate the relationship between rising and founding clone trajectories in relation to the contribution of driver genes to relapse and as potential drug targets. The potential of driver genes as drug targets is demonstrated by the fact that already at diagnosis the majority of the patients in this study displayed mutations in driver genes that encode proteins in pathways which are clinically actionable by approved drugs35. Bone marrow or blood samples collected at diagnosis, remission and relapse from 29 patients with ALL were analyzed, including two sequential relapse samples from nine patients. Leukemic cells were isolated from the samples by density-gradient centrifugation and the proportion of leukemic cells was estimated on May-Grünwald-Giemsa-stained cytocentrifugate preparations, using light microscopy. The estimated blast count in the bone marrow and/or blood was ~ 85–95% at diagnosis and ~ 70–95% at relapse. Cell pellets were frozen and stored at − 70 °C in established institutional tissue banks until extraction of DNA and RNA. DNA and RNA was extracted from the cell pellets using the AllPrep DNA/RNA Mini Kit (Qiagen), including a DNase digestion step using RNase-Free DNase. DNA and RNA samples were stored at − 80° until analysis. Prior to sequencing, the DNA and RNA quality and concentration were measured using the Qubit dsDNA or RNA Broad‐Range assay (Invitrogen). The integrity of the RNA was assessed by capillary electrophoreses on a Bioanalyzer.The ALL patients included in the study were diagnosed during 1993–2008 and treated at Swedish Pediatric Oncology centers  according to NOPHO ALL protocolsThe ALL patients included in the study represent eight BCP-ALL subtypes, the B-other subgroup and T-ALL. Table Sequencing libraries were prepared with 100 ng of input DNA from each patient sample using the TruSeq Nano protocol (Illumina Inc). The WGS libraries from diagnosis and relapse (except the sample ALL_244r2) were prepared in three technical replicates followed by sequencing on a HiSeqX instrument (Illumina Inc.) with paired-end 150 base pair (bp) reads and ~ 90 × coverage. Of the variant allele calls in each sample, 94.8%  were concordant between three libraries and an additional 4.5% of the allele calls (n = 2941) were concordant in two libraries, evidencing for the high quality of the library preparation and the sequencing process. This allele count distribution per samples is shown in Supplementary Fig. 36. Sequence reads were aligned to the human reference genome hg19 using Burrows Wheeler Algorithm (BWA)37 and duplicate reads were marked using Picard MarkDuplicates38. Local realignment of regions flanking indels and recalibration of base quality scores were performed using the Genome Analysis ToolKit (GATK) version 3.739.A single library was prepared from the germline (remission) samples, which were sequenced to ~ 30 × coverage. Data were processed using the Sarek workflow v1.140. SNVs and small insertions and deletions (indels) were also called by Strelka version 1.0.1541 by running both programs in the tumor-germline mode. Additionally, Manta version 1.0.342 was used to call small indels to curate the small indels from Strelka. The union of SNV calls by Mutect1 and Strelka was used for the downstream filtering and analysis. A downstream filter was used to exclude somatic mutations if (1) the sequencing depth was threefold higher than the chromosomal mean depth in each germline sample; or (2) the AF was below 5%, except in three patients at second relapse, where a bone marrow transplantation was performed prior to second relapse, or in samples where more than 90% of the SNVs overlapped SNVs in dbSNP by an AF below 10% 45. A list of somatic variants detected by WGS is provided as a supplementary data file in the aggregated variant caller (VCF) format (Supplementary data file).Somatic single nucleotide variants (SNVs) were called in the WGS data by Mutect113. The optimal number of mutational signatures was selected based on the residual sum of squares criterion13. We compared our de novo signatures to the 72 single base substitution (SBS) mutational signatures in the COSMIC database (v3.1)11 using cosine similarity as a distance measure. With a limited number of samples de novo signatures typically resemble a mixture of COSMIC signatures, and therefore a set of COSMIC signatures similar to our signatures based on a relatively relaxed threshold of cosine similarity > 0.65 was used. This criterion resulted in a set of 12 COSMIC signatures 14 and OncodriveFML15 software. Genes were defined as drivers of ALL in diagnostic and/or relapse samples based on somatic mutations (SNVs and indels) if : (1) at least one of the bioinformatic tools MutSigCV14 or Oncodrive-fml15 reported a gene as a potential driver with a p-value <  = 0.05; and (2) the mutations were recurrent among the ALL patients ; and (3) the mutations were putative non-silent somatic variants; and (4) the genes had clonal allelic variants with AF >  = 0.25; and (5) the mutant allele of a SNV was expressed at a detectable level in RNA in at least one of the same sample that carried the putative functional variant. For genes already known as ALL driver genes in the literature we required that the criteria 3–5 were fulfilled.Putative driver genes carrying non-silent somatic single nucleotide variants (SNVs) and insertion and deletions (indels) at diagnosis and/or relapse(s) were identified using the MutSigCV25 database and/or in four primary ALL blasts cells from the BLUEPRINT project24. In addition to the criteria listed in the section “46 which contains quality controlled WGS data from 12 populations. The variant alleles of the putative non-coding somatic variants in the 18 patients were not present in 7800 samples from the European population in the GnomAD v2.1.1 database. The absence of the somatic variant alleles in the large set of population samples provides further evidence for the somatic nature of the somatic candidate regulatory non-coding variants. The somatic candidate variants were evaluated for evolutionary conservation based on overlaps with the PhastCons score using the “phastConsElements46way” tool in the UCSC browser47. To identify non-protein coding regions with recurring mutations, candidate variants were clustered hierarchically, and partitioned to have a maximal distance between variants within a cluster smaller than 100 bp. For the non-coding mutations in Supplementary Table 26 on 1 Mb regions surrounding the non-coding variants to calculate the outlier high expression (OHE) and allele specific expression (ASE). For OHE we used the following cis-X results: size of the whole reference cohort, cohort rank and p-value in outlier test using the whole reference cohort. For each candidate gene in Supplementary Table Putative regulatory non-coding somatic mutations were annotated to predicted regulatory genomic regions with histone marks for active promoters (H3K4me3), active enhancers , or regions of open chromatin (DNase1 hypersensitivity) or transcription factor binding sites in the lymphoblastoid cell line GM12878 in the ENCODE48, applying the “gc” correction and “aspcf” segmentation to define complete digital karyotypes. LogR and B-allele frequency (BAF) values were computed using AlleleCount version 2.2.0. Large-scale somatic structural variants (SVs) ranging from 50 bp to several Mb in size were identified using the Manta software42, which uses the split-read and read-pair orientation for the detection of translocations, inversions, deletions and duplications. The somatic SVs were further filtered out (1) if they were present in the SweGen43 database of normal variation with 50% fraction overlap; or (2) if they overlapped any 10 kb genomic window with average coverage < 10 reads in the matched normal sample or in the UCSC assembly gap regions; or (3) if a deletion overlapped a duplication in the same sample. SVs were annotated with the Ensembl Variant Effect Predictor (VEP)45. SVs that overlap the T-cell receptor, human leukocyte antigen and immunoglobulin gene (TARP HLA IGH) were removed. SVs with known driver genes or fusion genes according to the ALL literature were not removed using the above filtering criteria.Somatic copy number alterations (CNAs) ranging from ~ 1 kb to full length chromosome arms and complete chromosomes were identified using the Allele-Specific Copy number Analysis of Tumors (ASCAT) software v2.549, followed by manual inspection of the identified candidates for coverage of heterozygous and homozygous deletions in the Integrated Genome Browser (IGV) to identify affected cancer genes50.The identified large-scale CNVs and SVs were overlaid with the tumor suppressor genes and oncogenes present in the OncoKB database51. Depending on the quality (RIN > 7) and amount of available RNA (> 100 ng), the TruSeq stranded total RNA protocol  was applied with 300 ng of input RNA. For the remaining samples, the TruSeq RNA access protocol was used with 30–100 ng of input RNA (Illumina Inc.). Thirty million sequence read pairs  per sample were generated per library on a HiSeq2500 instrument followed by mapping to the human reference genome hg19 using STAR52. For putative variants located in driver genes or genes flanking putative regulatory non-coding variants, gene expression levels were calculated in the samples containing the mutation and the log2 of a FPKM (fragments per kilobase per million mapped reads) value > 1 was used as a criterion for gene expression. To detect allele-specific expression of mutant alleles we used GATK for SNVs to calculate the mutant allele frequency and confirmed expression of the mutant allele manually for indels using IGV in RNA data50.RNA-sequencing (RNA-seq) libraries were prepared from RNA available for 22 ALL patients at diagnosis, 25 patients at first relapse, seven patients at second relapse and from control B-cells (CD19 +) and T-cells (CD3 +) from five healthy Swedish individuals . For density estimates, PyClone_beta_binomial was used. Tumor purities were estimated by ASCAT. Cluster and locus tables were prepared with the maximal number of clusters set to 10, except for ALL_832 where 11 was used. Clonal models were built using the ClonEvol software v0.99.1054 in the R package for clonal ordering, visualization and interpretation. AFs were adjusted manually to correct for tumor purity and clusters were manually adjusted to fit the most probable parent clusters  at diagnosis and relapse(s) using the PyClone software v0.13.1The study was performed in accordance with guidelines of the Helsinki Declaration. The study was approved by the Regional Ethical Board in Uppsala, Sweden . The samples were obtained with written informed consent for genetic research from the parents of the patients and/or the patients.All authors have read the manuscript and approved its submission for publication.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.