A prognostic neural epigenetic signature in high-grade glioma.

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is limited. We present an epigenetically defined neural signature of glioblastoma that independently predicts patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals a high abundance of malignant stemcell-like cells in high-neural glioblastoma, primarily of the neural lineage. These cells are further classified as neural-progenitor-cell-like, astrocyte-like and oligodendrocyte-progenitor-like, alongside oligodendrocytes and excitatory neurons. In line with these findings, high-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature is associated with decreased overall and progression-free survival. High-neural tumors also exhibit increased functional connectivity in magnetencephalography and resting-state magnet resonance imaging and can be detected via DNA analytes and brain-derived neurotrophic factor in patients' plasma. The prognostic importance of the neural signature was further validated in patients diagnosed with diffuse midline glioma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant. High-neural gliomas likely require a maximized surgical resection approach for improved outcomes.

Unraveling pH-Dependent Changes in Adsorption Structure of Uranyl on Alumina (012).

Mitigating uranium transport in groundwater is imperative for ensuring access to clean water across the globe. Here, in situ resonant anomalous X-ray reflectivity is used to investigate the adsorption of uranyl on alumina (012) in acidic aqueous solutions, representing typical UVI concentrations of contaminated water near mining sites. The analyses reveal that UVI adsorbs at two distinct heights of 2.4-3.2 and 5-5.3 Å from the surface terminal oxygens. The former is interpreted as the mixture of inner-sphere and outer-sphere complexes that adsorb closest to the surface. The latter is interpreted as an outer-sphere complex that shares one equatorial H2O with the terminal surface oxygen. With increasing pH, we observe an increasing prevalence of these outer-sphere complexes, indicating the enhanced role of the hydrogen bond that stabilizes adsorbed uranyl species. The presented work provides a molecular-scale understanding of sorption of uranyl on Al-based-oxide surfaces that has implications for environmental chemistry and materials science.

Morphology-based molecular classification of spinal cord ependymomas using deep neural networks.

Based on DNA-methylation, ependymomas growing in the spinal cord comprise two major molecular types termed spinal (SP-EPN) and myxopapillary ependymomas (MPE(-A/B)), which differ with respect to their clinical features and prognosis. Due to the existing discrepancy between histomorphogical diagnoses and classification using methylation data, we asked whether deep neural networks can predict the DNA methylation class of spinal cord ependymomas from hematoxylin and eosin stained whole-slide images. Using explainable AI, we further aimed to prospectively improve the consistency of histology-based diagnoses with DNA methylation profiling by identifying and quantifying distinct morphological patterns of these molecular ependymoma types. We assembled a case series of 139 molecularly characterized spinal cord ependymomas (nMPE = 84, nSP-EPN = 55). Self-supervised and weakly-supervised neural networks were used for classification. We employed attention analysis and supervised machine-learning methods for the discovery and quantification of morphological features and their correlation to the diagnoses of experienced neuropathologists. Our best performing model predicted the DNA methylation class with 98% test accuracy and used self-supervised learning to outperform pretrained encoder-networks (86% test accuracy). In contrast, the diagnoses of neuropathologists matched the DNA methylation class in only 83% of cases. Domain-adaptation techniques improved model generalization to an external validation cohort by up to 22%. Statistically significant morphological features were identified per molecular type and quantitatively correlated to human diagnoses. The approach was extended to recently defined subtypes of myxopapillary ependymomas (MPE-(A/B), 80% test accuracy). In summary, we demonstrated the accurate prediction of the DNA methylation class of spinal cord ependymomas (SP-EPN, MPE(-A/B)) using hematoxylin and eosin stained whole-slide images. Our approach may prospectively serve as a supplementary resource for integrated diagnostics and may even help to establish a standardized, high-quality level of histology-based diagnostics across institutions-in particular in low-income countries, where expensive DNA-methylation analyses may not be readily available.

Transcriptomic and epigenetic dissection of spinal ependymoma (SP-EPN) identifies clinically relevant subtypes enriched for tumors with and without NF2 mutation.

Ependymomas encompass multiple clinically relevant tumor types based on localization and molecular profiles. Tumors of the methylation class "spinal ependymoma" (SP-EPN) represent the most common intramedullary neoplasms in children and adults. However, their developmental origin is ill-defined, molecular data are scarce, and the potential heterogeneity within SP-EPN remains unexplored. The only known recurrent genetic events in SP-EPN are loss of chromosome 22q and NF2 mutations, but neither types and frequency of these alterations nor their clinical relevance have been described in a large, epigenetically defined series. Transcriptomic (n = 72), epigenetic (n = 225), genetic (n = 134), and clinical data (n = 112) were integrated for a detailed molecular overview on SP-EPN. Additionally, we mapped SP-EPN transcriptomes to developmental atlases of the developing and adult spinal cord to uncover potential developmental origins of these tumors. The integration of transcriptomic ependymoma data with single-cell atlases of the spinal cord revealed that SP-EPN display the highest similarities to mature adult ependymal cells. Unsupervised hierarchical clustering of transcriptomic data together with integrated analysis of methylation profiles identified two molecular SP-EPN subtypes. Subtype A tumors primarily carried previously known germline or sporadic NF2 mutations together with 22q loss (bi-allelic NF2 loss), resulting in decreased NF2 expression. Furthermore, they more often presented as multilocular disease and demonstrated a significantly reduced progression-free survival as compared to SP-EP subtype B. In contrast, subtype B predominantly contained samples without NF2 mutation detected in sequencing together with 22q loss (monoallelic NF2 loss). These tumors showed regular NF2 expression but more extensive global copy number alterations. Based on integrated molecular profiling of a large multi-center cohort, we identified two distinct SP-EPN subtypes with important implications for genetic counseling, patient surveillance, and drug development priorities.

Temporal change of DNA methylation subclasses between matched newly diagnosed and recurrent glioblastoma.

The longitudinal transition of phenotypes is pivotal in glioblastoma treatment resistance and DNA methylation emerged as an important tool for classifying glioblastoma phenotypes. We aimed to characterize DNA methylation subclass heterogeneity during progression and assess its clinical impact. Matched tissues from 47 glioblastoma patients were subjected to DNA methylation profiling, including CpG-site alterations, tissue and serum deconvolution, mass spectrometry, and immunoassay. Effects of clinical characteristics on temporal changes and outcomes were studied. Among 47 patients, 8 (17.0%) had non-matching classifications at recurrence. In the remaining 39 cases, 28.2% showed dominant DNA methylation subclass transitions, with 72.7% being a mesenchymal subclass. In general, glioblastomas with a subclass transition showed upregulated metabolic processes. Newly diagnosed glioblastomas with mesenchymal transition displayed increased stem cell-like states and decreased immune components at diagnosis and exhibited elevated immune signatures and cytokine levels in serum. In contrast, tissue of recurrent glioblastomas with mesenchymal transition showed increased immune components but decreased stem cell-like states. Survival analyses revealed comparable outcomes for patients with and without subclass transitions. This study demonstrates a temporal heterogeneity of DNA methylation subclasses in 28.2% of glioblastomas, not impacting patient survival. Changes in cell state composition associated with subclass transition may be crucial for recurrent glioblastoma targeted therapies.

Pituitary neuroendocrine tumors with PIT1/SF1 co-expression show distinct clinicopathological and molecular features.

Pituitary neuroendocrine tumors (PitNETs) are classified according to cell lineage, which requires immunohistochemistry for adenohypophyseal hormones and the transcription factors (TFs) PIT1, SF1, and TPIT. According to the current WHO 2022 classification, PitNETs with co-expression of multiple TFs are termed "plurihormonal". Previously, PIT1/SF1 co-expression was prevailingly reported in PitNETs, which otherwise correspond to the somatotroph lineage. However, little is known about such tumors and the WHO classification has not recognized their significance. We compiled an in-house case series of 100 tumors, previously diagnosed as somatotroph PitNETs. Following TF staining, histopathological features associated with PIT1/SF1 co-expression were assessed. Integration of in-house and publicly available sample data allowed for a meta-analysis of SF1-associated clinicopathological and molecular features across a total of 270 somatotroph PitNETs. The majority (74%, 52/70) of our densely granulated somatotroph PitNETs (DGST) unequivocally co-expressed PIT1 and SF1 (DGST-PIT1/SF1). None (0%, 0/30) of our sparsely granulated somatotroph PitNETs (SGST) stained positive for SF1 (SGST-PIT1). Among DGST, PIT1/SF1 co-expression was significantly associated with scarce FSH/LH expression and fewer fibrous bodies compared to DGST-PIT1. Integrated molecular analyses including publicly available samples confirmed that DGST-PIT1/SF1, DGST-PIT1 and SGST-PIT1 represent distinct tumor subtypes. Clinicopathological meta-analyses indicated that DGST-PIT1 respond more favorably towards treatment with somatostatin analogs compared to DGST-PIT1/SF1, while both these subtypes show an overall less aggressive clinical course than SGST-PIT1. In this study, we spotlight that DGST with co-expression of PIT1 and SF1 represent a common, yet underrecognized, distinct PitNET subtype. Our study questions the rationale of generally classifying such tumors as "plurihormonal", and calls for a refinement of the WHO classification. We propose the term "somatogonadotroph PitNET".

Integrated proteomics spotlight the proteasome as a therapeutic vulnerability in embryonal tumors with multilayered rosettes.

BACKGROUND: Embryonal tumors with multilayered rosettes (ETMR) are rare malignant embryonal brain tumors. The prognosis of ETMR is poor and novel therapeutic approaches are desperately needed. Comprehension of ETMR tumor biology is currently based on only few previous molecular studies, which mainly focused on the analyses of nucleic acids. In this study, we explored integrated ETMR proteomics. METHODS: Using mass spectrometry, proteome data were acquired from 16 ETMR and the ETMR cell line BT183. Proteome data were integrated with case-matched global DNA methylation data, publicly available transcriptome data, and proteome data of further embryonal and pediatric brain tumors. RESULTS: Proteome-based cluster analyses grouped ETMR samples according to histomorphology, separating neuropil-rich tumors with neuronal signatures from primitive tumors with signatures relating to stemness and chromosome organization. Integrated proteomics showcased that ETMR and BT183 cells harbor proteasome regulatory proteins in abundance, implicating their strong dependency on the proteasome machinery to safeguard proteostasis. Indeed, in vitro assays using BT183 highlighted that ETMR tumor cells are highly vulnerable toward treatment with the CNS penetrant proteasome inhibitor Marizomib. CONCLUSIONS: In summary, histomorphology stipulates the proteome signatures of ETMR, and proteasome regulatory proteins are pervasively abundant in these tumors. As validated in vitro, proteasome inhibition poses a promising therapeutic option in ETMR.

Mouse models of pediatric high-grade gliomas with MYCN amplification reveal intratumoral heterogeneity and lineage signatures.

Pediatric high-grade gliomas of the subclass MYCN (HGG-MYCN) are highly aggressive tumors frequently carrying MYCN amplifications, TP53 mutations, or both alterations. Due to their rarity, such tumors have only recently been identified as a distinct entity, and biological as well as clinical characteristics have not been addressed specifically. To gain insights into tumorigenesis and molecular profiles of these tumors, and to ultimately suggest alternative treatment options, we generated a genetically engineered mouse model by breeding hGFAP-cre::Trp53Fl/Fl::lsl-MYCN mice. All mice developed aggressive forebrain tumors early in their lifetime that mimic human HGG-MYCN regarding histology, DNA methylation, and gene expression. Single-cell RNA sequencing revealed a high intratumoral heterogeneity with neuronal and oligodendroglial lineage signatures. High-throughput drug screening using both mouse and human tumor cells finally indicated high efficacy of Doxorubicin, Irinotecan, and Etoposide as possible therapy options that children with HGG-MYCN might benefit from.

Direct 3D Sampling of the Embryonic Mouse Head: Layer-wise Nanosecond Infrared Laser (NIRL) Ablation from Scalp to Cortex for Spatially Resolved Proteomics.

Common workflows in bottom-up proteomics require homogenization of tissue samples to gain access to the biomolecules within the cells. The homogenized tissue samples often contain many different cell types, thereby representing an average of the natural proteome composition, and rare cell types are not sufficiently represented. To overcome this problem, small-volume sampling and spatial resolution are needed to maintain a better representation of the sample composition and their proteome signatures. Using nanosecond infrared laser ablation, the region of interest can be targeted in a three-dimensional (3D) fashion, whereby the spatial information is maintained during the simultaneous process of sampling and homogenization. In this study, we ablated 40 µm thick consecutive layers directly from the scalp through the cortex of embryonic mouse heads and analyzed them by subsequent bottom-up proteomics. Extra- and intracranial ablated layers showed distinct proteome profiles comprising expected cell-specific proteins. Additionally, known cortex markers like SOX2, KI67, NESTIN, and MAP2 showed a layer-specific spatial protein abundance distribution. We propose potential new marker proteins for cortex layers, such as MTA1 and NMRAL1. The obtained data confirm that the new 3D tissue sampling and homogenization method is well suited for investigating the spatial proteome signature of tissue samples in a layerwise manner. Characterization of the proteome composition of embryonic skin and bone structures, meninges, and cortex lamination in situ enables a better understanding of molecular mechanisms of development during embryogenesis and disease pathogenesis.

MYC overexpression and SMARCA4 loss cooperate to drive medulloblastoma formation in mice.

Group 3 medulloblastoma is one of the most aggressive types of childhood brain tumors. Roughly 30% of cases carry genetic alterations in MYC, SMARCA4, or both genes combined. While overexpression of MYC has previously been shown to drive medulloblastoma formation in mice, the functional significance of SMARCA4 mutations and their suitability as a therapeutic target remain largely unclear. To address this issue, we combined overexpression of MYC with a loss of SMARCA4 in granule cell precursors. Both alterations did not increase proliferation of granule cell precursors in vitro. However, combined MYC overexpression and SMARCA4 loss successfully induced tumor formation in vivo after orthotopic transplantation in recipient mice. Resulting tumors displayed anaplastic histology and exclusively consisted of SMARCA4-negative cells although a mixture of recombined and non-recombined cells was injected. These observations provide first evidence for a tumor-promoting role of a SMARCA4 deficiency in the development of medulloblastoma. In comparing the transcriptome of tumors to the cells of origin and an established Sonic Hedgehog medulloblastoma model, we gathered first hints on deregulated gene expression that could be specifically involved in SMARCA4/MYC driven tumorigenesis. Finally, an integration of RNA sequencing and DNA methylation data of murine tumors with human samples revealed a high resemblance to human Group 3 medulloblastoma on the molecular level. Altogether, the development of SMARCA4-deficient medulloblastomas in mice paves the way to deciphering the role of frequently occurring SMARCA4 alterations in Group 3 medulloblastoma with the perspective to explore targeted therapeutic options.

Direct Experimental Observations of Ion Distributions during Overcharging at the Muscovite-Water Interface by Adsorption of Rb+ and Halides (Cl- , Br- , I- ) at High Salinity.

The front cover artwork is provided by Argonne National Laboratory. The image shows the arrangement of correlated cations and anions at a charged solid surface in contact with highly concentrated electrolyte solutions. Read the full text of the Research Article at 10.1002/cphc.202300545.

Epigenetic neural glioblastoma enhances synaptic integration and predicts therapeutic vulnerability.

Neural-tumor interactions drive glioma growth as evidenced in preclinical models, but clinical validation is nascent. We present an epigenetically defined neural signature of glioblastoma that independently affects patients' survival. We use reference signatures of neural cells to deconvolve tumor DNA and classify samples into low- or high-neural tumors. High-neural glioblastomas exhibit hypomethylated CpG sites and upregulation of genes associated with synaptic integration. Single-cell transcriptomic analysis reveals high abundance of stem cell-like malignant cells classified as oligodendrocyte precursor and neural precursor cell-like in high-neural glioblastoma. High-neural glioblastoma cells engender neuron-to-glioma synapse formation in vitro and in vivo and show an unfavorable survival after xenografting. In patients, a high-neural signature associates with decreased survival as well as increased functional connectivity and can be detected via DNA analytes and brain-derived neurotrophic factor in plasma. Our study presents an epigenetically defined malignant neural signature in high-grade gliomas that is prognostically relevant.

Direct Experimental Observations of Ion Distributions during Overcharging at the Muscovite-Water Interface by Adsorption of Rb+ and Halides (Cl- , Br- , I- ) at High Salinity.

Classical electric double layer (EDL) models have been widely used to describe ion distributions at charged solid-water interfaces in dilute electrolytes. However, the chemistry of EDLs remains poorly constrained at high ionic strength where ion-ion correlations control non-classical behavior such as overcharging, i. e., the accumulation of counter-ions in amounts exceeding the substrate's surface charge. Here, we provide direct experimental observations of correlated cation and anion distributions adsorbed at the muscovite (001)-aqueous electrolyte interface as a function of dissolved RbBr concentration ([RbBr]=0.01-5.8 M) using resonant anomalous X-ray reflectivity. Our results show alternating cation-anion layers in the EDL when [RbBr]≳100 mM, whose spatial extension (i. e., ~20 Šfrom the surface) far exceeds the dimension of the classical Stern layer. Comparison to RbCl and RbI electrolytes indicates that these behaviors are sensitive to the choice of co-ion. This new in-depth molecular-scale understanding of the EDL structure during transition from classical to non-classical regimes supports the development of realistic EDL models for technologies operating at high salinity such as water purification applications or modern electrochemical storage.

Analysis of Urinary Glycosaminoglycans to Predict Outcome in COVID-19 and Community-Acquired Pneumonia-A Proof-of-Concept Study.

Although coronavirus disease 2019 (COVID-19) is considered a systemic disease associated with vascular inflammation and eventual destruction of the protective endothelial glycocalyx (eGC), biomarkers of eGC damage are not yet available in the clinic. The most prominent components of eGC are sulphated glycosaminoglycans (sGAGs) attached to core proteoglycans. We hypothesised that the amount of sGAG fragments shed in urine (as a surrogate for systemic eGC damage) would correlate with disease severity and outcome. Total urinary sGAG concentration was measured using an in-house optimised 1,9-dimethylmethylene blue (DMMB) assay, which is highly accurate and insensitive to interferences. The median urinary sGAG concentration was significantly higher in 67 hospitalised patients with COVID-19 compared to 72 hospitalised patients with community-acquired pneumonia (CAP). In both groups, urinary sGAG concentrations predicted a combined endpoint (including intubation and death) with an area under the receiver operator characteristic curve of 0.72 (95% CI 0.55-0.88, p = 0.01) and 0.70 (95% CI 0.57-0.83, p = 0.007), respectively. In conclusion, the inexpensive and easy-to-perform DMMB assay provides a surrogate parameter for eGC damage that may be useful for risk stratification of patients with COVID-19 and CAP.

Proteomics of immune cells from liver tumors reveals immunotherapy targets.

Elucidating the mechanisms by which immune cells become dysfunctional in tumors is critical to developing next-generation immunotherapies. We profiled proteomes of cancer tissue as well as monocyte/macrophages, CD4+ and CD8+ T cells, and NK cells isolated from tumors, liver, and blood of 48 patients with hepatocellular carcinoma. We found that tumor macrophages induce the sphingosine-1-phospate-degrading enzyme SGPL1, which dampened their inflammatory phenotype and anti-tumor function in vivo. We further discovered that the signaling scaffold protein AFAP1L2, typically only found in activated NK cells, is also upregulated in chronically stimulated CD8+ T cells in tumors. Ablation of AFAP1L2 in CD8+ T cells increased their viability upon repeated stimulation and enhanced their anti-tumor activity synergistically with PD-L1 blockade in mouse models. Our data reveal new targets for immunotherapy and provide a resource on immune cell proteomes in liver cancer.

Robust classification using average correlations as features (ACF).

MOTIVATION: In single-cell transcriptomics and other omics technologies, large fractions of missing values commonly occur. Researchers often either consider only those features that were measured for each instance of their dataset, thereby accepting severe loss of information, or use imputation which can lead to erroneous results. Pairwise metrics allow for imputation-free classification with minimal loss of data. RESULTS: Using pairwise correlations as metric, state-of-the-art approaches to classification would include the K-nearest-neighbor- (KNN) and distribution-based-classification-classifier. Our novel method, termed average correlations as features (ACF), significantly outperforms those approaches by training tunable machine learning models on inter-class and intra-class correlations. Our approach is characterized in simulation studies and its classification performance is demonstrated on real-world datasets from single-cell RNA sequencing and bottom-up proteomics. Furthermore, we demonstrate that variants of our method offer superior flexibility and performance over KNN classifiers and can be used in conjunction with other machine learning methods. In summary, ACF is a flexible method that enables missing value tolerant classification with minimal loss of data.

Y(III) Sorption at the Orthoclase (001) Surface Measured by X-ray Reflectivity.

Interactions of heavy metals with charged mineral surfaces control their mobility in the environment. Here, we investigate the adsorption of Y(III) onto the orthoclase (001) basal plane, the former as a representative of rare earth elements and an analogue of trivalent actinides and the latter as a representative of naturally abundant K-feldspar minerals. We apply in situ high-resolution X-ray reflectivity to determine the sorption capacity and molecular distribution of adsorbed Y species as a function of the Y3+ concentration, [Y3+], at pH 7 and 5. With [Y3+] ≥ 1 mM at pH 7, we observe an inner-sphere (IS) sorption complex at a distance of ∼1.5 Å from the surface and an outer-sphere (OS) complex at 3-4 Å. Based on the adsorption height of the IS complex, a bidentate, binuclear binding mode, in which Y3+ binds to two terminal oxygens, is proposed. In contrast, mostly OS sorption is observed at pH 5. The observed maximum Y coverage is ∼1.3 Y3+/AUC (AUC: area of the unit cell = 111.4 Å2) for all the investigated pH values and Y concentrations, which is in the expected range based on the estimated surface charge of orthoclase (001).

Combining batch experiments and spectroscopy for realistic surface complexation modelling of the sorption of americium, curium, and europium onto muscovite.

For a safe enclosure of contaminants, for instance in deep geological repositories of radioactive waste, any processes retarding metal migration are of paramount importance. This study focusses on the sorption of trivalent actinides (Am, Cm) and lanthanides (Eu) to the surface of muscovite, a mica and main component of most crystalline rocks (granites, granodiorites). Batch sorption experiments quantified the retention regarding parameters like pH (varied between 3 and 9), metal concentration (from 0.5 µM Cm to 10 µM Eu), or solid-to-liquid ratio (0.13 and 5.25 g·L-1). In addition, time-resolved laser fluorescence spectroscopy (TRLFS) using the actinide Cm(III) identified two distinct inner-sphere surface species. Combining both approaches allowed the development of a robust surface complexation model and the determination of stability constants of the spectroscopically identified species of (S-OH)2M3+ (logKo -8.89), (S-O)2M+ (logKo -4.11), and (S-O)2MOH (logKo -10.6), with all values extrapolated to infinite dilution. The inclusion of these stability constants into thermodynamic databases will improve the prognostic accuracy of lanthanide and actinide transport through groundwater channels in soils and crystalline rock systems.

Interleukin-17A and interleukin-22 production by conventional and non-conventional lymphocytes in three different end-stage lung diseases.

Objectives: The contribution of adaptive vs. innate lymphocytes to IL-17A and IL-22 secretion at the end stage of chronic lung diseases remains largely unexplored. In order to uncover tissue- and disease-specific secretion patterns, we compared production patterns of IL-17A and IL-22 in three different human end-stage lung disease entities. Methods: Production of IL-17A, IL-22 and associated cytokines was assessed in supernatants of re-stimulated lymphocytes by multiplex assays and multicolour flow cytometry of conventional T cells, iNKT cells, γδ T cells and innate lymphoid cells in bronchial lymph node and lung tissue from patients with emphysema (n = 19), idiopathic pulmonary fibrosis (n = 14) and cystic fibrosis (n = 23), as well as lung donors (n = 17). Results: We detected secretion of IL-17A and IL-22 by CD4+ T cells, CD8+ T cells, innate lymphoid cells, γδ T cells and iNKT cells in all end-stage lung disease entities. Our analyses revealed disease-specific contributions of individual lymphocyte subpopulations to cytokine secretion patterns. We furthermore found the high levels of microbial detection in CF samples to associate with a more pronounced IL-17A signature upon antigen-specific and unspecific re-stimulation compared to other disease entities and lung donors. Conclusion: Our results show that both adaptive and innate lymphocyte populations contribute to IL-17A-dependent pathologies in different end-stage lung disease entities, where they establish an IL-17A-rich microenvironment. Microbial colonisation patterns and cytokine secretion upon microbial re-stimulation suggest that pathogens drive IL-17A secretion patterns in end-stage lung disease.