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Supplementary Figure 1: Mouse generation and GSEA enrichment analysis of Sca1-BCR-ABLp190 mice. Supplementary Figure 2: Time course of the percentage of B220+ cells in PB of Sca1-BCR-ABLp190 and Sca1-BCR-ABLp190+Pax5+/- mice. Supplementary figure 3: pB-ALL is transplantable to secondary recipients. Supplementary figure 4: Sca1-BCR-ABLp190+Pax5+/- mice housed in SPF develop similar pB-ALL than Sca1-BCR-ABLp190+Pax5+/- mice housed in CF Supplementary Figure 5: transcriptome analysis of Sca1-BCR-ABLp190+Pax5+/- mice. Supplementary Figure 6: Mouse tumor exome sequencing data identified recurrent Pax5 mutations. Supplementary Figure 7: Polyclonal VDJ recombination events in pre-leukemic B220-positive cells from the BM of Sca1-BCR-ABLp190+Pax5+/- mice Supplementary Figure 8: Altered pathways in leukemic Sca1-BCR-ABLp190+Pax5+/-. Supplementary Figure 9: Quantitative differences of mRNA levels of glycolytic related genes between leukemic Sca1-BCR-ABLp190+Pax5+/-, Sca1-BCR-ABLp190 and wild-type B cells. Supplementary Figure 10: QRT-PCR validation of microarray data. Supplementary Figure 11: Metabolic signature in human pB-ALLs. Supplementary Figure 12: Analysis of ChIP-seq data revealed novel targets of PAX5 in human B lymphocytes. Supplementary Table 1: Percentage and total numbers of hematopoietic progenitor compartments in young Sca1-BCR-ABLp190 and wild type mice. Supplementary Table 3: pB-ALL genotype and phenotype of Sca1-BCR-ABLp190+Pax5+/- and Sca1-BCR-ABLp190 mice.

XLSX file - 655K, Supplementary Table S4 - annotated DNA methylation in confirmed promoter DMRs (RRBS) in HTB56.

Supplementary Figures S1-6. Supplementary Fig S1: Combined patient characteristics and subjected methods Supplementary Fig. S2 A: ROC curves were constructed by using the somatic scores from the sequenced genotypes. Supplementary Fig. S3: Circos plots of representative Ewing sarcomas. Supplementary Fig. S4: Sanger sequencing results confirmed (A) somatic inactivating mutations of the STAG2 gene (positions according to RefSeq NG 033796.2) and (B) the activating N546 mutation of FGFR1. Supplementary Fig. S5: FGFR-1 expression in Ewing Sarcomas by means of real time RT PCR (A, normalized to FGFR-1 expression in VH-64). (B) Array-based analysis of FGFR1 expression in ES compared to normal tissue (NBA; SI Materials and Methods). (C) Knock-down by means of lentiviral transduced FGFR1.shRNA effectively reduced FGFR1 mRNA expression by 70 % or more in Ewing sarcoma cell lines as compared to scrambled shRNA. (D) Knockdown was confirmed on the protein level by means of western blotting. Supplementary Fig. S6: Amplification of the FGFR-1 gene (A) as well as gene expression (B) are associated with a trend towards inferior survival in patients with ES. (C) Treatment with Ponatinib inhibited growth of ES cell lines significantly. (D) FGFR1 tyrosine kinase inhibitor therapy in a patient with relapsed Ewing sarcoma.

Supplementary Materials. Additional Material and Methods section and clinical data of the Ponatib treated patient.

Purpose: A low mutation rate seems to be a general feature of pediatric cancers, in particular in oncofusion gene-driven tumors. Genetically, Ewing sarcoma is defined by balanced chromosomal EWS/ETS translocations, which give rise to oncogenic chimeric proteins (EWS-ETS). Other contributing somatic mutations involved in disease development have only been observed at low frequency.Experimental Design: Tumor samples of 116 Ewing sarcoma patients were analyzed here. Whole-genome sequencing was performed on two patients with normal, primary, and relapsed tissue. Whole-exome sequencing was performed on 50 Ewing sarcoma and 22 matched normal tissues. A discovery dataset of 14 of these tumor/normal pairs identified 232 somatic mutations. Recurrent nonsynonymous mutations were validated in the 36 remaining exomes. Transcriptome analysis was performed in a subset of 14 of 50 Ewing sarcomas and DNA copy number gain and expression of FGFR1 in 63 of 116 Ewing sarcomas.Results: Relapsed tumors consistently showed a 2- to 3-fold increased number of mutations. We identified several recurrently mutated genes at low frequency (ANKRD30A, CCDC19, KIAA0319, KIAA1522, LAMB4, SLFN11, STAG2, TP53, UNC80, ZNF98). An oncogenic fibroblast growth factor receptor 1 (FGFR1) mutation (N546K) was detected, and the FGFR1 locus frequently showed copy number gain (31.7%) in primary tumors. Furthermore, high-level FGFR1 expression was noted as a characteristic feature of Ewing sarcoma. RNA interference of FGFR1 expression in Ewing sarcoma lines blocked proliferation and completely suppressed xenograft tumor growth. FGFR1 tyrosine kinase inhibitor (TKI) therapy in a patient with Ewing sarcoma relapse significantly reduced 18-FDG-PET activity.Conclusions: FGFR1 may constitute a promising target for novel therapeutic approaches in Ewing sarcoma. Clin Cancer Res; 21(21); 4935–46. ©2015 AACR.

Contents: Supplementary Table 1: Characteristics of the pediatric patients enrolled in whole-genome- and whole-exome-sequencing. Supplementary Table 2: Conventional Facility Health Monitoring Report. Supplementary Table 3: Percentage of hematopoietic subsets in young ETV6-RUNX1 and wild type mice housed in conventional facility. Supplementary Table 5: Differential regulation of histonemodifying genes in ETV6-RUNX1 mice compared to human ETV6-RUNX1 pB-ALL. Supplementary Table 6: Alterations in genes related to histone modification in pB-ALL human samples Supplementary Table 7: Summary of whole genome sequencing statistics. Supplementary Table 8: Summary of exome sequencing statistics. Supplementary Table 9: Validated SNVs in the patient cohort. Supplementary Fig. 1: Distribution of genomic ETV6-RUNX1 breakpoints amongst patients. Supplementary Fig. 2: Development of Sca1-ETV6-RUNX1 mice. Supplementary Fig. 3: pB-ALL development in Sca1-ETV6-RUNX1 mice and B cell development in young Sca1-ETV6-RUNX1 mice housed in SPF facility. Supplementary Fig. 4: Tumor exome sequencing data identify genes mutated in mouse and human. Supplementary Fig. 5: Study of proB cells in young Sca1-ETV6-RUNX1 mice.

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PDF file - 1572K, Figure S4 4A+B): Column charts depict hypo- and hypermethylated regions detected by RRBS in high metastatic A549 (A) and HTB56 (B) cells after 6 days of 5-Azacytidine-treatment and after 7 days of 5-Azacytidine-release. 4C+D) Smoothed scatter plot of 5-Azacytidine-treated (250nM) versus untreated high metastatic HTB56 cells. Shown are methylation levels for CpG-sites analyzed by RRBS. C: X-axis: Methylation levels in HTB56 high metastatic cells (d0=untreated) Y-axis: Methylation levels in HTB56 high metastatic cells (d6_250= after six days of 5-Azacytidinetreatment, 5-Azacytidine-concentration of 250 nM) D: X-axis: Methylation levels in HTB56 high metastatic cells (d0=untreated) Y-axis: Methylation levels in HTB56 high metastatic cells (d13_250= after thirteen days of 5- Azacytidine-treatment, 5-Azacytidine-concentration of 250 nM) Figure S5 Unsupervised hierarchical clustering shows that 5-Azacytidine exposed cells cluster more closely together than individual cell lines. Figure S6 Chromosomal distribution of clusters and DMRs. The curves show the density distribution of hypomethylated DMRs and CpG clusters in relation to their chromosomal position: 0 indicates chromosomal end and 1 the centromeric region. Hypomethylated DMRs (green), hypermethylated DMRs (red) and CpG clusters (black) are shown. DNA demethylation upon 5-Azacytidine occurs primarily on chromosome ends.

PDF file - 247K, Supplementary Figure 3 Unsupervised hierarchical clustering shows that independent samples at each step of selection cluster more closely together than individual cell lines A549 (A) and HTB56 (B). A high correlation between biological replicates from normal and highly aggressive cell lines from both cell types were observed. The clustering is based on a distance matrix, which has been derived from pearson product-moment correlation coefficients (r) by calculating 1-r.

PDF file - 219K, Supplementary Figure 2 Chart depicts viability of high and low metastatic cells before 5-Azacytidine-treatment and after 7 days of 5-Azacytidine-release.

PDF file - 233K, Supplementary Figure 1 Genomic changes in low and high metastatic A549 cells were analyzed by 500K SNP arrays.

Supplementary Tables S1-6. Table S1: Validation of somatic mutations by Sanger sequencing for construction of ROC Table S2: Characteristics of the discovery data set for exome sequencing Table S3: Somatic mutations in the discovery data set (filtered with MutSigCV1.6) Table S4: Recurrent non-synonymous mutations (filtered with MutSigCV1.6) Table S5: Characteristics of the expression data set. Table S6 : Shared mutations in consecutive sarcomas

PDF file - 96K, Supplementary Table 1: Mutations detected in parental and highly metastatic NSCLC cell lines A549 (A) and HTB56 (B) by SNP analysis Supplementary Table 2: Mutations detected in parental and highly metastatic NSCLC cell lines A549 and HTB56 by Exome sequencing Supplementary Table 3: Conversion rate uniquely mapped reads

Differentially expressed genes in bone marrow tumors from Sca1-BCR-ABLp190 mice compared to wild-type B220+ bone marrow B cells. (3692 genes-probesets with FDR = 0.05)

Supplementary Table 4: Differentially expressed genes in bone marrow proB and preB cells from ETV6-RUNX1 mice compared to wild-type bone marrow proB and preB cells. (12423 genes-probesets with FDR = 0.1).

Preleukemic clones carrying BCR-ABLp190 oncogenic lesions are found in neonatal cord blood, where the majority of preleukemic carriers do not convert into precursor B-cell acute lymphoblastic leukemia (pB-ALL). However, the critical question of how these preleukemic cells transform into pB-ALL remains undefined. Here, we model a BCR-ABLp190 preleukemic state and show that limiting BCR-ABLp190 expression to hematopoietic stem/progenitor cells (HS/PC) in mice (Sca1-BCR-ABLp190) causes pB-ALL at low penetrance, which resembles the human disease. pB-ALL blast cells were BCR-ABL–negative and transcriptionally similar to pro-B/pre-B cells, suggesting disease onset upon reduced Pax5 functionality. Consistent with this, double Sca1-BCR-ABLp190+Pax5+/− mice developed pB-ALL with shorter latencies, 90% incidence, and accumulation of genomic alterations in the remaining wild-type Pax5 allele. Mechanistically, the Pax5-deficient leukemic pro-B cells exhibited a metabolic switch toward increased glucose utilization and energy metabolism. Transcriptome analysis revealed that metabolic genes (IDH1, G6PC3, GAPDH, PGK1, MYC, ENO1, ACO1) were upregulated in Pax5-deficient leukemic cells, and a similar metabolic signature could be observed in human leukemia. Our studies unveil the first in vivo evidence that the combination between Sca1-BCR-ABLp190 and metabolic reprogramming imposed by reduced Pax5 expression is sufficient for pB-ALL development. These findings might help to prevent conversion of BCR-ABLp190 preleukemic cells.Significance: Loss of Pax5 drives metabolic reprogramming, which together with Sca1-restricted BCR-ABL expression enables leukemic transformation. Cancer Res; 78(10); 2669–79. ©2018 AACR.

Supplementary Figures and Table legends as well as supplementary methods

ETV6-RUNX1 is associated with the most common subtype of childhood leukemia. As few ETV6-RUNX1 carriers develop precursor B-cell acute lymphocytic leukemia (pB-ALL), the underlying genetic basis for development of full-blown leukemia remains to be identified, but the appearance of leukemia cases in time-space clusters keeps infection as a potential causal factor. Here, we present in vivo genetic evidence mechanistically connecting preleukemic ETV6-RUNX1 expression in hematopoetic stem cells/precursor cells (HSC/PC) and postnatal infections for human-like pB-ALL. In our model, ETV6-RUNX1 conferred a low risk of developing pB-ALL after exposure to common pathogens, corroborating the low incidence observed in humans. Murine preleukemic ETV6-RUNX1 pro/preB cells showed high Rag1/2 expression, known for human ETV6-RUNX1 pB-ALL. Murine and human ETV6-RUNX1 pB-ALL revealed recurrent genomic alterations, with a relevant proportion affecting genes of the lysine demethylase (KDM) family. KDM5C loss of function resulted in increased levels of H3K4me3, which coprecipitated with RAG2 in a human cell line model, laying the molecular basis for recombination activity. We conclude that alterations of KDM family members represent a disease-driving mechanism and an explanation for RAG off-target cleavage observed in humans. Our results explain the genetic basis for clonal evolution of an ETV6-RUNX1 preleukemic clone to pB-ALL after infection exposure and offer the possibility of novel therapeutic approaches. Cancer Res; 77(16); 4365–77. ©2017 AACR.

Application of next-generation sequencing may lead to the detection of secondary findings (SF) not related to the initially analyzed disease but to other severe medically actionable diseases. However, the analysis of SFs is not yet routinely performed. We mined whole-exome sequencing data of 231 pediatric cancer patients and their parents who had been treated in our center for the presence of SFs. By this approach, we identified in 6 children (2.6%) pathogenic germline variants in 5 of the noncancer-related genes on the American College of Medical Genetics and Genomics (ACMG) SF v3.0 list, of which the majority were related to cardiovascular diseases (RYR2, MYBPC3, KCNQ1). Interestingly, only the patient harboring the KCNQ1 variant showed at the time point of the analysis signs of the related Long QT syndrome. Moreover, we report 3 variants of unknown significance which, although not classified as pathogenic, have been reported in the literature to occur in individuals with the respective disease. While the frequency of patients with SFs is low, the impact of such findings on the patients' life is enormous, with regard to the potential prevention of life-threatening diseases. Hence, we are convinced that such actionable SF should be routinely analyzed.

Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed.Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation.Retrospective patient data (karyotype, age, height, weight, testis volume, follicle-stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition.Based on average performance, support vector machines and CatBoost were particularly well-suited models, with 100% sensitivity and93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p = 0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p = 0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on http://klinefelter-score-calculator.uni-muenster.de.Differentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46,XY) is a problem that can be solved with supervised machine learning techniques, improving patient care.Machine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less-experienced physicians.

Aim: We systematically review current clinical applications of artificial intelligence (AI) that use machine learning (ML) methods for decision support in surgical oncology with an emphasis on clinical translation. Methods: MEDLINE, Web of Science, and CENTRAL were searched on 19 January 2021 for a combination of AI and ML-related terms, decision support, and surgical procedures for abdominal malignancies. Data extraction included study characteristics, description of algorithms and their respective purpose, and description of key steps for scientific validation and clinical translation. Results: Out of 8302 articles, 107 studies were included for full-text analysis. Most of the studies were conducted in a retrospective setting (n = 105, 98%), with 45 studies (42%) using data from multiple centers. The most common tumor entities were colorectal cancer (n = 35, 33%), liver cancer (n = 21, 20%), and gastric cancer (n = 17, 16%). The most common prediction task was survival (n = 36, 34%), with artificial neural networks being the most common class of ML algorithms (n = 52, 49%). Key reporting and validation steps included, among others, a complete listing of patient features (n = 95, 89%), training of multiple algorithms (n = 73, 68%), external validation (n = 13, 12%), prospective validation (n = 2, 2%), robustness in terms of cross-validation or resampling (n = 89, 83%), treatment recommendations by ML algorithms (n = 9, 8%), and development of an interface (n = 12, 11%). Conclusion: ML for decision support in surgical oncology is receiving increasing attention with promising results, but robust and prospective clinical validation is mostly lacking. Furthermore, the integration of ML into AI applications is necessary to foster clinical translation.

ZusammenfassungIm Deutschen Lebendspende Register SOLKID-GNR (Safety of the Living Kidney Donor – The German National Register) werden medizinische und psychosoziale Daten zum Outcome von Lebendnierenspender erhoben. Die prospektive Datenerhebung ermöglicht erstmals in Deutschland eine wissenschaftlich fundierte Langzeitanalyse, wie sich eine Lebendnierenspende auf die psychische und physische Gesundheit von Lebendnierenspendern auswirkt. Dies trägt direkt zu einer Verbesserung der Information und Versorgung der Lebendnierenspender bei.

Key Points Expression of CALCRL is an independent prognostic factor and a potential therapeutic target in pediatric AML.CALCRL is linked with relapse risk in p ediatric AML, which supports its role as a master regulator of relapse-initiating drug-tolerant cells., Visual Abstract, We have recently identified the G protein-coupled neuropeptide receptor calcitonin receptor‐like (CALCRL) as an independent prognostic biomarker and a therapeutic target in more than 1500 adult patients with acute myeloid leukemia (AML). Here, we confirmed CALCRL expression as a prognostic factor in a cohort of 284 pediatric patients with AML. High CALCRL expression was independently associated with event-free survival (hazard ratio [HR], 1.87; 95% confidence interval [CI], 1.36-2.57; P = .0001), overall survival (HR, 1.55; 95% CI, 1.06-2.27; P = .025), and cumulative incidence of relapse (HR, 2.10; 95% CI, 1.49-1.96; P < .0001) when adjusting for age, white blood cell count, and genetic risk. Despite its association with leukemia stem cell signatures, CALCRL expression remained associated with all end points when compared with the 17-gene leukemic stem cell score. The strong association of CALCRL expression with the risk of relapse also in the pediatric population supports its role as novel age-independent master regulator of relapse-initiating, drug-tolerant AML cells in humans.

Motivation Each year, the number of published bulk and single-cell RNA-seq datasets is growing exponentially. Studies analyzing such data are commonly looking at gene-level differences, while the collected RNA-seq data inherently represents reads of transcript isoform sequences. Utilizing transcriptomic quantifiers, RNA-seq reads can be attributed to specific isoforms, allowing for analysis of transcript-level differences. A differential transcript usage (DTU) analysis is testing for proportional differences in a gene’s transcript composition, and has been of rising interest for many research questions, such as analysis of differential splicing or cell-type identification. Results We present the R package DTUrtle, the first DTU analysis workflow for both bulk and single-cell RNA-seq datasets, and the first package to conduct a ‘classical’ DTU analysis in a single-cell context. DTUrtle extends established statistical frameworks, offers various result aggregation and visualization options and a novel detection probability score for tagged-end data. It has been successfully applied to bulk and single-cell RNA-seq data of human and mouse, confirming and extending key results. In addition, we present novel potential DTU applications like the identification of cell-type specific transcript isoforms as biomarkers. Availability and implementation The R package DTUrtle is available at https://github.com/TobiTekath/DTUrtle with extensive vignettes and documentation at https://tobitekath.github.io/DTUrtle/. Supplementary information Supplementary data are available at Bioinformatics online.

Predisposing CHEK2 germline variants are associated with various adult-type malignancies, whereas their impact on cancer susceptibility in childhood cancer is unclear. To understand the frequency of germline variants in the CHEK2 gene and their impact on pediatric malignancies, we used whole-exome sequencing to search for CHEK2 variants in the germlines of 418 children diagnosed with cancer in our clinics. Moreover, we performed functional analysis of the pathogenic CHEK2 variants to analyze the effect of the alterations on CHK2 protein function. We detected a CHEK2 germline variant in 32/418 (7.7%) pediatric cancer patients and 46.8% of them had leukemia. Functional analysis of the pathogenic variants revealed that 5 pathogenic variants impaired CHK2 protein function. 6/32 patients carried one of these clearly damaging CHEK2 variants and two of them harbored a matching family history of cancer. In conclusion, we detected germline CHEK2 variants in 7.7% of all pediatric cancer patients, of which a minority but still relevant fraction of approximately 20% had a profound impact on protein expression or its phosphorylation after irradiation-induced DNA damage. Accordingly, we conclude that CHEK2 variants increase the risk for not only adult-onset but also pediatric cancer.

Clinical checklists are the current gold standard to determine whether a child with cancer shows indications for genetic testing. Nevertheless, the efficacy of these tests to reliably detect genetic cancer predisposition in children with cancer is still insufficiently investigated. Here, we assessed the validity of clinically recognizable signs to identify cancer predisposition by correlating a state-of-the-art clinical checklist to the corresponding whole exome sequencing analysis in an unselected single-center cohort of 139 child-parent datasets. We applied a strict testing to only include autosomal dominant or compound heterozygous cancer-related variants.Our study reflects a high consent rate for genetic testing (>90%). In total, 1/3rdof patients had a clinical indication for genetic testing according to current recommendations and 10.8% (n=15/139) of children harbored a proven cancer predisposition based on exome sequencing. Out of these only 73.3% (n=11/15) were identified through the clinical checklist. In addition, >2 clinical findings in the applied checklist increased the likelihood to identifying genetic predisposition from 15% to 50%. While our data revealed a high rate of genetic predisposition (50%, n=5/10) in Myelodysplastic Syndrome (MDS) cases, no cancer predisposition variants were identified in the sarcoma and lymphoma group.In summary, our data showed a low checklist specificity of 68.5%, and missed every 4thchild with genetic predisposition. This highlights the drawbacks of sole clinical evaluation to accurately identify all children at risk and underlines the need for routine germline sequencing of pediatric cancers.

The integration of routine medical care data into research endeavors promises great value. However, access to this extra-domain data is constrained by numerous technical and legal requirements. The German Medical Informatics Initiative (MII) - initiated by the Federal Ministry of Research and Education (BMBF) - is making progress in setting up Medical Data Integration Centers to consolidate data stored in clinical primary information systems. Unfortunately, for many research questions cross-organizational data sources are required, as one organization's data is insufficient, especially in rare disease research. A first step, for research projects exploring possible multi-centric study designs, is to perform a feasibility query, i.e., a cohort size calculation transcending organizational boundaries. Existing solutions for this problem, like the previously introduced feasibility process for the MII's HiGHmed consortium, perform well for most use cases. However, there exist use cases where neither centralized data repositories, nor Trusted Third Parties are acceptable for data aggregation. Based on open standards, such as BPMN 2.0 and HL7 FHIR R4, as well as the cryptographic techniques of secure Multi-Party Computation, we introduce a fully automated, decentral feasibility query process without any central component or Trusted Third Party. The open source implementation of the proposed solution is intended as a plugin process to the HiGHmed Data Sharing Framework. The process's concept and underlying algorithms can also be used independently.

With increasing numbers of patients recovering from COVID-19, there is increasing evidence for persistent symptoms and the need for follow-up studies. This retrospective study included patients without comorbidities, who recovered from COVID-19 and attended an outpatient clinic at a university hospital for follow-up care and potential convalescent plasma donation. Network analysis was applied to visualize symptom combinations and persistent symptoms. Comprehensive lab-testing was ascertained at each follow-up to analyze differences regarding patients with vs without persistent symptoms. 116 patients were included, age range was 18–69 years (median: 41) with follow-ups ranging from 22 to 102 days. The three most frequent persistent symptoms were Fatigue (54%), Dyspnea (29%) and Anosmia (25%). Lymphopenia was present in 13 of 112 (12%) cases. Five of 35 cases (14%) had Lymphopenia in the later follow-up range of 80–102 days. Serum IgA concentration was the only lab parameter with significant difference between patients with vs without persistent symptoms with reduced serum IgA concentrations in the patient cohort of persistent symptoms (p = 0.0219). Moreover, subgroup analyses showed that patients with lymphopenia experienced more frequently persistent symptoms. In conclusion, lymphopenia persisted in a noticeable percentage of recovered patients. Patients with persistent symptoms had significantly lower serum IgA levels. Furthermore, our data provides evidence that lymphopenia is associated with persistence of COVID-19 symptoms.

Background TH2 cells were thought to be a pivotal factor for initiation of the autoimmune blistering disease pemphigus. However, the role of other T-cell subsets in pemphigus pathogenesis remained unclear. Objective We aimed to characterize the exact phenotype of T cells responsible for the development of pemphigus. Methods Whole transcriptome shotgun sequencing was performed to determine differential gene expression in pemphigus lesions and skin of healthy individuals. The cutaneous cytokine signature was further evaluated by real-time quantitative PCR. In peripheral blood, the distribution of TH cell and folliclular helper (TFH) cell subsets was analyzed by flow cytometry. Finally, the capacity of TH and TFH cell subsets to induce desmoglein (Dsg)-specific autoantibodies by memory B cells was evaluated in coculture experiments. Results Transcriptome analysis of skin samples identified an IL-17A–dominated immune signature in patients with pemphigus, and Kyoto Encyclopedia of Genes and Genomes pathway analysis confirmed the dominance of the IL-17A signaling pathway. Increased expression of IL17A and associated cytokines was also detected by real-time quantitative PCR comparing lesional with perilesional or healthy skin. Interestingly, utilization of flow cytometry showed that patients with active pemphigus had elevated levels of circulating IL-17+, TH17, TFH17, and TFH17.1 cells. Notably, levels of TH17 and TFH17 cells correlated with levels of Dsg-specific CD19+CD27+ memory B cells, and patients with acute pemphigus showed higher levels of Dsg3-autoreactive TFH17 cells. Coculture experiments revealed TFH17 cells as primarily responsible for inducing Dsg-specific autoantibody production by B cells. Conclusion Our findings show that TFH17 cells are critically involved in the pathogenesis of pemphigus and offer novel targets for therapeutic intervention.

The process of spermatogenesis—when germ cells differentiate into sperm—is tightly regulated, and misregulation in gene expression is likely to be involved in the physiopathology of male infertility. The testis is one of the most transcriptionally rich tissues; nevertheless, the specific gene expression changes occurring during spermatogenesis are not fully understood. To better understand gene expression during spermatogenesis, we generated germ cell–specific whole transcriptome profiles by systematically comparing testicular transcriptomes from tissues in which spermatogenesis is arrested at successive steps of germ cell differentiation. In these comparisons, we found thousands of differentially expressed genes between successive germ cell types of infertility patients. We demonstrate our analyses’ potential to identify novel highly germ cell–specific markers (TSPY4 and LUZP4 for spermatogonia; HMGB4 for round spermatids) and identified putatively misregulated genes in male infertility (RWDD2A,CCDC183,CNNM1,SERF1B). Apart from these, we found thousands of genes showing germ cell–specific isoforms (includingSOX15,SPATA4,SYCP3,MKI67). Our approach and dataset can help elucidate genetic and transcriptional causes for male infertility.

A generic approach for assessment and continuous monitoring of data quality in ODM-based research data has been developed. The focus is on the two data quality indicators completeness and syntactic correctness. The main idea is to enable the generation of a data quality report without additional programming effort.

A generic approach for assessment and continuous monitoring of data quality in ODM-based research data has been developed. The focus is on the two data quality indicators completeness and syntactic correctness. The main idea is to enable the generation of a data quality report without additional programming effort.