Safeguard function of PU.1 shapes the inflammatory epigenome of neutrophils.

Neutrophils are essential first-line defense cells against invading pathogens, yet when inappropriately activated, their strong immune response can cause collateral tissue damage and contributes to immunological diseases. However, whether neutrophils can intrinsically titrate their immune response remains unknown. Here we conditionally deleted the Spi1 gene, which encodes the myeloid transcription factor PU.1, from neutrophils of mice undergoing fungal infection and then performed comprehensive epigenomic profiling. We found that as well as providing the transcriptional prerequisite for eradicating pathogens, the predominant function of PU.1 was to restrain the neutrophil defense by broadly inhibiting the accessibility of enhancers via the recruitment of histone deacetylase 1. Such epigenetic modifications impeded the immunostimulatory AP-1 transcription factor JUNB from entering chromatin and activating its targets. Thus, neutrophils rely on a PU.1-installed inhibitor program to safeguard their epigenome from undergoing uncontrolled activation, protecting the host against an exorbitant innate immune response.

Epigenetic control over the cell-intrinsic immune response antagonizes self-renewal in acute myeloid leukemia.

ABSTRACT: Epigenetic modulation of the cell-intrinsic immune response holds promise as a therapeutic approach for leukemia. However, current strategies designed for transcriptional activation of endogenous transposons and subsequent interferon type-I (IFN-I) response, show limited clinical efficacy. Histone lysine methylation is an epigenetic signature in IFN-I response associated with suppression of IFN-I and IFN-stimulated genes, suggesting histone demethylation as key mechanism of reactivation. In this study, we unveil the histone demethylase PHF8 as a direct initiator and regulator of cell-intrinsic immune response in acute myeloid leukemia (AML). Site-specific phosphorylation of PHF8 orchestrates epigenetic changes that upregulate cytosolic RNA sensors, particularly the TRIM25-RIG-I-IFIT5 axis, thereby triggering the cellular IFN-I response-differentiation-apoptosis network. This signaling cascade largely counteracts differentiation block and growth of human AML cells across various disease subtypes in vitro and in vivo. Through proteome analysis of over 200 primary AML bone marrow samples, we identify a distinct PHF8/IFN-I signature in half of the patient population, without significant associations with known clinically or genetically defined AML subgroups. This profile was absent in healthy CD34+ hematopoietic progenitor cells, suggesting therapeutic applicability in a large fraction of patients with AML. Pharmacological support of PHF8 phosphorylation significantly impairs the growth in samples from patients with primary AML. These findings provide novel opportunities for harnessing the cell-intrinsic immune response in the development of immunotherapeutic strategies against AML.

Germ line variant GFI1-36N affects DNA repair and sensitizes AML cells to DNA damage and repair therapy.

ABSTRACT: Growth factor independence 1 (GFI1) is a DNA-binding transcription factor and a key regulator of hematopoiesis. GFI1-36N is a germ line variant, causing a change of serine (S) to asparagine (N) at position 36. We previously reported that the GFI1-36N allele has a prevalence of 10% to 15% among patients with acute myeloid leukemia (AML) and 5% to 7% among healthy Caucasians and promotes the development of this disease. Using a multiomics approach, we show here that GFI1-36N expression is associated with increased frequencies of chromosomal aberrations, mutational burden, and mutational signatures in both murine and human AML and impedes homologous recombination (HR)-directed DNA repair in leukemic cells. GFI1-36N exhibits impaired binding to N-Myc downstream-regulated gene 1 (Ndrg1) regulatory elements, causing decreased NDRG1 levels, which leads to a reduction of O6-methylguanine-DNA-methyltransferase (MGMT) expression levels, as illustrated by both transcriptome and proteome analyses. Targeting MGMT via temozolomide, a DNA alkylating drug, and HR via olaparib, a poly-ADP ribose polymerase 1 inhibitor, caused synthetic lethality in human and murine AML samples expressing GFI1-36N, whereas the effects were insignificant in nonmalignant GFI1-36S or GFI1-36N cells. In addition, mice that received transplantation with GFI1-36N leukemic cells treated with a combination of temozolomide and olaparib had significantly longer AML-free survival than mice that received transplantation with GFI1-36S leukemic cells. This suggests that reduced MGMT expression leaves GFI1-36N leukemic cells particularly vulnerable to DNA damage initiating chemotherapeutics. Our data provide critical insights into novel options to treat patients with AML carrying the GFI1-36N variant.

Integration of Patient-Reported Outcome Data Collected Via Web Applications and Mobile Apps Into a Nation-Wide COVID-19 Research Platform Using Fast Healthcare Interoperability Resources: Development Study.

BACKGROUND: The Network University Medicine projects are an important part of the German COVID-19 research infrastructure. They comprise 2 subprojects: COVID-19 Data Exchange (CODEX) and Coordination on Mobile Pandemic Apps Best Practice and Solution Sharing (COMPASS). CODEX provides a centralized and secure data storage platform for research data, whereas in COMPASS, expert panels were gathered to develop a reference app framework for capturing patient-reported outcomes (PROs) that can be used by any researcher. OBJECTIVE: Our study aims to integrate the data collected with the COMPASS reference app framework into the central CODEX platform, so that they can be used by secondary researchers. Although both projects used the Fast Healthcare Interoperability Resources (FHIR) standard, it was not used in a way that data could be shared directly. Given the short time frame and the parallel developments within the CODEX platform, a pragmatic and robust solution for an interface component was required. METHODS: We have developed a means to facilitate and promote the use of the German Corona Consensus (GECCO) data set, a core data set for COVID-19 research in Germany. In this way, we ensured semantic interoperability for the app-collected PRO data with the COMPASS app. We also developed an interface component to sustain syntactic interoperability. RESULTS: The use of different FHIR types by the COMPASS reference app framework (the general-purpose FHIR Questionnaire) and the CODEX platform (eg, Patient, Condition, and Observation) was found to be the most significant obstacle. Therefore, we developed an interface component that realigns the Questionnaire items with the corresponding items in the GECCO data set and provides the correct resources for the CODEX platform. We extended the existing COMPASS questionnaire editor with an import function for GECCO items, which also tags them for the interface component. This ensures syntactic interoperability and eases the reuse of the GECCO data set for researchers. CONCLUSIONS: This paper shows how PRO data, which are collected across various studies conducted by different researchers, can be captured in a research-compatible way. This means that the data can be shared with a central research infrastructure and be reused by other researchers to gain more insights about COVID-19 and its sequelae.

The CHK2 kinase is recurrently mutated and functionally impaired in the germline of pediatric cancer patients.

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.

Bi-allelic Mutations in M1AP Are a Frequent Cause of Meiotic Arrest and Severely Impaired Spermatogenesis Leading to Male Infertility.

Male infertility affects ∼7% of men, but its causes remain poorly understood. The most severe form is non-obstructive azoospermia (NOA), which is, in part, caused by an arrest at meiosis. So far, only a few validated disease-associated genes have been reported. To address this gap, we performed whole-exome sequencing in 58 men with unexplained meiotic arrest and identified the same homozygous frameshift variant c.676dup (p.Trp226LeufsTer4) in M1AP, encoding meiosis 1 associated protein, in three unrelated men. This variant most likely results in a truncated protein as shown in vitro by heterologous expression of mutant M1AP. Next, we screened four large cohorts of infertile men and identified three additional individuals carrying homozygous c.676dup and three carrying combinations of this and other likely causal variants in M1AP. Moreover, a homozygous missense variant, c.1166C>T (p.Pro389Leu), segregated with infertility in five men from a consanguineous Turkish family. The common phenotype between all affected men was NOA, but occasionally spermatids and rarely a few spermatozoa in the semen were observed. A similar phenotype has been described for mice with disruption of M1ap. Collectively, these findings demonstrate that mutations in M1AP are a relatively frequent cause of autosomal recessive severe spermatogenic failure and male infertility with strong clinical validity.

Dose-dependent expression of GFI1 alters metabolism in the haematopoietic progenitors and MLL::AF9-induced leukaemic cells.

Growth factor independence 1 (GFI1) is a transcriptional repressor protein that plays an essential role in the differentiation of myeloid and lymphoid progenitors. We and other groups have shown that GFI1 has a dose-dependent role in the initiation, progression, and prognosis of acute myeloid leukaemia (AML) patients by inducing epigenetic changes. We now demonstrate a novel role for dose-dependent GFI1 expression in regulating metabolism in haematopoietic progenitor and leukaemic cells. Using in-vitro and ex-vivo murine models of MLL::AF9-induced human AML and extra-cellular flux assays, we now demonstrate that a lower GFI1 expression enhances oxidative phosphorylation rate via upregulation of the FOXO1- MYC axis. Our findings underscore the significance of therapeutic exploitation in GFI1-low-expressing leukaemia cells by targeting oxidative phosphorylation and glutamine metabolism.

A clinical screening tool to detect genetic cancer predisposition in pediatric oncology shows high sensitivity but can miss a substantial percentage of affected children.

PURPOSE: Clinical checklists are the standard of care 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. METHODS: We assessed the validity of clinically recognizable signs to identify cancer predisposition by correlating a state-of-the-art clinical checklist to the corresponding exome sequencing analysis in an unselected single-center cohort of 139 child-parent data sets. RESULTS: In total, one-third of patients had a clinical indication for genetic testing according to current recommendations, and 10.1% (14 of 139) of children harbored a cancer predisposition. Of these, 71.4% (10 of 14) were identified through the clinical checklist. In addition, >2 clinical findings in the checklist increased the likelihood to identifying genetic predisposition from 12.5% to 50%. Furthermore, our data revealed a high rate of genetic predisposition (40%, 4 of 10) in myelodysplastic syndrome cases, while no (likely) pathogenic variants were identified in the sarcoma and lymphoma group. CONCLUSION: In summary, our data show high checklist sensitivity, particularly in identifying childhood cancer predisposition syndromes. Nevertheless, the checklist used here also missed 29% of children with a cancer predisposition, highlighting the drawbacks of sole clinical evaluation and underlining the need for routine germline sequencing in pediatric oncology.

Integrative genomic analysis of pediatric T-cell lymphoblastic lymphoma reveals candidates of clinical significance.

T-cell lymphoblastic lymphoma (T-LBL) is a heterogeneous malignancy of lymphoblasts committed to T-cell lineage. The dismal outcomes (15%-30%) after T-LBL relapse warrant establishing risk-based treatment. To our knowledge, this study presents the first comprehensive, systematic, integrated, genome-wide analysis including relapsed cases that identifies molecular markers of prognostic relevance for T-LBL. NOTCH1 was identified as the putative driver for T-LBL. An activated NOTCH/PI3K-AKT signaling axis and alterations in cell cycle regulators constitute the core oncogenic program for T-LBL. Mutated KMT2D was identified as a prognostic marker. The cumulative incidence of relapse was 47% ± 17% in patients with KMT2D mutations, compared with 14% ± 3% in wild-type KMT2D. Structural analysis of the mutated domains of KMT2D revealed a plausible impact on structure and functional consequences. These findings provide new insights into the pathogenesis of T-LBL, including high translational potential. The ongoing LBL 2018 trial (www.clinicaltrials.gov #NCT04043494) allows for prospective validation and subsequent fine tuning of the stratification criteria for T-LBL risk groups to improve survival of pediatric patients.

A Carboxy-terminal Smarcb1 Point Mutation Induces Hydrocephalus Formation and Affects AP-1 and Neuronal Signalling Pathways in Mice.

The BAF (BRG1/BRM-associated factor) chromatin remodelling complex is essential for the regulation of DNA accessibility and gene expression during neuronal differentiation. Mutations of its core subunit SMARCB1 result in a broad spectrum of pathologies, including aggressive rhabdoid tumours or neurodevelopmental disorders. Other mouse models have addressed the influence of a homo- or heterozygous loss of Smarcb1, yet the impact of specific non-truncating mutations remains poorly understood. Here, we have established a new mouse model for the carboxy-terminal Smarcb1 c.1148del point mutation, which leads to the synthesis of elongated SMARCB1 proteins. We have investigated its impact on brain development in mice using magnetic resonance imaging, histology, and single-cell RNA sequencing. During adolescence, Smarcb11148del/1148del mice demonstrated rather slow weight gain and frequently developed hydrocephalus including enlarged lateral ventricles. In embryonic and neonatal stages, mutant brains did not differ anatomically and histologically from wild-type controls. Single-cell RNA sequencing of brains from newborn mutant mice revealed that a complete brain including all cell types of a physiologic mouse brain is formed despite the SMARCB1 mutation. However, neuronal signalling appeared disturbed in newborn mice, since genes of the AP-1 transcription factor family and neurite outgrowth-related transcripts were downregulated. These findings support the important role of SMARCB1 in neurodevelopment and extend the knowledge of different Smarcb1 mutations and their associated phenotypes.

Federated electronic data capture (fEDC): Architecture and prototype.

In clinical research as well as patient care, structured documentation of findings is an important task. In many cases, this is achieved by means of electronic case report forms (eCRF) using corresponding information technology systems. To avoid double data entry, eCRF systems can be integrated with electronic health records (EHR). However, when researchers from different institutions collaborate in collecting data, they often use a single joint eCRF system on the Internet. In this case, integration with EHR systems is not possible in most cases due to information security and data protection restrictions. To overcome this shortcoming, we propose a novel architecture for a federated electronic data capture system (fEDC). Four key requirements were identified for fEDC: Definitions of forms have to be available in a reliable and controlled fashion, integration with electronic health record systems must be possible, patient data should be under full local control until they are explicitly transferred for joint analysis, and the system must support data sharing principles accepted by the scientific community for both data model and data captured. With our approach, sites participating in a joint study can run their own instance of an fEDC system that complies with local standards (such as being behind a network firewall) while also being able to benefit from using identical form definitions by sharing metadata in the Operational Data Model (ODM) format published by the Clinical Data Interchange Standards Consortium (CDISC) throughout the collaboration. The fEDC architecture was validated with a working open-source prototype at five German university hospitals. The fEDC architecture provides a novel approach with the potential to significantly improve collaborative data capture: Efforts for data entry are reduced and at the same time, data quality is increased since barriers for integrating with local electronic health record systems are lowered. Further, metadata are shared and patient privacy is ensured at a high level.

Noncancer-related Secondary Findings in a Cohort of 231 Children With Cancer and Their Parents.

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.

Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy: a prospective annotation study.

BACKGROUND: With Surgomics, we aim for personalized prediction of the patient's surgical outcome using machine-learning (ML) on multimodal intraoperative data to extract surgomic features as surgical process characteristics. As high-quality annotations by medical experts are crucial, but still a bottleneck, we prospectively investigate active learning (AL) to reduce annotation effort and present automatic recognition of surgomic features. METHODS: To establish a process for development of surgomic features, ten video-based features related to bleeding, as highly relevant intraoperative complication, were chosen. They comprise the amount of blood and smoke in the surgical field, six instruments, and two anatomic structures. Annotation of selected frames from robot-assisted minimally invasive esophagectomies was performed by at least three independent medical experts. To test whether AL reduces annotation effort, we performed a prospective annotation study comparing AL with equidistant sampling (EQS) for frame selection. Multiple Bayesian ResNet18 architectures were trained on a multicentric dataset, consisting of 22 videos from two centers. RESULTS: In total, 14,004 frames were tag annotated. A mean F1-score of 0.75 ± 0.16 was achieved for all features. The highest F1-score was achieved for the instruments (mean 0.80 ± 0.17). This result is also reflected in the inter-rater-agreement (1-rater-kappa > 0.82). Compared to EQS, AL showed better recognition results for the instruments with a significant difference in the McNemar test comparing correctness of predictions. Moreover, in contrast to EQS, AL selected more frames of the four less common instruments (1512 vs. 607 frames) and achieved higher F1-scores for common instruments while requiring less training frames. CONCLUSION: We presented ten surgomic features relevant for bleeding events in esophageal surgery automatically extracted from surgical video using ML. AL showed the potential to reduce annotation effort while keeping ML performance high for selected features. The source code and the trained models are published open source.

Design of a targeted next-generation DNA sequencing panel for pediatric T-cell lymphoblastic lymphoma to unravel biology and optimize treatment.

Low incidence and molecular heterogeneity of pediatric T-cell lymphoblastic lymphoma (T-LBL) require an international, large-scale effort to identify novel clinical biomarkers. The ongoing international clinical trial LBL2018 (NCT04043494) represents an ideal opportunity to implement a common analytic approach. Targeted next-generation sequencing is well-suited for this purpose; however, selection of relevant target genes for T-LBL remains subject of ongoing debates. Our group has recently designed and evaluated a first target panel of 80 candidate genes for T-LBL. The present study aimed at developing a novel optimized gene panel for large-scale application and to promote an international agreement on a common core panel. Small sequence variants obtained from our former study were systematically analyzed and classified with regards to pathogenic relevance, to prioritize candidate genes. Additional genes were curated from literature and online databases for a more comprehensive analysis of relevant functions and signaling pathways. The new target panel TGP-T-LBL entails 84 candidate genes which are key actors in NOTCH, PI3K-AKT, JAK-STAT, RAS signaling, epigenetic regulation, transcription, DNA repair, cell cycle regulation, and ribosomal function. From our former gene panel, 35 out of 80 candidate genes were selected for the novel panel. Forty-six out of 84 genes are currently being analyzed in the ongoing international trial LBL2018. Exploratory analysis of prognostic relevance on mutation-level suggested a potential association of PIK3CA variants c.1624G>A(p.Glu542Lys) and c.1633G>A(p.Glu545Lys) to occurrence of relapse, emphasizing particular relevance of mutation analysis in PI3K-AKT signaling. Our approach promotes comprehensive and clinically relevant mutational profiling of pediatric T-LBL.

Cogito: automated and generic comparison of annotated genomic intervals.

BACKGROUND: Genetic and epigenetic biological studies often combine different types of experiments and multiple conditions. While the corresponding raw and processed data are made available through specialized public databases, the processed files are usually limited to a specific research question. Hence, they are unsuitable for an unbiased, systematic overview of a complex dataset. However, possible combinations of different sample types and conditions grow exponentially with the amount of sample types and conditions. Therefore the risk to miss a correlation or to overrate an identified correlation should be mitigated in a complex dataset. Since reanalysis of a full study is rarely a viable option, new methods are needed to address these issues systematically, reliably, reproducibly and efficiently. RESULTS: Cogito "COmpare annotated Genomic Intervals TOol" provides a workflow for an unbiased, structured overview and systematic analysis of complex genomic datasets consisting of different data types (e.g. RNA-seq, ChIP-seq) and conditions. Cogito is able to visualize valuable key information of genomic or epigenomic interval-based data, thereby providing a straightforward analysis approach for comparing different conditions. It supports getting an unbiased impression of a dataset and developing an appropriate analysis strategy for it. In addition to a text-based report, Cogito offers a fully customizable report as a starting point for further in-depth investigation. CONCLUSIONS: Cogito implements a novel approach to facilitate high-level overview analyses of complex datasets, and offers additional insights into the data without the need for a full, time-consuming reanalysis. The R/Bioconductor package is freely available at https://bioconductor.org/packages/release/bioc/html/Cogito.html , a comprehensive documentation with detailed descriptions and reproducible examples is included.

Lmo2 expression defines tumor cell identity during T-cell leukemogenesis.

The impact of LMO2 expression on cell lineage decisions during T-cell leukemogenesis remains largely elusive. Using genetic lineage tracing, we have explored the potential of LMO2 in dictating a T-cell malignant phenotype. We first initiated LMO2 expression in hematopoietic stem/progenitor cells and maintained its expression in all hematopoietic cells. These mice develop exclusively aggressive human-like T-ALL In order to uncover a potential exclusive reprogramming effect of LMO2 in murine hematopoietic stem/progenitor cells, we next showed that transient LMO2 expression is sufficient for oncogenic function and induction of T-ALL The resulting T-ALLs lacked LMO2 and its target-gene expression, and histologically, transcriptionally, and genetically similar to human LMO2-driven T-ALL We next found that during T-ALL development, secondary genomic alterations take place within the thymus. However, the permissiveness for development of T-ALL seems to be associated with wider windows of differentiation than previously appreciated. Restricted Cre-mediated activation of Lmo2 at different stages of B-cell development induces systematically and unexpectedly T-ALL that closely resembled those of their natural counterparts. Together, these results provide a novel paradigm for the generation of tumor T cells through reprogramming in vivo and could be relevant to improve the response of T-ALL to current therapies.

Dexamethasone-mediated inhibition of Notch signalling blocks the interaction of leukaemia and mesenchymal stromal cells.

Acute myeloid leukaemia (AML) is a haematological malignancy characterized by a poor prognosis. Bone marrow mesenchymal stromal cells (BM MSCs) support leukaemic cells in preventing chemotherapy-induced apoptosis. This encouraged us to investigate leukaemia-BM niche-associated signalling and to identify signalling cascades supporting the interaction of leukaemic cells and BM MSC. Our study demonstrated functional differences between MSCs originating from leukaemic (AML MSCs) and healthy donors (HD MSCs). The direct interaction of leukaemic and AML MSCs was indispensable in influencing AML cell proliferation. We further identified an important role for Notch expression and its activation in AML MSCs contributing to the enhanced proliferation of AML cells. Supporting this observation, overexpression of the intracellular Notch domain (Notch ICN) in AML MSCs enhanced AML cells' proliferation. From a therapeutic point of view, dexamethasone treatment impeded Notch signalling in AML MSCs resulting in reduced AML cell proliferation. Concurrent with our data, Notch inhibitors had only a marginal effect on leukaemic cells alone but strongly influenced Notch signalling in AML MSCs and abrogated their cytoprotective function on AML cells. In vivo, dexamethasone treatment impeded Notch signalling in AML MSCs leading to a reduced number of AML MSCs and improved survival of leukaemic mice. In summary, targeting the interaction of leukaemic cells and AML MSCs using dexamethasone or Notch inhibitors might further improve treatment outcomes in AML patients.

Differential transcript usage analysis of bulk and single-cell RNA-seq data with DTUrtle.

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.

Site-specific methylation of 18S ribosomal RNA by SNORD42A is required for acute myeloid leukemia cell proliferation.

Noncoding RNAs, including small nucleolar RNAs (snoRNAs), play important roles in leukemogenesis, but the relevant mechanisms remain incompletely understood. We performed snoRNA-focused CRISPR-Cas9 knockout library screenings that targeted the entire snoRNAnome and corresponding host genes. The C/D box containing SNORD42A was identified as an essential modulator for acute myeloid leukemia (AML) cell survival and proliferation in multiple human leukemia cell lines. In line, SNORD42A was consistently expressed at higher levels in primary AML patient samples than in CD34+ progenitors, monocytes, and granulocytes. Functionally, knockout of SNORD42A reduced colony formation capability and inhibited proliferation. The SNORD42A acts as a C/D box snoRNA and directs 2'-O-methylation at uridine 116 of 18S ribosomal RNA (rRNA). Deletion of SNORD42A decreased 18S-U116 2'-O-methylation, which was associated with a specific decrease in the translation of ribosomal proteins. In line, the cell size of SNORD42A deletion carrying leukemia cells was decreased. Taken together, these findings establish that high-level expression of SNORD42A with concomitant U116 18S rRNA 2'-O-methylation is essential for leukemia cell growth and survival.

ELaPro, a LOINC-mapped core dataset for top laboratory procedures of eligibility screening for clinical trials.

BACKGROUND: Screening for eligible patients continues to pose a great challenge for many clinical trials. This has led to a rapidly growing interest in standardizing computable representations of eligibility criteria (EC) in order to develop tools that leverage data from electronic health record (EHR) systems. Although laboratory procedures (LP) represent a common entity of EC that is readily available and retrievable from EHR systems, there is a lack of interoperable data models for this entity of EC. A public, specialized data model that utilizes international, widely-adopted terminology for LP, e.g. Logical Observation Identifiers Names and Codes (LOINC®), is much needed to support automated screening tools. OBJECTIVE: The aim of this study is to establish a core dataset for LP most frequently requested to recruit patients for clinical trials using LOINC terminology. Employing such a core dataset could enhance the interface between study feasibility platforms and EHR systems and significantly improve automatic patient recruitment. METHODS: We used a semi-automated approach to analyze 10,516 screening forms from the Medical Data Models (MDM) portal's data repository that are pre-annotated with Unified Medical Language System (UMLS). An automated semantic analysis based on concept frequency is followed by an extensive manual expert review performed by physicians to analyze complex recruitment-relevant concepts not amenable to automatic approach. RESULTS: Based on analysis of 138,225 EC from 10,516 screening forms, 55 laboratory procedures represented 77.87% of all UMLS laboratory concept occurrences identified in the selected EC forms. We identified 26,413 unique UMLS concepts from 118 UMLS semantic types and covered the vast majority of Medical Subject Headings (MeSH) disease domains. CONCLUSIONS: Only a small set of common LP covers the majority of laboratory concepts in screening EC forms which supports the feasibility of establishing a focused core dataset for LP. We present ELaPro, a novel, LOINC-mapped, core dataset for the most frequent 55 LP requested in screening for clinical trials. ELaPro is available in multiple machine-readable data formats like CSV, ODM and HL7 FHIR. The extensive manual curation of this large number of free-text EC as well as the combining of UMLS and LOINC terminologies distinguishes this specialized dataset from previous relevant datasets in the literature.