[Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative]. / Grundlagen für die wissenschaftliche Nutzung umfangreicher Versorgungsdaten in Deutschland ­ Ergebnisse der AG Data Sharing der Medizininformatik-Initiative.

Healthcare data are an important resource in applied medical research. They are available multicentrically. However, it remains a challenge to enable standardized data exchange processes between federal states and their individual laws and regulations. The Medical Informatics Initiative (MII) was founded in 2016 to implement processes that enable cross-clinic access to healthcare data in Germany. Several working groups (WGs) have been set up to coordinate standardized data structures (WG Interoperability), patient information and declarations of consent (WG Consent), and regulations on data exchange (WG Data Sharing). Here we present the most important results of the Data Sharing working group, which include agreed terms of use, legal regulations, and data access processes. They are already being implemented by the established Data Integration Centers (DIZ) and Use and Access Committees (UACs). We describe the services that are necessary to provide researchers with standardized data access. They are implemented with the Research Data Portal for Health, among others. Since the pilot phase, the processes of 385 active researchers have been used on this basis, which, as of April 2024, has resulted in 19 registered projects and 31 submitted research applications.

Towards a National Portal for Medical Research Data (FDPG): Vision, Status, and Lessons Learned.

Harmonizing medical data sharing frameworks is challenging. Data collection and formats follow local solutions in individual hospitals; thus, interoperability is not guaranteed. The German Medical Informatics Initiative (MII) aims to provide a Germany-wide, federated, large-scale data sharing network. In the last five years, numerous efforts have been successfully completed to implement the regulatory framework and software components for securely interacting with decentralized and centralized data sharing processes. 31 German university hospitals have today established local data integration centers that are connected to the central German Portal for Medical Research Data (FDPG). Here, we present milestones and associated major achievements of various MII working groups and subprojects which led to the current status. Further, we describe major obstacles and the lessons learned during its routine application in the last six months.

Aligning Biobanks and Data Integration Centers Efficiently (ABIDE_MI).

ABIDE_MI is a complementary funded 18 months project within the German Medical Informatics Initiative (MII), which aims to align IT infrastructures and regulatory/governance structures between biobanks/biobanking IT and the MII data integration centres (DIC) at German university hospitals. A major task in 2021 was the systematic collection of all documents describing rules, as well as proposal/contract templates for data and biosample use and access at each of the participating 24 university hospitals and their comparison with MII-wide consented data sharing principles, documents and governance structures. This comparison revealed large heterogeneity across the ABIDE_MI sites and further, redundant structures/regulations currently established at the German university hospitals. A second task was the design and stepwise development of an IT network infrastructure with central components (data and biosample query portal) and decentralized standardized FHIR servers to capture the standardized FHIR-based core data set modules (resources) defined within the MII working group "Interoperability". Subsequent steps in the project are the harmonization of the data and biosample sharing governance/regulation frameworks at each ABIDE_MI site, creating synergies for the research infrastructures at the German university hospitals and to link those resources to the German Portal for Medical Research Data and with the BBMRI-ERIC Directory and Negotiator tools.

Myeloid leukemia with transdifferentiation plasticity developing from T-cell progenitors.

Unfavorable patient survival coincides with lineage plasticity observed in human acute leukemias. These cases are assumed to arise from hematopoietic stem cells, which have stable multipotent differentiation potential. However, here we report that plasticity in leukemia can result from instable lineage identity states inherited from differentiating progenitor cells. Using mice with enhanced c-Myc expression, we show, at the single-cell level, that T-lymphoid progenitors retain broad malignant lineage potential with a high capacity to differentiate into myeloid leukemia. These T-cell-derived myeloid blasts retain expression of a defined set of T-cell transcription factors, creating a lymphoid epigenetic memory that confers growth and propagates myeloid/T-lymphoid plasticity. Based on these characteristics, we identified a correlating human leukemia cohort and revealed targeting of Jak2/Stat3 signaling as a therapeutic possibility. Collectively, our study suggests the thymus as a source for myeloid leukemia and proposes leukemic plasticity as a driving mechanism. Moreover, our results reveal a pathway-directed therapy option against thymus-derived myeloid leukemogenesis and propose a model in which dynamic progenitor differentiation states shape unique neoplastic identities and therapy responses.

mBISON: Finding miRNA target over-representation in gene lists from ChIP-sequencing data.

BACKGROUND: Over-representation of predicted miRNA targets in sets of genes regulated by a given transcription factor (e.g. as defined by ChIP-sequencing experiments) helps to identify biologically relevant miRNA targets and is useful to get insight into post-transcriptional regulation. FINDINGS: To facilitate the application of this approach we have created the mBISON web-application. mBISON calculates the significance of over-representation of miRNA targets in a given non-ranked gene set. The gene set can be specified either by a list of genes or by one or more ChIP-seq datasets followed by a user-defined peak-gene association procedure. mBISON is based on predictions from TargetScan and uses a randomization step to calculate False-Discovery-Rates for each miRNA, including a correction for gene set specific properties such as 3'UTR length. The tool can be accessed from the following web-resource: http://cbdm.mdc-berlin.de/~mgebhardt/cgi-bin/mbison/home . CONCLUSION: mBISON is a web-application that helps to extract functional information about miRNAs from gene lists, which is in contrast to comparable applications easy to use by everyone and can be applied on ChIP-seq data directly.

Similarity in targets with REST points to neural and glioblastoma related miRNAs.

There are groups of genes that need coordinated repression in multiple contexts, for example if they code for proteins that work together in a pathway or in a protein complex. Redundancy of biological regulatory networks implies that such coordinated repression might occur at both the pre- and post-transcriptional level, though not necessarily simultaneously or under the same conditions. Here, we propose that such redundancy in the global regulatory network can be detected by the overlap between the putative targets of a transcriptional repressor, as identified by a ChIP-seq experiment, and predicted targets of a microRNA (miRNA). To test this hypothesis, we used publicly available ChIP-seq data of the neural transcriptional repressor RE1 silencing transcription factor (REST) from 15 different cell samples. We found 20 miRNAs, each of which shares a significant amount of predicted targets with REST. The set of predicted associations between these 20 miRNAs and the overlapping REST targets is enriched in known miRNA targets. Many of the detected miRNAs have functions related to neural identity and glioblastoma, which could be expected from their overlap in targets with REST. We propose that the integration of experimentally determined transcription factor binding sites with miRNA-target predictions provides functional information on miRNAs.

PU.1 level-directed chromatin structure remodeling at the Irf8 gene drives dendritic cell commitment.

Dendritic cells (DCs) are essential regulators of immune responses; however, transcriptional mechanisms that establish DC lineage commitment are poorly defined. Here, we report that the PU.1 transcription factor induces specific remodeling of the higher-order chromatin structure at the interferon regulatory factor 8 (Irf8) gene to initiate DC fate choice. An Irf8 reporter mouse enabled us to pinpoint an initial progenitor stage at which DCs separate from other myeloid lineages in the bone marrow. In the absence of Irf8, this progenitor undergoes DC-to-neutrophil reprogramming, indicating that DC commitment requires an active, Irf8-dependent escape from alternative myeloid lineage potential. Mechanistically, myeloid Irf8 expression depends on high PU.1 levels, resulting in local chromosomal looping and activation of a lineage- and developmental-stage-specific cis-enhancer. These data delineate PU.1 as a concentration-dependent rheostat of myeloid lineage selection by controlling long-distance contacts between regulatory elements and suggest that specific higher-order chromatin remodeling at the Irf8 gene determines DC differentiation.