Neuropilin2 in Mesenchymal Stromal Cells as a Potential Novel Therapeutic Target in Myelofibrosis.

Bone marrow fibrosis in myeloproliferative neoplasm (MPN), myelodysplastic syndromes (MDS), MPN/MDS overlap syndromes and acute myeloid leukemia (AML) is associated with poor prognosis and early treatment failure. Myelofibrosis (MF) is accompanied by reprogramming of multipotent bone marrow mesenchymal stromal cells (MSC) into osteoid and fiber-producing stromal cells. We demonstrate NRP2 and osteolineage marker NCAM1 (neural cell adhesion molecule 1) expression within the endosteal niche in normal bone marrow and aberrantly in MPN, MDS MPN/MDS overlap syndromes and AML (n = 99), as assessed by immunohistochemistry. Increased and diffuse expression in mesenchymal stromal cells and osteoblasts correlates with high MF grade in MPN (p < 0.05 for NRP2 and NCAM1). Single cell RNA sequencing (scRNAseq) re-analysis demonstrated NRP2 expression in endothelial cells and partial co-expression of NRP2 and NCAM1 in normal MSC and osteoblasts. Potential ligands included transforming growth factor ß1 (TGFB1) from osteoblasts and megakaryocytes. Murine ThPO and JAK2V617F myelofibrosis models showed co-expression of Nrp2 and Ncam1 in osteolineage cells, while fibrosis-promoting MSC only express Nrp2. In vitro experiments with MC3T3-E1 pre-osteoblasts and analysis of Nrp2-/- mouse femurs suggest that Nrp2 is functionally involved in osteogenesis. In summary, NRP2 represents a potential novel druggable target in patients with myelofibrosis.

Machine Learning for Decision-Support in Acute Abdominal Pain - Proof of Concept and Central Considerations.

Acute abdominal pain is a common presenting symptom in the emergency department and represents heterogeneous causes and diagnoses. There is often a decision to be made regarding emergency surgical care. Machine learning (ML) could be used here as a decision-support and relieve the time and personnel resource shortage.Patients with acute abdominal pain presenting to the Department of Surgery at Bonn University Hospital in 2020 and 2021 were retrospectively analyzed. Clinical parameters as well as laboratory values were used as predictors. After randomly splitting into a training and test data set (ratio 80 to 20), three ML algorithms were comparatively trained and validated. The entire procedure was repeated 20 times.A total of 1357 patients were identified and included in the analysis, with one in five (n = 276, 20.3%) requiring emergency abdominal surgery within 24 hours. Patients operated on were more likely to be male (p = 0.026), older (p = 0.006), had more gastrointestinal symptoms (nausea: p < 0.001, vomiting p < 0.001) as well as a more recent onset of pain (p < 0.001). Tenderness (p < 0.001) and guarding (p < 0.001) were more common in surgically treated patients and blood analyses showed increased inflammation levels (white blood cell count: p < 0.001, CRP: p < 0.001) and onset of organ dysfunction (creatinine: p < 0.014, quick p < 0.001). Of the three trained algorithms, the tree-based methods (h2o random forest and cforest) showed the best performance. The algorithms classified patients, i.e., predicted surgery, with a median AUC ROC of 0.81 and 0.79 and AUC PRC of 0.56 in test sets.A proof-of-concept was achieved with the development of an ML model for predicting timely surgical therapy for acute abdomen. The ML algorithm can be a valuable tool in decision-making. Especially in the context of heavily used medical resources, the algorithm can help to use these scarce resources more effectively. Technological progress, especially regarding artificial intelligence, increasingly enables evidence-based approaches in surgery but requires a strictly interdisciplinary approach. In the future, the use and handling of ML should be integrated into surgical training.

Induction of Interferon-Stimulated Genes Correlates with Reduced Growth of Influenza A Virus in Lungs after RIG-I Agonist Treatment of Ferrets.

Intracellular RIG-I receptors represent key innate sensors of RNA virus infection, and RIG-I activation results in the induction of hundreds of host effector genes, including interferon-stimulated genes (ISGs). Synthetic RNA agonists targeting RIG-I have shown promise as antivirals against a broad spectrum of viruses, including influenza A virus (IAV), in both in vitro and mouse models of infection. Herein, we demonstrate that treatment of a ferret airway epithelial (FRL) cell line with a RIG-I agonist rapidly and potently induced expression of a broad range of ISGs and resulted in potent inhibition of growth of different IAV strains. In ferrets, a single intravenous injection of RIG-I agonist was associated with upregulated ISG expression in peripheral blood mononuclear cells and lung tissue, but not in nasal tissues. In a ferret model of viral contact transmission, a single treatment of recipient animals 24 h prior to cohousing with IAV-infected donors did not reduce virus transmission and shedding but did result in reduced lung virus titers 6 days after treatment. A single treatment of the IAV-infected donor animals also resulted in reduced virus titers in the lungs 2 days later. Thus, a single intravenous treatment with RIG-I agonist prior to infection or to ferrets with an established IAV infection can reduce virus growth in the lungs. These findings support further development of RIG-I agonists as effective antiviral treatments to limit the impact of IAV infections, particularly in reducing virus replication in the lower airways. IMPORTANCE RIG-I agonists have shown potential as broad-spectrum antivirals in vitro and in mouse models of infection. However, their antiviral potential has not been reported in outbred animals such as ferrets, which are widely regarded as the gold standard small animal model for human IAV infections. Herein, we demonstrate that RIG-I agonist treatment of a ferret airway cell line resulted in ISG induction and inhibition of a broad range of human influenza viruses. A single intravenous treatment of ferrets also resulted in systemic induction of ISGs, including in lung tissue, and when delivered to animals prior to IAV exposure or to animals with established IAV infection treatment resulted in reduced virus replication in the lungs. These data demonstrate the effectiveness of single RIG-I treatment against IAV in the ferret model and highlight the importance of future studies to optimize treatment regimens and delivery routes to maximize their ability to ameliorate IAV infections.

Reconstruction of the origin of the first major SARS-CoV-2 outbreak in Germany.

The first major COVID-19 outbreak in Germany occurred in Heinsberg in February 2020 with 388 officially reported cases. Unexpectedly, the first outbreak happened in a small town with little to no travelers. We used phylogenetic analyses to investigate the origin and spread of the virus in this outbreak. We sequenced 90 (23%) SARS-CoV-2 genomes from the 388 reported cases including the samples from the first documented cases. Phylogenetic analyses of these sequences revealed mainly two circulating strains with 74 samples assigned to lineage B.3 and 6 samples assigned to lineage B.1. Lineage B.3 was introduced first and probably caused the initial spread. Using phylogenetic analysis tools, we were able to identify closely related strains in France and hypothesized the possible introduction from France.

EPIGENE: genome-wide transcription unit annotation using a multivariate probabilistic model of histone modifications.

BACKGROUND: Understanding the transcriptome is critical for explaining the functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription units (TUs) use RNA-seq that, however, require large quantities of mRNA rendering the identification of inherently unstable TUs, e.g. miRNA precursors, difficult. This problem can be alleviated by chromatin-based approaches due to a correlation between histone modifications and transcription. RESULTS: Here, we introduce EPIGENE, a novel chromatin segmentation method for the identification of active TUs using transcription-associated histone modifications. Unlike the existing chromatin segmentation approaches, EPIGENE uses a constrained, semi-supervised multivariate hidden Markov model (HMM) that models the observed combination of histone modifications using a product of independent Bernoulli random variables, to identify active TUs. Our results show that EPIGENE can identify genome-wide TUs in an unbiased manner. EPIGENE-predicted TUs show an enrichment of RNA Polymerase II at the transcription start site and in gene body indicating that they are indeed transcribed. Comprehensive validation using existing annotations revealed that 93% of EPIGENE TUs can be explained by existing gene annotations and 5% of EPIGENE TUs in HepG2 can be explained by microRNA annotations. EPIGENE outperformed the existing RNA-seq-based approaches in TU prediction precision across human cell lines. Finally, we identified 232 novel TUs in K562 and 43 novel cell-specific TUs all of which were supported by RNA Polymerase II ChIP-seq and Nascent RNA-seq data. CONCLUSION: We demonstrate the applicability of EPIGENE to identify genome-wide active TUs and to provide valuable information about unannotated TUs. EPIGENE is an open-source method and is freely available at: https://github.com/imbbLab/EPIGENE.