Background: Plant meristems are structured organs consisting of distinct layers of stem cells, which differentiate into new plant tissue. Mutations in meristematic layers can propagate into large sectors of the plant. However, the characteristics of meristematic mutations remain unclear, limiting our understanding of the genetic basis of somaclonal phenotypic variation. Results: Here, we analyse the frequency and distribution of somatic mutations in an apricot tree. We separately sequence the epidermis (developing from meristem layer 1) and the flesh (developing from meristem layer 2) of several fruits sampled across the entire tree. We find that most somatic mutations (> 90%) are specific to individual layers. Interestingly, layer 1 shows a higher mutation load than layer 2, implying different mutational dynamics between the layers. The distribution of somatic mutations follows the branching of the tree. This suggests that somatic mutations are propagated to developing branches through axillary meristems. In turn, this leads us to the unexpected observation that the genomes of layer 1 of distant branches are more similar to each other than to the genomes of layer 2 of the same branches. Finally, using single-cell RNA sequencing, we demonstrate that layer-specific mutations were only transcribed in the cells of the respective layers and can form the genetic basis of somaclonal phenotypic variation. Conclusions: Here, we analyse the frequency and distribution of somatic mutations with meristematic origin. Our observations on the layer specificity of somatic mutations outline how they are distributed, how they propagate, and how they can impact clonally propagated crops.

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.

Plant meristems consist of three distinct stem cells layers. The identities of these layers are maintained in developing organs. Somatic mutations in the meristem thus propagate only into the respective layers of the differentiated organs. We analysed the genomes of individual cell layers in an apricot tree and found an unexpectedly high mutation load, where >90% of the somatic mutations were layer-specific. The layers had similar mutation spectra but layer 1 (epidermis) had substantially more mutations than layer 2 (mesocarp). Most somatic mutations were shared between fruits and neighbouring leaves corroborating their meristematic origin. Single-cell transcriptomics revealed their layer-specific effects forming the basis for layer-specific phenotypes. Our work unveils the hidden abundance of layer-specific meristematic mutations and provides insights into their identification and understanding. One sentence summary Somatic mutations in fruit trees occur in specific cell layers.

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 β1 (TGFB1) from osteoblasts and megakaryocytes. Murine ThPO and JAK2^V617F 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.

Background: Understanding transcriptome is critical for explaining functional as well as regulatory roles of genomic regions. Current methods for the identification of transcription unit (TU) uses RNA-seq which, however, requires large quantities of mRNA limiting the identification of inherently unstable TUs e.g. for miRNA precursors. This problem can be resolved 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 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 unbiasedly. EPIGENE predicted TUs showed an enrichment of RNA Polymerase II in transcription start site and gene body indicating that they have been transcribed. Comprehensive validation with 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 outperforms existing RNA-Seq based approaches in TU prediction precision across human cell lines. Finally, we identify 381 novel TUs in K562 and 43 novel cell-specific TUs all of which are supported by RNA Polymerase II data.Conclusions: We demonstrate the applicability of HMM to identify genome-wide active TUs and provides valuable information about unannotated TUs. EPIGENE is an open-source method and is freely available at:https://github.com/imbeLab/EPIGENE.