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Uropathogenic Escherichia coli (UPEC) strains cause symptomatic urinary tract infections in humans whereas commensal-like E. coli strains in the urinary bladder cause long-term asymptomatic bacteriuria (ABU). We previously reported that UPEC and ABU strains differentially regulate key DNA methylation and histone acetylation components in the surrogate insect host Galleria mellonella to epigenetically modulate innate immunity-related gene expression, which in turn controls bacterial growth. In this follow-up study, we infected G. mellonella larvae with UPEC strain CFT073 or ABU strain 83972 to identify differences in the expression of microRNAs (miRNAs), a class of non-coding RNAs that regulate gene expression at the post-transcriptional level. Our small RNA sequencing analysis showed that UPEC and ABU infections caused significant changes in the abundance of miRNAs in the larvae, and highlighted the differential expression of 147 conserved miRNAs and 95 novel miRNA candidates. We annotated the G. mellonella genome sequence to investigate the miRNA-regulated expression of genes encoding antimicrobial peptides, signaling proteins, and enzymatic regulators of DNA methylation and histone acetylation in infected larvae. Our results indicate that miRNAs play a role in the epigenetic reprograming of innate immunity in G. mellonella larvae to distinguish between pathogenic and commensal strains of E. coli. [ABSTRACT FROM AUTHOR]

Introduction: Small fiber neuropathies (SFN) are associated with a reduction in quality of life. In adults, epidermal nerve fiber density (END) analysis is recommended for the diagnosis of SFN. In children, END assessment is not often performed. We analyzed small nerve fiber innervation to elucidate the potential diagnostic role of skin biopsies in young patients with pain.Methods: Epidermal nerve fiber density and sudomotor neurite density (SND) were assessed in skin biopsies from 26 patients aged 7 to 20 years (15 female patients) with unexplained chronic pain. The results were compared with clinical data.Results: Epidermal nerve fiber density was abnormal in 50% and borderline in 35% of patients. An underlying medical condition was found in 42% of patients, including metabolic, autoimmune, and genetic disorders.Discussion: Reduction of epidermal nerve fibers can be associated with treatable conditions. Therefore, the analysis of END in children with pain may help to uncover a possible cause and guide potential treatment options. [ABSTRACT FROM AUTHOR]

This article explores the distribution and composition of the ferritin complex in the adult brain and its connection to neuroferritinopathy (NF), a type of neurodegeneration with brain iron accumulation (NBIA). NF is characterized by symptoms like movement disturbances and tremors. The study identifies a novel variant in the ferritin heavy chain gene (FTH1) that is associated with NF and provides insights into the role of ferritin in both healthy and diseased brains. The authors, a diverse group of researchers, present their findings in a respectful and culturally sensitive manner, making this report a valuable resource for library patrons researching this topic. [Extracted from the article]

Background: MicroRNAs carry out post-transcriptional gene regulation in animals by binding to the 3' untranslated regions of mRNAs, causing their degradation or translational repression. MicroRNAs influence many biological functions, and dysregulation can therefore disrupt development or even cause death. High-throughput sequencing and the mining of animal small RNA data has shown that microRNA genes can yield differentially expressed isoforms, known as isomiRs. Such isoforms are particularly relevant during early development, and the extension or truncation of the 5' end can change the profile of mRNA targets compared to the original mature sequence. We used the publicly available small RNA dataset of the model beetle Tribolium castaneum to create the first comparative isomiRome of early developmental stages in this species. Standard microRNA analysis software does not specifically account for isomiRs. We therefore carried out the first comparative evaluation of the specialized tools isomiRID, isomiR-SEA and miraligner, which can be downloaded for local use and can handle next generation sequencing data. Results: We compared the performance of isomiRID, isomiR-SEA and miraligner using simulated Illumina HiSeq2000 and MiSeq data to test the impact of technical errors. We also created artificial microRNA isoforms to determine the effect of biological variants on the performance of each algorithm. We found that isomiRID achieved the best true positive rate among the three algorithms, but only accounted for one mutation at a time. In contrast, miraligner reported all variations simultaneously but with 78% sensitivity, yielding isomiRs with 3' or 5' deletions. Finally, isomiRSEA achieved a sensitivity of 25-33% when the seed region was mutated or partly deleted, but was the only tool that could accommodate more than one mismatch. Using the best tool, we performed a complete isomiRome analysis of the early developmental stages of T. castaneum. Conclusions: Our findings will help researchers to select the most suitable isomiR analysis tools for their experiments. We confirmed the dynamic expression of 3' non-template isomiRs and expanded the isomiRome by all known isomiR modifications during the early development of T. castaneum. [ABSTRACT FROM AUTHOR]

The current state‐of‐the‐art analysis of central nervous system (CNS) tumors through DNA methylation profiling relies on the tumor classifier developed by Capper and colleagues, which centrally harnesses DNA methylation data provided by users. Here, we present a distributed‐computing‐based approach for CNS tumor classification that achieves a comparable performance to centralized systems while safeguarding privacy. We utilize the t‐distributed neighborhood embedding (t‐SNE) model for dimensionality reduction and visualization of tumor classification results in two‐dimensional graphs in a distributed approach across multiple sites (DistSNE). DistSNE provides an intuitive web interface (https://gin-tsne.med.uni-giessen.de) for user‐friendly local data management and federated methylome‐based tumor classification calculations for multiple collaborators in a DataSHIELD environment. The freely accessible web interface supports convenient data upload, result review, and summary report generation. Importantly, increasing sample size as achieved through distributed access to additional datasets allows DistSNE to improve cluster analysis and enhance predictive power. Collectively, DistSNE enables a simple and fast classification of CNS tumors using large‐scale methylation data from distributed sources, while maintaining the privacy and allowing easy and flexible network expansion to other institutes. This approach holds great potential for advancing human brain tumor classification and fostering collaborative precision medicine in neuro‐oncology. [ABSTRACT FROM AUTHOR]

Simple Summary: Meningioma is the most common primary intracranial tumor. DNA methylation-based subtyping, while highly useful for diagnosis and treatment planning, is costly and not widely available. Therefore, the identification of methylation classes based on histological sections would be highly beneficial as it could greatly support and accelerate diagnostic and treatment decisions. We developed and systematically evaluated an AI framework to perform the classification of the most prevalent methylation subtypes based on histological sections. The model achieved a balanced accuracy of 0.870 for benign-1 vs benign-2 and 0.749 for benign-1 vs. intermediate-A in a narrow validation set. Combined with the network's assessed focus on key tumor regions these results provide a promising proof-of-concept of such an AI-driven classification approach in precision medicine. Convolutional neural networks (CNNs) are becoming increasingly valuable tools for advanced computational histopathology, promoting precision medicine through exceptional visual decoding abilities. Meningiomas, the most prevalent primary intracranial tumors, necessitate accurate grading and classification for informed clinical decision-making. Recently, DNA methylation-based molecular classification of meningiomas has proven to be more effective in predicting tumor recurrence than traditional histopathological methods. However, DNA methylation profiling is expensive, labor-intensive, and not widely accessible. Consequently, a digital histology-based prediction of DNA methylation classes would be advantageous, complementing molecular classification. In this study, we developed and rigorously assessed an attention-based multiple-instance deep neural network for predicting meningioma methylation classes using tumor methylome data from 142 (+51) patients and corresponding hematoxylin-eosin-stained histological sections. Pairwise analysis of sample cohorts from three meningioma methylation classes demonstrated high accuracy in two combinations. The performance of our approach was validated using an independent set of 51 meningioma patient samples. Importantly, attention map visualization revealed that the algorithm primarily focuses on tumor regions deemed significant by neuropathologists, offering insights into the decision-making process of the CNN. Our findings highlight the capacity of CNNs to effectively harness phenotypic information from histological sections through computerized images for precision medicine. Notably, this study is the first demonstration of predicting clinically relevant DNA methylome information using computer vision applied to standard histopathology. The introduced AI framework holds great potential in supporting, augmenting, and expediting meningioma classification in the future. [ABSTRACT FROM AUTHOR]

Overview of the fully automated AI-based valve tracking system used for deriving several mitral and tricuspid valve-related motion parameters. [Display omitted] In cardiac MRI, valve motion parameters can be useful for the diagnosis of cardiac dysfunction. In this study, a fully automated AI-based valve tracking system was developed and evaluated on 2- or 4-chamber view cine series on a large cardiac MR dataset. Automatically derived motion parameters include atrioventricular plane displacement (AVPD), velocities (AVPV), mitral or tricuspid annular plane systolic excursion (MAPSE, TAPSE), or longitudinal shortening (LS). Two sequential neural networks with an intermediate processing step are applied to localize the target and track the landmarks throughout the cardiac cycle. Initially, a localisation network is used to perform heatmap regression of the target landmarks, such as mitral, tricuspid valve annulus as well as apex points. Then, a registration network is applied to track these landmarks using deformation fields. Based on these outputs, motion parameters were derived. The accuracy of the system resulted in deviations of 1.44 ± 1.32 mm, 1.51 ± 1.46 cm/s, 2.21 ± 1.81 mm, 2.40 ± 1.97 mm, 2.50 ± 2.06 mm for AVPD, AVPV, MAPSE, TAPSE and LS, respectively. Application on a large patient database (N = 5289) revealed a mean MAPSE and LS of 9.5 ± 3.0 mm and 15.9 ± 3.9 % on 2-chamber and 4-chamber views, respectively. A mean TAPSE and LS of 13.4 ± 4.7 mm and 21.4 ± 6.9 % was measured. The results demonstrate the versatility of the proposed system for automatic extraction of various valve-related motion parameters. [ABSTRACT FROM AUTHOR]

Summary In the era of next generation sequencing and beyond, the Sanger technique is still widely used for variant verification of inconclusive or ambiguous high-throughput sequencing results or as a low-cost molecular genetical analysis tool for single targets in many fields of study. Many analysis steps need time-consuming manual intervention. Therefore, we present here a pipeline-capable high-throughput solution with an optional Shiny web interface, that provides a binary mutation decision of hotspots together with plotted chromatograms including annotations via flat files. Availability and implementation SangeR is freely available at https://github.com/Neuropathology-Giessen/SangeR and https://hub.docker.com/repository/docker/kaischmid/sange%5fr Contact Kai.Schmid@patho.med.uni-giessen.de or Daniel.Amsel@patho.med.uni-giessen.de Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

tRNA-NTs (tRNA nucleotidyltransferases) are required for the maturation or repair of tRNAs by ensuring that they have an intact cytidine-cytidine-adenosine sequence at their 3-termini. Therefore this enzymatic activity is found in all cellular compartments, namely the nucleus, cytoplasm, plastids and mitochondria, in which tRNA synthesis or translation occurs. A single gene codes for tRNA-NT in plants, suggesting a complex targeting mechanism. Consistent with this, distinct signals have been proposed for plastidic, mitochondrial and nuclear targeting. Our previous research has shown that in addition to N-terminal targeting information, the mature domain of the protein itself modifies targeting to mitochondria and plastids. This suggests the existence of an as yet unknown determinate for the distribution of dual-targeted proteins between these two organelles. In the present study, we explore the enzymatic and physicochemical properties of tRNA-NT variants to correlate the properties of the enzyme with the intracellular distribution of the protein. We show that alteration of tRNA-NT stability influences its intracellular distribution due to variations in organelle import capacities. Hence the fate of the protein is determined not only by the transit peptide sequence, but also by the physicochemical properties of the mature protein. [ABSTRACT FROM AUTHOR]