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Biology subjects

Seep, L.

Publications and source records attributed to Seep, L..

4 recordsLinked to original sources

HDL-associated phosphatidylserine blunts myeloid activation and protects from atherosclerosis

Background and aimsLipids play a critical role in atherosclerosis. Low-density lipoprotein (LDL)-cholesterol and certain lipid classes like sphingomyelins are associated with inflammation and poor cardiovascular outcomes. Phosphatidylserine (PS), on the other hand, is a negatively charged anti-inflammatory phospholipid class involved in efferocytosis. In this study, we sought to investigate its anti-atherosclerotic properties through a combination of complementary human lipidomics analyses, in vitro assays and in vivo experiments in Apoe-/- mice. MethodsHuman lipidomics studies were performed on the 300OB cohort comprising 300 obese and overweight individuals at risk of cardiovascular disease. In vitro assays were carried out using human monocytes and macrophages, and in vivo experiments included histopathological, immunophenotyping and single-cell transcriptomic analyses. ResultsIn humans, we identified PS as an anti-inflammatory and atheroprotective biomarker. Hence, we developed a high-density lipoprotein (HDL)-like formulation enriched in PS to exploit its properties in a targeted fashion in mice. In vitro, this formulation potently inhibited inflammatory cytokine production on human myeloid cells. Our in-depth in vivo experiments provided evidence of the formulations potent plaque-stabilizing and anti-inflammatory actions. These effects were mediated by a shift in the monocyte/macrophage compartment toward homeostatic/repairing phenotypes. ConclusionsCollectively, our results demonstrate that HDL-associated PS potently suppresses inflammation and atheroprogression, and holds promise as a viable approach to improve immunomodulatory therapies.

immunology↗

cOmicsArt - a customizable Omics Analysis and reporting tool

MotivationThe availability of bulk-omic data is steadily increasing, necessitating collaborative efforts between experimental and computational researchers. While software tools with graphical user interfaces (GUIs) enable rapid and interactive data assessment, they are limited to pre-implemented methods, often requiring transitions to custom code for further adjustments. However, most available tools lack GUI-independent reproducibility such as direct integration with R, resulting in very limited support for transition. ResultsWe introduce the customizable Omics Analysis and reporting tool - cOmicsArt. cOmicsArt aims to enhance collaboration through integration of GUI-based analysis with R. The GUI allows researchers to perform user-friendly exploratory and statistical analyses with interactive visualizations and automatic documentation. Downloadable R scripts and results ensure reproducibility and seamless integration with R, supporting both novice and experienced programmers by enabling easy customizations and serving as a foundation for more advanced analyses. This versatility also allows for usage in educational settings guiding students from GUI-based analysis to R Code. AvailabilitycOmicsArt is freely available at https://shiny.iaas.uni-bonn.de/cOmicsArt/ User documentation is available at https://icb-dcm.github.io/cOmicsArt/ Source code is available on GitHub https://github.com/ICB-DCM/cOmicsArt A docker image can be retrieved from https://hub.docker.com/r/pauljonasjost/comicsart/tags A snapshot upon publication can be found on Zenodo: https://zenodo.org/records/13740904 A screen recording of cOmicsArt is available at: https://www.youtube.com/watch?v=pTGjtIYQOakp Contactjan.hasenauer@uni-bonn.de Supplementary informationSupplementary data are available online.

bioinformatics↗

From Planning Stage To FAIR Data: A Practical Metadatasheet For Biomedical Scientists

Datasets consist of measurement data and metadata. Metadata provides context, essential for understanding and (re-)using data. Various metadata standards exist for different methods, systems and contexts. However, relevant information resides at differing stages across the data-lifecycle. Often, this information is defined and standardized only at publication stage, which can lead to data loss and workload increase. In this study, we developed Metadatasheet, a metadata standard based on interviews with members of two biomedical consortia and systematic screening of data repositories. It aligns with the data-lifecycle allowing synchronous metadata recording within Microsoft Excel, a widespread data recording software. Additionally, we provide an implementation, the Metadata Workbook, that offers user-friendly features like automation, dynamic adaption, metadata integrity checks, and export options for various metadata standards. By design and due to its extensive documentation, the proposed metadata standard simplifies recording and structuring of metadata for biomedical scientists, promoting practicality and convenience in data management. This framework can accelerate scientific progress by enhancing collaboration and knowledge transfer throughout the intermediate steps of data creation.

bioinformatics↗

Diagnostic value of blood gene expression-based classifiers as exemplified for acute myeloid leukemia

ABSTRACTAcute Myeloid Leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. Despite nearly two decades of promising results using gene expression profiling, international recommendations for diagnosis and differential diagnosis of AML remain based on classical approaches including assessment of morphology, immunophenotyping, cytochemistry, and cytogenetics. Concerns about the translation of whole transcriptome profiling include the robustness of derived predictors when taking into account factors such as study- and site-specific effects and whether achievable levels of accuracy are sufficient for practical use. In the present study, we sought to shed light on these issues via a large-scale analysis using machine learning methods applied to a total of 12,029 samples from 105 different studies. Taking advantage of the breadth of data and the now much improved understanding of high-dimensional modeling, we show that AML can be predicted with high accuracy. High-dimensional approaches - in which multivariate signatures are learned directly from genome-wide data with no prior biological knowledge - are highly effective and robust. We explore also the relationship between predictive signatures, differential expression and known AML-related genes. Taken together, our results support the notion that transcriptome assessment could be used as part of an integrated genomic approach in cancer diagnosis and treatment to be implemented early on for diagnosis and differential diagnosis of AML.\n\nOne Sentence SummaryBlood gene expression data and machine learning were used to develop robust and accurate classifiers for diagnosis and differential diagnosis of acute myeloid leukemia based on analysis of more than 12,000 samples derived from more than 100 individual studies

genomics↗