Biology subjects

Ewing, E.

Publications and source records attributed to Ewing, E..

7 recordsLinked to original sources

bioRxiv2025

Systematic comparison of dCas9-based DNA methylation epimodifiers over time indicates efficient on-target and widespread off-target effects

CRISPR/dCas9-based epigenome editing systems, including DNA methylation epimodifiers, have greatly advanced molecular functional studies revolutionizing their precision and applicability. Despite their promise, challenges such as the magnitude and stability of the on-target editing and unwanted off-target effects underscore the need for improved tool characterization and design. We systematically compared specific targeting of the BACH2 gene promoter and genome-wide off-target effects of available and novel dCas9-based DNA methylation editing tools over time. We demonstrate that multimerization of the catalytic domain of DNA methyltransferase 3A enhances editing potency but also induces widespread, early methylation deposition at low-to-medium methylated promoter-related regions with specific gRNAs and, interestingly, also with non-targeting gRNAs. A small fraction of the methylation changes associated with transcriptional dysregulation and mapped predominantly to bivalent chromatin associating both with transcriptional repression and activation. Additionally, specific non-targeting control gRNA caused pervasive and long-lasting methylation-independent transcriptional alterations particularly in genes linked to RNA and energy metabolism. CRISPRoff emerged as the most efficient tool for stable targeting of the BACH2 promoter, with fewer and less stable off-target effects compared to other epimodifiers but with persistent transcriptome alterations. Our findings highlight the delicate balance between potency and specificity of epigenome editing and provide critical insights into the design and application of future tools to improve their precision and minimize unintended consequences.

molecular biology↗

bioRxiv2024

GeneSetCluster 2.0: a comprehensive toolset for summarizing and integrating gene-sets analysis

BackgroundGene-Set Analysis (GSA) is commonly used to analyze high-throughput experiments. However, GSA cannot readily disentangle clusters or pathways due to redundancies in upstream knowledge bases, which hinders comprehensive exploration and interpretation of biological findings. To address this challenge, we developed GeneSetCluster, an R package designed to summarize and integrate GSA results. Over time, we and users as well identified limitations in the original version, such as difficulties in managing redundancies across multiple gene-sets, large computational times, and its lack of accessibility for users without programming expertise. ResultsWe present GeneSetCluster 2.0, a comprehensive upgrade that delivers methodological, computational, interpretative, and user-experience enhancements. Methodologically, GeneSetCluster 2.0 introduces a novel approach to address duplicated gene-sets and implements a seriation-based clustering algorithm that reorders results, aiding pattern identification. Computationally, the package is optimized for parallel processing, significantly reducing execution time. GeneSetCluster 2.0 enhances cluster annotations by associating clusters with relevant tissues and biological processes to improve biological interpretation, particularly for human and mouse data. To broaden accessibility, we have developed a user-friendly web application enabling non-programmers to use it. This version also ensures seamless integration between the R package, catering to users with programming expertise, and the web application for broader audiences. We evaluated the updates in a single-cell RNA public dataset. ConclusionGeneSetCluster 2.0 offers substantial improvements over its predecessor. Furthermore, by bridging the gap between bioinformaticians and clinicians in multidisciplinary teams, GeneSetCluster 2.0 facilitates collaborative research. The R package and web application, along with detailed installation and usage guides, are available on GitHub (https://github.com/TranslationalBioinformaticsUnit/GeneSetCluster2.0), and the web application can be accessed at https://translationalbio.shinyapps.io/genesetcluster/.

bioinformatics↗

bioRxiv2023

Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from in vivo publications

BackgroundSystematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain systematic review steps such as data extraction are labour-intensive which hampers their applicability, not least with the rapidly expanding body of biomedical literature. ObjectiveTo bridge this gap, we aimed at developing a data mining tool in the R programming environment to automate data extraction from neuroscience in vivo publications. The function was trained on a literature corpus (n=45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n=31 publications; multiple sclerosis, n=244 publications). ResultsOur data mining tool Auto-STEED (Automated and STructured Extraction of Experimental Data) was able to extract key experimental parameters such as animal models and species as well as risk of bias items such as randomization or blinding from in vivo studies. Sensitivity and specificity were over 85 and 80%, respectively, for most items in both validation corpora. Accuracy and F-scores were above 90% and 0.9 for most items in the validation corpora. Time savings were above 99%. ConclusionsOur developed text mining tool Auto-STEED is able to extract key experimental parameters and risk of bias items from the neuroscience in vivo literature. With this, the tool can be deployed to probe a field in a research improvement context or to replace one human reader during data extraction resulting in substantial time-savings and contribute towards automation of systematic reviews. The function is available on Github.

neuroscience↗

bioRxiv2023

The scheduling of adolescence with Netrin-1 and UNC5C

Dopamine axons are the only axons known to grow during adolescence. Here, using rodent models, we examined how two proteins, Netrin-1 and its receptor, UNC5C, guide dopamine axons towards the prefrontal cortex and shape behaviour. We demonstrate in mice (Mus musculus) that dopamine axons reach the cortex through a transient gradient of Netrin-1 expressing cells - disrupting this gradient reroutes axons away from their target. Using a seasonal model (Siberian hamsters; Phodopus sungorus) we find that mesocortical dopamine development can be regulated by a natural environmental cue (daylength) in a sexually dimorphic manner - delayed in males, but advanced in females. The timings of dopamine axon growth and UNC5C expression are always phase-locked. Adolescence is an ill-defined, transitional period; we pinpoint neurodevelopmental markers underlying this period.

neuroscience↗

bioRxiv2021

Genomic characterization of three novel Desulfobacterota classes expand the metabolic and phylogenetic diversity of the Phylum

An overwhelming majority of bacterial life remains uncharacterized. Recent efforts to assemble genomes from metagenomes have provided invaluable insights into these yet-uncultured bacterial lineages. We report on the characterization of 30 genomes belonging to three novel classes within the phylum Desulfobacterota. One class (proposed name Candidatus "Anaeroferrophillalia") was characterized by the capacity for heterotrophic growth, either fermentatively or utilizing polysulfide, tetrathionate and thiosulfate as electron acceptors. Autotrophic growth using the Wood Ljungdahl pathway and hydrogen or Fe(II) as an electron donor could also occur in absence of organic carbon sources. The second class (proposed name Candidatus "Anaeropigmentia") was characterized by its capacity for fermentative or aerobic growth at low oxygen thresholds using a broad range of sugars and amino acids, and the capacity to synthesize the methyl/alkyl carrier CoM, an ability that is prevalent in the archaeal but rare in the bacterial domain. Pigmentation is inferred from the capacity for carotenoids (lycopene) production, as well as the occurrence of the majority of genes involved in bacteriochlorophyll a biosynthesis. The third class (proposed name Candidatus "Zymogenia") was characterized by the capacity for heterotrophic growth fermentatively using broad sugars and amino acids as carbon sources, and the adaptation of some of its members to hypersaline habitats. Analysis of the distribution pattern of all three classes showed their occurrence as rare community members in multiple habitats, with preferences for anaerobic terrestrial (e.g. hydrocarbon contaminated environments, wetlands, bioreactors), freshwater (e.g. ground water and gas-saturated temperate lakes), and marine (e.g. hydrothermal vents, marine sediments, and coastal sediments) environments, over oxygenated (e.g. pelagic ocean and agricultural land) settings. Special preference for some members of the class Candidatus "Zymogenia" to hypersaline environments, e.g. hypersaline microbial mats and lagoons was observed. ImportanceCulture-independent diversity surveys conducted in the last three decades have clearly demonstrated that the scope of microbial diversity is much broader than that inferred from isolation efforts. Multiple reasons have been put forth to explain the refractiveness of a wide range of the earths microbiome to isolation efforts. Documenting the scope of high-rank phylogenetic diversity on earth, as well as deciphering and documenting the metabolic capacities, physiological preferences, and putative ecological roles of these yet-uncultured lineages represents one of the central goals in current microbial ecology research. Recent efforts to assemble genomes from metagenomes have provided invaluable insights into these yet-uncultured lineages. This study expands our knowledge of the phylum Desulfobacterota through the characterization of 30 genomes belonging to three novel classes. The analyzed genomes were either recovered from Zodletone Spring in southwestern Oklahoma in this study, or recently binned from public metagenomes as part of the Global Earth Microbiome initiative. Our results expand the high-rank diversity within the bacterial tree of life by describing three novel classes within the phylum Desulfobacterota, document the utilization of multiple metabolic processes, e.g. iron-oxidation, aromatic hydrocarbon degradation, reduction of sulfur-cycling intermediates, and features, e.g. coenzyme M biosynthesis, and pigmentation, as salient characteristics in these novel Desulfobacterota classes.

microbiology↗

bioRxiv2020

STATegra: Multi-omics data integration - A conceptual scheme with a bioinformatics pipeline

Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.

bioinformatics↗

bioRxiv2020

Gsta4 controls apoptosis of differentiating adult oligodendrocytes during homeostasis and remyelination via the mitochondria-associated Fas/Casp8/Bid-axis

Arrest of oligodendrocyte (OL) differentiation and remyelination following myelin damage in Multiple Sclerosis (MS) are associated with disease progression but the underlying mechanism are elusive. We show that Glutathione S-transferase 4 (Gsta4) is highly expressed during adult OL differentiation and that its loss prevents differentiation into myelinating OLs. Also, Gsta4 appeared to be a novel target for Clemastine, in clinical trial for MS. Over-expression of Gsta4 reduced the expression of Fas and activity along the mitochondria-associated Casp8/Bid-axis in adult pre-OLs from the corpus callosum, together promoting enhanced pre-OL survival during differentiation. The Gsta4-mediated input on apoptosis during adult OL differentiation was further verified in the LPC and EAE model, where Casp8 were reduced in pre-OLs with high Gsta4 expression in an immune response-independent fashion. Our results place Gsta4 as a key regulator of intrinsic mechanisms beneficial for OL differentiation and remyelination, and as a possible target for future MS therapies.

neuroscience↗