Biology subjects

Symeonidi, A.

Publications and source records attributed to Symeonidi, A..

6 recordsLinked to original sources

bioRxiv2026

A transient CD87-centred axis enhances the Th17 potential of cDC2 in neonates

Type 2 conventional dendritic cells (cDC2) orchestrate T cell immunity, yet the signals that regulate their tissue-specific functions across development remain poorly defined. We demonstrate that splenic cDC2/DC3 subset heterogeneity is established perinatally and that splenic ESAMhi cDC2A undergo a transcriptional and functional transition around the time of weaning, which occurs independently of microbiota. Comparative transcriptomics identified neonatal-specific regulatory programs, including elevated expression of Plaur, encoding CD87/uPAR. CD87 sensitizes neonatal ESAMhi cDC2A to coagulation factor XII (FXII), thereby enhancing their capacity to promote Th17 differentiation. In human infants, CD87 expression was high on cDC2, its expression declined with age and in plasma of preterm infants CD87 positively correlated with Th17-associated cytokines. Together, we identify a conserved link between coagulation pathways and cDC2 developmental programming that may offer new opportunities to modulate Th17 responses in infancy. SummarySplenic cDC2 undergo a transcriptional and functional remodeling around weaning. Elevated CD87 expression on neonatal cDC2 enhances their FXII-driven Th17 potential. Conserved in human infants, elevated CD87 expression reveals a coagulation-linked pathway shaping early-life dendritic cell function and suggests targets to improve vaccination.

immunology↗

bioRxiv2024

Benchmarking scRNA-seq copy number variation callers

Copy number variations (CNVs), the gain or loss of genomic regions, are associated with different diseases and cancer types, where they are related to tumor progression and treatment outcome. Single cell technologies offer new possibilities to measure CNVs in individual cells, allowing to assess population heterogeneity and to delineate subclonal structures. Single cell whole-genome sequencing is considered the gold-standard for the quantification of CNVs in single cells. However, the majority of existing single cell datasets interrogate gene expression, using scRNA-seq. Consequently, several computational approaches have been developed to identify CNVs from that data modality. Nevertheless, an independent benchmarking of these methods is lacking. We used 15 scRNA-seq datasets and evaluated six popular computational methods in their ability to recover the ground truth CNVs using a large set of performance metrics. Additionally, we explored whether they could correctly identify euploid cells, especially also in fully diploid samples, and subclonal structures in heterogeneous tumor samples. We discovered several dataset-specific factors that influence the performance of the methods, such as the dataset size and the number and type of CNVs in the analyzed sample. We found that the choice of the reference dataset can have a large impact on the performance. Methods which included additional allelic information from the scRNA-seq reads performed more robustly across scenarios, but at the cost of higher runtime. Furthermore, the methods differed substantially in their additional functionalities and resource requirements. We offer a benchmarking pipeline to help identify the optimal CNV calling method for newly generated scRNA-seq datasets, and to benchmark and improve new methods performance.

bioinformatics↗

bioRxiv2024

Multi-generational biotic stress increases the rate of spontaneous epimutations in a ROS1-dependent manner

Mistakes in the maintenance of CG methylation are a source of spontaneous epimutations in plants that can be inherited across generations. The extent to which these stochastic events are affected by prolonged exposure to biotic and abiotic stress remains poorly characterized. Here, we grew Arabidopsis Mutation Accumulation (MA) lines for 12 generations in the presence of two biotic stressors: Pseudomonas syringae (Pst) and salicylic acid (SA). We found that multi-generational exposure to Pst and SA led to an 18%-32% and 23%-61% increase in the genome-wide epimutation rate, respectively. These rate increases were mainly targeted to subsets of genes characterized by low steady-state methylation (LM), on average, and a lack of transcriptional responsiveness to stress. We show that these effects are mediated by the DNA demethylase Repressor of Silencing 1 (ROS1). Loss of ROS1 not only buffers transcriptional responses to biotic stress, but also stabilizes epimutation rates, particularly in LM genes, rendering them insensitive to environmental perturbations. Taken together, our data demonstrates that stress can induce heritable epimutations and highlights a ROS1-mediated link between transcriptional plasticity and DNA methylation maintenance fidelity over generations.

evolutionary biology↗

bioRxiv2022

epiAneufinder: identifying copy number variations from single-cell ATAC-seq data

Single-cell open chromatin profiling via the single-cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq) assay has become a mainstream measurement of open chromatin in single-cells. Here we present a novel algorithm, epiAneufinder, that exploits the read count information from scATAC-seq data to extract genome-wide copy number variations (CNVs) for individual cells, allowing to explore the CNV heterogeneity present in a sample at the single-cell level. Using different cancer scATAC-seq datasets, we show how epiAneufinder can identify intratumor clonal heterogeneity in populations of single cells based on their CNV profiles. These profiles are concordant with the ones inferred from single-cell whole genome sequencing data for the same samples. epiAneufinder allows the addition of single-cell CNV information to scATAC-seq data, without the need of additional experiments, unlocking a layer of genomic variation which is otherwise unexplored.

bioinformatics↗

bioRxiv2020

Recursive Convolutional Neural Networks for Epigenomics

Deep learning methods have proved to be powerful classification tools in the fields of structural and functional genomics. In this paper, we introduce a Recursive Convolutional Neural Networks (RCNN) for the analysis of epigenomic data. We focus on the task of predicting gene expression from the intensity of histone modifications. The proposed RCNN architecture can be applied to data of an arbitrary size, and has a single meta-parameter that quantifies the models capacity, thus making it flexible for experimenting. The proposed architecture outperforms state-of-the-art systems, while having several orders of magnitude fewer parameters.

bioinformatics↗

bioRxiv2019

AlphaBeta: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants

IntroductionHeritable changes in cytosine methylation can arise stochastically in plant genomes independently of DNA sequence alterations. These so-called spontaneous epimutations appear to be a byproduct of imperfect DNA methylation maintenance during mitotic or meitotic cell divisions. Accurate estimates of the rate and spectrum of these stochastic events are necessary to be able to quantify how epimutational processes shape methylome diversity in the context of plant evolution, development and aging. MethodHere we describe AlphaBeta, a computational method for estimating epimutation rates and spectra from pedigree-based high-throughput DNA methylation data. The approach requires that the topology of the pedigree is known, which is typically the case in the experimental construction of mutation accumulation lines (MA-lines) in sexually or clonally reproducing species. However, this method also works for inferring somatic epimutation rates in long-lived perennials, such as trees, using leaf methylomes and coring data as input. In this case, we treat the tree branching structure as an intra-organismal phylogeny of somatic lineages and leverage information about the epimutational history of each branch. ResultsTo illustrate the method, we applied AlphaBeta to multi-generational data from selfing- and asexually-derived MA-lines in Arabidopsis and dandelion, as well as to intra-generational leaf methylome data of a single poplar tree. Our results show that the epimutation landscape in plants is deeply conserved across angiosperm species, and that heritable epimutations originate mainly during somatic development, rather than from DNA methylation reinforcement errors during sexual reproduction. Finally, we also provide the first evidence that DNA methylation data, in conjunction with statistical epimutation models, can be used as a molecular clock for age-dating trees. ConclusionAlphaBeta faciliates unprecedented quantitative insights into epimutational processes in a wide range of plant systems. Software implementing our method is available as a Bioconductor R package at http://bioconductor.org/packages/3.10/bioc/html/AlphaBeta.html

genomics↗