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Extrachromosomal circular DNAs (eccDNAs) have emerged as important intra-cellular mobile genetic elements that affect gene copy number and exert in trans regulatory roles within the cell nucleus. Here, we describe scCircle-seq, a method for profiling eccDNAs and unraveling their diversity and complexity in single cells. We implement and validate scCircle-seq in normal and cancer cell lines, demonstrating that most eccDNAs vary largely between cells and are stochastically inherited during cell division, although their genomic landscape is cell type-specific and can be used to accurately cluster cells of the same origin. eccDNAs are preferentially produced from chromatin regions enriched in H3K9me3 and H3K27me3 histone marks and are induced during replication stress conditions. Concomitant sequencing of eccDNAs and RNA from the same cell uncovers the absence of correlation between eccDNA copy number and gene expression levels, except for a few oncogenes, including MYC, contained within a large eccDNA in colorectal cancer cells. Lastly, we apply scCircle-seq to one prostate cancer and two breast cancer specimens, revealing cancer-specific eccDNA landscapes and a higher propensity of eccDNAs to form in amplified genomic regions. scCircle-seq is a scalable tool that can be used to dissect the complexity of eccDNAs across different cell and tissue types, and further expands the potential of eccDNAs for cancer diagnostics.
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http://dx.doi.org/10.1038/s41467-024-45972-y | DOI Listing |
J Chem Inf Model
September 2025
Department of Chemistry, Delaware State University, Dover, Delaware 19901, United States.
The calculation of the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO-LUMO) gap for chemical molecules is computationally intensive using quantum mechanics (QM) methods, while experimental determination is often costly and time-consuming. Machine Learning (ML) offers a cost-effective and rapid alternative, enabling efficient predictions of HOMO-LUMO gap values across large data sets without the need for extensive QM computations or experiments. ML models facilitate the screening of diverse molecules, providing valuable insights into complex chemical spaces and integrating seamlessly into high-throughput workflows to prioritize candidates for experimental validation.
View Article and Find Full Text PDFCrit Care Sci
September 2025
Universitätsklinikum Carl Gustav Carus - Dresden, Sachsen, Germany.
The PROtective VEntilation (PROVE) Network is a globally-recognized collaborative research group dedicated to advancing research, education, and collaboration in the field of mechanical ventilation. Established to address critical questions in intraoperative and intensive care ventilation, the network focuses on improving outcomes for patients undergoing mechanical ventilation in diverse settings, including operating rooms, intensive care units, burn units, and resource-limited environments in low- and middle-income countries. The PROVE Network is committed to generating high-quality evidence through a comprehensive portfolio of investigations, including randomized clinical trials, observational research, and meta-analyses.
View Article and Find Full Text PDFPhys Rev Lett
August 2025
Jilin University, State Key Laboratory of Integrated Optoelectronics, JLU Region, College of Electronic Science and Engineering, Changchun 130012, China.
Exceptional rings (ERs) are high-dimensional non-Hermitian topologies formed by exceptional points, significantly enriching the topological properties of non-Hermitian systems. Because of the intricate topology and symmetry requirements, the realization of ERs generally demands complex structures and precise parameter tuning, resulting in relatively few experimental observations in high-dimensional periodic systems. Here, we show that even the simplest 1D non-Hermitian periodic systems can support multiple ERs, enabled by the system's multiple degrees of freedom which naturally accommodate diverse non-Hermitian perturbations.
View Article and Find Full Text PDFPLoS One
September 2025
Laboratório de Termitologia, Departamento de Sistemática e Ecologia, Centro de Ciências Exatas e da Natureza, Universidade Federal da Paraíba, João Pessoa, Paraíba, Brazil.
With the aim of expanding the possibilities of identifying termite species, in the present study we generated genetic data based on sequences of the mitochondrial gene encoding cytochrome c oxidase subunit II (COII) for termites (Blattodea: Isoptera) occurring in the state of Paraíba, northeastern Brazil. The genetic data were obtained from 135 COII sequences identified in 28 genera and 48 species. These are the first COII sequences for 15 taxa (31.
View Article and Find Full Text PDFBioinformatics
September 2025
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Motivation: RNA velocity has become a powerful tool for uncovering transcriptional dynamics in snapshot single-cell data. However, current RNA velocity approaches often assume constant transcriptional rates and treat genes independently with gene-specific times, which may introduce biases and deviate from biological realities. Here, we present InterVelo, a novel deep learning framework that simultaneously learns cellular pseudotime and RNA velocity.
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