Biology subjects

Sukosd, F.

Publications and source records attributed to Sukosd, F..

6 recordsLinked to original sources

bioRxiv2026

Intrahepatic reporter assay reveals leaky somatic blockade of L1 retrotransposition in mice

Long interspersed element-1 (LINE-1, L1) retrotransposition has long been proposed to occur in somatic tissues, yet direct experimental evidence distinguishing adult somatic events from early embryonic insertions has remained limited. Here we establish an intrahepatic L1 reporter assay that enables immunohistochemical detection and quantitative analysis of L1 retrotransposition in vivo. Using autonomous and non-autonomous L1 reporter variants, we demonstrate clearly detectable somatic L1 activity in the mouse liver. Comparative analysis of L1 activity in liver tissue and tumor-derived cell culture reveals that tumor cells preferentially restrict L1 at early regulatory stages, consistent with epigenetic control, whereas downstream defence mechanisms are comparatively permissive. In contrast, normal liver tissue shows stronger restriction at later stages of the L1 life cycle. Together, our results provide direct experimental evidence for somatic L1 retrotransposition in vivo in adult liver and reveal distinct regulatory strategies that shape L1 activity in tumor versus normal somatic cells. TeaserGenome destabilizing L1 retrotransposon activity is present in somatic tissues, where it likely contributes to cancer development.

genetics↗

bioRxiv2025

Subphase-Labeled Mitotic Dataset for AI-powered Cell Division Analysis

Mitosis detection represents a critical task in the field of digital pathology, as determination of the mitotic index (MI) plays an important role in the tumor grading and prognostic assessment of patients. Manual determination of MI is a labor-intensive and time-consuming task for practitioners with rather high interobserver variability, thus, automation has become a priority. There has been substantial progress towards creating robust mitosis detection algorithms in recent years, primarily driven by the Mitosis Domain Generalization (MIDOG) challenges. In parallel, there has been growing interest in the molecular characterization of mitosis with the goal of achieving a more comprehensive understanding of its underlying mechanisms in a subphase-specific manner. Here, we introduce a new mitotic figure dataset annotated with subphase information based on the MIDOG++ dataset as well as a previously unrepresented tumor domain to enhance the diversity and applicability of the dataset. We envision a new perspective for domain generalization by improving the performance of models with subtyping mitotic cells into the 5 main stages of normal mitosis, complemented with an atypical mitotic class. We believe that our work broadens the horizon in digital pathology: subtyping information could provide useful help for mitosis detection, while also providing promising new directions in answering biological questions, such as molecular analysis of the subphases on a single cell level.

bioinformatics↗

bioRxiv2025

The Cause of Most Common Antler Abnormality is Not a Fracture But an Osteomyelitis Like Disease the Pedunculitis Chronica Deformans

Climate change is increasing the incidence of certain diseases in wildlife, offering a deeper understanding of their underlying causes. Traditionally, the most common antler abnormalities have been attributed to trauma, based on their morphology. However, this explanation does not account for the epidemic-like spread of abnormalities in specific regions, suggesting that they might be a previously undescribed aspect of this anomaly. In fallow deer (Dama dama), using advanced medical diagnostic tools, we identified an osteomyelitis-like condition, Pedunculitis Chronica Deformans (PCD) which is also observed in other Cervidae. The incidence of this disease shows a strong correlation with the production of mycotoxins by molds that are becoming more prevalent with global warming. Mycotoxins can inhibit wound healing through scar formation after the antlers are cast, leading to the development of PCD. In this report we characterize PCD pathomorphology and introduce a scoring system to differentiate it from other deformities. Moreover, we argue that the most common anomalies such as fractures, antler losses, fatal meningoencephalitis and brain abscesses are complications of PCD. Our research underscores the potential of antlers to serve as biomarkers for mycotoxin exposure.

zoology↗

bioRxiv2022

Show me your neighbour and I tell what you are: fisheye transformation for deep learning-based single-cell phenotyping

Recently we have concluded that image-based features derived from the microenvironment have an enormous impact on successfully determining the class of an object1. Here we demonstrate that deep learning-based phenotypic analysis of cells with a properly chosen microenvironment-size provides results comparable to our earlier neighbourhood-based methods that utilise hand-crafted image features. We hypothesised that treating cells with equal weight, regardless of their position within the cellular microenvironment, is suboptimal, and direct neighbours have a larger impact on the phenotype of the cell-of-interest than cells in its larger proximity. Hence we present a novel approach that (1) considers the fully featured view of the cell-of-interest, (2) includes the neighbourhood and (3) gives lesser weight to cells that are far from the cell. To achieve this, we present a transformation similar to those characteristic for fisheye cameras. Such a transformation satisfies all the above defined criteria, with a fast rate of transform for any images. Using the proposed transformation with proper settings we could significantly increase the accuracy of single-cell phenotyping, both in case of cell culture and tissue-based microscopy images. The range of potential applications of the proposed method goes beyond microscopy, as we present improved results on the iWildCam 2020 dataset containing images of wild animals.

bioinformatics↗

bioRxiv2019

Efficient treatment of a preclinical inflammatory bowel disease model with engineered bacteria

We developed an engineered bacterium based, RNA interference mediated therapeutic method to significantly reduce the symptoms in the most frequently used animal model of inflammatory bowel disease. This transkingdom RNA interference strategy was based on the non-pathogenic E. coli MDS42 strain, which was engineered to constitutively produce invasin and histeriolysin cytolysin. These proteins enabled the bacteria first to invade the colon epithelium and then degrade in the phagosome. This allowed the delivery of a plasmid encoding shRNA targeting TNF alpha into the cytoplasm of the target cells. The expression levels of TNF alpha and other cytokines significantly decreased and the inflammatory symptoms were significantly reduced. With further safety modifications this method could serve as a safe and side-effect free alternative to biologicals targeting TNF-alpha.

synthetic biology↗

bioRxiv2017

Show me your neighbours, and I’ll tell you what you are – cellular microenvironment matters

To answer major questions of cell biology, it is essential to understand cellular complexity. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-based analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro- and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various microenvironmental features contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the microenvironment of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's microenvironment significantly improves the accuracy of machine learning-based phenotyping.

bioinformatics↗