Biology subjects

Migh, E.

Publications and source records attributed to Migh, E..

9 recordsLinked to original sources

bioRxiv2026

Revamped transposon-induced hepatocellular carcinoma model reveals deep similarities of cancer-driver landscapes across mice and humans

Genome-scale cancer-driver discovery is limited by statistical uncertainty in driver calling and by experimental constraints on mutations available for selection. We developed 3xfOnc3 - a three-frame Sleeping Beauty (SB) mutagenesis system designed to broaden functional perturbations and reduce integration biases of traditional SB screens - and tested it in vivo in mouse liver. Within 12 months, 60% of mice developed heterogeneous hepatocellular tumors. Tumor-selected integrations were interpreted against an empirical reference from SB-based therapeutic gene delivery to hepatocytes, enabling correction for gene targetability. Background-aware filtering prioritized 315 driver candidates, including 243 protein-coding genes. Tumors were multiclonal, and identical integration-marked lineages expanded differently across lesions, supporting context-dependent clonal expansion. SB hits within young L1 subfamily members were selectively enriched in tumors. Human orthologues of candidate drivers were enriched for Cancer Gene Census genes and protein-altering pan-cancer mutations. Together, 3xfOnc3 reveals a background-corrected driver landscape linking driver selection to clonal tumor evolution and cross-species cancer relevance.

cancer biology↗

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↗

bioRxiv2026

Prevalent Gut Phages Encode Modular Adhesins Mediating Epithelial Binding and Endoplasmic Reticulum Trafficking

Bacteriophages are crucial components of the human microbiome and hold promise as therapeutic agents. Yet, their physical interactions with mammalian cells remain poorly understood. Here, we developed a high-throughput platform to identify phages that adhere to epithelial layers and the proteins that mediate this interaction. The identified phages encode immunoglobulin (Ig)-like domain-containing proteins that, when displayed on a non-adherent phage, confer epithelial binding and internalization in vitro, and increased phage retention in the mouse gut in vivo. Phages encoding these adhesins are among the most abundant and prevalent human gut phages, including crAss-like phages and a distinct cluster of uncharacterized myophages. Domain sequence variation alters epithelial interaction profiles, and internalized phages traffic to the endoplasmic reticulum through the Golgi apparatus, suggesting access to non-degradative internalization pathways. These findings reveal widespread phage-human interactions in the human virome, with potential impacts on health and implications for next-generation phage therapeutics.

microbiology↗

bioRxiv2025

Microenvironmental information significantly improves the recognition of cell types in human lung cancer patients

Accurate single-cell phenotypic classification in histopathological tissue sections is essential for understanding tumor behavior, identifying potential therapeutic targets, and improving prognostic assessments in cancer research. In this study, we applied deep learning to classify lung cancer cell phenotypes in hematoxylin and eosin-stained tissue sections. Using 11 whole slide images from 11 patients, we annotated nearly 20,000 cells into seven distinct phenotypes for training and validation. We used a fisheye transformation technique, which modifies images to mimic fisheye camera effects in order to incorporate cellular microenvironment information to enhance deep learning models. We evaluated its effectiveness on lung cancer tissue sections, optimizing transformation parameters and assessing classification performance through multiple cross-validation strategies. Our results demonstrate that the transformation significantly improves classification accuracy, approaching human level performance, particularly for phenotypes that rely on subtle morphological differences. The approach enhances model generalizability across patient samples, highlighting the importance of integrating spatial context in computational pathology. These findings suggest that incorporating adaptive image transformations can significantly improve automated histopathological analysis, with potential implications for more robust and clinically applicable AI-driven diagnostics.

bioinformatics↗

bioRxiv2025

A Morpho-Proteomic Atlas of Mitosis at Sub-Minute Resolution

Precise spatiotemporal protein organization is critical for fundamental biological processes including cell division1,2. Indeed, aberrant mitosis and mitotic factors are involved in diverse diseases, including various cancers3,4, Alzheimers disease5, and rare diseases6. During mitosis, complex spatial rearrangements and regulation ensure the accurate separation of replicated sister chromatids to produce genetically identical daughter cells7-9. Previous studies employed high-throughput methodologies to follow specific proteins during mitosis10-15. Still a temporally refined systems-level approach capable of monitoring morphological and proteomic changes throughout mitosis has been lacking. Here, we achieved unprecedented resolution by phenotypically decomposing mitosis into 40 subsections of a regression plane for proteomic analysis using deep learning and regression techniques. Our deep visual proteomics (DVP) workflow16, revealed rapid, dynamic proteomic changes throughout mitosis. We quantified 4,350 proteins with high confidence, demonstrating that 147 show significant dynamic abundance changes during mitotic progression. Clustering revealed coordinated patterns of protein regulation, while network analysis uncovered tight regulation of core cell cycle proteins and a link between cell cycle and cancer-linked mutations. Immunofluorescence validated abundance changes and linked previously uncharacterised proteins, like C19orf53, to mitosis. To facilitate data navigation, we developed Mito-Omix, a user-friendly online platform that integrates intricate morphological and molecular data. Our morphological and proteomic dataset spans mitosis at high resolution, providing a rich resource for understanding healthy and aberrant cell division.

cell biology↗

bioRxiv2025

Deep visual multi-omics profiling reveals mechanisms that underly cancer cell differentiation and aggressiveness in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits significant intra-tumoral heterogeneity (ITH) at both morphological and genetic levels, complicating treatment and contributing to disease progression. Among these, ccRCCs with focal rhabdoid differentiation stand out as highly aggressive tumors distinguished by cells with unique morphological features. However, the correlation between distinct morphological phenotypes, specific molecular alterations, and their influence on tumor behavior remains poorly understood. In this study, we integrated advanced AI-based image analysis with single-cell isolation and multi-omics profiling to dissect the link between clinically relevant morphological and molecular features of ccRCC cells. Using a novel digital pathology workflow, we quantified low-grade, high-grade, and rhabdoid morphologies in ccRCC diagnostic images with unprecedented precision. Subsequently, isolation of two sets of 1,000 morphologically distinct cells for detailed mRNA and protein expression analyses, revealed significant increasing dysregulation associating with higher histopathological grades. Rhabdoid ccRCC cells (grade 4) demonstrated unique molecular profiles, including upregulated FOXM1-driven proliferation, disrupted cell-matrix interactions, and enhanced immune evasion pathways. Despite high T-cell infiltration in rhabdoid areas, we identified a rhabdoid-specific immunosuppressive network driven by cytokines, IFN-beta, and integrin signaling, likely contributing to T-cell exhaustion. Rhabdoid ccRCC cells develop a distinct immunosuppressive signaling network, involving PD-L1 and novel immunomodulatory factors such as CD38 and ITGB2. These findings provide a basis for novel therapeutic strategies targeting these pathways in combination with immunotherapy to improve outcomes for patients with aggressive rhabdoid ccRCC. Key PointsO_LIccRCC is characterized by well-established morphological heterogeneity but the correlation with the underlying molecular aberrations remained elusive. C_LIO_LIBy integrating AI-based image analysis with single cell isolation and deep multi-omics profiling, we dissect the molecular intricacies of ccRCC, from targeted collection of 1,000 morphologically distinct cells. C_LIO_LIOur results demonstrate significant dysregulation of gene and protein expression correlating with higher histopathological grades in ccRCC. C_LIO_LIAggressive ccRCC cells with rhabdoid differentiation (grade 4) display distinct molecular profiles, as they upregulate FOXM1-mediated proliferation, ECM remodeling and the immune evasion responses, suggesting new therapeutic avenues enhancing ICI efficacy in these patients. C_LI

cancer biology↗

bioRxiv2023

The chloroplastic phosphate transporter CrPHT4-7 supports phosphate homeostasis and photosynthesis in Chlamydomonas

In eukaryotic cells, phosphorus is assimilated and utilized primarily as phosphate (Pi). Pi homeostasis is mediated by transporters that have not yet been adequately characterized in green algae. This study reports on CrPHT4-7 from Chlamydomonas reinhardtii, a member of the PHT4 transporter family, which exhibits remarkable similarity to AtPHT4;4 from Arabidopsis thaliana, a chloroplastic ascorbate transporter. Using fluorescent protein tagging we show that CrPHT4-7 resides in the chloroplast envelope membrane. Crpht4-7 mutants, generated by the CRISPR/Cas12a-mediated single-strand templated repair, show retarded growth especially in high light, enhanced sensitivity to phosphorus limitation, reduced ATP level, strong ascorbate accumulation and diminished non-photochemical quenching in high light. Conversely, CrPHT4-7 overexpressing lines exhibit enhanced biomass accumulation under high light conditions in comparison with the wild-type strain. Expressing CrPHT4-7 in a yeast strain lacking Pi transporters substantially recovered its slow growth phenotype demonstrating that it transports Pi. Even though CrPHT4-7 shows a high degree of similarity to AtPHT4;4, it does not display any significant ascorbate transport activity in yeast or intact algal cells. Thus, the results demonstrate that CrPHT4-7 functions as a chloroplastic Pi transporter essential for maintaining Pi homeostasis and photosynthesis in Chlamydomonas reinhardtii. One-sentence summaryWe demonstrate that the CrPHT4-7 transporter of Chlamydomonas reinhardtii is located in the chloroplast envelope membrane and contributes to maintaining phosphate homeostasis and photosynthesis.

plant biology↗

bioRxiv2020

Regression plane concept: analysing continuous cellular processes with machine learning

Biological processes are inherently continuous, and the chance of phenotypic discovery is significantly restricted by discretising them. Using multi-parametric active regression we introduce a novel concept to describe and explore biological data in a continuous manner. We have implemented Regression Plane (RP), the first user-friendly discovery tool enabling class-free phenotypic supervised machine learning.

bioinformatics↗

bioRxiv2019

A deep learning framework for nucleus segmentation using image style transfer

Single cell segmentation is typically one of the first and most crucial tasks of image-based cellular analysis. We present a deep learning approach aiming towards a truly general method for localizing nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods submitted to the 2018 Data Science Bowl on images representing a variety of realistic conditions, some of which were not represented in the training data. The key to our approach is to adapt our model to unseen and unlabeled data using image style transfer to generate augmented training samples. This allows the model to recognize nuclei in new and different experiments without requiring expert annotations.

bioinformatics↗