Biology subjects

Molnar, C.

Publications and source records attributed to Molnar, C..

9 recordsLinked to original sources

bioRxiv2026

The Drosophila FET orthologue Cabeza is an essential cofactor for ETV4-mediated activation of GGAA microsatellite neoenhancers

The conversion of transcriptionally silent GGAA microsatellites (GGAA{micro}Sats) into functional enhancers by FET::ETS oncogenic fusions is a hallmark of Ewing sarcoma. However, emerging evidence implicates non-fused, full-length oncogenic ETS transcription factors in activating these repeats in other malignancies. Evaluating the in vivo transcriptional requirements of various human ETS factors in Drosophila, we found that human ETV4 uniquely binds and robustly activates GGAA{micro}Sats in a tissue-specific manner. This activation is strongly inhibited by the human ETS repressor ETV6. Taking advantage of low genetic redundancy in Drosophila, we identified Cabeza (Caz), the single fly FET orthologue, as a necessary cofactor for ETV4-mediated transcription at GGAA{micro}Sats. Conversely, EWS::FLI1-mediated transcriptional activation of GGAA{micro}Sats is entirely independent of endogenous Caz, highlighting the distinct mechanics of covalent tethering versus non-covalent physical complexes. Collectively, our findings provide definitive in vivo evidence that non-fused ETS factors cooperate with endogenous FET proteins to drive transcription from silent GGAA repeats, mechanistically validating this regulatory transformation known to operate as an oncogenic mechanism beyond Ewing sarcoma.

genetics↗

bioRxiv2026

EVEE: Interpretable variant effect prediction from genomic foundation model embeddings

Scientific foundation models learn high-dimensional representations from diverse data modalities, yet what they encode and how to extract that knowledge remain open questions. Here we show that probing the internal representations of Evo 2, a 7-billion-parameter genomic foundation model, enables accurate and interpretable genetic variant effect prediction. We introduce a covariance-based probe that captures second-order structure from Evo 2 sequence embeddings to predict variant pathogenicity across variant types and functional consequences, matching or exceeding specialized predictors within their domains. To ground these predictions in known biological mechanisms, we train a complementary panel of probes on existing annotations to detect which genomic properties are disrupted by a variant. This categorized evidence is then integrated with each variants genomic context through a language model to generate variant-specific mechanistic hypotheses. Our pathogenicity predictions correlate with experimental measures of variant function, clinical penetrance, and biobank disease associations while the mechanistic hypotheses are consistent with expert reviews, known mechanism classes, and downstream molecular readouts. We release pathogenicity scores, disruption profiles, and contextualized interpretations for 4.2 million variants from the ClinVar database as an open resource through the Evo Variant Effect Explorer (EVEE). More broadly, this structured probing approach offers a general framework for interrogating foundation models across scientific disciplines and grounding their outputs in existing domain concepts.

genomics↗

bioRxiv2025

Human Oncogene EWS::FLI1 Functions as a Pioneer Factor in Saccharomyces cerevisiae.

Ewing sarcoma (EwS) is an aggressive, human-exclusive tumor typically driven by the EWS::FLI1 fusion protein. To assess whether the neomorphic functions of EWS::FLI1 are fundamentally dependent on evolutionarily recent cofactors such as ETS transcription factors (ETS-TFs), Plycomb group (PcG) proteins, CBP/p300, or specific subunits of the BAF complex, we expressed EWS::FLI1 in the model organism Saccharomyces cerevisiae. This minimal system was chosen because several key EWS::FLI1s cofactors possess greatly reduced sequence homology (e.g., BAF) or are lacking altogether (e.g., ETS-TFs, PcG, or CBP/p300). We used co-IP/MS to map the yeast interactome, Chip-Seq to identify gDNA binding sequences, RNA-Seq for global gene expression, and engineered reporters to test conversion of (GGAA) tandem repeats (GGAASat) into neoenhancers. We found that the yeast EWS::FLI1 interactome was more limited and qualitatively distinct from its human counterpart, sharing core machinery (e.g. RNA Polymerase II, FACT) but lacking the BAF/SWI-SNF and spliceosome complexes, and showing strong enrichment for the SAGA chromatin remodeling complex. We also found that EWS::FLI1 binds to hundreds of sites in the yeast genome with a clear preference for putative ETS-TF consensus sequences and (CA) dinucleotide repeats. Yet, EWS::FLI1 expressing cells presented only minimal transcriptional dysregulation, a stark contrast to the extensive changes observed in humans and Drosophila cells. Finally, we found that EWS::FLI1 successfully converted silent GGAASat sequences into active enhancers in yeast. This remarkable result occurs despite the absence of homologs for key human activators, such as CBP/p300, strongly suggesting that EWS::FLI1 can mobilize functionally related, non-homologous pathways to establish neoenhancers at GGAASat sites. Altogether, our results indicate that EWS::FLI1s core ability to drive GGAASat-dependent gene expression is a conserved, ancient property, while GGAASat-independent extensive transcriptome reprogramming is dependent on co-factors and pathways specific to animal cells.

cancer biology↗

bioRxiv2024

Hematological phenotypes in GATA2 deficiency syndrome arise from secondary injuries and maladaptation to proliferation

The GATA2 transcription factor is a pivotal regulator of hematopoiesis. Disruptions in the GATA2 gene drive severe hematologic abnormalities and are associated with an increased risk of myelodysplastic syndromes and acute myeloid leukemia; however, the mechanisms underlying the pathophysiology of GATA2 deficiency remain still unclear. We developed two different mouse models that are based on serial and limiting donor cell transplantation of (aged) GATA2 haploinsufficient cells and mirror the symptoms of GATA2 deficiency. Similar to what has been observed in patients, our models show that GATA2 haploinsufficiency leads to B lymphopenia, monocytopenia, lethal bone marrow failure (BMF), myelodysplasia and leukemia. Leukemia arises exclusively as a result of BMF, driven by somatic aberrations and accompanied by increased Myc target expression and genomic instability. These findings were confirmed in human GATA2+/- K562 cell lines showing defects in cytokinesis and are in line with the fact that monosomy 7 and trisomy 8 are frequent events in patients with MDS. Key pointsO_LIIn a mouse model for GATA2 deficiency, leukemia emerges from bone marrow failure C_LIO_LIMaladaptation to proliferative signals and chromosomal segregation defects contribute to the hematological phenotypes in GATA2 deficiency C_LI

cell biology↗

bioRxiv2024

Piximi - An Images to Discovery web tool for bioimages and beyond

Deep learning has greatly accelerated research in biological image analysis yet it often requires programming skills and specialized tool installation. Here we present Piximi, a modern, no-programming image analysis tool leveraging deep learning. Implemented as a web application at Piximi.app, Piximi requires no installation and can be accessed by any modern web browser. Its client-only architecture preserves the security of researcher data by running all computation locally. Piximi offers four core modules: a deep learning classifier, an image annotator, measurement modules, and pre-trained deep learning segmentation modules. Piximi is interoperable with existing tools and workflows by supporting import and export of common data and model formats. The intuitive researcher interface and easy access to Piximi allows biological researchers to obtain insights into images within just a few minutes. Piximi aims to bring deep learning-powered image analysis to a broader community by eliminating barriers to entry.

cell biology↗

bioRxiv2023

The Drosophila cancer-germline, head-to-head gene pair TrxT and dhd is dispensable for normal brain development but plays a major role in l(3)malignant brain tumour growth.

Drosophila thioredoxins Deadhead (Dhd) and Thioredoxin-T (TrxT) are encoded by a head-to-head gene pair in the X chromosome. Akin to human cancer-germline (CG) genes, dhd and TrxT expression is normally sex and germline specific, but becomes upregulated in brain tumours of either sex caused by mutation in l(3)malignant brain tumour (l(3)mbt). Using CRISPR/Cas9-mediated knock-out alleles and RNA-seq, we show that while both TrxT and dhd are dispensable for normal brain development, each of them has a significant but partial effect on l(3)mbt brain tumour development as well as on tumour-linked transcriptomic signatures. Moreover, this effect is enhanced by the concomitant removal of both thioredoxins that strongly inhibits l(3)mbt larval brain tumour traits, reduces gene expression differences between tumour and wild-type tissue, and essentially erases all traces of gene expression differences between tumours from larvae of different sex. These results show that TrxT and dhd play a major synergistic role in the emergence of l(3)mbt tumour-linked transcriptomic signatures and tumour development, which is remarkable taking into account that these two genes are never expressed together under normal conditions. We have also found that TrxT is crucial but dhd has no effect on the growth of male-derived l(3)mbt allografts, hence suggesting that the initial stages of tumour development and sustained, long-term tumour growth may depend upon different molecular pathways. In humans, head-to-head inverted gene pairs are abundant among the CG genes that map to the X chromosome, some of which have been shown to work as a unit cancer. Our results identify the first instance of an X-linked, head-to-head CG gene pair in Drosophila and underpin the potential of such CG genes that are dispensable for normal development and homeostasis of somatic tissue, as targets to curtail malignant growth with minimal impact on overall health.

cancer biology↗

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↗

bioRxiv2017

A deep convolutional neural network approach for astrocyte detection

Astrocytes are involved in brain pathologies such as trauma or stroke, neurodegenerative disorders like Alzheimers and Parkinsons disease, chronic pain, and many others. Determining cell density and timing of morphological and biochemical changes is important for a proper understanding of the role of astrocytes in physiological and pathological conditions. One of the most important of such analyses is astrocytes count within a complex tissue environment in microscopy images. The most widely used approaches for the quantification of microscopy images data are either manual stereological cell counting or semi-automatic segmentation techniques. Detecting astrocytes automatically is a highly challenging computational task, for which we currently lack efficient image analysis tools. In this study, we developed a fast and fully automated software that assesses the number of astrocytes using Deep Convolutional Neural Networks (DCNN). The method highly outperforms state-of-the-art image analysis and machine learning methods and provides detection accuracy and precision comparable to that of human experts. Additionally, the runtime of cell detection is significantly less than other three analyzed computational methods, and it is faster than human observers by orders of magnitude. We applied DCNN-based method to examine the number of astrocytes in different brain regions of rats with opioid-induced hyperalgesia/tolerance (OIH/OIT) as morphine tolerance is believed to activate glial cells in the brain. We observed strong positive correlation between manual cell detection and DCNN-based analysis method for counting astrocytes in the brains of experimental animals.

bioinformatics↗

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↗