Biology subjects

Jung, V.

Publications and source records attributed to Jung, V..

7 recordsLinked to original sources

bioRxiv2026

Resolving context-specific protein-protein interactomes forbiological discovery and therapeutic target prioritisation

Protein function is shaped by cellular context, yet most protein representations and interaction maps remain context-agnostic. Here we present ProtScape, a multiscale graph-learning framework integrating global protein interactions, cell-type gene expression and protein language models to learn context-specific representations and infer interactomes across more than 200 cell types. ProtScape substantially outperforms existing approaches in interaction reconstruction, increasing the area under the precision-recall curve by 40 percentage points. Its predicted interactions were supported by held-out continuous STRING global evidence, while its representations recovered higher-order protein organisation. In patient-derived amyotrophic lateral sclerosis motor neurons, ProtScape revealed stage-specific network changes implicating RAB-dependent trafficking as a candidate early disease mechanism. In Parkinson's disease, it recovered clinically supported therapeutic targets from a proteome-wide search space 16-fold smaller than that required by competing representations. Together, ProtScape provides a scalable framework for translating context-specific interactome organisation into experimentally testable disease mechanisms and therapeutic hypotheses.

bioinformatics↗

bioRxiv2026

MDA5 multimerization on LINE RNA drives pathogenic extracellular immune complexes in autoimmunity

Autoantibodies are hallmarks of many autoimmune diseases, but their potential pathogenic roles, particularly for those targeting intracellular proteins, remain unclear. Anti-MDA5-positive dermatomyositis (anti-MDA5 DM) is characterized by autoantibodies against the intracellular protein MDA51,2, a conserved innate immune receptor that recognizes viral dsRNA by forming filaments3. Here, using four patient-derived monoclonal autoantibodies (mAbs), we reconstitute and define the molecular architecture, biogenesis, and immunological activity of pathogenic MDA5 immune complexes. Our cryo-EM analysis revealed that these mAbs bind dsRNA-scaffolded MDA5 filaments in at least two distinct binding modes, each exhibiting striking epitope convergence using germline-encoded residues. Extracellular immune complexes formed between mAbs and filamentous, but not monomeric, MDA5 potently activate multiple innate immune pathways, with the magnitude of activation determined by antibody binding mode and immune complex stoichiometry. Antibody bivalency further crosslinks MDA5 filaments into higher-order aggregates with heightened immunostimulatory activity, demonstrating an active role of autoantibodies in shaping immune complex architecture. Analysis of patient plasma reveals elevated levels of extracellular MDA5 filaments and identifies LINE retroelement-derived dsRNA as a structural scaffold. Notably, MDA5 immune complexes induce endogenous LINE dsRNA expression, likely promoting additional MDA5 filament formation and extracellular release through inflammatory cell death. These data thus support a self-amplifying inflammatory cycle as a pathogenic mechanism for anti-MDA5 DM. Collectively, our study defines a broadly applicable architectural principle, in which higher-order organization and binding modes of autoantibodies--beyond antibody affinity or nucleic acid presence alone--govern innate immune activation.

biochemistry↗

bioRxiv2025

Extended pre-training of histopathology foundation models uncovers co-existing breast cancer archetypes characterized by RNA splicing or TGF-β dysregulation

In recent years, histopathology foundation models (hFM) have rapidly advanced in size and complexity, achieving excellent performance in tasks such as cancer diagnosis and biomarker discovery. Here, we reveal novel capabilities of these models by specializing hFMs, originally trained on diverse tissue types, specifically to invasive tumor tissue. It enables unprecedented discrimination of visually similar yet molecularly distinct tumor regions, that were previously indistinguishable by baseline models, which eventually leads to uncovering new biological insights into breast cancer. Our contributions are threefold. First, to the best of our knowledge, this is the first study to systematically evaluate the biological concepts encoded within hFM representations across multiple scales. Second, we explore extended pre-training to identify optimal conditions that enhance the models ability to encode richer, tumor tissue-specific biological concepts. We show that this refinement strategy transforms generalist models into a specialist one capable of resolving subtle, recurrent tumor regions with distinct morphological and molecular identities, called tumor archetypes. Finally, leveraging this specialized model, we uncover two dominant tumor archetypes in invasive breast cancer characterized by distinct aberrant gene expression signatures, notably RNA metabolism dysregulation and TGF-{beta} signaling. Strikingly, these archetypes coexist within the same tumors as spatially distinct regions with varying densities and patterns, and are recurrent across patients, highlighting their universality and potential clinical relevance for patient stratification. Altogether our study demonstrates how extended pre-training of state-of-the-art hFM with specific tumor tissues can unlock rich molecular and morphological information encoded in H&E images. By providing a more accessible approach to investigating tumor heterogeneity, this work opens new avenues for precision oncology, using routine histopathology slides and low computational resources. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/645192v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@38e660org.highwire.dtl.DTLVardef@19ce335org.highwire.dtl.DTLVardef@108e8e6org.highwire.dtl.DTLVardef@1f25bc0_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

cancer biology↗

bioRxiv2025

BitBIRCH Clustering Refinement Strategies

Chemical libraries are becoming not only increasingly bigger, but they are doing so at an accelerated pace. Keeping up with this explosion in chemical data demands more than just hardware upgrades, we need dramatically more efficient algorithms as well. We have been working in this direction, with the introduction of the iSIM framework, which uses n-ary similarity to speed up the processing of very large sets. Recently, we showed how to use this technique to cluster billions of molecules with unprecedented efficiency through the BitBIRCH algorithm. In this Application Note we present a package fully-dedicated to expanding on the BitBIRCH method, including multiple options that give the user appreciable control over the tree structure, while dramatically improving the quality of the final partitions. Remarkably, this is achieved without compromising the efficiency of the original method. We also present new post-processing tools that help dissect the clustering information, as well as ample examples showcasing the new functionalities. BitBIRCH is publicly available at: https://github.com/mqcomplab/bitbirch.

bioinformatics↗

bioRxiv2024

Efficient clustering of large molecular libraries

The widespread use of Machine Learning (ML) techniques in chemical applications has come with the pressing need to analyze extremely large molecular libraries. In particular, clustering remains one of the most common tools to dissect the chemical space. Unfortunately, most current approaches present unfavorable time and memory scaling, which makes them unsuitable to handle million- and billion-sized sets. Here, we propose to bypass these problems with a time- and memory-efficient clustering algorithm, BitBIRCH. This method uses a tree structure similar to the one found in the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm to ensure O(N) time scaling. BitBIRCH leverages the instant similarity (iSIM) formalism to process binary fingerprints, allowing the use of Tanimoto similarity, and reducing memory requirements. Our tests show that BitBIRCH is already > 1,000 times faster than standard implementations of the Taylor-Butina clustering for libraries with 1,500,000 molecules. BitBIRCH increases efficiency without compromising the quality of the resulting clusters. We explore strategies to handle large sets, which we applied in the clustering of one billion molecules under 5 hours using a parallel/iterative BitBIRCH approximation.

bioinformatics↗

bioRxiv2024

Beyond Kaftrio: mechanistic insights to maximize N1303K-CFTR rescue

IntroductionN1303K is the fourth most frequent Cystic Fibrosis (CF) causing mutation. People with CF (pwCF) clinical status can be improved by Elexacaftor(ELX)/Tezacaftor(TEZ)/Ivacaftor (ETI) combotherapy. We investigated the mechanism underlying N1303K-CFTR rescue. MethodsN1303K-CFTR expression and maturation was evaluated by Western Blot in cell lines and Human Nasal Epithelial Primary Cells (HNECs). Cell surface expression was studied by nanoluciferase complementation assay and TurboID proximity labeling. Functional rescue was tested in vitro by YFP-Based Assay and Short Circuit Current. ResultsCorrection by ELX/TEZ increases N1303K-CFTR amounts, but not its maturation in CFTR-expressing HEK and 16HBEge cell lines and in HNECs. In control conditions, N1303K-CFTR is more distributed at the cell surface and significantly more surface partners are identified in the N1303K-CFTR interactome as compared to F508del-CFTR in HEK cells. ELX/TEZ induces a global stabilization of N1303K-CFTR without favoring its plasma membrane relocation in contrast to F508del-CFTR which is redistributed to the membrane. ETI increases N1303K-CFTR activity in HNECs and can be increased by API co-potentiation with a predicted increase in Forced Expiratory Volume in 1 second (ppFEV1) by respectively 13(2)% and 18%(3). This is consistent with a gain in ppFEV1 reported in pwCF carrying the N1303K mutation and additional improvement by API in a patient. ConclusionThese results support the expansion of ETI approval to N1303K mutation but highlight different mechanisms of action than for F508del.

physiology↗

bioRxiv2020

From taxonomic to functional dark diversity: exploring the causes of potential biodiversity and its implications for conservation

O_LIDark diversity is an emerging and promising concept proposed to estimate the recruitment potential of natural communities and guide conservation and restoration policies. It represents all the species that could be present in a community due to favourable environmental conditions, but are currently lacking. To date, experimental approaches only measured taxonomic dark diversity, mainly based on species coexistence, which relies partly on neutral processes. Thus, these approaches may fail to identify the biodiversity which is lacking for deterministic reasons, and can hence hardly bring out suitable restoration methods. C_LIO_LIHere, we propose a novel method to estimate dark diversity, which is based on more deterministic coexistence: the coexistence of species functional features. We adapted the Beals co-occurrence index using functional groups, and we estimated functional dark diversity based on coexistence of functional groups. We then made use of functional dark diversity to address a persistent issue of restoration ecology: how does passive rewilding impact the ecological integrity of recovered communities? We compared spontaneous, secondary woodlands with ancient forests, in terms of taxonomic and functional dark diversity of vascular plants and spiders. C_LIO_LIOur results indicated that functional dark diversity does not equate to taxonomic dark diversity. Considering plants, recent woodlands surprisingly harboured less functional dark diversity than ancient forests, while they had a very similar amount of taxonomic dark diversity. Concerning spiders, recent woodlands harboured a similar amount of functional dark diversity as ancient forests, but more taxonomic dark diversity. Also, the composition of functional dark diversity differed between forest types, shedding light on their past assembly processes and unveiling their potential for conservation and effective restoration. C_LIO_LISynthesis and applications. Functional dark diversity brings novel perspectives for ecological diagnostic and restoration. Combined to taxonomic dark diversity, it enables to identify easily the deterministic constrains which limit the re-assembly of ecological communities after land-use changes and to predict the realistic, possible establishments of functional features. Here, we showed that spontaneous woodlands can have very similar, sometimes even higher, ecological integrity than that of ancient forests, and hence may be valuable habitats to be conserved from an ecological perspective. C_LI

ecology↗