Biology subjects

Froehlich, H.

Publications and source records attributed to Froehlich, H..

7 recordsLinked to original sources

bioRxiv2026

Magel2 deficiency drives microglial synaptic engulfment and alters neuron-microglia interactions in Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder characterized by hypothalamic dysfunction, developmental delay, hyperphagia, and cognitive impairment, yet the cellular mechanisms underlying circuit abnormalities remain unclear. Here, we show that loss of the imprinted gene Magel2 remodels microglial state and function in a mouse model of PWS. Three-dimensional morphometric analysis of hypothalamic microglia revealed reduced structural complexity and contracted territorial domains, emerging early in development and largely independent of obesity. Single-nucleus RNA sequencing of hypothalamic brain tissue identified extensive transcriptional reprogramming involving immune signaling, cellular communication, ribosome function, synaptic organization, and protein translation, with stronger effects in females. Magel2 deficiency also increased microglia-neuron contacts and synaptic engulfment, accompanied by reduced synaptic marker density, suggesting aberrant refinement of hypothalamic circuits. Analyses of human post-mortem hypothalamic tissue from individuals with PWS confirmed microglial structural alterations and increased complement engagement. Ligand-receptor inference further revealed reorganization of microglia-centered communication networks, including altered adhesion, trophic, and immune signaling consistent with enhanced phagocytic states. Together, these findings identify microglial remodeling and increased synaptic engagement as central features of Magel2 deficiency, implicate microglia in hypothalamic circuit dysfunction in PWS, and highlight excessive synaptic pruning as a novel dysfunctional mechanism of PWS.

neuroscience↗

bioRxiv2025

ELITE: E3 Ligase Inference for Tissue specific Elimination: A LLM Based E3 Ligase Prediction System for Precise Targeted Protein Degradation

Targeted protein degradation (TPD) has transformed modern drug discovery by harnessing the ubiquitin-proteasome system to eliminate disease-driving proteins previously deemed undruggable. However, current approaches predominantly rely on a narrow set of ubiquitously expressed E3 ligases, such as Cereblon (CRBN) and Von Hippel-Lindau (VHL), which limits tissue specificity, increases systemic toxicity, and fosters resistance. Here, we present an AI-driven framework for the rational identification of tissue-specific E3 ligases suitable for precision-targeted degradation. Our model leverages an BERT-based protein language architecture trained on billions of sequences to generate contextual embeddings that capture structural and functional motifs relevant for E3-substrate compatibility. By integrating these embeddings with tissue-resolved protein-protein interaction data, the framework predicts ligase-target interactions that are both biologically plausible and context-restricted. This enables the prioritization of ligases capable of driving selective degradation of pathogenic proteins within disease-relevant tissues. The proposed approach offers a scalable path to expand the E3 ligase repertoire and advance TPD toward true precision medicine. The AI architecture used in this study builds upon a well-established, state-of-the-art protein language modeling framework rather than introducing a fundamentally new model design. The principal contribution instead lies in the deliberate engineering of a task-specific dataset that integrates large-scale protein sequence embeddings with tissue-resolved expression and interaction information tailored to targeted protein degradation. By curating and harmonizing these heterogeneous data modalities for the explicit purpose of identifying tissue-selective E3 ligases, we enable a generalizable foundation model to be effectively adapted to a previously unexplored application context. This data-centric advance underpins the frameworks ability to generate biologically meaningful, context-aware predictions and represents a central contribution of the present work. SignificanceCurrent computational strategies for targeted protein degradation (TPD) largely ignore biological context, relying on global E3-substrate datasets that overlook tissue specificity and thus cannot anticipate off-target toxicity or resistance. Our work introduces a context-aware, AI-driven framework that integrates large-scale protein language model embeddings with tissue-resolved protein-protein interaction data to predict compatible and tissue-selective E3 ligases for any pathogenic target. By learning biochemical compatibilities directly from sequence space, this approach transcends the limitations of curated interaction databases and enables generalization to novel ligases or disease mutations. The resulting ligand-target rankings combine biochemical plausibility with spatial expression constraints, providing a scalable foundation for designing degraders that act precisely within disease-relevant tissues. This represents a conceptual and technical advance toward precision degradomics - a next generation of targeted protein degradation where therapeutic selectivity is defined not only by molecular affinity but also by cellular and tissue context. Subsequent steps will include structural integration for ternary complex modeling, biochemical validation of predicted pairs, and the deployment of a public platform to guide tissue-specific degrader discovery. Data science maturityDSML3: Development/pre-production: Data science output has been rolled out/validated across multiple domains/problems

bioinformatics↗

bioRxiv2022

Analysis of the hypothalamic oxytocin system and oxytocin receptor-expressing astrocytes in a mouse model of Prader-Willi syndrome

Prader-Willi syndrome (PWS) is a neurodevelopmental disorder characterized by hyperphagia, obesity, developmental delay and intellectual disability. Studies suggest dysfunctional signaling of the neuropeptide oxytocin as one of the key mechanisms in PWS, and administration of oxytocin via intranasal or systemic routes yielded promising results in both humans and mouse models. However, a detailed assessment of the oxytocin system in mouse models of PWS such as the Magel2-deficient Magel2tm1.Stw mouse, is lacking. In this study, we performed an automated counting of oxytocin cells in the entire paraventricular nucleus of the hypothalamus of Magel2tm1.Stw and wild-type control mice and found a significant reduction in the caudal part, which represents the parvocellular subdivision. In addition, based on the recent discovery that some astrocytes express the oxytocin receptor (OTR), we performed detailed analysis of astrocyte numbers and morphology in various brain regions, and assessed expression levels of the astrocyte marker GFAP, which was significantly decreased in the hypothalamus, but not other brain regions in Magel2tm1.Stw mice. Finally, we analyzed the number of OTR-expressing astrocytes in various brain regions and found a significant reduction in the nucleus accumbens of Magel2tm1.Stw mice, as well as a sex-specific difference in the lateral septum. This study suggests a role for caudal PVN OT neurons as well as OTR-expressing astrocytes in a mouse model of PWS, provides novel information about sex-specific expression of astrocytic OTRs, and presents several new brain regions containing OTR-expressing astrocytes in the mouse brain.

neuroscience↗

bioRxiv2022

Accurate Prediction of Virus-Host Protein-Protein Interactions via a Siamese Neural Network Using Deep Protein Sequence Embeddings

Prediction and understanding of tissue-specific virus-host interactions have relevance for the development of novel therapeutic interventions strategies. In addition, virus-like particles (VLPs) open novel opportunities to deliver therapeutic compounds to targeted cell types and tissues. Given our incomplete knowledge of virus-host interactions on one hand and the cost and time associated with experimental procedures on the other, we here propose a novel deep learning approach to predict virus-host protein-protein interactions (PPIs). Our method (Siamese Tailored deep sequence Embedding of Proteins - STEP) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. For the SARS-CoV-2 spike protein our method predicts an interaction with the sigma 2 receptor, which has been suggested as a drug target. As a second use case, we apply STEP to predict interactions of the JCV VP1 protein showing an enrichment of PPIs with neurotransmitters, which are known to function as an entry point of the virus into glial brain cells. In both cases we demonstrate how recent techniques from the field of Explainable AI (XAI) can be employed to identify those parts of a pair of sequences, which most likely contribute to the protein-protein interaction. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as XAI methods for the analysis of biological sequences. We have made our method publicly available via GitHub. The bigger pictureDevelopment of novel cell and tissue specific therapies requires a profound knowledge about protein-protein interactions (PPIs). Identifying these PPIs with experimental approaches such as biochemical assays or yeast two-hybrid screens is cumbersome, costly, and at the same time difficult to scale. Computational approaches can help to prioritize huge amounts of possible PPIs by learning from biological sequences plus already-known PPIs. In this work, we developed a novel approach (Siamese Tailored deep sequence Embedding of Proteins - STEP) that is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network architecture. We use this approach to train models by utilizing protein sequence information and known PPIs. After evaluating the high prediction performance of STEP in comparison to an existing method, we apply it to two use cases, SARS-CoV-2 and John Cunningham polyomavirus (JCV), to predict virus protein to human host interactions. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of natural language processing as well as Explainable AI methods for the analysis of biological sequence data. HighlightsO_LIA novel deep learning approach (STEP) predicts virus protein to human host protein interactions based on recent deep protein sequence embedding and a Siamese neural network architecture C_LIO_LIPrediction of protein-protein interactions of the JCV VP1 protein and of the SARS-CoV-2 spike protein C_LIO_LIIdentification of parts of sequences that most likely contribute to the protein-protein interaction using Explainable AI (XAI) techniques C_LI Data Science MaturityDSML 3: Development/Pre-production: Data science output has been rolled out/validated across multiple domains/problems

bioinformatics↗

bioRxiv2021

Mitochondrial dysfunction and oxidative stress contribute to cognitive and motor impairment in FOXP1 syndrome

There is increasing evidence that mitochondrial homeostasis - influenced by both genetic and environmental factors - is crucial in neurodevelopment. FOXP1 syndrome is a neurodevelopmental disorder that manifests motor dysfunction, intellectual disability, autism and language impairment. In this study, we used a Foxp1+/- mouse model to address whether cognitive and motor deficits in FOXP1 syndrome are associated with mitochondrial dysfunction and oxidative stress. Here we show that genes with a role in mitochondrial biogenesis and dynamics (e.g. Foxo1, Pgc-1, Tfam, Opa1, and Drp1) were dysregulated in the striatum of Foxp1+/- mice at different postnatal stages. Furthermore, in the striatum of Foxp1+/- animals, mitochondrial membrane potential was disrupted, and reactive oxygen species, lipid peroxidation and cytochrome c release were significantly elevated. These features can explain the reduced neurite branching, learning and memory, endurance, and motor coordination that we observed in these animals. Taken together, we provide strong evidence of mitochondrial dysfunction in Foxp1+/- mice, suggesting that insufficient energy supply and excessive oxidative stress underlies the cognitive and motor impairment in FOXP1 deficiency.

molecular biology↗

bioRxiv2020

The COVID-19 PHARMACOME: Rational Selection of Drug Repurposing Candidates from Multimodal Knowledge Harmonization

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific communitys massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

bioinformatics↗

bioRxiv2019

GuiltyTargets: Prioritization of Novel Therapeutic Targets with Deep Network Representation Learning

The majority of clinical trial failures are caused by low efficacy of investigated drugs, often due to a poor choice of target protein. Computational prioritization approaches aim to support target selection by ranking candidate targets in the context of a given disease. We propose a novel target prioritization approach, GuiltyTargets, which relies on deep network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled machine learning for candidate ranking. We evaluated our approach on six diseases of different types (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.94, significantly outperforming a previous approach, which relies on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. Applying GuiltyTargets to Alzheimers disease resulted into a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinsons disease, highlighting the potential for target repositioning of our method.\n\nAvailabilityThe GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets.\n\nAuthor summaryMany drug candidates fail in clinical trials due to low efficacy. One of the reasons is the choice of the wrong target protein, i.e. perturbation of the protein does not effectively modulate the disease phenotype on a molecular level. In consequence many patients do not demonstrate a clear response to the drug candidate. Traditionally, targets are selected based on evidence from the literature and follow-up experiments. However, this process is very labor intensive and often biased by subjective choices. Computational tools could help a more rational and unbiased choice of target proteins and thus increase the chance of drug discovery programs. In this work we propose a novel machine learning based method for target candidate ranking. The method (GuiltyTargets) captures properties of known targets to learn a ranking of candidates. GuiltyTargets compares favorably against existing machine learning based target prioritization methods and allowed us to propose novel targets for Alzheimers disease.

bioinformatics↗