Biology subjects

Jung, F.

Publications and source records attributed to Jung, F..

13 recordsLinked to original sources

bioRxiv2026

Multi-omics and biophysical phosphoproteomics upon BRAF inhibition uncover functional networks of BRAFV600E-driven signaling

Dysregulated kinase activity drives oncogenic signaling, disrupts cellular homeostasis, and promotes tumour progression. The BRAFV600E mutation constitutively activates the MAPK pathway and is a key therapeutic target in melanoma and other cancers, but the functional relevance of most downstream phosphorylation events and mechanisms of drug resistance remain unclear. To address this, a global multi-omic model of BRAF inhibition response was established in BRAFV600E-mutant cells by integrating time-resolved and biophysical phosphoproteomics, transcriptomics, and thermal proteome profiling. Ultradeep phosphoproteomics revealed extensive phosphorylation changes upon BRAF inhibitor treatment, while biophysical phosphoproteomics identified phosphorylation events linked to altered protein solubility and subcellular localization, suggesting changes in nucleic acid interactions and nuclear reorganisation. Network-based integration of these datasets prioritized functionally relevant phosphorylation sites and kinases. Experimental validation identified CDK9, CLK3, and TNIK as critical regulators of BRAFV600E signaling and candidate targets for combinatorial inhibition capable of re-sensitising resistant cells. The transcription factor ETV3 emerged as a previously unrecognised effector of BRAF signaling. Biophysical proteomics data confirmed that ETV3 phosphorylation modulates DNA-binding, while functional assays combining knockdown, metabolomics, and drug screening demonstrated its role in coordinating transcriptional and metabolic adaptations to BRAF inhibition. This study provides a systems-level framework linking phosphorylation dynamics to protein function and phenotype, identifies ETV3 as a new node in oncogenic BRAF signaling, and illustrates how integrated, site-resolved models can reveal mechanisms of kinase-driven oncogenesis. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=43 SRC="FIGDIR/small/704793v1_ufig1.gif" ALT="Figure 1"> View larger version (13K): org.highwire.dtl.DTLVardef@1df1401org.highwire.dtl.DTLVardef@9a77a5org.highwire.dtl.DTLVardef@124f819org.highwire.dtl.DTLVardef@1c6b57_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LITime- and cell type-resolved phosphoproteomics maps BRAF inhibition dynamics C_LIO_LIBiophysical phosphoproteomics, combining quantitative phosphoproteomics with solubility profiling or nuclear fractionation, reveals phosphorylation-driven changes of protein solubility and localization C_LIO_LIIntegration of abundance and biophysical phosphoproteomics data identifies functionally relevant phosphorylation events of BRAFV600E signaling C_LIO_LINetwork integration of multimodal phosphoproteomic, transcriptomic and thermal proteome profiling data links signaling to protein function and cellular phenotypes C_LIO_LIBiophysical evidence improves models and identifies non-canonical kinases driving BRAF signaling as well as novel downstream regulators such as ETV3 C_LIO_LIFollow-up experiments reveal a ETV3-GLUT3-mediated metabolic adaptation in BRAFV600E cells C_LI

systems biology↗

bioRxiv2025

Cancer pathway connectivity resolved by drug perturbation and RNA sequencing

Pathway inhibitors are a backbone of cancer treatment. The configuration of pathway dependencies varies from tumour to tumour. Better treatment for individual patients could be designed if the pathway wiring were readily measurable. Here, we characterise the transcriptional responses of 116 lymphoma patient samples exposed to ten drug perturbations. We used factor analysis to decompose individual and shared drug effects, thereby generating a pathway connectivity map of chronic lymphocytic leukemia (CLL). The expression profiles of the major disease subgroups, defined by IGHV mutation status, became more similar to each other after BTK inhibition, consistent with B-cell receptor (BCR) signalling as a driver of their phenotypic difference. An even stronger convergence was observed with combined IRAK4 and BTK inhibition, indicating cooperation of BCR and toll-like receptor (TLR) signalling in CLL. We identified genetic aberrations (BRAF, TP53, deletion 17p, deletion 15q, trisomy 12) that modulated drug effects in CLL and constituted specific interaction patterns. IRAK4 inhibition effects depended on the presence of trisomy 12, a finding that suggests that the trisomy 12 driver event in CLL acts by gene dosage-dependent IRAK4 upregulation and amplification of the BCR/TLR cooperation. Our results highlight the potential of systematic drug perturbation assays with transcriptome readout to map pathway interconnectivity and functionally annotate tumour drivers.

systems biology↗

bioRxiv2025

DMC-BrainMap - an open-source, end-to-end tool for multi-feature brain mapping across species

A central goal of current neuroscience research is to understand how behavior emerges from neuronal circuit activity. For this, cellular and circuit components in intact brains need to be identified and studied using e.g. viral tracing, electrophysiology, imaging, activity perturbations, and behavior. A commonality of these approaches is the need to establish the anatomical locations of both experimental parameters (e.g. recording/injection sites) and circuit components (e.g. cell bodies/projections). The development of standardized coordinate systems, i.e. reference brain atlases, has proven essential for reproducible, cross-laboratory mapping of anatomical data. However, the process of mapping, analyzing, and visualizing anatomical data remains challenging, especially for users lacking programming expertise. Here, we introduce DMC-BrainMap, an open-source napari plugin designed as a user-friendly tool for streamlined processing and whole-brain analysis of anatomical data. The working principle and core functionalities include all steps after image acquisition, i.e., preprocessing of images, registration of images to a reference atlas, segmentation of different anatomical features, and data analysis/visualization. DMC-BrainMap can be applied to histological data obtained from a variety of model organisms at different developmental stages, including mouse, rat, and zebrafish. We demonstrate the utility of DMC-BrainMap by mapping and quantifying cell bodies, axonal densities, injection sites as well as placement of optical fibers and Neuropixels probes. Further, we show how spatial transcriptomics data can be integrated into the DMC-BrainMap workflow. By eliminating the need for programming by the user, DMC-BrainMap provides an easy-to-use tool for increased rigor, reproducibility, and data sharing in neuroscientific research involving animal models.

neuroscience↗

bioRxiv2024

A Prefrontal Cortex Map based on Single Neuron Activity

The intrinsic organization underlying the central cognitive role of the prefrontal cortex (PFC) is poorly understood. The work to date has been dominated by cytoarchitecture as a canvas for studies on the PFC, constraining concepts, analyses, results, and their interpretations to pre-configured delimitations that might not be relevant to function. We approached organization by profiling the activity and spatial location of >23,000 neurons recorded in awake mice. Regularly firing neurons were over-represented in most PFC subregions, yet a fine-grained activity map of the PFC did not align with cytoarchitecturally defined subregions. Instead, we observed a robust relationship between spontaneous activity patterns and intra-PFC hierarchy, suggesting internal organization principles transcending cytoarchitecture. Single neuron responses to sounds did not reflect intra-PFC hierarchy but were linked to spontaneous firing rate, indicating that responsiveness increases with excitability and is decoupled from the PFCs intrinsic operational structure. Our data-driven approach provides a scalable roadmap to explore functional organizations in diverse brain regions and species, opening avenues to link activity, structure, and function in the brain.

neuroscience↗

bioRxiv2024

A striosomal accumbens pathway drives compulsive seeking behaviors through an aversive Esr1+ hypothalamic-habenula circuit.

The lateral hypothalamic area (LHA) integrates external stimuli with internal states to drive the choice between competing innate or value-driven motivated behaviors. Projections from the LHA to the lateral habenula (LHb) shape internal states, with excitatory estrogen receptor 1-expressing (Esr1+) LHA-LHb neurons driving aversive responses and sustained negative states. Here, we identify and functionally characterize a specific projection from the nucleus accumbens (ACB) that targets Esr1+ LHA-LHb neurons. Using cell-type-specific tracing of monosynaptic inputs, single-nucleus RNA sequencing, and neuroanatomical mapping, we demonstrate that the Esr1+ LHA-LHb pathway receives a major input from a striosomal Tac1+/Tshz1+/Oprm1+ ACB neuron subtype. Intersectional cell-type-specific and input-output defined optogenetic manipulation of this ACB-LHA-LHb pathway revealed its role in signaling aversion after repeated activation, with the negative behavioral state being dependent on recruitment of Esr1+ LHA-LHb neurons. Importantly, we found that activation of the D1+ ACB-LHA pathway drives reward-independent compulsive-like seeking behaviors, expressed as compulsive digging or poking behaviors. We found that these complex yet stereotyped behaviors compete with highly motivated states and can override the need for natural rewards or social stimuli. Our findings reveal a discrete striosomal Tac1+ ACB projection targeting the aversive Esr1+ LHA-LHb pathway as a key circuit that promotes compulsive seeking behaviors over goal-directed actions.

neuroscience↗

bioRxiv2024

A priority code in presequences: mitochondrial targeting signals assign specific import characteristics to precursor proteins

The biogenesis of mitochondria relies on the import of hundreds of different precursor proteins from the cytosol. Most of these proteins are synthesized with N-terminal presequences which serve as mitochondrial targeting signals. Presequences consistently form amphipathic helices, but they considerably differ in respect to their primary structure and length. Here we show that presequences can be classified into seven different groups based on their specific features. Using a test set of different presequences, we observed that group A presequences endow precursor proteins with improved in vitro import characteristics. We developed IQ-Compete (for Import and de-Quenching Competition assay), a novel assay based on fluorescence de-quenching, to monitor the import efficiencies of mitochondrial precursors in vivo. With this assay, we confirmed the increased import competence of group A presequences. Using mass spectrometry, we found that the presequence of the group A protein Oxa1 specifically recruits the tetratricopeptide repeat (TPR) containing protein TOMM34 to the cytosolic precursor protein. TOMM34 apparently serves as a presequence-specific targeting factor which increases the import efficiency of a specific subset of mitochondrial precursor proteins. Our results suggest that presequences contain a protein-specific priority code that encrypts the targeting mechanism of individual mitochondrial precursor proteins.

biochemistry↗

bioRxiv2024

Escape from X inactivation is directly modulated by levels of Xist non-coding RNA

In placental females, one copy of the two X chromosomes is largely silenced during a narrow developmental time window, in a process mediated by the non-coding RNA Xist 1. Here, we demonstrate that Xist can initiate X-chromosome inactivation (XCI) well beyond early embryogenesis. By modifying its endogenous level, we show that Xist has the capacity to actively silence genes that escape XCI both in neuronal progenitor cells (NPCs) and in vivo, in mouse embryos. We also show that Xist plays a direct role in eliminating TAD-like structures associated with clusters of escapee genes on the inactive X chromosome, and that this is dependent on Xists XCI initiation partner, SPEN 2. We further demonstrate that Xists function in suppressing gene expression of escapees and topological domain formation is reversible for up to seven days post-induction, but that sustained Xist up-regulation leads to progressively irreversible silencing and CpG island DNA methylation of facultative escapees. Thus, the distinctive transcriptional and regulatory topologies of the silenced X chromosome is actively, directly - and reversibly - controlled by Xist RNA throughout life.

developmental biology↗

bioRxiv2023

Dextromethorphan inhibits collagen transport in the endoplasmic reticulum eliciting an anti-fibrotic response in ex-vivo and in vitro models of pulmonary fibrosis

Excessive deposition of fibrillar collagen in the interstitial extracellular matrix (ECM) of human lung tissue causes fibrosis, which can ultimately lead to organ failure. Despite our understanding of the molecular mechanisms underlying the disease, a cure for pulmonary fibrosis has not yet been found. In this study, we screened an FDA-approved drug library containing 712 drugs and found that Dextromethorphan (DXM), a cough expectorant, significantly reduces the amount of excess fibrillar collagen deposited in the ECM in in-vitro cultured primary human lung fibroblasts (NHLF) and ex-vivo cultured human precision-cut lung slice (hPCLS) models of lung fibrosis. Reduced extracellular fibrillar collagen levels in the ECM upon DXM treatment are due to a reversible trafficking inhibition of collagen type I (COL1) in the endoplasmic reticulum (ER) in TANGO1 and HSP47 positive structures. Mass spectrometric analysis shows that DXM causes hyper-hydroxylation of proline and lysine residues on Collagen (COL1, COL3, COL4, COL5, COL7, COL12) and Latent-transforming growth factor beta-binding protein (LTBP1 and LTBP2) peptides coinciding with their secretion block. In addition, thermal proteome profiling of cells treated with DXM shows increased thermal stability of prolyl-hydroxylases such as P3H2, P3H3, P3H4, P4HA1 and P4HA2, suggesting a change in activity. Transcriptome analysis of pro-fibrotic stimulated NHLFs and hPCLS upon DXM treatment showed activation of an anti-fibrotic program via regulation of pathways such as those involved in the MMP-ADAMTS axis, WNT, and fibroblast-to-myofibroblast differentiation. Taken together, the data obtained from both in-vitro and ex-vivo models of fibrogenesis show that Dextromethorphan has potent anti-fibrotic activity by efficient inhibition of COL1 membrane trafficking in the ER.

cell biology↗

bioRxiv2022

Esr1+ hypothalamic-habenula neurons shape aversive states.

Excitatory projections from the lateral hypothalamic area (LHA) to the lateral habenula (LHb) drive aversive responses. We used Patch-seq guided multimodal classification to define the structural and functional heterogeneity of the LHA-LHb pathway. Our classification identified six glutamatergic neuron types with unique electrophysiological properties, molecular profiles, and projection patterns. We found that genetically-defined LHA-LHb neurons signal distinct aspects of emotional or naturalistic behaviors: Esr1+ LHA-LHb neurons induce aversion, whereas Npy+ LHA-LHb neurons control rearing behavior. Repeated optogenetic drive of Esr1+ LHA-LHb neurons induces a behaviorally persistent aversive state, and large-scale recordings showed a region-specific neural representation of the aversive state in the prelimbic region of the prefrontal cortex. We further found that exposure to unpredictable mild shocks induced a sex-specific sensitivity to develop a stress state in female mice, which was associated with a specific shift in the intrinsic properties of bursting-type Esr1+ LHA-LHb neurons. In summary, we describe the diversity of LHA-LHb neuron types, and provide evidence for the role of Esr1+ neurons in aversion and sexually dimorphic stress sensitivity.

neuroscience↗

bioRxiv2022

Can the Kuznetsov Model Replicate and Predict Cancer Growth in Humans?

Several mathematical models to predict tumor growth over time have been developed in the last decades. A central aspect of such models is the interaction of tumor cells with immune effector cells. The Kuznetsov model (Kuznetsov et al. (1994), Bulletin of Mathematical Biology, vol. 56, no. 2, pp. 295-321) is the most prominent of these models and has been used as a basis for many other related models and theoretical studies. However, none of these models have been validated with large-scale real-world data of human patients treated with cancer immunotherapy. In addition, parameter estimation of these models remains a major bottleneck on the way to model-based and data-driven medical treatment. In this study, we quantitatively fit Kuznetsovs model to a large dataset of 1472 patients, of which 210 patients have more than six data points, by estimating the model parameters of each patient individually. We also conduct a global practical identifiability analysis for the estimated parameters. We thus demonstrate that several combinations of parameter values could lead to accurate data fitting. This opens the potential for global parameter estimation of the model, in which the values of all parameters are fixed for all patients. Furthermore, by omitting the last two or three data points, we show that the model can be extrapolated and predict future tumor dynamics. This paves the way for a more clinically relevant application of mathematical tumor modeling, in which the treatment strategy could be adjusted in advance according to the models future predictions.

bioinformatics↗

bioRxiv2021

Drivers and Determinants of Strain Dynamics Following Faecal Microbiota Transplantation

Faecal microbiota transplantation (FMT) is an efficacious therapeutic intervention, but its clinical mode of action and underlying microbiome dynamics remain poorly understood. Here, we analysed the metagenomes associated with 142 FMTs, in a time series-based meta-study across five disease indications. We quantified strain-level dynamics of 1,089 microbial species based on their pangenome, complemented with 47,548 newly constructed metagenome-assembled genomes. Using subsets of procedural-, host- and microbiome-based variables, LASSO-regularised regression models accurately predicted the colonisation and resilience of donor and recipient microbes, as well as turnover of individual species. Linking this to putative ecological mechanisms, we found these sets of variables to be informative of the underlying processes that shape the post-FMT gut microbiome. Recipient factors and complementarity of donor and recipient microbiomes, encompassing entire communities to individual strains, were the main determinants of individual strain population dynamics, and mostly independent of clinical outcomes. Recipient community state and the degree of residual strain depletion provided a neutral baseline for donor strain colonisation success, in addition to inhibitive priority effects between species and conspecific strains, as well as putatively adaptive processes. Our results suggest promising tunable parameters to enhance donor flora colonisation or recipient flora displacement in clinical practice, towards the development of more targeted and personalised therapies.

microbiology↗

bioRxiv2020

Model-based prediction of muscarinic receptor function from auditory mismatch negativity responses

AO_SCPLOWBSTRACTC_SCPLOWDrugs affecting neuromodulation, for example by dopamine or acetylcholine, take centre stage among therapeutic strategies in psychiatry. These neuromodulators can change both neuronal gain and synaptic plasticity and therefore affect electrophysiological measures. An important goal for clinical diagnostics is to exploit this effect in the reverse direction, i.e., to infer the status of specific neuromodulatory systems from electrophysiological measures. In this study, we provide proof-of-concept that the functional status of cholinergic (specifically muscarinic) receptors can be inferred from electrophysiological data using generative (dynamic causal) models. To this end, we used epidural EEG recordings over two auditory cortical regions during a mismatch negativity (MMN) paradigm in rats. All animals were treated, across sessions, with muscarinic receptor agonists and antagonists at different doses. Together with a placebo condition, this resulted in five levels of muscarinic receptor status. Using a dynamic causal model - embodying a small network of coupled cortical microcircuits - we estimated synaptic parameters and their change across pharmacological conditions. The ensuing parameter estimates associated with (the neuromodulation of) synaptic efficacy showed both graded muscarinic effects and predictive validity between agonistic and antagonistic pharmacological conditions. This finding illustrates the potential utility of generative models of electrophysiological data as computational assays of muscarinic function. In application to EEG data of patients from heterogeneous spectrum diseases, e.g. schizophrenia, such models might help identify subgroups of patients that respond differentially to cholinergic treatments. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWIn psychiatry, the vast majority of pharmacological treatments affect actions of neuromodulatory transmitters, e.g. dopamine or acetylcholine. As treatment is largely trial-and-error based, one of the goals for computational psychiatry is to construct mathematical models that can serve as "computational assays" and infer the status of specific neuromodulatory systems in individual patients. This neuro-modelling strategy has great promise for electrophysiological data in particular but requires careful validation. The present study demonstrates that the functional status of cholinergic (muscarinic) receptors can be inferred from electrophysiological data using dynamic causal models of neural circuits. While accuracy needs to be enhanced and our results must be replicated in larger samples, our current results provide proof-of-concept for computational assays of muscarinic function using EEG.

neuroscience↗

bioRxiv2019

Respiration competes with theta for modulating parietal cortex neurons

Recent work has shown that nasal respiration entrains local field potential (LFP) and neuronal activity in widespread regions of the brain. This includes non-olfactory regions where respiration-coupled oscillations have been described in different mammals, such as rodents, cats and humans. They may, thus, constitute a global signal aiding interregional communication. Nevertheless, the brain produces other widespread slow rhythms, such as theta oscillations, which also mediate long-range synchronization of neuronal activity. It is completely unknown how these different signals interact to control neuronal network activity. In this work, we characterized respiration- and theta-coupled activity in the posterior parietal cortex of mice. Our results show that respiration-coupled and theta oscillations have different laminar profiles, in which respiration preferentially entrains LFPs and units in more superficial layers, whereas theta modulation does not differ across the parietal cortex. Interestingly, we find that the percentage of theta-modulated units increases in the absence of respiration-coupled oscillations, suggesting that both rhythms compete for modulating parietal cortex neurons. We further show through intracellular recordings that synaptic inhibition is likely to play a role in generating respiration-coupled oscillations at the membrane potential level. Finally, we provide anatomical and electrophysiological evidence of reciprocal monosynaptic connections between the anterior cingulate and posterior parietal cortices, suggesting a possible source of respiration-coupled activity in the parietal cortex.

neuroscience↗