Biology subjects

Falter-Braun, P.

Publications and source records attributed to Falter-Braun, P..

15 recordsLinked to original sources

bioRxiv2026

The PP2A phosphatase associates with the Arabidopsis TRAPPII tethering complex and dephosphorylates a TRAPPII-derived phosphopeptide in vitro

The transport protein particle II (TRAPPII) complex is a conserved regulator of post-Golgi membrane trafficking. In Arabidopsis, phosphorylation of the TRAPPII-specific subunit TRS120 by SHAGGY-like kinases modulates adaptive growth responses, but the phosphatases that reverse this phosphorylation remain unknown. Here, proteomic analyses identified subunits of Protein Phosphatase 2A (PP2A) in the TRAPPII interactome. PP2A subunits physically associated with TRAPPII, and double-mutant analyses revealed genetic interactions between PP2A and TRAPPII. Loss of TRAPPII function reduced the relative membrane association of PP2A scaffolding subunits. We established an in vitro assay for Arabidopsis PP2A holoenzyme activity using complexes transiently co-expressed and affinity-purified from Nicotiana benthamiana. Structural modelling and interface analysis predicted binding of a phosphorylated peptide encompassing a TRS120 phosphosite cluster at the PP2A catalytic interface, while biochemical assays showed that a PP2A holoenzyme containing the B2 regulatory subunit dephosphorylated this peptide. Together, these findings identify a B2-containing PP2A holoenzyme as a candidate phosphatase for TRS120 and support a model in which antagonistic SHAGGY-like kinase and PP2A activities couple signalling to membrane trafficking during plant development and environmental adaptation.

plant biology↗

bioRxiv2026

Symbiont effectors modulate plant signalling to increase host stress resilience

Plant colonizing mutualistic symbionts confer beneficial effects to their hosts, which often includes increased growth, and biotic and abiotic stress resilience. How these benefits are activated on a molecular level is mostly unknown. Here, we describe effector candidates of the fungal symbiont Serendipita indica (Si), which modulate plant stress signalling pathways. By analyzing the Si effector interactome, we reveal frequent targeting of stress related host proteins, which are linked to the identified effector signalling functions. Moreover, functional data indicate that Si effectors modulate abiotic stress response of Arabidopsis, as well as resistance to pathogen infection. Analysis of symbiont effectors might not only uncover previously unreported molecular mechanisms that increase plant fitness but might also be used to identify potential genetic traits for crop improvement under changing climates.

plant biology↗

bioRxiv2026

Conserved metabolic vulnerabilities across pathogenic coronaviruses nominate host-directed therapeutic targets

Pathogenic coronaviruses profoundly rewire host cell metabolism to support viral replication, yet whether these metabolic alterations expose shared and actionable vulnerabilities remains unclear. By integrating transcriptomic profiles from cells infected with SARS-CoV, SARS-CoV-2, and MERS-CoV with genome-scale metabolic models, we identify conserved and virus-specific metabolic perturbations affecting mitochondrial transport, nucleotide biosynthesis, fatty acid metabolism, and redox balance. Despite distinct transcriptional responses, all three viruses converge on a limited set of metabolic reactions whose flux ranges deviate strongly from healthy states. Using a network-based predictive framework, we systematically identify gene-pair perturbations that restore perturbed reaction fluxes toward non-infected metabolic states. Predicted rescue mechanisms reveal shared metabolic dependencies across coronaviruses, as well as time-dependent virus-specific vulnerabilities, and nominate druggable host targets. Notably, several top predictions align with independent experimental and clinical evidence, including metabolic interventions shown to reduce viral replication or disease severity in COVID-19 patients. Together, our results define conserved metabolic rescue pathways in coronavirus infection and provide a general strategy for identifying host-directed therapeutic opportunities from transcriptomic data. HighlightsO_LICoronaviruses converge on shared metabolic vulnerabilities in host cells C_LIO_LINiTRO predicts gene pairs that rescue viral-induced metabolic states C_LIO_LIMitochondrial transport emerges as a key pan-coronavirus target C_LIO_LITop predictions validated by clinical trials and in vitro evidence C_LI eTOC BlurbDohai et al. develop NiTRO, a network-based algorithm that integrates coronavirus-induced transcriptomic changes with genome-scale metabolic models to identify gene-pair perturbations capable of rescuing infected metabolic states. The approach reveals shared and virus-specific druggable metabolic vulnerabilities, with top predictions corroborated by clinical evidence.

systems biology↗

bioRxiv2025

Hidden sampling biases inflate performance in gene regulatory network inference

Accurate reconstruction of gene regulatory networks (GRNs) from single-cell transcriptomic data remains a major methodological challenge. Recent machine learning approaches, particularly graph neural networks and graph autoencoders, have reported improved performance, yet these gains do not consistently translate to realistic biological settings. Here, we show that a key reason for that is the way negative regulatory interactions are sampled for supervised training and evaluation. We find that widely used sampling strategies introduce node-degree biases that allow models to exploit trivial graph-structural cues rather than biological signals. Across multiple benchmarks, simple degree-based heuristics match or exceed state-of-the-art graph neural network models under these biased evaluation protocols. We further introduce a degree-aware sampling approach that eliminates these artifacts and provides more reliable assessments of GRN inference methods. Our results call for standardized, bias-aware benchmarking practices to ensure meaningful progress in supervised GRN inference from single-cell RNA-seq data.

bioinformatics↗

bioRxiv2025

Systematic Discovery of Pathogen Effector Functions across Human Pathogens and Pathways

Pathogens deploy effector proteins to exploit host cell biology, and most pathogen open reading frames (ORFs) are rapidly evolving and lack functional annotation. We developed the eORFeome, a scalable functional genomics platform encompassing 3,835 effector ORFs from diverse viruses, bacteria, and parasites. High-throughput barcoded screens across NF{kappa}B, apoptosis, p53, cGAS-STING and MHC-I pathways revealed functions for hundreds of uncharacterized eORFs, unexpected new activities for known effectors, and distinct pathway-specific functions encoded by single ORFs. Illustrating the power of the approach, we identify HHV6A U14 as a p53 antagonist, HHV7 U21 as a dual-function STING antagonist and MHC-I antigen display inhibitor, and adenoviral 13.6K/i-leader protein as a de novo evolved TAP inhibitor that suppresses MHC-I display. These results establish a general framework for systematic effector annotation, uncover new mechanisms of host-pathogen interaction across kingdoms, and highlight pathogen effectors as a versatile toolkit for rewiring and probing human cellular pathways.

systems biology↗

bioRxiv2025

Comparative gene regulatory network mapping of Brassicaceae members with differential drought tolerance

Climate change is projected to intensify weather extremes like droughts that threaten global crop yields. While many crops are sensitive to water deprivation, they have drought-tolerant wild relatives; however, the molecular basis for this differential tolerance among closely related species remains unclear. We previously investigated this using transcriptomics in the Brassicaceae family, comparing the drought-sensitive reference plant Arabidopsis thaliana (Ath) with its drought-tolerant relatives Arabidopsis lyrata (Aly) and Eutrema salsugineum (Esa), uncovering key differences that may underlie their respective phenotypes. Here, to elucidate the regulatory architecture driving these differences, we mapped drought-responsive gene regulatory networks (GRNs) for all three species using the yeast one-hybrid (Y1H) approach. We cloned promoters of orthologs with divergent drought-induced expression patterns and screened them against the Ath transcription factor (TF) collection, generating a quality-controlled cross-species drought-responsive GRN map encompassing 1,137 high-confidence TF-promoter interactions. Comparative analysis revealed that the tolerant species share greater regulatory similarity and exhibit higher network connectivity than Ath. An Esa-specific expansion of bZIP TF interactions was observed, consistent with an enrichment of G-box motifs in its genome and in drought-upregulated genes. Several observed changes affect the ABA signalling pathway: Ath gained a susceptibility linked GBF3-CYP707A1 edge, whereas Esa acquired tolerance associated ABF-mTERF10 interactions. Furthermore, network-rewiring analysis uncovered a novel role for ASIL2 in stress response. Finally, our network highlights TFs (CSDP1, ERF4, HB6, and MYB73) that likely contribute to the "stress-primed" state of Aly and Esa. Our work provides a broadly applicable framework for comparative GRN mapping and a valuable resource for improving drought tolerance in crops.

plant biology↗

bioRxiv2025

CaV1.2-dependent excitation-transcription coupling modulates nociception

Peripheral nociceptive sensory neurons integrate various noxious inputs, resulting in local depolarization that triggers the firing of action potentials and thus the sensation of pain. We recently reported that nociceptor depolarization itself initiates signaling by the calcium channel Cav1.2 causing acute hyperalgesia in vivo. However, whether this mechanism initiates excitation-transcription (E-T) coupling and thereby leads to long-lasting modulation of nociceptor activity remains poorly understood. Using high content imaging of dorsal root ganglion (DRG) neurons, we here found that depolarization of nociceptors induces phosphorylation of the transcription factor (TF) cAMP-response element binding protein (CREB), which was affected by inhibition of protein kinase A (PKA) and calcineurin, but not Ca2+/calmodulin-dependent protein kinases. Genetic deletion or pharmacological inhibition of Cav1.2 confirmed its role in calcium-dependent kinase signaling and CREB phosphorylation after depolarization. In line with this, pharmacological modulation of Cav1 channels affected the expression of a subset of depolarization-regulated immediate early genes known to orchestrate a broader transcriptional response. Indeed, RNA-Seq analysis of DRG neurons from mice with a tissue-specific deletion of Cav1.2 in nociceptive sensory neurons (SNS-Cacna1c-/- mice) revealed downregulation of multiple calcium and potassium channel subunits as well as proteins involved in synaptic vesicle release and cell adhesion. Furthermore, repetitive firing of action potentials and release of the neuropeptide CGRP was impaired in Cav1.2-deficient sensory neurons. SNS-Cacna1c-/- mice showed increased sensitivity to noxious heat and exacerbated inflammatory but not neuropathic pain. In conclusion, our data suggest a Cav1.2-dependent E-T coupling mechanism in nociceptors that counteracts nociception in vivo. HighlightsO_LIDepolarization induces Cav1.2- and PKA-dependent phosphorylation of the transcriptional regulator CREB in nociceptors C_LIO_LICav1.2 regulates the expression of depolarization-induced immediate early genes and induces reorganization of the signaling network in nociceptors C_LIO_LICav1.2-deficient nociceptors downregulate multiple ion channel subunits as well as proteins involved in synaptic vesicle release and cell adhesion C_LIO_LICav1.2-deficient nociceptors show impaired depolarization-induced PKA-II signaling, CREB phosphorylation, repetitive firing of action potentials, and release of the neuropeptide CGRP C_LIO_LINociceptor-specific Cav1.2-deficiency in mice increases the sensitivity to noxious heat and exacerbates inflammatory pain C_LI In briefIsensee et al. report that depolarization of nociceptors induces calcium influx through Cav1.2 channels, thereby initiating a signaling cascade that links neuronal excitation to transcriptional regulation of immediate early genes (E-T coupling). Nociceptor-specific knockout of Cav1.2 led to transcriptional changes in nociceptor-specific genes and was associated with impaired depolarization-induced signaling, action potential firing, and neuropeptide release. As the Cav1.2-deficient mice show increased noxious heat sensitivity and exacerbated inflammatory pain, these findings suggest that Cav1.2-dependent E-T coupling mechanism counteracts nociception in vivo.

neuroscience↗

bioRxiv2023

Inferring protein from mRNA concentrations using convolutional neural networks

Transcript abundance is a widely used but poor predictor of protein abundance. As proteins are the actual agents executing biological functions, and because signaling outcome depends in a non-linear manner on the concentration of the network components, we aimed to develop a convolutional neural network-(CNN-) based predictor for Homo sapiens and the reference plant Arabidopsis thaliana. After hyperparameter optimization and initial analysis of the training data, we employed a distinct training module for value and sequence data, respectively, predicting 40% of the variance in protein levels in Homo sapiens, respectively 48% in Arabidopsis thaliana. Codon counts and peptides had the greatest predictive power. Extracting the learned weight revealed generally similar trends but also some intriguing differences between human and Arabidopsis. Many learned motifs in the 5 and 3 UTRs correspond to previously described regulatory features demonstrating that the model can learn ab initio mechanistically relevant features.

bioinformatics↗

bioRxiv2023

A gut meta-interactome map reveals modulation of human immunity by microbiome effectors

The molecular mechanisms by which the gut microbiome influences human health remain largely unknown. Pseudomonadota is the third most abundant phylum in normal gut microbiomes. Several pathogens in this phylum can inject so-called virulence effector proteins into host cells. We report the identification of intact type 3 secretion systems (T3SS) in 5 - 20% of commensal Pseudomonadota in normal human gut microbiomes. To understand their functions, we experimentally generated a high-quality protein-protein meta-interactome map consisting of 1,263 interactions between 289 bacterial effectors and 430 human proteins. Effector targets are enriched for metabolic and immune functions and for genetic variation of microbiome-influenced traits including autoimmune diseases. We demonstrate that effectors modulate NF-{kappa}B signaling, cytokine secretion, and adhesion molecule expression. Finally, effectors are enriched in metagenomes of Crohns disease, but not ulcerative colitis patients pointing toward complex contributions to the etiology of inflammatory bowel diseases. Our results suggest that effector-host protein interactions are an important regulatory layer by which the microbiome impacts human health.

microbiology↗

bioRxiv2023

Regulation of adaptive growth decisions via phosphorylation of the TRAPPII complex in Arabidopsis

Plants often adapt to adverse or stress conditions via differential growth. The trans-Golgi Network (TGN) has been implicated in stress responses, but it is not clear in what capacity it mediates adaptive growth decisions. In this study, we assess the role of the TGN in stress responses by exploring the interactome of the Transport Protein Particle II (TRAPPII) complex, required for TGN structure and function. We identified physical and genetic interactions between TRAPPII and shaggy-like kinases (GSK3/AtSKs). Kinase assays and pharmacological inhibition provided in vitro and in vivo evidence that AtSKs target the TRAPPII-specific subunit AtTRS120/TRAPPC9. GSK3/AtSK phosphorylation sites in AtTRS120/TRAPPC9 were mutated, and the resulting AtTRS120 phosphovariants subjected to a variety of single and multiple stress conditions in planta. The non-phosphorylatable TRS120 mutant exhibited enhanced adaptation to multiple stress conditions and to osmotic stress whereas the phosphomimetic version was less resilient. Higher order inducible trappii atsk mutants had a synthetically enhanced defect in root gravitropism. Our results suggest that the TRAPPII phosphostatus mediates adaptive responses to abiotic cues. AtSKs are multifunctional kinases that integrate a broad range of signals. Similarly, the TRAPPII interactome is vast and considerably enriched in signaling components. An AtSK-TRAPPII interaction would integrate all levels of cellular organization and instruct the TGN, a central and highly discriminate cellular hub, as to how to mobilize and allocate resources to optimize growth and survival under limiting or adverse conditions.

plant biology↗

bioRxiv2023

Speos: An ensemble graph representation learning framework to predict core genes for complex diseases

Understanding phenotype-to-genotype relationships is a grand challenge of 21st century biology with translational implications. The recently proposed "omnigenic" model postulates that effects of genetic variation on traits are mediated by core-genes and -proteins whose activities mechanistically influence the phenotype, whereas peripheral genes encode a regulatory network that indirectly affects phenotypes via core gene products. We have developed a positive-unlabeled graph representation-learning ensemble-approach to predict core genes for diverse diseases using Mendelian disorder genes for training. Employing mouse knockout phenotypes for external validation, we demonstrate that our most confident predictions validate at rates on par with the Mendelian disorder genes, and all candidates exhibit core-gene properties like transcriptional deregulation in diseases and loss-of-function intolerance. Predicted candidates are enriched for drug targets and druggable proteins and, in contrast to Mendelian disorder genes, also for druggable but yet untargeted gene products. Model interpretation suggests key molecular mechanisms and physical interactions for core gene predictions. Our results demonstrate the potential of graph representation learning and pave the way for studying core gene properties and future drug development.

genetics↗

bioRxiv2021

Cell-ACDC: a user-friendly toolset embedding state-of-the-art neural networks for segmentation, tracking and cell cycle annotations of live-cell imaging data

Live-cell imaging is a powerful tool to study dynamic cellular processes on the level of single cells with quantitative detail. Microfluidics enables parallel high-throughput imaging, creating a downstream bottleneck at the stage of data analysis. Recent progress on deep learning image analysis dramatically improved cell segmentation and tracking. Nevertheless, manual data validation and correction is typically still required and broadly used tools spanning the complete range of live-cell imaging analysis, from cell segmentation to pedigree analysis and signal quantification, are still needed. Here, we present Cell-ACDC, a user-friendly graphical user-interface (GUI)-based framework written in Python, for segmentation, tracking and cell cycle annotation. We included two state-of-the-art and high-accuracy deep learning models for single-cell segmentation of yeast and mammalian cells implemented in the most used deep learning frameworks TensorFlow and PyTorch. Additionally, we developed and implemented a cell tracking method and embedded it into an intuitive, semi-automated workflow for label-free cell cycle annotation of single cells. The open-source and modularized nature of Cell-ACDC will enable simple and fast integration of new deep learning-based and traditional methods for cell segmentation or downstream image analysis. Source codehttps://github.com/SchmollerLab/Cell_ACDC

cell biology↗

bioRxiv2021

A map of direct SARS-CoV-2 protein interactions implicates specific human host processes

Key steps in viral propagation, immune suppression, and pathology are mediated by direct, binary, physical interactions between viral and host proteins. To understand the biology of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, we generated an unbiased systematic map of binary interactions between viral and host proteins, complementing previous co-complex association maps by conveying more direct mechanistic understanding and potentially enabling targeted disruption of direct interactions. To this end, we deployed two parallel strategies, identifying 205 virus-host and 27 intraviral binary interactions amongst 171 host and 19 viral proteins, and confirming high quality of these interactions via a calibrated orthogonal assay. Host proteins interacting with SARS-CoV-2 proteins are enriched in various cellular processes, including immune signaling and inflammation, protein ubiquitination, and membrane trafficking. Specific subnetworks provide new hypotheses related to viral modulation of host protein homeostasis and T-cell regulation. The binary virus-host protein interactions we identified can now be prioritized as targets for therapeutic intervention. More generally, we provide a resource of systematic maps describing which SARS-CoV-2 and human proteins interact directly.

systems biology↗

bioRxiv2020

Global BioID-based SARS-CoV-2 proteins proximal interactome unveils novel ties between viral polypeptides and host factors involved in multiple COVID19-associated mechanisms

The worldwide SARS-CoV-2 outbreak poses a serious challenge to human societies and economies. SARS-CoV-2 proteins orchestrate complex pathogenic mechanisms that underlie COVID-19 disease. Thus, understanding how viral polypeptides rewire host protein networks enables better-founded therapeutic research. In complement to existing proteomic studies, in this study we define the first proximal interaction network of SARS-CoV-2 proteins, at the whole proteome level in human cells. Applying a proximity-dependent biotinylation (BioID)-based approach greatly expanded the current knowledge by detecting interactions within poorly soluble compartments, transient, and/or of weak affinity in living cells. Our BioID study was complemented by a stringent filtering and uncovered 2,128 unique cellular targets (1,717 not previously associated with SARS-CoV-1 or 2 proteins) connected to the N- and C-ter BioID-tagged 28 SARS-CoV-2 proteins by a total of 5,415 (5,236 new) proximal interactions. In order to facilitate data exploitation, an innovative interactive 3D web interface was developed to allow customized analysis and exploration of the landscape of interactions (accessible at http://www.sars-cov-2-interactome.org/). Interestingly, 342 membrane proteins including interferon and interleukin pathways factors, were associated with specific viral proteins. We uncovered ORF7a and ORF7b protein proximal partners that could be related to anosmia and ageusia symptoms. Moreover, comparing proximal interactomes in basal and infection-mimicking conditions (poly(I:C) treatment) allowed us to detect novel links with major antiviral response pathway components, such as ORF9b with MAVS and ISG20; N with PKR and TARB2; NSP2 with RIG-I and STAT1; NSP16 with PARP9-DTX3L. Altogether, our study provides an unprecedented comprehensive resource for understanding how SARS-CoV-2 proteins orchestrate host proteome remodeling and innate immune response evasion, which can inform development of targeted therapeutic strategies.

systems biology↗

bioRxiv2019

Proteomic studies of the Arabidopsis TRAPP complexes reveal conserved organization and a novel plant-specific component with a role in plant development

How the membrane trafficking system spatially organizes intracellular activities and intercellular signaling networks is not well understood in plants. The Transport Protein Particle (TRAPP) complexes are known to play key roles in selective delivery of membrane vesicles to various subcellular compartments in yeast and animals, but remain to be fully characterized in plants. Here we interrogate the TRAPP complexes in Arabidopsis using quantitative proteomic approaches. TRS33 is a component shared by all TRAPP complexes in yeast and animals, and the Arabidopsis AtTRS33 is essential for the subcellular dynamics of other TRAPP components. Affinity purification of AtTRS33 followed by quantitative mass spectrometry identified fourteen interacting proteins; these include not only thirteen homologs of all known TRAPP components in yeast and mammals but also a novel protein we named TRAPP-interacting plant protein (TRIPP), which is conserved in multi-cellular photosynthetic organisms. Proteomic and molecular analyses showed that TRIPP specifically associates with the TRAPPII complex in vivo and directly interacts with the TRAPPII-specific subunits but not the subunits shared with TRAPPIII. TRIPP co-localizes with a subset of TRS33 compartments, and its localization is disrupted in the trs33 mutant. Loss-of-function tripp mutation caused growth and reproductive development defects, including partial photomorphogenesis in the dark. Our study demonstrates that plants possess at least two distinct TRAPP complexes similar to metazoan, and identifies TRIPP as a novel plant-specific component of the TRAPPII complex with important functions in plant growth and development.

plant biology↗