Biology subjects

Koehler, N.

Publications and source records attributed to Koehler, N..

16 recordsLinked to original sources

bioRxiv2026

Decoy diversification underpins the regulation of an NLR-mediated autoimmunity

Appropriate activation of innate immune receptors is vital for the plant immune system. Immune activation must be strong and robust upon invasion by rapidly evolving pathogen species, while staying inactive in the absence of a pathogen to avoid constitutive defense responses that inhibit plant growth. Nucleotide-binding leucine-rich repeat receptors (NLRs) are intracellular proteins that surveil for pathogen invasion, often by direct or indirect perception of pathogen effector proteins. Indirect recognition of pathogen effector proteins, through monitoring the integrity of guardee or decoy proteins, can allow for a single NLR to detect a wide range of pathogen effectors. In this work, we investigated how the Solanaceae NLR NbPtr1 can recognize six different effector proteins from the bacterial pathogens Pseudomonas syringae, Xanthomonas perforans, and Ralstonia pseudosolanacearum. We identified several NOI-domain containing proteins that are guarded by NbPtr1. These NOI proteins share homology with, but are distinct from, RPM1 INTERACTING PROTEIN 4 (RIN4), a well-known guardee in Arabidopsis. Virus-induced gene silencing and CRISPR/Cas9-assisted deletion of NbNOI genes resulted in stunted growth dependent on NbPtr1 activity. Negative regulation of NbPtr1 requires the interaction between NbPtr1 and the suppressor NbNOIs. A conserved threonine residue of the NOI proteins is required for this interaction and fits into a putative binding pocket of NbPtr1 based on protein modeling. This threonine residue is modified by some of the recognized effector proteins. Our study uncovers the regulatory mechanism of an autoactive NLR and highlights the importance of decoy diversification in establishing compatibility with NLRs.

plant biology↗

bioRxiv2026

Bivalent bispecific CD28 antibodies reinforce T-cell responsiveness and revert anergy/quiescence in patients treated with bispecific CD3 antibodies

Bispecific T-cell engagers stimulating CD3 (TCEs) rapidly gain momentum in oncological therapy. We report that single-agent TCE-treatment of cancer patients induces T cell hyporesponsiveness including abolished proliferative and lytic capacity. Single-cell RNA-sequencing identified transcriptional features of anergy/quiescence like upregulation of CBLB and BACH2, and suppression of AP 1-dependent activation programs due to isolated provision of T cell signal-1. To restore T cell functionality, we engineered bispecific costimulators (BiCos) which deliver signal-2 via highly efficient bivalent CD28-binding yet maintain strictly target-restricted activity after binding antigens expressed not only by tumor cells but also the extracellular matrix. Preclinical analyses documented that BiCos potentiate efficacy of single-agent TCE treatment with activity exceeding that of univalent Knob-into-Hole CD28-costimulators and revert T cell hyporesponsiveness. In humanized mice, BiCos induced elimination of established tumors in combination with very low doses of TCE. Finally, BiCos reversed molecular hallmark features of quiescence such as high expression of BACH2 and KLF2 in T cells of TCE-treated patients resulting in complete restoration of cellular function, thereby reinstating durable antitumor immunity.

immunology↗

bioRxiv2026

Membrane localisation and checkpoint blockade enhance xenoantigen delivery to redirect pre-existing immunity against tumours

Cancer immunotherapies often rely on the recognition of tumour antigens, which strongly limits their efficacy upon heterogeneous antigen expression or downregulation. A strategy to overcome this limitation is to redirect pre-existing antiviral immunity against tumours through the delivery of xenoantigens. While many studies have addressed this by repurposing licensed vaccines, we here investigated the underlying mechanisms of immune redirection via the delivery of non-adjuvanted xenoantigen proteins, thereby avoiding confounding adjuvant- or pathogen-specific effects. Using B16F10 melanoma cells engineered to express the model antigen OVA, we found that tumour rejection in pre-immunised mice depends on the subcellular localisation of the xenoantigen, with membrane-bound antigens eliciting stronger rejection than dose-matched soluble cytoplasmic antigens. Enhanced rejection of membrane-bound OVA expressing tumours was associated with stronger CD4+ T cell responses. In addition, pre-immunisation also increased recruitment of inflammatory monocytes and macrophages at the tumour site. To translate this concept therapeutically, we developed a membrane-targeting OVA fusion protein which, upon intratumoural delivery, redirected pre-existing immunity and made tumours responsive to anti-PD-1 therapy. Importantly, these findings were further validated using the clinically relevant varicella zoster virus (VZV) glycoprotein E (gE) antigen and the licensed varicella vaccine Varivax. Our approach provides a mechanistic and translational perspective for treating poorly immunogenic tumours, leveraging widespread pathogen-specific immune memory in combination with anti-PD1 therapy in cancer patients.

cancer biology↗

bioRxiv2025

Controlling TCR and CAR activation by targeting LCK recruitment with a first-in-class small-molecule inhibitor

T-cell activation is driven by the recruitment of lymphocyte-specific protein tyrosine kinase (LCK) to the T-cell receptor (TCR), a critical step in initiating immune responses. Existing LCK inhibitors lack specificity because they target the conserved kinase domain shared by Src family kinases, resulting in off-target effects. Here, we introduce a novel strategy to selectively modulate T-cell activation by disrupting the interaction between the SH3 domain of LCK and the receptor kinase (RK) motif of CD3{varepsilon}. Using computational modeling and high-throughput virtual screening, we identified candidate compounds targeting the SH3(LCK) domain. Functional validation revealed that one compound, C10, selectively disrupted the SH3-RK interaction, leading to reduced TCR-driven activation and proliferation, while sparing activation via alternative receptors and B-cell responses. Moreover, C10 modulated the activity of CD3{varepsilon}-containing CAR and TRuC T cells, attenuating cytokine production and promoting a central-memory-like phenotype associated with enhanced persistence. These findings establish targeted disruption of LCK recruitment as a viable strategy for fine-tuning T-cell responses and propose SH3(LCK) as a druggable domain with therapeutic potential for autoimmune diseases, graft-versus-host disease, and optimizing CAR T-cell therapies.

immunology↗

bioRxiv2025

Navigating the Lipid Universe with LipidLibrarian: A Cross-Linked Database for Lipidomics Data Integration

There are numerous public resources and guidelines available for lipidomics research, including standard nomenclatures, classification systems, and lipid databases. However, these resources are not always aligned with one another, making it difficult to find and compare information on the same lipid across different databases. To tackle these challenges we present LipidLibrarian, a lipid search engine that enables a combined search of all major lipid databases by aggregating the available information and presenting it in a unified manner. The three main sources of information that build the foundation of LipidLibrarian as a comprehensive search-engine are SwissLipids, LIPID MAPS and ALEX123. Furthermore, various secondary resources such as LION/web, LINEX, LipidLynxX, and Goslin were incorporated to enhance the results and conduct name and hierarchy conversions. LipidLibrarian is accessible via a user-friendly website, allowing the user to query lipids using their trivial names, shorthand notations, database identifiers, or their masses. Alternatively, LipidLibrarian can be accessed as a Python package for integration into high-throughput lipidomics pipelines. The output of a LipidLibrarian query is split into multiple categories, such as nomenclature, database identifiers, masses, adducts, fragments, ontology terms, and reactions. For each of these categories, LipidLi-brarian aggregates the results from all databases and provides the source from which each value originates. This enables the user to quickly assess if the databases contain differing or conflicting information. In summary, LipidLibrarian provides an effortless, comprehensive and automated search for lipid information, thereby accelerating the research workflow and making it a meaningful tool for the scientific community.

bioinformatics↗

bioRxiv2025

A microbial metabolite protects against graft-versus-host disease via mTORC1 and STING-dependent intestinal regeneration

Changes in the intestinal microbiome and microbiota-derived metabolites predict clinical outcomes after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Here, we report that desaminotyrosine (DAT), a product of bacterial flavonoid metabolism, correlates with improved overall survival and reduced relapse rates in allo-HSCT patients. In preclinical mouse models, treatment with synthetic DAT prevents graft-versus-host disease by protecting the intestinal barrier and promoting intestinal regeneration and contributes to graft-vs.-leukemia responses. DATs beneficial effects on intestinal regeneration remain effective despite broad-spectrum antibiotics-induced dysbiosis, also when administered by fecal microbiota transfer with flavonoid-degrading F. plautii. Mechanistically, DAT promotes mTORC1-dependent activation and proliferation of intestinal stem cells, with concomitant engagement of the innate immune receptor STING required to mitigate metabolic stress and maintain an undifferentiated stem cell state independently of type-I interferon responses. Additionally, DAT can skew T cells towards an effector phenotype to modulate graft-versus-leukemia responses. Our data uncover DATs dual, tissue- and immune-modulating properties and underscore its potential in precision microbiome-based therapies to improve tissue regeneration and minimize immune-mediated side effects.

immunology↗

bioRxiv2024

LipiDetective - a deep learning model for the identification of molecular lipid species in tandem mass spectra

Lipids are involved in many vital processes within the cell, and alterations in lipid homeostasis have been associated with various diseases such as cancer or type 2 diabetes. Confidently identifying lipids in samples is a prerequisite for understanding the multiple functions lipids fulfill in health and disease. However, the accurate identification of molecular lipid species based on tandem mass spectrometry data is still a key challenge in lipidomics. Most current approaches rely on using a custom pipeline to process and match the measured spectra against an in-house spectra reference library, which hinders the comparability of results. To address this challenge, a transformer model called LipiDetective was developed and trained on a dataset composed of reference spectra measured from lipid standards, spectra from databases, and published experiments, utilizing both shotgun as well as liquid-chromatography mass spectrometry. LipiDetective demonstrates, for the first time, that artificial neural networks can learn the characteristic lipid fragmentation patterns to automatically and accurately annotate molecular lipids species in tandem mass spectra independently of the experimental setup. The model can even correctly predict lipid species for which it has never seen a spectrum before as it is able to generalize the learned lipid fragmentation patterns. Analysis of the integrated gradients reveals that LipiDetective focuses on relevant peaks that can be matched to known fragments and are thus humanly interpretable. Therefore, LipiDetective has the potential to be a valuable tool to aid in the lipid identification process and support the comparability of results from different sources. Aside from Lipidetective as a "ready-to-use" application, this work primarily offers a deeper understanding of how the model functions and how future deep learning models for lipid identification in mass spectra could be improved.

bioinformatics↗

bioRxiv2024

Engineering pathogen-inducible promoters for conferring disease resistance in tomato

Many disease resistance genes utilized in crop breeding encode nucleotide-binding leucine-rich repeat receptors (NLRs), which are frequently race-specific and fail to provide durable protection in the field. Consequently, engineering broad-spectrum resistance has become a primary goal. While autoactive NLRs can provide broad-spectrum immunity by bypassing specific pathogen recognition, they often impose severe fitness costs. Here, we hypothesized that precise transcriptional control could mitigate these penalties and developed a synthetic strategy coupling pathogen-inducible (PI) promoters with autoactive NLRs. Through a transcriptomic meta-analysis, we identified and validated native tomato PI promoters that respond to diverse pathogens while remaining insensitive to abiotic stress. We established a tunable system by pairing these promoters varying in basal and inducible expression with NLRs exhibiting different degrees of autoactivity. Systematic characterization revealed that balancing these components is critical to prevent leaky immune activation in the absence of pathogens. Transgenic tomatoes expressing a weakly autoactive NLR under a PI phenylalanine ammonia-lyase promoter exhibited enhanced resistance to two taxonomically distinct pathogens without growth impairment. Disruption of the PI sequence largely abolished this resistance, confirming the necessity of pathogen-mediated transcriptional induction. Finally, we added an additional layer of tunability by engineering the copy number of inducible elements and the core promoter to fine-tune promoter strength while preserving inducibility. Together, our results showcase a versatile framework for engineering multi-pathogen resistance and provide new insights into using transcriptional control in overcoming the fitness costs associated with autoactive NLRs.

synthetic biology↗

bioRxiv2024

Satellite glial cells from adult DRG dedifferentiate in vitro and can be reprogrammed into nociceptor-like neurons

In dorsal root ganglia (DRG), neuronal loss has been reported in patients with neuropathic pain, raising the question of whether the DRG, as part of the peripheral nervous system (PNS), harbor an endogenous cell source for neural repair. We found that adult mouse DRG harbor glial cells that dedifferentiate in vitro into Sox2/Sox10-positive glial progenitor-like cells. Coexpression of the developmental transcription factors Neurog1 and Neurog2 was sufficient to induce both neuronal and glial phenotypes. Nerve growth factor supported the maturation of a subset of neurons into nociceptor-like cells expressing functional TrpA1, TrpV1, and TTX-resistant NaV channels. We report the limitation that we miss factors allowing consistent maturation to the sensory neuron profile. In summary, in the PNS, adult DRG-derived glial cells can acquire neural progenitor-like properties, show bipotent reprogramming competence, and may serve as an intrinsic cell source for sensory circuit regeneration.

neuroscience↗

bioRxiv2023

ATF6 activation alters colonic lipid metabolism causing tumor-associated microbial adaptation

Endoplasmic reticulum unfolded protein responses (UPRER) contribute to cancer development and the activating transcription factor 6 (ATF6) is involved in microbiota-dependent tumorigenesis. Here, we substantiate the clinical relevance of ATF6 in early-onset and late colorectal cancer patient cohorts. Transcriptional analysis in intestinal epithelial cells (IEC) of ATF6 transgenic mice (nATF6IEC) identified bacteria-specific changes in cellular metabolism enriched for fatty acid biosynthesis. Untargeted metabolomics and isotype-labeling confirmed ATF6-related enrichment of long chain fatty acids in colonic tissue of patients, mice and organoid cultures. FASN inhibition and microbiota transfer in germ-free nATF6IEC mice confirmed the causal involvement of ATF6-induced lipid alterations in tumorigenesis. The selective expansion of tumor-relevant microbial taxa was mechanistically linked to long chain fatty acid exposure, using bioorthogonal non-canonical amino acid tagging (BONCAT) and growth analysis of Desulfovibrio isolates. We postulate chronic ATF6 signaling in the epithelium to select for tumor-promoting microbiota by altering lipid metabolism. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/565267v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@be3756org.highwire.dtl.DTLVardef@231da4org.highwire.dtl.DTLVardef@16f3aaeorg.highwire.dtl.DTLVardef@1758fe3_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical Abstract:C_FLOATNO Chronic ATF6 signaling in the colonic epithelium alters lipid metabolism to select a tumor-promoting microbiota C_FIG O_LIBiallelic expression of activated ATF6 (p50 nuclear fragment) in intestinal epithelial cells (nATF6IEC) induces spontaneous colon tumors in SPF but not GF mice C_LIO_LIMechanistically, biallelic SPF nATF6IEC mice alter colonic lipid metabolism, including the upregulation of LCFAs and Fasn C_LIO_LIInhibition of FASN prevents colon tumor formation in mice, and reduces the tumor-promoting potential of the intestinal microbiota (FMT) C_LIO_LIExposure of fl/fl control mouse microbiota to LCFAs ex vivo translationally activates tumor-associated bacteria, including Desulfovibrio fairfieldensis C_LIO_LIHuman CRC patients show ATF6 upregulation, FASN co-occurrence and increased LCFAs in T tissue C_LIO_LIATF6 activity links with CRC-associated microbiota in patients, including Desulfovibrio C_LI Created with BioRender.com nATF6: activated activating transcription factor 6; LCFA: long-chain fatty acids; SAFA: saturated fatty acids; Fasn: fatty acid synthase; C75 i.p.: intraperitoneal injection of the Fasn inhibitor C75.

cancer biology↗

bioRxiv2023

Barley shows reduced Fusarium Head Blight under drought and modular expression of differential expressed genes under combined stress

Plants often face simultaneous abiotic and biotic stress conditions. However, physiological and transcriptional responses of plants under combined stress situations are little understood. Spring barley is susceptible to Fusarium Head Blight (FHB), which is strongly affected by weather conditions. We therefore studied the potential influence of drought on FHB severity and responses in three differently susceptible spring barley varieties and found strongly reduced FHB severity in susceptible varieties under drought. Quantity of differentially expressed genes (DEGs) and strength of transcriptomic regulation reflected the concentration of physiological stress markers such as abscisic acid or fungal DNA contents. Infection-related gene expression associated rather with susceptibility than resistance. Weighted gene correlation network analysis uncovered 18 modules of co-expressed genes, which reflect the pathogen or drought response in the used varieties. A generally infection-related module contained co-expressed genes for defence, programmed cell death and mycotoxin-detoxification indicating that diverse genotypes use a similar defence strategy towards FHB albeit with different success. Further DEGs showed co-expression in drought or genotype-associated modules correlating with measured phytohormones or the osmolyte proline. The combination of drought stress with infection lead to highest numbers of DEGs and provoked a modular composition of single stress responses rather than a specific transcriptional readout. HighlightCo-expression network analysis reveals association of physiological stress markers and gene expression modules under biotic and abiotic stress

plant biology↗

bioRxiv2022

Integrated Analysis of T cell Repertoire and Transcriptome Identifies Mechanisms of Regulatory T cell (Treg) Suppression of Acute Graft-versus-Host-Disease

CD4+FOXP3+ regulatory T cells have demonstrated efficacy in graft-versus-host disease (GvHD) prevention and treatment. Preclinical and clinical studies indicate that Treg are able to protect from GvHD without interfering with the graft-versus-tumor (GvT) effect of hematopoietic cell transplantation (HCT), although the underlying molecular mechanisms are largely unknown. To elucidate Treg suppressive function during in vivo suppression of acute GvHD, we performed paired T cell receptor (TCR, TCR{beta} genes) repertoire sequencing and RNA sequencing analysis on conventional T cells (Tcon) and Treg before and after transplantation in an MHC major-mismatch mouse model of HCT. We show that both Treg and Tcon underwent clonal restriction and that Treg did not interfere with the activation of alloreactive Tcon clones and the breadth of their TCR repertoire, however, markedly suppressed their expansion. Transcriptomic analysis revealed that Treg predominantly affected the transcriptome of CD4 Tcon and to a lesser extent of CD8 Tcon, modulating the transcription of genes encoding pro- and anti-inflammatory molecules as well as enzymes involved in metabolic processes, inducing a switch from glycolysis to oxidative phosphorylation. Finally, Treg did not interfere with the induction of gene sets involved in the GvT effect. Our results shed light into the mechanisms of acute GvHD suppression by Treg and will support the clinical translation of this immunoregulatory approach. Key Points- Regulatory T cells modulate conventional T cells transcriptome during GvHD suppression by affecting several, non-redundant pathways. - Regulatory T cells undergo activation and clonal expansion during GvHD suppression.

immunology↗

bioRxiv2022

Lipid network and moiety analyses reveal enzymatic dysregulation and altered mechanisms from lipidomics

Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present LINEX2 (Lipid Network Explorer), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases we generate dataset-specific lipid interaction networks. To aid interpretation of these networks we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/479101v2_ufig1.gif" ALT="Figure 1"> View larger version (29K): org.highwire.dtl.DTLVardef@10d722dorg.highwire.dtl.DTLVardef@1c3256borg.highwire.dtl.DTLVardef@cd9deforg.highwire.dtl.DTLVardef@13befc4_HPS_FORMAT_FIGEXP M_FIG C_FIG LINEX2 (Lipid Network Explorer) is a framework to visualize and analyze quantitative lipidomics data. The included algorithms offer new perspectives on the lipidome and can propose potential mechanisms of dysregulation. O_LIUsing the Reactome and Rhea databases, a comprehensive set of lipid class reactions is included and utilized to map the lipidome on custom data-specific networks. C_LIO_LIWith a novel network enrichment method, enzymatic dysregulation can be recovered from lipidomics data. C_LIO_LIWe validate its usability on data with a central lipid enzymatic deficiency. C_LIO_LILINEX2 is the first tool capable of such analysis and includes complimentary analysis options for structural lipid analysis. It is freely available as a web service (https://exbio.wzw.tum.de/linex2). C_LI

systems biology↗

bioRxiv2021

MoSBi: Automated signature mining for molecular stratification and subtyping

The improving access to increasing amounts of biomedical data provides completely new chances for advanced patient stratification and disease subtyping strategies. This requires computational tools that produce uniformly robust results across highly heterogeneous molecular data. Unsupervised machine learning methodologies are able to discover de-novo patterns in such data. Biclustering is especially suited by simultaneously identifying sample groups and corresponding feature sets across heterogeneous omics data. The performance of available biclustering algorithms heavily depends on individual parameterization and varies with their application. Here, we developed MoSBi (Molecular Signature identification using Biclustering), an automated multi-algorithm ensemble approach that integrates results utilizing an error model-supported similarity network. We evaluated the performance of MoSBi on transcriptomics, proteomics and metabolomics data, as well as synthetic datasets covering various data properties. Profiting from multi-algorithm integration, MoSBi identified robust group and disease specific signatures across all scenarios overcoming single algorithm specificities. Furthermore, we developed a scalable network-based visualization of bicluster communities that support biological hypothesis generation. MoSBi is available as an R package and web-service to make automated biclustering analysis accessible for application in molecular sample stratification.

bioinformatics↗

bioRxiv2021

Susceptibility to diet induced obesity at thermoneutral conditions is independent of UCP1

ObjectiveActivation of uncoupling protein 1 (UCP1) in brown adipose tissue (BAT) upon cold stimulation leads to substantial increase in energy expenditure to defend body temperature. Increases in energy expenditure after a high caloric food intake, termed diet-induced thermogenesis, are also attributed to BAT. These properties render BAT a potential target to combat diet-induced obesity. However, studies investigating the role of UCP1 to protect against diet-induced obesity are controversial and rely on the phenotyping of a single constitutive UCP1-knockout model. To address this issue, we generated a novel UCP1-knockout model by Cre-mediated deletion of Exon 2 in the UCP1 gene. We studied the effect of constitutive UCP1 knockout on metabolism and the development of diet-induced obesity. MethodsUCP1 knockout and wildtype mice were housed at 30{degrees}C and fed a control diet for 4-weeks followed by 8-weeks of high-fat diet. Body weight and food intake were monitored continuously over the course of the study and indirect calorimetry was used to determine energy expenditure during both feeding periods. ResultsBased on Western blot analysis, thermal imaging and noradrenaline test, we confirmed the lack of functional UCP1 in knockout mice. However, body weight gain, food intake and energy expenditure were not affected by deletion of UCP1 gene function during both feeding periods. ConclusionConclusively, we show that UCP1 does not protect against diet-induced obesity at thermoneutrality. Further we introduce a novel UCP1-KO mouse enabling the generation of conditional UCP1-knockout mice to scrutinize the contribution of UCP1 to energy metabolism in different cell types or life stages.

physiology↗

bioRxiv2016

Deep Learning for Imaging Flow Cytometry: Cell Cycle Analysis of Jurkat Cells

We show that deep convolutional neural networks combined with non-linear dimension reduction enable reconstructing biological processes based on raw image data. We demonstrate this by recon-structing the cell cycle of Jurkat cells and disease progression in diabetic retinopathy. In further analysis of Jurkat cells, we detect and separate a subpopulation of dead cells in an unsupervised manner and, in classifying discrete cell cycle stages, we reach a 6-fold reduction in error rate compared to a recent approach based on boosting on image features. In contrast to previous methods, deep learning based predictions are fast enough for on-the-fly analysis in an imaging flow cytometer.

cell biology↗