Biology subjects

Wagner, F.

Publications and source records attributed to Wagner, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Monitoring Sphingomyelin Biosynthesis at Nanoscale Resolution by Expansion Microscopy

Sphingomyelin is the most abundant sphingolipid in mammalian cells and is synthesized by two isoenzymes, sphingomyelin synthase 1 and 2, located in the Golgi and in the plasma membrane. Abnormal sphingomyelin synthesis is associated with infections and diseases such as diabetes and cancer. Measuring cellular sphingomyelin synthase activity fosters our understanding of how these enzymes are involved in pathological processes and can be crucial for the identification of therapeutic compounds modifying sphingomyelin biosynthesis. We have developed a novel fluorometric assay that enables microscopic detection of cellular sphingomyelin synthase activity. We show that sphingomyelin synthases use propargyl choline and -NH2-{omega}-N3-C6-ceramide to generate trifunctional sphingomyelin, a lipid derivative detectable with spatial resolution via Forster resonance energy transfer. By combining this assay with expansion microscopy, a super-resolution imaging technique, we measured the distribution of de novo synthesized trifunctional sphingomyelin in cells at nanoscale resolution, thereby directly demonstrating sphingomyelin biosynthesis at the Golgi and the plasma membrane. By monitoring sphingomyelin biosynthesis and degradation in cells infected with the obligate intracellular pathogen Chlamydia trachomatis, we dissected the complex sphingolipid metabolization of these bacteria with unprecedented resolution. By correlating spatial metabolic information with lipidomics, we provide a powerful tool for investigating cellular sphingomyelin metabolism. TeaserCombining functional lipids, expansion microscopy and FRET helps visualise sphingolipid metabolism at unprecedented resolution.

biochemistry↗

bioRxiv2026

Chlamydia trachomatis deploys sphingolipids for genome organisation

Chlamydia trachomatis is an obligate intracellular bacterial pathogen and a leading cause of sexually transmitted infections worldwide. During its biphasic developmental cycle, infectious, non-replicative elementary bodies alternate with replicative reticulate bodies within a membrane-bound intracellular niche known as the inclusion. C. trachomatis relies heavily on host-derived metabolites, including sphingolipids, which are essential for inclusion integrity, bacterial growth and production of infectious progeny. Here, using expansion microscopy, we uncover an unexpected localization of sphingolipid derivatives within the highly condensed DNA nucleoids of elementary bodies. These sphingolipids are released from nucleoids prior to DNA decondensation during the elementary-to-reticulate body transition, the earliest phenotypic event in the complex developmental cycle of these bacteria. Thereafter, nucleoids undergo a characteristic DNA decondensation process that we visualized by expansion microscopy. By combining super-resolution imaging with a FRET-based metabolic tracking approach and lipidomics, we identified sphingomyelin derived from the sphingolipid analogues as the sphingolipid species predominantly associated with the condensing nucleoids of elementary bodies. Notably, reticulate bodies arrested in their developmental stage fail to accumulate sphingomyelin, suggesting a role for this lipid in stage-specific DNA condensation. Together, our findings suggest an unanticipated role for sphingolipids in bacterial DNA organization and developmental regulation in C. trachomatis.

microbiology↗

bioRxiv2026

Behavior of downstream swimming brown trout in accelerating and high flow velocity - Movement matters

Studies on active and sedated fish passing through turbines and pumps show different mortality and injury rates for both cases. Consequently, fish behavior appears to play a substantial role in these outcomes. However, direct behavioral observations in hydraulic machines using quantitative parameters to draw conclusions about the underlying mechanisms are hardly possible and remain understudied. In this study, we examined the behavior of adult brown trout (Salmo trutta) in an experimental flume under hydraulic conditions characterized by strong flow acceleration and high velocities typical of turbine and pump intakes. Fish movement behavior was analyzed based on a quantitative approach to enable the analysis of swimming behavior even in flow velocities exceeding the sprint swimming speed of fish. The application of Hidden Markov Models (HMM) to analyze activity states and movement modes of fish from video tracking data demonstrated significant effects of the spatial velocity gradient (SVG) and flow velocity on fish behavior. Notably, SVG emerged as the primary trigger for avoidance reactions when exceeding a threshold of [Formula]. Fish exhibited distinct movement patterns under dark and daylight conditions, with more avoidance reactions in darkness. Whereas a considerable proportion of fish in daylight increased their swimming activity in the zone were flow velocity exceeded sprint swimming speed, in dark conditions no activity peak occurred in the same zone. The results illustrate how hydraulic conditions and lighting influence fish behavior. Integrating the behavioral rules identified in this study into numerical mortality-risk models could substantially improve their predictive accuracy. Thus, the findings allow for the development of less fish harming engineering solutions for hydropower facilities and pumping stations.

animal behavior and cognition↗

bioRxiv2026

Virtual prototyping of non-invasive spinal cord electrical stimulation targeting upper limb motor function

Transcutaneous spinal cord stimulation (tSCS) applied over the cervical or lumbar spinal cord facilitates motor function after paralysis. However, emerging electrophysiological evidence indicates mechanistic differences between cervical and lumbar tSCS and across different cervical tSCS paradigms. To clarify these discrepancies, we developed and validated a multi-scale, whole-body computational model of tSCS-induced volume conduction, axonal recruitment, and synaptic transmission. Across 24 cervical and four lumbar tSCS paradigms, simulations showed that somatosensory afferents consistently exhibit lower stimulation thresholds than motor efferents. In turn, region-specific synaptic transmission differences may explain electrophysiological discrepancies between cervical and lumbar tSCS. Across cervical tSCS paradigms, substantial volume conduction and axonal recruitment differences were observed that explain electrophysiological discrepancies. Specifically, clinically-prevalent paradigms, including those with anodes placed over the clavicles or iliac crests, engaged peripheral nerves in addition to spinal roots. This effect was amplified by multiphasic waveforms, which introduced recruitment sites near the anodes. By integrating simulations with electrophysiological recordings in 14 able-bodied individuals, we investigated previously unexplored cervical tSCS paradigms on their capacity to recruit somatosensory afferents relative to motor threshold.

neuroscience↗

bioRxiv2023

Evolutionary constraint and innovation across hundreds of placental mammals

Evolutionary constraint and acceleration are powerful, cell-type agnostic measures of functional importance. Previous studies in mammals were limited by species number and reliance on human-referenced alignments. We explore the evolution of placental mammals, including humans, through reference-free whole-genome alignment of 240 species and protein-coding alignments for 428 species. We estimate 10.7% of the human genome is evolutionarily constrained. We resolve constraint to single nucleotides, pinpointing functional positions, and refine and expand by over seven-fold the catalog of ultraconserved elements. Overall, 48.5% of constrained bases are as yet unannotated, suggesting yet-to-be-discovered functional importance. Using species-level phenotypes and an updated phylogeny, we associate coding and regulatory variation with olfaction and hibernation. Focusing on biodiversity conservation, we identify genomic metrics that predict species at risk of extinction.

genomics↗

bioRxiv2022

High resolution mapping of the breast cancer tumor microenvironment using integrated single cell, spatial and in situ analysis of FFPE tissue

Single cell and spatial technologies that profile gene expression across a whole tissue are revolutionizing the resolution of molecular states in clinical tissue samples. Commercially available methods that characterize either single cell or spatial gene expression are currently limited by low sample throughput and/or gene plexy, lack of on-instrument analysis, and the destruction of histological features and epitopes during the workflow. Here, we analyzed large, serial formalin-fixed, paraffin-embedded (FFPE) human breast cancer sections using a novel FFPE-compatible single cell gene expression workflow (Chromium Fixed RNA Profiling; scFFPE-seq), spatial transcriptomics (Visium CytAssist), and automated microscopy-based in situ technology using a 313-plex gene panel (Xenium In Situ). Whole transcriptome profiling of the FFPE tissue using scFFPE-seq and Visium facilitated the identification of 17 different cell types. Xenium allowed us to spatially resolve these cell types and their gene expression profiles with single cell resolution. Due to the non-destructive nature of the Xenium workflow, we were able to perform H&E staining and immunofluorescence on the same section post-processing which allowed us to spatially register protein, histological, and RNA data together into a single image. Integration of data from Chromium scFFPE-seq, Visium, and Xenium across serial sections allowed us to do extensive benchmarking of sensitivity and specificity between the technologies. Furthermore, data integration inspired the interrogation of three molecularly distinct tumor subtypes (low-grade and high-grade ductal carcinoma in situ (DCIS), and invasive carcinoma). We used Xenium to characterize cellular composition and differentially expressed genes within these subtypes. This analysis allowed us to draw biological insights about DCIS progression to infiltrating carcinoma, as the myoepithelial layer degrades and tumor cells invade the surrounding stroma. Xenium also allowed us to further predict the hormone receptor status of tumor subtypes, including a small 0.1 mm2 DCIS region that was triple positive for ESR1 (estrogen receptor), PGR (progesterone receptor), and ERBB2 (human epidermal growth factor receptor 2, a.k.a. HER2) RNA. In order to derive whole transcriptome information from these cells, we used Xenium data to interpolate the cell composition of Visium spots, and used Visium whole transcriptome information to discover new biomarkers of breast tumor subtypes. We demonstrate that scFFPE-seq, Visium, and Xenium independently provide information about molecular signatures relevant to understanding cancer heterogeneity. However, it is the integration of these technologies that leads to even deeper insights, ushering in discoveries that will progress oncology research and the development of diagnostics and therapeutics.

cancer biology↗

bioRxiv2022

Human photoreceptor cell transplants integrate into human retina organoids

Cell transplantation is a promising therapeutic approach to recover loss of neurons and vision in patient retinas. So far, human photoreceptor transplants restored some visual function in degenerating mouse retina. Whether retinal cell transplants also integrate into human retina, and how to optimize this for different pathologies are still unknown. Here, we sought to determine if human retina organoids generated from pluripotent stem cells might assist cell replacement therapy development in a human-to-human setting. Models for intra- and subretinal cell transplantation strategies were explored: Photoreceptor donor cells carrying a transgenic fluorescent reporter were enriched from acutely dissociated human retinal organoids. Donor cells were precisely transplanted by microinjection into the retina of host organoids, but high cell numbers might require multiple injections posing potential damage. Alternatively, donor cells were transplanted in large numbers by placing them in subretinal-like contact to the apical organoid surface. Using postmitotic retinal organoids (age >170-days) as a source for donor cells and as hosts, we show that six weeks after subretinal-like transplantation, large clusters of photoreceptors reproducibly incorporate into the host retina. Transplanted clusters frequently are located within or across the host photoreceptor layer, include cone and rod photoreceptors, and become infiltrated by cell processes of host Muller glia, indicative of structural integration. Histological and ultrastructural data of virally-labeled photoreceptor transplants show characteristic morphological and structural features of polarized photoreceptors: inner segments and ribbon synapses, and donor-host cell contacts develop contributing to the retinal outer limiting membrane. These results demonstrate that human retinal organoids provide a preclinical research system for cell replacement therapies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/500037v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@3a405dorg.highwire.dtl.DTLVardef@57c4deorg.highwire.dtl.DTLVardef@3de9c5org.highwire.dtl.DTLVardef@be9820_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2021

Multidose evaluation of 6,710 drug repurposing library identifies potent SARS-CoV-2 infection inhibitors In Vitro and In Vivo.

Identification of host factors contributing to replication of viruses and resulting disease progression remains a promising approach for development of new therapeutics. Here, we evaluated 6710 clinical and preclinical compounds targeting 2183 host proteins by immunocytofluorescence-based screening to identify SARS-CoV-2 infection inhibitors. Computationally integrating relationships between small molecule structure, dose-response antiviral activity, host target and cell interactome networking produced cellular networks important for infection. This analysis revealed 389 small molecules, >12 scaffold classes and 813 host targets with micromolar to low nanomolar activities. From these classes, representatives were extensively evaluated for mechanism of action in stable and primary human cell models, and additionally against Beta and Delta SARS-CoV-2 variants and MERS-CoV. One promising candidate, obatoclax, significantly reduced SARS-CoV-2 viral lung load in mice. Ultimately, this work establishes a rigorous approach for future pharmacological and computational identification of novel host factor dependencies and treatments for viral diseases.

microbiology↗

bioRxiv2021

Phenotyping in the era of genomics: MaTrics a digital character matrix to document mammalian phenotypic traits coded numerically

A new and uniquely structured matrix of mammalian phenotypes, MaTrics (Mammalian Traits for Comparative Genomics) is presented in a digital form. By focussing on mammalian species for which genome assemblies are available, MaTrics provides an interface between mammalogy and comparative genomics. MaTrics was developed as part of a project to link phenotypic differences between mammals to differences in their genomes using Forward Genomics. Apart from genomes this approach requires information on homologous phenotypes that are numerically encoded (presence-absence; multistate character coding*) in a matrix. MaTrics provides these data, links them to at least one reference (e.g., literature, photographs, histological sections, CT-scans, or museum specimens) and makes them available in a machine actionable NEXUS-format. By making the data computer readable, MatTrics opens a new way for digitizing collections. Currently, MaTrics covers 147 mammalian species and includes 207 characters referring to structure, morphology, physiology, ecology and ethology. Researching these traits revealed substantial knowledge gaps, highlighting the need for substantial phenotyping efforts in the genomic era. Using the trait information documented in MaTrics, previous Forward Genomics screens identified changes in genes that are associated with various phenotypes, ranging from fully-aquatic lifestyle to dietary specializations. These results motivate the continuous expansion of phenotype information, both by filling research gaps or by adding additional taxa and traits. MaTrics is digitally available online within the data repository Morph{middle dot}D{middle dot}Base (www.morphdbase.de).

zoology↗

bioRxiv2020

Extensive proteomic and transcriptomic changes quench the TCR/CD3 activation signal of latently HIV-1 infected T cells

Although the ability of HIV-1 to reside in a latent state in CD4+ T cells constitutes a critical hurdle to a curative therapy, the biomolecular mechanisms by which latent HIV-1 infection is established and maintained are only partially understood. Ex vivo studies have shown that T cell receptor/CD3 stimulation only triggered HIV-1 reactivation in a fraction of the latently infected CD4+ T cell reservoir, suggesting that parts of the T cell population hosting latent HIV-1 infection events are altered to be TCR/CD3-activation-inert. We provide experimental evidence that HIV-1 infection of primary T cells and T cell lines indeed generates a substantial amount of TCR/CD3 activation-inert latently infected T cells. HIV-1 induced host cell TCR/CD3 inertness is thus a conserved mechanism that contributes to the stability of latent HIV-1 infection. Proteomic and genome-wide RNA-level analysis comparing CD3-responsive and CD3-inert latently HIV-1 infected T cells, followed by software-based integration of the data into protein-protein interaction networks (PINs) suggested two phenomena to govern CD3-inertness: (i) the presence of extensive transcriptomic noise that affected the efficacy of CD3 signaling and (ii) defined changes to specific signaling pathways. Validation experiments demonstrated that compounds known to increase transcriptomic noise further diminished the ability of TCR/CD3 stimulation to trigger HIV-1 reactivation. Conversely, targeting specific central nodes in the generated PINs such as STAT3 improved the ability of TCR/CD3 activation to trigger HIV-1 reactivation in T cell lines and primary T cells. The data emphasize that latent HIV-1 infection is largely the result of extensive, stable biomolecular changes to the signaling network of the host T cells harboring latent HIV-1 infection events. In extension, the data imply that therapeutic restoration of host cell TCR/CD3 responsiveness could enable gradual reservoir depletion without the need for therapeutic activators, driven by cognate antigen recognition. AUTHOR SUMMARYA curative therapy for HIV-1 infection will at least require the eradication of a small pool of CD4+ helper T cells in which the virus can persist in a latent state, even after years of successful antiretroviral therapy. It has been assumed that activation of these viral reservoir T cells will also reactivate the latent virus, which is a prerequisite for the destruction of these cells. Remarkably, this is not the case and following application of even the most potent stimuli that activate normal T cells through their T cell receptor, a large portion of the latent virus pool remains in a dormant state. Herein we demonstrate that a large part of latent HIV-1 infection events reside in T cells that have been rendered activation inert by the actual infection event. We provide a systemwide, biomolecular description of the changes that render latently HIV-1 infected T cells activation inert and using this description, devise pharmacologic interference strategies that render initially activation inert T cells responsive to stimulation. This in turn allows for efficient triggering of HIV-1 reactivation in a large part of the latently HIV-1 infected T cell reservoir.

microbiology↗

bioRxiv2020

A High Content Screen for Mucin-1-Reducing Compounds Identifies Fostamatinib as a Candidate for Rapid Repurposing for Acute Lung Injury during the COVID-19 pandemic

Drug repurposing is the only method capable of delivering treatments on the shortened time-scale required for patients afflicted with lung disease arising from SARS-CoV-2 infection. Mucin-1 (MUC1), a membrane-bound molecule expressed on the apical surfaces of most mucosal epithelial cells, is a biochemical marker whose elevated levels predict the development of acute lung injury (ALI) and respiratory distress syndrome (ARDS), and correlate with poor clinical outcomes. In response to the pandemic spread of SARS-CoV-2, we took advantage of a high content screen of 3,713 compounds at different stages of clinical development to identify FDA-approved compounds that reduce MUC1 protein abundance. Our screen identified Fostamatinib (R788), an inhibitor of spleen tyrosine kinase (SYK) approved for the treatment of chronic immune thrombocytopenia, as a repurposing candidate for the treatment of ALI. In vivo, Fostamatinib reduced MUC1 abundance in lung epithelial cells in a mouse model of ALI. In vitro, SYK inhibition by Fostamatinib promoted MUC1 removal from the cell surface. Our work reveals Fostamatinib as a repurposing drug candidate for ALI and provides the rationale for rapidly standing up clinical trials to test Fostamatinib efficacy in patients with COVID-19 lung injury.

cell biology↗

bioRxiv2020

Monet: An open-source Python package for analyzing and integrating scRNA-Seq data using PCA-based latent spaces

Single-cell RNA-Seq is a powerful technology that enables the transcriptomic profiling of the different cell populations that make up complex tissues. However, the noisy and high-dimensional nature of the generated data poses significant challenges for its analysis and integration. Here, I describe Monet, an open-source Python package designed to provide effective and computationally efficient solutions to some of the most common challenges encountered in scRNA-Seq data analysis, and to serve as a toolkit for scRNA-Seq method development. At its core, Monet implements algorithms to infer the dimensionality and construct a PCA-based latent space from a given dataset. This latent space, represented by a MonetModel object, then forms the basis for data analysis and integration. In addition to validating these core algorithms, I provide demonstrations of some more advanced analysis tasks currently supported, such as batch correction and label transfer, which are useful for analyzing multiple datasets from the same tissue. Monet is available at https://github.com/flo-compbio/monet. Ongoing work is focused on providing electronic notebooks with tutorials for individual analysis tasks, and on developing interoperability with other Python scRNA-Seq software. The author welcomes suggestions for future improvements.

bioinformatics↗

bioRxiv2019

Straightforward clustering of single-cell RNA-Seq data with t-SNE and DBSCAN

Clustering of cells by cell type is arguably the most common and repetitive task encountered during the analysis of single-cell RNA-Seq data. However, as popular clustering methods operate largely independently of visualization techniques, the fine-tuning of clustering parameters can be unintuitive and time-consuming. Here, I propose Galapagos, a simple and effective clustering workflow based on t-SNE and DBSCAN that does not require a gene selection step. In practice, Galapagos only involves the fine-tuning of two parameters, which is straightforward, as clustering is performed directly on the t-SNE visualization results. Using peripheral blood mononuclear cells as a model tissue, I validate the effectiveness of Galapagos in different ways. First, I show that Galapagos generates clusters corresponding to all main cell types present. Then, I demonstrate that the t-SNE results are robust to parameter choices and initialization points. Next, I employ a simulation approach to show that clustering with Galapagos is accurate and robust to the high levels of technical noise present. Finally, to demonstrate Galapagos accuracy on real data, I compare clustering results to true cell type identities established using CITE-Seq data. In this context, I also provide an example of the primary limitation of Galapagos, namely the difficulty to resolve related cell types in cases where t-SNE fails to clearly separate the cells. Galapagos helps to make clustering scRNA-Seq data more intuitive and reproducible, and can be implemented in most programming languages with only a few lines of code.

bioinformatics↗

bioRxiv2019

ENHANCE: Accurate denoising of single-cell RNA-Seq data

Single-cell RNA-Seq measurements are commonly affected by high levels of technical noise, posing challenges for data analysis and visualization. A diverse array of methods has been proposed to computationally remove noise by sharing information across similar cells or genes, however their respective accuracies have been difficult to establish. Here, we propose a simple denoising strategy based on principal component analysis (PCA). We show that while PCA performed on raw data is biased towards highly expressed genes, this bias can be mitigated with a cell aggregation step, allowing the recovery of denoised expression values for both highly and lowly expressed genes. We benchmark our resulting ENHANCE algorithm and three previously described methods on simulated data that closely mimic real datasets, showing that ENHANCE provides the best overall denoising accuracy, recovering modules of co-expressed genes and cell subpopulations. Implementations of our algorithm are available at https://github.com/yanailab/enhance.

bioinformatics↗

bioRxiv2019

Armadillo repeat-containing protein 1 is a dual localization protein associated with mitochondrial intermembrane space bridging complex

Cristae architecture is important for the function of mitochondria, the organelles that play the central role in many cellular processes. The mitochondrial contact site and cristae organizing system (MICOS) together with the sorting and assembly machinery (SAM) forms the mitochondrial intermembrane space bridging complex (MIB), a large protein complex present in mammalian mitochondria that partakes in the formation and maintenance of cristae. We report here a new subunit of the mammalian MICOS/MIB complex, an armadillo repeat-containing protein 1 (ArmC1). ArmC1 localizes both to cytosol and mitochondria, where it associates with the outer mitochondrial membrane through its carboxy-terminus. ArmC1 interacts with other constituents of the MICOS/MIB complex and its amounts are reduced upon MICOS/MIB complex depletion. Mitochondria lacking ArmC1 do not show defects in cristae structure, respiration or protein content, but appear fragmented and with reduced motility. ArmC1 represents therefore a peripheral MICOS/MIB component that appears to play a role in mitochondrial distribution in the cell.

cell biology↗

bioRxiv2019

Dynamic architecture of the Escherichia coli structural maintenance of chromosomes (SMC) complex, MukBEF

Structural Maintenance of Chromosomes (SMC) complexes use a proteinaceous ring-shaped architecture to organise chromosomes, thereby facilitating chromosome segregation. They utilise cycles of ATP binding and hydrolysis to transport themselves rapidly with respect to DNA, a process requiring protein conformational changes and multiple DNA contacts. We have analysed changes in the architecture of the Escherichia coli SMC complex, MukBEF, as a function of nucleotide binding to MukB and subsequent ATP hydrolysis. This builds upon previous work showing that MukF kleisin directs formation of a MukBEF tripartite ring as a consequence of functional interactions between the C- and N-terminal domains of MukF with the MukB head and neck, respectively (Zawadzka et al., 2018). Using both model truncated substrates and complexes containing full length MukB, we now demonstrate formation of MukBEF dimers of dimers, dependent on MukF dimerization, MukB head-engagement and MukE, which plays an essential role in organizing MukBEF complexes.

biochemistry↗

bioRxiv2018

Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq data

Single-cell RNA-Seq (scRNA-Seq) enables the systematic molecular characterization of heterogeneous tissues at an unprecedented resolution and scale. However, it is currently unclear how to establish formal cell type definitions, which impedes the systematic analysis of scRNA-Seq data across experiments and studies. To address this challenge, we have developed Moana, a hierarchical machine learning framework that enables the construction of robust cell type classifiers from heterogeneous scRNA-Seq datasets. To demonstrate Moanas capabilities, we construct cell type classifiers for human immune cells that accurately distinguish between closely related cell types in the presence of experimental perturbations and systematic differences between scRNA-Seq protocols. We show that Moana is generally applicable and scales to datasets with more than ten thousand cells, thus enabling the construction of tissue-specific cell type atlases that can be directly applied to analyze new scRNASeq datasets. A Python implementation of Moana can be found at https://github.com/yanailab/moana.

bioinformatics↗

bioRxiv2018

Synthetic Lethality of Wnt Pathway Activation and Asparaginase in Drug-Resistant Acute Leukemias

Resistance to asparaginase, an antileukemic enzyme that depletes asparagine, is a common clinical problem. Using a genome-wide CRISPR/Cas9 screen, we found a synthetic lethal interaction between Wnt pathway activation and asparaginase in acute leukemias resistant to this enzyme. Wnt pathway activation induced asparaginase sensitivity in distinct treatment-resistant subtypes of acute leukemia, including T-lymphoblastic, hypodiploid B-lymphoblastic, and acute myeloid leukemias, but not in normal hematopoietic progenitors. Sensitization to asparaginase was mediated by Wnt-dependent stabilization of proteins (Wnt/STOP), which inhibits GSK3-dependent protein ubiquitination and degradation. Inhibiting the alpha isoform of GSK3 phenocopied this effect, and pharmacologic GSK3 inhibition profoundly sensitized drug-resistant leukemias to asparaginase. Our findings provide a molecular rationale for activation of Wnt/STOP signaling to improve the therapeutic index of asparaginase.\n\nSIGNIFICANCEThe intensification of asparaginase-based therapy has improved outcomes for several subtypes of acute leukemia, but the development of treatment resistance has a poor prognosis. We hypothesized, from the concept of synthetic lethality, that gain-of-fitness alterations in drug-resistant cells had conferred a survival advantage that could be exploited therapeutically. We found a synthetic lethal interaction between activation of Wnt-dependent stabilization of proteins (Wnt/STOP) and asparaginase in acute leukemias resistant to this enzyme. Inhibition of the alpha isoform of GSK3 was sufficient to phenocopy this effect, and the combination of GSK3-selective inhibitors and asparaginase had marked therapeutic activity against leukemias resistant to monotherapy with either agent. These data indicate that drug-drug synthetic lethal interactions can improve the therapeutic index of cancer therapy.

cancer biology↗