Artemisinins Target GABAA Receptor Signaling and Impair α Cell Identity.

Type 1 diabetes is characterized by the destruction of pancreatic ß cells, and generating new insulin-producing cells from other cell types is a major aim of regenerative medicine. One promising approach is transdifferentiation of developmentally related pancreatic cell types, including glucagon-producing α cells. In a genetic model, loss of the master regulatory transcription factor Arx is sufficient to induce the conversion of α cells to functional ß-like cells. Here, we identify artemisinins as small molecules that functionally repress Arx by causing its translocation to the cytoplasm. We show that the protein gephyrin is the mammalian target of these antimalarial drugs and that the mechanism of action of these molecules depends on the enhancement of GABAA receptor signaling. Our results in zebrafish, rodents, and primary human pancreatic islets identify gephyrin as a druggable target for the regeneration of pancreatic ß cell mass from α cells.

Type I Interferon Signaling Disrupts the Hepatic Urea Cycle and Alters Systemic Metabolism to Suppress T Cell Function.

Infections induce complex host responses linked to antiviral defense, inflammation, and tissue damage and repair. We hypothesized that the liver, as a central metabolic hub, may orchestrate systemic metabolic changes during infection. We infected mice with chronic lymphocytic choriomeningitis virus (LCMV), performed RNA sequencing and proteomics of liver tissue, and integrated these data with serum metabolomics at different infection phases. Widespread reprogramming of liver metabolism occurred early after infection, correlating with type I interferon (IFN-I) responses. Viral infection induced metabolic alterations of the liver that depended on the interferon alpha/beta receptor (IFNAR1). Hepatocyte-intrinsic IFNAR1 repressed the transcription of metabolic genes, including Otc and Ass1, which encode urea cycle enzymes. This led to decreased arginine and increased ornithine concentrations in the circulation, resulting in suppressed virus-specific CD8+ T cell responses and ameliorated liver pathology. These findings establish IFN-I-induced modulation of hepatic metabolism and the urea cycle as an endogenous mechanism of immunoregulation. VIDEO ABSTRACT.

24-Norursodeoxycholic acid reshapes immunometabolism in CD8+ T cells and alleviates hepatic inflammation.

BACKGROUND & AIMS: 24-Norursodeoxycholic acid (NorUDCA) is a novel therapeutic bile acid used to treat immune-mediated cholestatic liver diseases, such as primary sclerosing cholangitis (PSC), where dysregulated T cells including CD8+ T cells contribute to hepatobiliary immunopathology. We hypothesized that NorUDCA may directly modulate CD8+ T cell function thus contributing to its therapeutic efficacy. METHODS: NorUDCA's immunomodulatory effects were first studied in Mdr2-/- mice, as a cholestatic model of PSC. To differentiate NorUDCA's immunomodulatory effects on CD8+ T cell function from its anticholestatic actions, we also used a non-cholestatic model of hepatic injury induced by an excessive CD8+ T cell immune response upon acute non-cytolytic lymphocytic choriomeningitis virus (LCMV) infection. Studies included molecular and biochemical approaches, flow cytometry and metabolic assays in murine CD8+ T cells in vitro. Mass spectrometry was used to identify potential CD8+ T cell targets modulated by NorUDCA. The signaling effects of NorUDCA observed in murine cells were validated in circulating T cells from patients with PSC. RESULTS: NorUDCA demonstrated immunomodulatory effects by reducing hepatic innate and adaptive immune cells, including CD8+ T cells in the Mdr2-/- model. In the non-cholestatic model of CD8+ T cell-driven immunopathology induced by acute LCMV infection, NorUDCA ameliorated hepatic injury and systemic inflammation. Mechanistically, NorUDCA demonstrated strong immunomodulatory efficacy in CD8+ T cells affecting lymphoblastogenesis, expansion, glycolysis and mTORC1 signaling. Mass spectrometry identified that NorUDCA regulates CD8+ T cells by targeting mTORC1. NorUDCA's impact on mTORC1 signaling was further confirmed in circulating PSC CD8+ T cells. CONCLUSIONS: NorUDCA has a direct modulatory impact on CD8+ T cells and attenuates excessive CD8+ T cell-driven hepatic immunopathology. These findings are relevant for treatment of immune-mediated liver diseases such as PSC. LAY SUMMARY: Elucidating the mechanisms by which 24-norursodeoxycholic acid (NorUDCA) works for the treatment of immune-mediated liver diseases, such as primary sclerosing cholangitis, is of considerable clinical interest. Herein, we uncovered an unrecognized property of NorUDCA in the immunometabolic regulation of CD8+ T cells, which has therapeutic relevance for immune-mediated liver diseases, including PSC.

Genome-Wide Mutation Scoring for Machine-Learning-Based Antimicrobial Resistance Prediction.

The prediction of antimicrobial resistance (AMR) based on genomic information can improve patient outcomes. Genetic mechanisms have been shown to explain AMR with accuracies in line with standard microbiology laboratory testing. To translate genetic mechanisms into phenotypic AMR, machine learning has been successfully applied. AMR machine learning models typically use nucleotide k-mer counts to represent genomic sequences. While k-mer representation efficiently captures sequence variation, it also results in high-dimensional and sparse data. With limited training data available, achieving acceptable model performance or model interpretability is challenging. In this study, we explore the utility of feature engineering with several biologically relevant signals. We propose to predict the functional impact of observed mutations with PROVEAN to use the predicted impact as a new feature for each protein in an organism's proteome. The addition of the new features was tested on a total of 19,521 isolates across nine clinically relevant pathogens and 30 different antibiotics. The new features significantly improved the predictive performance of trained AMR models for Pseudomonas aeruginosa, Citrobacter freundii, and Escherichia coli. The balanced accuracy of the respective models of those three pathogens improved by 6.0% on average.

Effects of Alanyl-Glutamine Treatment on the Peritoneal Dialysis Effluent Proteome Reveal Pathomechanism-Associated Molecular Signatures.

Peritoneal dialysis (PD) is a modality of renal replacement therapy in which the high volumes of available PD effluent (PDE) represents a rich source of biomarkers for monitoring disease and therapy. Although this information could help guide the management of PD patients, little is known about the potential of PDE to define pathomechanism-associated molecular signatures in PD.We therefore subjected PDE to a high-performance multiplex proteomic analysis after depletion of highly-abundant plasma proteins and enrichment of low-abundance proteins. A combination of label-free and isobaric labeling strategies was applied to PDE samples from PD patients (n = 20) treated in an open-label, randomized, two-period, cross-over clinical trial with standard PD fluid or with a novel PD fluid supplemented with alanyl-glutamine (AlaGln).With this workflow we identified 2506 unique proteins in the PDE proteome, greatly increasing coverage beyond the 171 previously-reported proteins. The proteins identified range from high abundance plasma proteins to low abundance cellular proteins, and are linked to larger numbers of biological processes and pathways, some of which are novel for PDE. Interestingly, proteins linked to membrane remodeling and fibrosis are overrepresented in PDE compared with plasma, whereas the proteins underrepresented in PDE suggest decreases in host defense, immune-competence and response to stress. Treatment with AlaGln-supplemented PD fluid is associated with reduced activity of membrane injury-associated mechanisms and with restoration of biological processes involved in stress responses and host defense.Our study represents the first application of the PDE proteome in a randomized controlled prospective clinical trial of PD. This novel proteomic workflow allowed detection of low abundance biomarkers to define pathomechanism-associated molecular signatures in PD and their alterations by a novel therapeutic intervention.

Characterization of host proteins interacting with the lymphocytic choriomeningitis virus L protein.

RNA-dependent RNA polymerases (RdRps) play a key role in the life cycle of RNA viruses and impact their immunobiology. The arenavirus lymphocytic choriomeningitis virus (LCMV) strain Clone 13 provides a benchmark model for studying chronic infection. A major genetic determinant for its ability to persist maps to a single amino acid exchange in the viral L protein, which exhibits RdRp activity, yet its functional consequences remain elusive. To unravel the L protein interactions with the host proteome, we engineered infectious L protein-tagged LCMV virions by reverse genetics. A subsequent mass-spectrometric analysis of L protein pulldowns from infected human cells revealed a comprehensive network of interacting host proteins. The obtained LCMV L protein interactome was bioinformatically integrated with known host protein interactors of RdRps from other RNA viruses, emphasizing interconnected modules of human proteins. Functional characterization of selected interactors highlighted proviral (DDX3X) as well as antiviral (NKRF, TRIM21) host factors. To corroborate these findings, we infected Trim21-/- mice with LCMV and found impaired virus control in chronic infection. These results provide insights into the complex interactions of the arenavirus LCMV and other viral RdRps with the host proteome and contribute to a better molecular understanding of how chronic viruses interact with their host.

Complement Activation in Peritoneal Dialysis-Induced Arteriolopathy.

Cardiovascular disease (CVD) is the leading cause of increased mortality in patients with CKD and is further aggravated by peritoneal dialysis (PD). Children are devoid of preexisting CVD and provide unique insight into specific uremia- and PD-induced pathomechanisms of CVD. We obtained peritoneal specimens from children with stage 5 CKD at time of PD catheter insertion (CKD5 group), children with established PD (PD group), and age-matched nonuremic controls (n=6/group). We microdissected omental arterioles from tissue layers not directly exposed to PD fluid and used adjacent sections of four arterioles per patient for transcriptomic and proteomic analyses. Findings were validated in omental and parietal arterioles from independent pediatric control (n=5), CKD5 (n=15), and PD (n=15) cohorts. Transcriptomic analysis revealed differential gene expression in control versus CKD5 arterioles and in CKD5 versus PD arterioles. Gene ontology analyses revealed activation of metabolic processes in CKD5 arterioles and of inflammatory, immunologic, and stress-response cascades in PD arterioles. PD arterioles exhibited particular upregulation of the complement system and respective regulatory pathways, with concordant findings at the proteomic level. In the validation cohorts, PD specimens had the highest abundance of omental and parietal arteriolar C1q, C3d, terminal complement complex, and phosphorylated SMAD2/3, a downstream effector of TGF-ß Furthermore, in the PD parietal arterioles, C1q and terminal complement complex abundance correlated with the level of dialytic glucose exposure, abundance of phosphorylated SMAD2/3, and degree of vasculopathy. We conclude that PD fluids activate arteriolar complement and TGF-ß signaling, which quantitatively correlate with the severity of arteriolar vasculopathy.

Mass spectrometric analysis of synaptosomal membrane preparations for the determination of brain receptors, transporters and channels.

The molecular composition of synaptic signal transduction machineries shapes synaptic neurotransmission. The repertoire of receptors, transporters and channels (RTCs) comprises major signaling events in the brain. RTCs are conventionally studied by candidate immunohistochemistry and biochemistry, which are low throughput with resolution greatly affected by available immunoreagents and membrane interference. Therefore, a comprehensive resource of synaptic brain RTCs is still lacking. In particular, studies on the detergent-soluble synaptosomal fraction, known to contain transporters and channels, are limited. We, therefore, performed sub-synaptosomal fractionation of rat cerebral cortex, followed by trypsin/chymotrypsin sequential digestion of a detergent-soluble synaptosomal fraction and a postsynaptic density preparation, stable-isotope tryptic peptide labeling and liquid chromatography mass spectrometry. Based on the current study, a total of 4784 synaptic proteins were submitted to the ProteomExchange database (PXD001948), including 274 receptors, 394 transporters/channels and 1377 transmembrane proteins. Function-based classification assigned 1781 proteins as probable drug targets with 834 directly linked to brain disorders. The analytical approach identified 499 RTCs that are not listed in the largest, curated database for synaptosomal proteins (SynProt). This is a threefold RTC increase over all other data collected to date. Taken together, we present a protein discovery resource that can serve as a benchmark for future molecular interrogation of synaptic connectivity.

FASIL-MS: An Integrated Proteomic and Bioinformatic Workflow To Universally Quantitate In Vivo-Acetylated Positional Isomers.

Dynamic changes in histone post-translational modifications (PTMs) regulate gene transcription leading to fine-tuning of biological processes such as DNA replication and cell cycle progression. Moreover, specific histone modifications constitute docking sites for recruitment of DNA damage repair proteins and mediation of subsequent cell survival. Therefore, understanding and monitoring changes in histone PTMs that can alter cell proliferation and thus lead to disease progression are of considerable medical interest. In this study, stable isotope labeling with N-acetoxy-D3-succinimide (D3-NAS) was utilized to efficiently derivatize unmodified lysine residues at the protein level. The sample preparation method was streamlined to facilitate buffer exchange between the multiple steps of the protocol by coupling chemical derivatization to filter-aided sample preparation (FASP). Additionally, the mass spectrometry method was adapted to simultaneously coisolate and subsequently cofragment all differentially H3/D3-acetylated histone peptide clusters. Combination of these multiplexed MS(2) spectra with the implementation of a data analysis algorithm enabled the quantitation of each and every in vivo-acetylated DMSO- and SAHA-treated H4(4-17) and H3(18-26) peptide. We have termed our new approach FASIL-MS for filter-aided stable isotopic labeling coupled to mass spectrometry. FASIL-MS enables the universal and site-specific quantitation of peptides with multiple in vivo-acetylated lysine residues. Data are available via ProteomeXchange (PXD003611).

Early-onset inflammatory bowel disease and common variable immunodeficiency-like disease caused by IL-21 deficiency.

BACKGROUND: Alterations of immune homeostasis in the gut can result in development of inflammatory bowel disease (IBD). Recently, Mendelian forms of IBD have been discovered, as exemplified by deficiency of IL-10 or its receptor subunits. In addition, other types of primary immunodeficiency disorders might be associated with intestinal inflammation as one of their leading clinical presentations. OBJECTIVE: We investigated a large consanguineous family with 3 children who presented with early-onset IBD within the first year of life, leading to death in infancy in 2 of them. METHODS: Homozygosity mapping combined with exome sequencing was performed to identify the molecular cause of the disorder. Functional experiments were performed to assess the effect of IL-21 on the immune system. RESULTS: A homozygous mutation in IL21 was discovered that showed perfect segregation with the disease. Deficiency of IL-21 resulted in reduced numbers of circulating CD19(+) B cells, including IgM(+) naive and class-switched IgG memory B cells, with a concomitant increase in transitional B-cell numbers. In vitro assays demonstrated that mutant IL-21(Leu49Pro) did not induce signal transducer and activator of transcription 3 phosphorylation and immunoglobulin class-switch recombination. CONCLUSION: Our study uncovers IL-21 deficiency as a novel cause of early-onset IBD in human subjects accompanied by defects in B-cell development similar to those found in patients with common variable immunodeficiency. IBD might mask an underlying primary immunodeficiency, as illustrated here with IL-21 deficiency.

Metagenomic Antimicrobial Susceptibility Testing from Simulated Native Patient Samples.

Genomic antimicrobial susceptibility testing (AST) has been shown to be accurate for many pathogens and antimicrobials. However, these methods have not been systematically evaluated for clinical metagenomic data. We investigate the performance of in-silico AST from clinical metagenomes (MG-AST). Using isolate sequencing data from a multi-center study on antimicrobial resistance (AMR) as well as shotgun-sequenced septic urine samples, we simulate over 2000 complicated urinary tract infection (cUTI) metagenomes with known resistance phenotype to 5 antimicrobials. Applying rule-based and machine learning-based genomic AST classifiers, we explore the impact of sequencing depth and technology, metagenome complexity, and bioinformatics processing approaches on AST accuracy. By using an optimized metagenomics assembly and binning workflow, MG-AST achieved balanced accuracy within 5.1% of isolate-derived genomic AST. For poly-microbial infections, taxonomic sample complexity and relatedness of taxa in the sample is a key factor influencing metagenomic binning and downstream MG-AST accuracy. We show that the reassignment of putative plasmid contigs by their predicted host range and investigation of whole resistome capabilities improved MG-AST performance on poly-microbial samples. We further demonstrate that machine learning-based methods enable MG-AST with superior accuracy compared to rule-based approaches on simulated native patient samples.

Automated antimicrobial susceptibility testing and antimicrobial resistance genotyping using Illumina and Oxford Nanopore Technologies sequencing data among Enterobacteriaceae.

Whole-genome sequencing (WGS) enables the molecular characterization of bacterial pathogens. We compared the accuracy of the Illumina and Oxford Nanopore Technologies (ONT) sequencing platforms for the determination of AMR classes and antimicrobial susceptibility testing (AST) among 181 clinical Enterobacteriaceae isolates. Sequencing reads for each isolate were uploaded to AREScloud (Ares Genetics) to determine the presence of AMR markers and the predicted WGS-AST profile. The profiles of both sequencing platforms were compared to broth microdilution (BMD) AST. Isolates were delineated by resistance to third-generation cephalosporins and carbapenems as well as the presence of AMR markers to determine clinically relevant AMR classes. The overall categorical agreement (CA) was 90% (Illumina) and 88% (ONT) across all antimicrobials, 96% for the prediction of resistance to third-generation cephalosporins for both platforms, and 94% (Illumina) and 91% (ONT) for the prediction of resistance to carbapenems. Carbapenem resistance was overestimated on ONT with a major error of 16%. Sensitivity for the detection of carbapenemases, extended-spectrum ß-lactamases, and plasmid-mediated ampC genes was 98, 95, and 70% by ONT compared to the Illumina dataset as the reference. Our results highlight the potential of the ONT platform's use in clinical microbiology laboratories. When combined with robust bioinformatics methods, WGS-AST predictions may be a future approach to guide effective antimicrobial decision-making.

Learning From Limited Data: Towards Best Practice Techniques for Antimicrobial Resistance Prediction From Whole Genome Sequencing Data.

Antimicrobial resistance prediction from whole genome sequencing data (WGS) is an emerging application of machine learning, promising to improve antimicrobial resistance surveillance and outbreak monitoring. Despite significant reductions in sequencing cost, the availability and sampling diversity of WGS data with matched antimicrobial susceptibility testing (AST) profiles required for training of WGS-AST prediction models remains limited. Best practice machine learning techniques are required to ensure trained models generalize to independent data for optimal predictive performance. Limited data restricts the choice of machine learning training and evaluation methods and can result in overestimation of model performance. We demonstrate that the widely used random k-fold cross-validation method is ill-suited for application to small bacterial genomics datasets and offer an alternative cross-validation method based on genomic distance. We benchmarked three machine learning architectures previously applied to the WGS-AST problem on a set of 8,704 genome assemblies from five clinically relevant pathogens across 77 species-compound combinations collated from public databases. We show that individual models can be effectively ensembled to improve model performance. By combining models via stacked generalization with cross-validation, a model ensembling technique suitable for small datasets, we improved average sensitivity and specificity of individual models by 1.77% and 3.20%, respectively. Furthermore, stacked models exhibited improved robustness and were thus less prone to outlier performance drops than individual component models. In this study, we highlight best practice techniques for antimicrobial resistance prediction from WGS data and introduce the combination of genome distance aware cross-validation and stacked generalization for robust and accurate WGS-AST.

High-throughput drug screening identifies the ATR-CHK1 pathway as a therapeutic vulnerability of CALR mutated hematopoietic cells.

Mutations of calreticulin (CALR) are the second most prevalent driver mutations in essential thrombocythemia and primary myelofibrosis. To identify potential targeted therapies for CALR mutated myeloproliferative neoplasms, we searched for small molecules that selectively inhibit the growth of CALR mutated cells using high-throughput drug screening. We investigated 89 172 compounds using isogenic cell lines carrying CALR mutations and identified synthetic lethality with compounds targeting the ATR-CHK1 pathway. The selective inhibitory effect of these compounds was validated in a co-culture assay of CALR mutated and wild-type cells. Of the tested compounds, CHK1 inhibitors potently depleted CALR mutated cells, allowing wild-type cell dominance in the co-culture over time. Neither CALR deficient cells nor JAK2V617F mutated cells showed hypersensitivity to ATR-CHK1 inhibition, thus suggesting specificity for the oncogenic activation by the mutant CALR. CHK1 inhibitors induced replication stress in CALR mutated cells revealed by elevated pan-nuclear staining for γH2AX and hyperphosphorylation of RPA2. This was accompanied by S-phase cell cycle arrest due to incomplete DNA replication. Transcriptomic and phosphoproteomic analyses revealed a replication stress signature caused by oncogenic CALR, suggesting an intrinsic vulnerability to CHK1 perturbation. This study reveals the ATR-CHK1 pathway as a potential therapeutic target in CALR mutated hematopoietic cells.

Allosteric transitions of supramolecular systems explored by network models: application to chaperonin GroEL.

Identification of pathways involved in the structural transitions of biomolecular systems is often complicated by the transient nature of the conformations visited across energy barriers and the multiplicity of paths accessible in the multidimensional energy landscape. This task becomes even more challenging in exploring molecular systems on the order of megadaltons. Coarse-grained models that lend themselves to analytical solutions appear to be the only possible means of approaching such cases. Motivated by the utility of elastic network models for describing the collective dynamics of biomolecular systems and by the growing theoretical and experimental evidence in support of the intrinsic accessibility of functional substates, we introduce a new method, adaptive anisotropic network model (aANM), for exploring functional transitions. Application to bacterial chaperonin GroEL and comparisons with experimental data, results from action minimization algorithm, and previous simulations support the utility of aANM as a computationally efficient, yet physically plausible, tool for unraveling potential transition pathways sampled by large complexes/assemblies. An important outcome is the assessment of the critical inter-residue interactions formed/broken near the transition state(s), most of which involve conserved residues.

A coarse-grained potential for fold recognition and molecular dynamics simulations of proteins.

A coarse-grained potential for protein simulations and fold ranking is presented. The potential is based on a two-point model of individual amino acids and a specific implementation of hydrogen bonding. Parameters are determined for distance dependent pair interactions, pseudo bonds, angles, and torsions. A scaling factor for a hydrogen bonding term is also determined. Iterative sampling for 4867 proteins reproduces distributions of internal coordinates and distances observed in the Protein Data Bank. The adjustment of the potential and resampling are in the spirit of the generalized ensemble approach. No native structure information (e.g., secondary structure) is used in the calculation of the potential or in the simulation of a particular protein. The potential is subject to two tests as follows: (i) simulations of 956 globular proteins in the neighborhood of their native folds (these proteins were not used in the training set) and (ii) discrimination between native and decoy structures for 2470 proteins with 305,000 decoys and the "Decoys 'R' Us" dataset. In the first test, 58% of tested proteins stay within 5 A from the native fold in Molecular Dynamics simulations of more than 20 nanoseconds using the new potential. The potential is also useful in differentiating between correct and approximate folds providing significant signal for structure prediction algorithms. Sampling with the potential consistently regenerates the distribution of distances and internal coordinates it learned. Nevertheless, during Molecular Dynamics simulations structures are found that reproduce the learned distributions but are far from the native fold.

Building and assessing atomic models of proteins from structural templates: learning and benchmarks.

One approach to predict a protein fold from a sequence (a target) is based on structures of related proteins that are used as templates. We present an algorithm that examines a set of candidates for templates, builds from each of the templates an atomically detailed model, and ranks the models. The algorithm performs a hierarchical selection of the best model using a diverse set of signals. After a quick and suboptimal screening of template candidates from the protein data bank, the current method fine-tunes the selection to a few models. More detailed signals test the compatibility of the sequence and the proposed structures, and are merged to give a global fitness measure using linear programming. This algorithm is a component of the prediction server LOOPP (http://www.loopp.org). Large-scale training and tests sets were designed and are presented. Recent results of the LOOPP server in CASP8 are discussed.

Mapping the Human Kinome in Response to DNA Damage.

We provide a catalog for the effects of the human kinome on cell survival in response to DNA-damaging agents, covering all major DNA repair pathways. By treating 313 kinase-deficient cell lines with ten diverse DNA-damaging agents, including seven commonly used chemotherapeutics, we identified examples of vulnerability and resistance that are kinase specific. To investigate synthetic lethal interactions, we tested the response to carmustine for 25 cell lines by establishing a phenotypic fluorescence-activated cell sorting (FACS) assay designed to validate gene-drug interactions. We show apoptosis, cell cycle changes, and DNA damage and proliferation after alkylation- or crosslink-induced damage. In addition, we reconstitute the cellular sensitivity of DYRK4, EPHB6, MARK3, and PNCK as a proof of principle for our study. Furthermore, using global phosphoproteomics on cells lacking MARK3, we provide evidence for its role in the DNA damage response. Our data suggest that cancers with inactivating mutations in kinases, including MARK3, are particularly vulnerable to alkylating chemotherapeutic agents.

Systematic characterization of BAF mutations provides insights into intracomplex synthetic lethalities in human cancers.

Aberrations in genes coding for subunits of the BRG1/BRM associated factor (BAF) chromatin remodeling complexes are highly abundant in human cancers. Currently, it is not understood how these mostly loss-of-function mutations contribute to cancer development and how they can be targeted therapeutically. The cancer-type-specific occurrence patterns of certain subunit mutations suggest subunit-specific effects on BAF complex function, possibly by the formation of aberrant residual complexes. Here, we systematically characterize the effects of individual subunit loss on complex composition, chromatin accessibility and gene expression in a panel of knockout cell lines deficient for 22 BAF subunits. We observe strong, specific and sometimes discordant alterations dependent on the targeted subunit and show that these explain intracomplex codependencies, including the synthetic lethal interactions SMARCA4-ARID2, SMARCA4-ACTB and SMARCC1-SMARCC2. These data provide insights into the role of different BAF subcomplexes in genome-wide chromatin organization and suggest approaches to therapeutically target BAF-mutant cancers.

The ERBB-STAT3 Axis Drives Tasmanian Devil Facial Tumor Disease.

The marsupial Tasmanian devil (Sarcophilus harrisii) faces extinction due to transmissible devil facial tumor disease (DFTD). To unveil the molecular underpinnings of this transmissible cancer, we combined pharmacological screens with an integrated systems-biology characterization. Sensitivity to inhibitors of ERBB tyrosine kinases correlated with their overexpression. Proteomic and DNA methylation analyses revealed tumor-specific signatures linked to the evolutionary conserved oncogenic STAT3. ERBB inhibition blocked phosphorylation of STAT3 and arrested cancer cells. Pharmacological blockade of ERBB or STAT3 prevented tumor growth in xenograft models and restored MHC class I expression. This link between the hyperactive ERBB-STAT3 axis and major histocompatibility complex class I-mediated tumor immunosurveillance provides mechanistic insights into horizontal transmissibility and puts forward a dual chemo-immunotherapeutic strategy to save Tasmanian devils from DFTD. VIDEO ABSTRACT.