Discovery of phage determinants that confer sensitivity to bacterial immune systems.

Over the past few years, numerous anti-phage defense systems have been discovered in bacteria. Although the mechanism of defense for some of these systems is understood, a major unanswered question is how these systems sense phage infection. To systematically address this question, we isolated 177 phage mutants that escape 15 different defense systems. In many cases, these escaper phages were mutated in the gene sensed by the defense system, enabling us to map the phage determinants that confer sensitivity to bacterial immunity. Our data identify specificity determinants of diverse retron systems and reveal phage-encoded triggers for multiple abortive infection systems. We find general themes in phage sensing and demonstrate that mechanistically diverse systems have converged to sense either the core replication machinery of the phage, phage structural components, or host takeover mechanisms. Combining our data with previous findings, we formulate key principles on how bacterial immune systems sense phage invaders.

A cross-disorder dosage sensitivity map of the human genome.

Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.

Drug-Driven Phenotypic Convergence Supports Rational Treatment Strategies of Chronic Infections.

Chronic Pseudomonas aeruginosa infections evade antibiotic therapy and are associated with mortality in cystic fibrosis (CF) patients. We find that in vitro resistance evolution of P. aeruginosa toward clinically relevant antibiotics leads to phenotypic convergence toward distinct states. These states are associated with collateral sensitivity toward several antibiotic classes and encoded by mutations in antibiotic resistance genes, including transcriptional regulator nfxB. Longitudinal analysis of isolates from CF patients reveals similar and defined phenotypic states, which are associated with extinction of specific sub-lineages in patients. In-depth investigation of chronic P. aeruginosa populations in a CF patient during antibiotic therapy revealed dramatic genotypic and phenotypic convergence. Notably, fluoroquinolone-resistant subpopulations harboring nfxB mutations were eradicated by antibiotic therapy as predicted by our in vitro data. This study supports the hypothesis that antibiotic treatment of chronic infections can be optimized by targeting phenotypic states associated with specific mutations to improve treatment success in chronic infections.

SLFN5-mediated chromatin dynamics sculpt higher-order DNA repair topology.

Repair of DNA double-strand breaks (DSBs) elicits three-dimensional (3D) chromatin topological changes. A recent finding reveals that 53BP1 assembles into a 3D chromatin topology pattern around DSBs. How this formation of a higher-order structure is configured and regulated remains enigmatic. Here, we report that SLFN5 is a critical factor for 53BP1 topological arrangement at DSBs. Using super-resolution imaging, we find that SLFN5 binds to 53BP1 chromatin domains to assemble a higher-order microdomain architecture by driving damaged chromatin dynamics at both DSBs and deprotected telomeres. Mechanistically, we propose that 53BP1 topology is shaped by two processes: (1) chromatin mobility driven by the SLFN5-LINC-microtubule axis and (2) the assembly of 53BP1 oligomers mediated by SLFN5. In mammals, SLFN5 deficiency disrupts the DSB repair topology and impairs non-homologous end joining, telomere fusions, class switch recombination, and sensitivity to poly (ADP-ribose) polymerase inhibitor. We establish a molecular mechanism that shapes higher-order chromatin topologies to safeguard genomic stability.

ARAF protein kinase activates RAS by antagonizing its binding to RASGAP NF1.

RAF protein kinases are effectors of the GTP-bound form of small guanosine triphosphatase RAS and function by phosphorylating MEK. We showed here that the expression of ARAF activated RAS in a kinase-independent manner. Binding of ARAF to RAS displaced the GTPase-activating protein NF1 and antagonized NF1-mediated inhibition of RAS. This reduced ERK-dependent inhibition of RAS and increased RAS-GTP. By this mechanism, ARAF regulated the duration and consequences of RTK-induced RAS activation and supported the RAS output of RTK-dependent tumor cells. In human lung cancers with EGFR mutation, amplification of ARAF was associated with acquired resistance to EGFR inhibitors, which was overcome by combining EGFR inhibitors with an inhibitor of the protein tyrosine phosphatase SHP2 to enhance inhibition of nucleotide exchange and RAS activation.

A proteomic and phosphoproteomic landscape of KRAS mutant cancers identifies combination therapies.

KRAS mutant cancer, characterized by the activation of a plethora of phosphorylation signaling pathways, remains a major challenge for cancer therapy. Despite recent advancements, a comprehensive profile of the proteome and phosphoproteome is lacking. This study provides a proteomic and phosphoproteomic landscape of 43 KRAS mutant cancer cell lines across different tissue origins. By integrating transcriptomics, proteomics, and phosphoproteomics, we identify three subsets with distinct biological, clinical, and therapeutic characteristics. The integrative analysis of phosphoproteome and drug sensitivity information facilitates the identification of a set of drug combinations with therapeutic potentials. Among them, we demonstrate that the combination of DOT1L and SHP2 inhibitors is an effective treatment specific for subset 2 of KRAS mutant cancers, corresponding to a set of TCGA clinical tumors with the poorest prognosis. Together, this study provides a resource to better understand KRAS mutant cancer heterogeneity and identify new therapeutic possibilities.

Using CombiCells, a platform for titration and combinatorial display of cell surface ligands, to study T-cell antigen sensitivity modulation by accessory receptors.

Understanding cellular decisions due to receptor-ligand interactions at cell-cell interfaces has been hampered by the difficulty of independently varying the surface density of multiple different ligands. Here, we express the synthetic binder protein SpyCatcher, designed to form spontaneous covalent bonds with interactors carrying a Spytag, on the cell surface. Using this, we show that addition of different concentrations and combinations of native Spytag-fused ligands allows for the combinatorial display of ligands on cells within minutes. We use this combinatorial display of cell surface ligands-called CombiCells-to assess T cell antigen sensitivity and the impact of T cell co-stimulation and co-inhibition receptors. We find that the T cell receptor (TCR) displayed greater sensitivity to peptides on major-histocompatibility complexes (pMHC) than synthetic chimeric antigen receptor (CARs) and bi-specific T cell engager (BiTEs) display to their target antigen, CD19. While TCR sensitivity was greatly enhanced by CD2/CD58 interactions, CAR sensitivity was primarily but more modestly enhanced by LFA-1/ICAM-1 interactions. Lastly, we show that PD-1/PD-L1 engagement inhibited T cell activation triggered solely by TCR/pMHC interactions, as well as the amplified activation induced by CD2 and CD28 co-stimulation. The ability to easily produce cells with different concentrations and combinations of ligands should accelerate the study of receptor-ligand interactions at cell-cell interfaces.

TSC2 regulates tumor susceptibility to TRAIL-mediated T-cell killing by orchestrating mTOR signaling.

Resistance to cancer immunotherapy continues to impair common clinical benefit. Here, we use whole-genome CRISPR-Cas9 knockout data to uncover an important role for Tuberous Sclerosis Complex 2 (TSC2) in determining tumor susceptibility to cytotoxic T lymphocyte (CTL) killing in human melanoma cells. TSC2-depleted tumor cells had disrupted mTOR regulation following CTL attack, which was associated with enhanced cell death. Wild-type tumor cells adapted to CTL attack by shifting their mTOR signaling balance toward increased mTORC2 activity, circumventing apoptosis, and necroptosis. TSC2 ablation strongly augmented tumor cell sensitivity to CTL attack in vitro and in vivo, suggesting one of its functions is to critically protect tumor cells. Mechanistically, TSC2 inactivation caused elevation of TRAIL receptor expression, cooperating with mTORC1-S6 signaling to induce tumor cell death. Clinically, we found a negative correlation between TSC2 expression and TRAIL signaling in TCGA patient cohorts. Moreover, a lower TSC2 immune response signature was observed in melanomas from patients responding to immune checkpoint blockade. Our study uncovers a pivotal role for TSC2 in the cancer immune response by governing crosstalk between TSC2-mTOR and TRAIL signaling, aiding future therapeutic exploration of this pathway in immuno-oncology.

Signal Transduction Network Principles Underlying Bacterial Collective Behaviors.

Bacteria orchestrate collective behaviors and accomplish feats that would be unsuccessful if carried out by a lone bacterium. Processes undertaken by groups of bacteria include bioluminescence, biofilm formation, virulence factor production, and release of public goods that are shared by the community. Collective behaviors are controlled by signal transduction networks that integrate sensory information and transduce the information internally. Here, we discuss network features and mechanisms that, even in the face of dramatically changing environments, drive precise execution of bacterial group behaviors. We focus on representative quorum-sensing and second-messenger cyclic dimeric GMP (c-di-GMP) signal relays. We highlight ligand specificity versus sensitivity, how small-molecule ligands drive discrimination of kin versus nonkin, signal integration mechanisms, single-input sensory systems versus coincidence detectors, and tuning of input-output dynamics via feedback regulation. We summarize how different features of signal transduction systems allow groups of bacteria to successfully interpret and collectively react to dynamically changing environments.

Interleukin-10 Directly Inhibits CD8+ T Cell Function by Enhancing N-Glycan Branching to Decrease Antigen Sensitivity.

Chronic viral infections remain a global health concern. The early events that facilitate viral persistence have been linked to the activity of the immunoregulatory cytokine IL-10. However, the mechanisms by which IL-10 facilitates the establishment of chronic infection are not fully understood. Herein, we demonstrated that the antigen sensitivity of CD8+ T cells was decreased during chronic infection and that this was directly mediated by IL-10. Mechanistically, we showed that IL-10 induced the expression of Mgat5, a glycosyltransferase that enhances N-glycan branching on surface glycoproteins. Increased N-glycan branching on CD8+ T cells promoted the formation of a galectin 3-mediated membrane lattice, which restricted the interaction of key glycoproteins, ultimately increasing the antigenic threshold required for T cell activation. Our study identified a regulatory loop in which IL-10 directly restricts CD8+ T cell activation and function through modification of cell surface glycosylation allowing the establishment of chronic infection.

Metabolic Diversity in Human Non-Small Cell Lung Cancer Cells.

Intermediary metabolism in cancer cells is regulated by diverse cell-autonomous processes, including signal transduction and gene expression patterns, arising from specific oncogenotypes and cell lineages. Although it is well established that metabolic reprogramming is a hallmark of cancer, we lack a full view of the diversity of metabolic programs in cancer cells and an unbiased assessment of the associations between metabolic pathway preferences and other cell-autonomous processes. Here, we quantified metabolic features, mostly from the 13C enrichment of molecules from central carbon metabolism, in over 80 non-small cell lung cancer (NSCLC) cell lines cultured under identical conditions. Because these cell lines were extensively annotated for oncogenotype, gene expression, protein expression, and therapeutic sensitivity, the resulting database enables the user to uncover new relationships between metabolism and these orthogonal processes.

Sea-surface temperature pattern effects have slowed global warming and biased warming-based constraints on climate sensitivity.

The observed rate of global warming since the 1970s has been proposed as a strong constraint on equilibrium climate sensitivity (ECS) and transient climate response (TCR)-key metrics of the global climate response to greenhouse-gas forcing. Using CMIP5/6 models, we show that the inter-model relationship between warming and these climate sensitivity metrics (the basis for the constraint) arises from a similarity in transient and equilibrium warming patterns within the models, producing an effective climate sensitivity (EffCS) governing recent warming that is comparable to the value of ECS governing long-term warming under CO[Formula: see text] forcing. However, CMIP5/6 historical simulations do not reproduce observed warming patterns. When driven by observed patterns, even high ECS models produce low EffCS values consistent with the observed global warming rate. The inability of CMIP5/6 models to reproduce observed warming patterns thus results in a bias in the modeled relationship between recent global warming and climate sensitivity. Correcting for this bias means that observed warming is consistent with wide ranges of ECS and TCR extending to higher values than previously recognized. These findings are corroborated by energy balance model simulations and coupled model (CESM1-CAM5) simulations that better replicate observed patterns via tropospheric wind nudging or Antarctic meltwater fluxes. Because CMIP5/6 models fail to simulate observed warming patterns, proposed warming-based constraints on ECS, TCR, and projected global warming are biased low. The results reinforce recent findings that the unique pattern of observed warming has slowed global-mean warming over recent decades and that how the pattern will evolve in the future represents a major source of uncertainty in climate projections.

Unraveling emergent network indeterminacy in complex ecosystems: A random matrix approach.

Indeterminacy of ecological networks-the unpredictability of ecosystem responses to persistent perturbations-is an emergent property of indirect effects a species has on another through interaction chains. Thus, numerous indirect pathways in large, complex ecological communities could make forecasting the long-term outcomes of environmental changes challenging. However, a comprehensive understanding of ecological structures causing indeterminacy has not yet been reached. Here, using random matrix theory (RMT), we provide mathematical criteria determining whether network indeterminacy emerges across various ecological communities. Our analytical and simulation results show that indeterminacy intricately depends on the characteristics of species interaction. Specifically, contrary to conventional wisdom, network indeterminacy is unlikely to emerge in large competitive and mutualistic communities, while it is a common feature in top-down regulated food webs. Furthermore, we found that predictable and unpredictable perturbations can coexist in the same community and that indeterminate responses to environmental changes arise more frequently in networks where predator-prey relationships predominate than competitive and mutualistic ones. These findings highlight the importance of elucidating direct species relationships and analyzing them with an RMT perspective on two fronts: It aids in 1) determining whether the network's responses to environmental changes are ultimately indeterminate and 2) identifying the types of perturbations causing less predictable outcomes in a complex ecosystem. In addition, our framework should apply to the inverse problem of network identification, i.e., determining whether observed responses to sustained perturbations can reconstruct their proximate causalities, potentially impacting other fields such as microbial and medical sciences.

Reconciling carbon quality with availability predicts temperature sensitivity of global soil carbon mineralization.

Soil organic carbon (SOC) mineralization is a key component of the global carbon cycle. Its temperature sensitivity Q10 (which is defined as the factor of change in mineralization with a 10 °C temperature increase) is crucial for understanding the carbon cycle-climate change feedback but remains uncertain. Here, we demonstrate the universal control of carbon quality-availability tradeoffs on Q10. When carbon availability is not limited, Q10 is controlled by carbon quality; otherwise, substrate availability controls Q10. A model driven by such quality-availability tradeoffs explains 97% of the spatiotemporal variability of Q10 in incubations of soils across the globe and predicts a global Q10 of 2.1 ± 0.4 (mean ± one SD) with higher Q10 in northern high-latitude regions. We further reveal that global Q10 is predominantly governed by the mineralization of high-quality carbon. The work provides a foundation for predicting SOC dynamics under climate and land use changes which may alter soil carbon quality and availability.

Bmal1 integrates circadian function and temperature sensing in the suprachiasmatic nucleus.

Circadian regulation and temperature dependency are important orchestrators of molecular pathways. How the integration between these two drivers is achieved, is not understood. We monitored circadian- and temperature-dependent effects on transcription dynamics of cold-response protein RNA Binding Motif 3 (Rbm3). Temperature changes in the mammalian master circadian pacemaker, the suprachiasmatic nucleus (SCN), induced Rbm3 transcription and regulated its circadian periodicity, whereas the core clock gene Per2 was unaffected. Rbm3 induction depended on a full Brain And Muscle ARNT-Like Protein 1 (Bmal1) complement: reduced Bmal1 erased Rbm3 responses and weakened SCN circuit resilience to temperature changes. By focusing on circadian and temperature dependency, we highlight weakened transmission between core clock and downstream pathways as a potential route for reduced circadian resilience.

Relative decline in density of Northern Hemisphere tree species in warm and arid regions of their climate niches.

Although climate change is expected to drive tree species toward colder and wetter regions of their distribution, broadscale empirical evidence is lacking. One possibility is that past and present human activities in forests obscure or alter the effects of climate. Here, using data from more than two million monitored trees from 73 widely distributed species, we quantify changes in tree species density within their climatic niches across Northern Hemisphere forests. We observe a reduction in mean density across species, coupled with a tendency toward increasing tree size. However, the direction and magnitude of changes in density exhibit considerable variability between species, influenced by stand development that results from previous stand-level disturbances. Remarkably, when accounting for stand development, our findings show a significant change in density toward cold and wet climatic conditions for 43% of the species, compared to only 14% of species significantly changing their density toward warm and arid conditions in both early- and late-development stands. The observed changes in climate-driven density showed no clear association with species traits related to drought tolerance, recruitment and dispersal capacity, or resource use, nor with the temperature or aridity affiliation of the species, leaving the underlying mechanism uncertain. Forest conservation policies and associated management strategies might want to consider anticipated long-term species range shifts alongside the integration of contemporary within-distribution density changes.

An RNA-informed dosage sensitivity map reflects the intrinsic functional nature of genes.

Understanding dosage sensitivity or why Mendelian diseases have dominant vs. recessive modes of inheritance is crucial for uncovering the etiology of human disease. Previous knowledge of dosage sensitivity is mainly based on observations of rare loss-of-function mutations or copy number changes, which are underpowered due to ultra rareness of such variants. Thus, the functional underpinnings of dosage constraint remain elusive. In this study, we aim to systematically quantify dosage perturbations from cis-regulatory variants in the general population to yield a tissue-specific dosage constraint map of genes and further explore their underlying functional logic. We reveal an inherent divergence of dosage constraints in genes by functional categories with signaling genes (transcription factors, protein kinases, ion channels, and cellular machinery) being dosage sensitive, while effector genes (transporters, metabolic enzymes, cytokines, and receptors) are generally dosage resilient. Instead of being a metric of functional dispensability, we show that dosage constraint reflects underlying homeostatic constraints arising from negative feedback. Finally, we employ machine learning to integrate DNA and RNA metrics to generate a comprehensive, tissue-specific map of dosage sensitivity (MoDs) for autosomal genes.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction.

A major challenge of precision oncology is the identification and prioritization of suitable treatment options based on molecular biomarkers of the considered tumor. In pursuit of this goal, large cancer cell line panels have successfully been studied to elucidate the relationship between cellular features and treatment response. Due to the high dimensionality of these datasets, machine learning (ML) is commonly used for their analysis. However, choosing a suitable algorithm and set of input features can be challenging. We performed a comprehensive benchmarking of ML methods and dimension reduction (DR) techniques for predicting drug response metrics. Using the Genomics of Drug Sensitivity in Cancer cell line panel, we trained random forests, neural networks, boosting trees and elastic nets for 179 anti-cancer compounds with feature sets derived from nine DR approaches. We compare the results regarding statistical performance, runtime and interpretability. Additionally, we provide strategies for assessing model performance compared with a simple baseline model and measuring the trade-off between models of different complexity. Lastly, we show that complex ML models benefit from using an optimized DR strategy, and that standard models-even when using considerably fewer features-can still be superior in performance.

D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer.

As key oncogenic drivers in non-small-cell lung cancer (NSCLC), various mutations in the epidermal growth factor receptor (EGFR) with variable drug sensitivities have been a major obstacle for precision medicine. To achieve clinical-level drug recommendations, a platform for clinical patient case retrieval and reliable drug sensitivity prediction is highly expected. Therefore, we built a database, D3EGFRdb, with the clinicopathologic characteristics and drug responses of 1339 patients with EGFR mutations via literature mining. On the basis of D3EGFRdb, we developed a deep learning-based prediction model, D3EGFRAI, for drug sensitivity prediction of new EGFR mutation-driven NSCLC. Model validations of D3EGFRAI showed a prediction accuracy of 0.81 and 0.85 for patients from D3EGFRdb and our hospitals, respectively. Furthermore, mutation scanning of the crucial residues inside drug-binding pockets, which may occur in the future, was performed to explore their drug sensitivity changes. D3EGFR is the first platform to achieve clinical-level drug response prediction of all approved small molecule drugs for EGFR mutation-driven lung cancer and is freely accessible at https://www.d3pharma.com/D3EGFR/index.php.