Conformational surveillance of Orai1 by a rhomboid intramembrane protease prevents inappropriate CRAC channel activation.

Calcium influx through plasma membrane calcium release-activated calcium (CRAC) channels, which are formed of hexamers of Orai1, is a potent trigger for many important biological processes, most notably in T cell-mediated immunity. Through a bioinformatics-led cell biological screen, we have identified Orai1 as a substrate for the rhomboid intramembrane protease RHBDL2. We show that RHBDL2 prevents stochastic calcium signaling in unstimulated cells through conformational surveillance and cleavage of inappropriately activated Orai1. A conserved disease-linked proline residue is responsible for RHBDL2's recognizing the active conformation of Orai1, which is required to sharpen switch-like signaling triggered by store-operated calcium entry. Loss of RHBDL2 control of CRAC channel activity causes severe dysregulation of downstream CRAC channel effectors, including transcription factor activation, inflammatory cytokine expression, and T cell activation. We propose that this surveillance function may represent an ancient activity of rhomboid proteases in degrading unwanted signaling proteins.

Targeting SWI/SNF ATPases in enhancer-addicted prostate cancer.

The switch/sucrose non-fermentable (SWI/SNF) complex has a crucial role in chromatin remodelling1 and is altered in over 20% of cancers2,3. Here we developed a proteolysis-targeting chimera (PROTAC) degrader of the SWI/SNF ATPase subunits, SMARCA2 and SMARCA4, called AU-15330. Androgen receptor (AR)+ forkhead box A1 (FOXA1)+ prostate cancer cells are exquisitely sensitive to dual SMARCA2 and SMARCA4 degradation relative to normal and other cancer cell lines. SWI/SNF ATPase degradation rapidly compacts cis-regulatory elements bound by transcription factors that drive prostate cancer cell proliferation, namely AR, FOXA1, ERG and MYC, which dislodges them from chromatin, disables their core enhancer circuitry, and abolishes the downstream oncogenic gene programs. SWI/SNF ATPase degradation also disrupts super-enhancer and promoter looping interactions that wire supra-physiologic expression of the AR, FOXA1 and MYC oncogenes themselves. AU-15330 induces potent inhibition of tumour growth in xenograft models of prostate cancer and synergizes with the AR antagonist enzalutamide, even inducing disease remission in castration-resistant prostate cancer (CRPC) models without toxicity. Thus, impeding SWI/SNF-mediated enhancer accessibility represents a promising therapeutic approach for enhancer-addicted cancers.

Development of an orally bioavailable mSWI/SNF ATPase degrader and acquired mechanisms of resistance in prostate cancer.

Mammalian switch/sucrose nonfermentable (mSWI/SNF) ATPase degraders have been shown to be effective in enhancer-driven cancers by functioning to impede oncogenic transcription factor chromatin accessibility. Here, we developed AU-24118, an orally bioavailable proteolysis-targeting chimera (PROTAC) degrader of mSWI/SNF ATPases (SMARCA2 and SMARCA4) and PBRM1. AU-24118 demonstrated tumor regression in a model of castration-resistant prostate cancer (CRPC) which was further enhanced with combination enzalutamide treatment, a standard of care androgen receptor (AR) antagonist used in CRPC patients. Importantly, AU-24118 exhibited favorable pharmacokinetic profiles in preclinical analyses in mice and rats, and further toxicity testing in mice showed a favorable safety profile. As acquired resistance is common with targeted cancer therapeutics, experiments were designed to explore potential mechanisms of resistance that may arise with long-term mSWI/SNF ATPase PROTAC treatment. Prostate cancer cell lines exposed to long-term treatment with high doses of a mSWI/SNF ATPase degrader developed SMARCA4 bromodomain mutations and ABCB1 (ATP binding cassette subfamily B member 1) overexpression as acquired mechanisms of resistance. Intriguingly, while SMARCA4 mutations provided specific resistance to mSWI/SNF degraders, ABCB1 overexpression provided broader resistance to other potent PROTAC degraders targeting bromodomain-containing protein 4 and AR. The ABCB1 inhibitor, zosuquidar, reversed resistance to all three PROTAC degraders tested. Combined, these findings position mSWI/SNF degraders for clinical translation for patients with enhancer-driven cancers and define strategies to overcome resistance mechanisms that may arise.

Cabozantinib plus Nivolumab and Ipilimumab in Renal-Cell Carcinoma.

BACKGROUND: The efficacy and safety of treatment with cabozantinib in combination with nivolumab and ipilimumab in patients with previously untreated advanced renal-cell carcinoma are unknown. METHODS: In this phase 3, double-blind trial, we enrolled patients with advanced clear-cell renal-cell carcinoma who had not previously received treatment and had intermediate or poor prognostic risk according to the International Metastatic Renal-Cell Carcinoma Database Consortium categories. Patients were randomly assigned to receive 40 mg of cabozantinib daily in addition to nivolumab and ipilimumab (experimental group) or matched placebo in addition to nivolumab and ipilimumab (control group). Nivolumab (3 mg per kilogram of body weight) and ipilimumab (1 mg per kilogram) were administered once every 3 weeks for four cycles. Patients then received nivolumab maintenance therapy (480 mg once every 4 weeks) for up to 2 years. The primary end point was progression-free survival, as determined by blinded independent review according to Response Evaluation Criteria in Solid Tumors, version 1.1, and was assessed in the first 550 patients who had undergone randomization. The secondary end point was overall survival, assessed in all patients who had undergone randomization. RESULTS: Overall, 855 patients underwent randomization: 428 were assigned to the experimental group and 427 to the control group. Among the first 550 patients who had undergone randomization (276 in the experimental group and 274 in the control group), the probability of progression-free survival at 12 months was 0.57 in the experimental group and 0.49 in the control group (hazard ratio for disease progression or death, 0.73; 95% confidence interval, 0.57 to 0.94; P = 0.01); 43% of the patients in the experimental group and 36% in the control group had a response. Grade 3 or 4 adverse events occurred in 79% of the patients in the experimental group and in 56% in the control group. Follow-up for overall survival is ongoing. CONCLUSIONS: Among patients with previously untreated, advanced renal-cell carcinoma who had intermediate or poor prognostic risk, treatment with cabozantinib plus nivolumab and ipilimumab resulted in significantly longer progression-free survival than treatment with nivolumab and ipilimumab alone. Grade 3 or 4 adverse events were more common in the experimental group than in the control group. (Funded by Exelixis; COSMIC-313 ClinicalTrials.gov number, NCT03937219.).

SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction.

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy. Taking full account of intricate biological interactions is highly important in accurately predicting drug synergy. However, the extremely limited prior knowledge poses great challenges in developing current computational methods. To address this, we introduce SynergyX, a multi-modality mutual attention network to improve anti-tumor drug synergy prediction. It dynamically captures cross-modal interactions, allowing for the modeling of complex biological networks and drug interactions. A convolution-augmented attention structure is adopted to integrate multi-omic data in this framework effectively. Compared with other state-of-the-art models, SynergyX demonstrates superior predictive accuracy in both the General Test and Blind Test and cross-dataset validation. By exhaustively screening combinations of approved drugs, SynergyX reveals its ability to identify promising drug combination candidates for potential lung cancer treatment. Another notable advantage lies in its multidimensional interpretability. Taking Sorafenib and Vorinostat as an example, SynergyX serves as a powerful tool for uncovering drug-gene interactions and deciphering cell selectivity mechanisms. In summary, SynergyX provides an illuminating and interpretable framework, poised to catalyze the expedition of drug synergy discovery and deepen our comprehension of rational combination therapy.

AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences.

The optimization of therapeutic antibodies through traditional techniques, such as candidate screening via hybridoma or phage display, is resource-intensive and time-consuming. In recent years, computational and artificial intelligence-based methods have been actively developed to accelerate and improve the development of therapeutic antibodies. In this study, we developed an end-to-end sequence-based deep learning model, termed AttABseq, for the predictions of the antigen-antibody binding affinity changes connected with antibody mutations. AttABseq is a highly efficient and generic attention-based model by utilizing diverse antigen-antibody complex sequences as the input to predict the binding affinity changes of residue mutations. The assessment on the three benchmark datasets illustrates that AttABseq is 120% more accurate than other sequence-based models in terms of the Pearson correlation coefficient between the predicted and experimental binding affinity changes. Moreover, AttABseq also either outperforms or competes favorably with the structure-based approaches. Furthermore, AttABseq consistently demonstrates robust predictive capabilities across a diverse array of conditions, underscoring its remarkable capacity for generalization across a wide spectrum of antigen-antibody complexes. It imposes no constraints on the quantity of altered residues, rendering it particularly applicable in scenarios where crystallographic structures remain unavailable. The attention-based interpretability analysis indicates that the causal effects of point mutations on antibody-antigen binding affinity changes can be visualized at the residue level, which might assist automated antibody sequence optimization. We believe that AttABseq provides a fiercely competitive answer to therapeutic antibody optimization.

Correction: Phospholipid scramblase 1 interacts with influenza A virus NP, impairing its nuclear import and thereby suppressing virus replication.

[This corrects the article DOI: 10.1371/journal.ppat.1006851.].

Dichotomous Expression of TNF Superfamily Ligands on Antigen-Presenting Cells Controls Post-priming Anti-viral CD4+ T Cell Immunity.

T cell antigen-presenting cell (APC) interactions early during chronic viral infection are crucial for determining viral set point and disease outcome, but how and when different APC subtypes contribute to these outcomes is unclear. The TNF receptor superfamily (TNFRSF) member GITR is important for CD4+ T cell accumulation and control of chronic lymphocytic choriomeningitis virus (LCMV). We found that type I interferon (IFN-I) induced TNFSF ligands GITRL, 4-1BBL, OX40L, and CD70 predominantly on monocyte-derived APCs and CD80 and CD86 predominantly on classical dendritic cells (cDCs). Mice with hypofunctional GITRL in Lyz2+ cells had decreased LCMV-specific CD4+ T cell accumulation and increased viral load. GITR signals in CD4+ T cells occurred after priming to upregulate OX40, CD25, and chemokine receptor CX3CR1. Thus IFN-I (signal 3) induced a post-priming checkpoint (signal 4) for CD4+ T cell accumulation, revealing a division of labor between cDCs and monocyte-derived APCs in regulating T cell expansion.

Key processes required for the different stages of fungal carnivory by a nematode-trapping fungus.

Nutritional deprivation triggers a switch from a saprotrophic to predatory lifestyle in soil-dwelling nematode-trapping fungi (NTF). In particular, the NTF Arthrobotrys oligospora secretes food and sex cues to lure nematodes to its mycelium and is triggered to develop specialized trapping devices. Captured nematodes are then invaded and digested by the fungus, thus serving as a food source. In this study, we examined the transcriptomic response of A. oligospora across the stages of sensing, trap development, and digestion upon exposure to the model nematode Caenorhabditis elegans. A. oligospora enacts a dynamic transcriptomic response, especially of protein secretion-related genes, in the presence of prey. Two-thirds of the predicted secretome of A. oligospora was up-regulated in the presence of C. elegans at all time points examined, and among these secreted proteins, 38.5% are predicted to be effector proteins. Furthermore, functional studies disrupting the t-SNARE protein Sso2 resulted in impaired ability to capture nematodes. Additionally, genes of the DUF3129 family, which are expanded in the genomes of several NTF, were highly up-regulated upon nematode exposure. We observed the accumulation of highly expressed DUF3129 proteins in trap cells, leading us to name members of this gene family as Trap Enriched Proteins (TEPs). Gene deletion of the most highly expressed TEP gene, TEP1, impairs the function of traps and prevents the fungus from capturing prey efficiently. In late stages of predation, we observed up-regulation of a variety of proteases, including metalloproteases. Following penetration of nematodes, these metalloproteases facilitate hyphal growth required for colonization of prey. These findings provide insights into the biology of the predatory lifestyle switch in a carnivorous fungus and provide frameworks for other fungal-nematode predator-prey systems.

A universal trade-off between growth and lag in fluctuating environments.

The rate of cell growth is crucial for bacterial fitness and drives the allocation of bacterial resources, affecting, for example, the expression levels of proteins dedicated to metabolism and biosynthesis1,2. It is unclear, however, what ultimately determines growth rates in different environmental conditions. Moreover, increasing evidence suggests that other objectives are also important3-7, such as the rate of physiological adaptation to changing environments8,9. A common challenge for cells is that these objectives cannot be independently optimized, and maximizing one often reduces another. Many such trade-offs have indeed been hypothesized on the basis of qualitative correlative studies8-11. Here we report a trade-off between steady-state growth rate and physiological adaptability in Escherichia coli, observed when a growing culture is abruptly shifted from a preferred carbon source such as glucose to fermentation products such as acetate. These metabolic transitions, common for enteric bacteria, are often accompanied by multi-hour lags before growth resumes. Metabolomic analysis reveals that long lags result from the depletion of key metabolites that follows the sudden reversal in the central carbon flux owing to the imposed nutrient shifts. A model of sequential flux limitation not only explains the observed trade-off between growth and adaptability, but also allows quantitative predictions regarding the universal occurrence of such tradeoffs, based on the opposing enzyme requirements of glycolysis versus gluconeogenesis. We validate these predictions experimentally for many different nutrient shifts in E. coli, as well as for other respiro-fermentative microorganisms, including Bacillus subtilis and Saccharomyces cerevisiae.

PROTAC-DB 3.0: an updated database of PROTACs with extended pharmacokinetic parameters.

Proteolysis-targeting chimera (PROTAC) is an emerging therapeutic technology that leverages the ubiquitin-proteasome system to target protein degradation. Due to its event-driven mechanistic characteristics, PROTAC has the potential to regulate traditionally non-druggable targets. Recently, AI-aided drug design has accelerated the development of PROTAC drugs. However, the rational design of PROTACs remains a considerable challenge. Here, we present an updated online database, PROTAC-DB 3.0. In this third version, we have expanded the database to include 6111 PROTACs (87% increase compared to the 2.0 version). Additionally, the database now contains 569 warheads (small molecules targeting the protein), 2753 linkers, and 107 E3 ligands (small molecules recruiting E3 ligases). The number of target-PROTAC-E3 ternary complex structures has also increased to 959. Recognizing the importance of druggability in PROTAC design, we have incorporated pharmacokinetic data to PROTAC-DB 3.0. To enhance user experience, we have added features for sorting based on molecular similarity and literature publication date. PROTAC-DB 3.0 is accessible at http://cadd.zju.edu.cn/protacdb/.

Targeting the lipid kinase PIKfyve upregulates surface expression of MHC class I to augment cancer immunotherapy.

Despite the remarkable clinical success of immunotherapies in a subset of cancer patients, many fail to respond to treatment and exhibit resistance. Here, we found that genetic or pharmacologic inhibition of the lipid kinase PIKfyve, a regulator of autophagic flux and lysosomal biogenesis, upregulated surface expression of major histocompatibility complex class I (MHC-I) in cancer cells via impairing autophagic flux, resulting in enhanced cancer cell killing mediated by CD8+ T cells. Genetic depletion or pharmacologic inhibition of PIKfyve elevated tumor-specific MHC-I surface expression, increased intratumoral functional CD8+ T cells, and slowed tumor progression in multiple syngeneic mouse models. Importantly, enhanced antitumor responses by Pikfyve-depletion were CD8+ T cell- and MHC-I-dependent, as CD8+ T cell depletion or B2m knockout rescued tumor growth. Furthermore, PIKfyve inhibition improved response to immune checkpoint blockade (ICB), adoptive cell therapy, and a therapeutic vaccine. High expression of PIKFYVE was also predictive of poor response to ICB and prognostic of poor survival in ICB-treated cohorts. Collectively, our findings show that targeting PIKfyve enhances immunotherapies by elevating surface expression of MHC-I in cancer cells, and PIKfyve inhibitors have potential as agents to increase immunotherapy response in cancer patients.

Direct interaction of ß-catenin with nuclear ESM1 supports stemness of metastatic prostate cancer.

Wnt/ß-catenin signaling is frequently activated in advanced prostate cancer and contributes to therapy resistance and metastasis. However, activating mutations in the Wnt/ß-catenin pathway are not common in prostate cancer, suggesting alternative regulations may exist. Here, we report that the expression of endothelial cell-specific molecule 1 (ESM1), a secretory proteoglycan, is positively associated with prostate cancer stemness and progression by promoting Wnt/ß-catenin signaling. Elevated ESM1 expression correlates with poor overall survival and metastasis. Accumulation of nuclear ESM1, instead of cytosolic or secretory ESM1, supports prostate cancer stemness by interacting with the ARM domain of ß-catenin to stabilize ß-catenin-TCF4 complex and facilitate the transactivation of Wnt/ß-catenin signaling targets. Accordingly, activated ß-catenin in turn mediates the nuclear entry of ESM1. Our results establish the significance of mislocalized ESM1 in driving metastasis in prostate cancer by coordinating the Wnt/ß-catenin pathway, with implications for its potential use as a diagnostic or prognostic biomarker and as a candidate therapeutic target in prostate cancer.

Optogenetically engineered Ca2+ oscillation-mediated DRP1 activation promotes mitochondrial fission and cell death.

Mitochondrial dynamics regulate the quality and morphology of mitochondria. Calcium (Ca2+) plays an important role in regulating mitochondrial function. Here, we investigated the effects of optogenetically engineered Ca2+ signaling on mitochondrial dynamics. More specifically, customized illumination conditions could trigger unique Ca2+ oscillation waves to trigger specific signaling pathways. In this study, we found that modulating Ca2+ oscillations by increasing the light frequency, intensity and exposure time could drive mitochondria toward the fission state, mitochondrial dysfunction, autophagy and cell death. Moreover, illumination triggered phosphorylation at the Ser616 residue but not the Ser637 residue of the mitochondrial fission protein, dynamin-related protein 1 (DRP1, encoded by DNM1L), via the activation of Ca2+-dependent kinases CaMKII, ERK and CDK1. However, optogenetically engineered Ca2+ signaling did not activate calcineurin phosphatase to dephosphorylate DRP1 at Ser637. In addition, light illumination had no effect on the expression levels of the mitochondrial fusion proteins mitofusin 1 (MFN1) and 2 (MFN2). Overall, this study provides an effective and innovative approach to altering Ca2+ signaling for controlling mitochondrial fission with a more precise resolution than pharmacological approaches in the temporal dimension.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Nuclear factors NF-YC3 and NF-YBs positively regulate arbuscular mycorrhizal symbiosis in tomato.

The involvement of nuclear factor Y (NF-Y) in transcriptional reprogramming during arbuscular mycorrhizal symbiosis has been demonstrated in several plant species. However, a comprehensive picture is lacking. We showed that the spatial expression of NF-YC3 was observed in cortical cells containing arbuscules via the cis-regulatory element GCC boxes. Moreover, the NF-YC3 promoter was transactivated by the combination of CYCLOPS and autoactive calcium and calmodulin-dependent kinase (CCaMK) via GCC boxes. Knockdown of NF-YC3 significantly reduced the abundance of all intraradical fungal structures and affected arbuscule size. BCP1, SbtM1, and WRI5a, whose expression associated with NF-YC3 levels, might be downstream of NF-YC3. NF-YC3 interacted with NF-YB3a, NF-YB5c, or NF-YB3b, in yeast (Saccharomyces cerevisiae) and in planta, and interacted with NF-YA3a in yeast. Spatial expression of three NF-YBs was observed in all cell layers of roots under both mock and mycorrhizal conditions. Simultaneous knockdown of three NF-YBs, but not individually, reduced the fungal colonization level, suggesting that there might be functional redundancy of NF-YBs to regulate AM symbiosis. Collectively, our data suggest that NF-YC3 and NF-YBs positively regulate AM symbiosis in tomato, and arbuscule-related NF-YC3 may be an important downstream gene of the common symbiosis signaling pathway.

Advancing Ligand Docking through Deep Learning: Challenges and Prospects in Virtual Screening.

Molecular docking, also termed ligand docking (LD), is a pivotal element of structure-based virtual screening (SBVS) used to predict the binding conformations and affinities of protein-ligand complexes. Traditional LD methodologies rely on a search and scoring framework, utilizing heuristic algorithms to explore binding conformations and scoring functions to evaluate binding strengths. However, to meet the efficiency demands of SBVS, these algorithms and functions are often simplified, prioritizing speed over accuracy.The emergence of deep learning (DL) has exerted a profound impact on diverse fields, ranging from natural language processing to computer vision and drug discovery. DeepMind's AlphaFold2 has impressively exhibited its ability to accurately predict protein structures solely from amino acid sequences, highlighting the remarkable potential of DL in conformation prediction. This groundbreaking advancement circumvents the traditional search-scoring frameworks in LD, enhancing both accuracy and processing speed and thereby catalyzing a broader adoption of DL algorithms in binding pose prediction. Nevertheless, a consensus on certain aspects remains elusive.In this Account, we delineate the current status of employing DL to augment LD within the VS paradigm, highlighting our contributions to this domain. Furthermore, we discuss the challenges and future prospects, drawing insights from our scholarly investigations. Initially, we present an overview of VS and LD, followed by an introduction to DL paradigms, which deviate significantly from traditional search-scoring frameworks. Subsequently, we delve into the challenges associated with the development of DL-based LD (DLLD), encompassing evaluation metrics, application scenarios, and physical plausibility of the predicted conformations. In the evaluation of LD algorithms, it is essential to recognize the multifaceted nature of the metrics. While the accuracy of binding pose prediction, often measured by the success rate, is a pivotal aspect, the scoring/screening power and computational speed of these algorithms are equally important given the pivotal role of LD tools in VS. Regarding application scenarios, early methods focused on blind docking, where the binding site is unknown. However, recent studies suggest a shift toward identifying binding sites rather than solely predicting binding poses within these models. In contrast, LD with a known pocket in VS has been shown to be more practical. Physical plausibility poses another significant challenge. Although DLLD models often achieve higher success rates compared to traditional methods, they may generate poses with implausible local structures, such as incorrect bond angles or lengths, which are disadvantageous for postprocessing tasks like visualization. Finally, we discuss the future perspectives for DLLD, emphasizing the need to improve generalization ability, strike a balance between speed and accuracy, account for protein conformation flexibility, and enhance physical plausibility. Additionally, we delve into the comparison between generative and regression algorithms in this context, exploring their respective strengths and potential.

Plasticity of growth laws tunes resource allocation strategies in bacteria.

Bacteria like E. coli grow at vastly different rates on different substrates, however, the precise reason for this variability is poorly understood. Different growth rates have been attributed to 'nutrient quality', a key parameter in bacterial growth laws. However, it remains unclear to what extent nutrient quality is rooted in fundamental biochemical constraints like the energy content of nutrients, the protein cost required for their uptake and catabolism, or the capacity of the plasma membrane for nutrient transporters. Here, we show that while nutrient quality is indeed reflected in protein investment in substrate-specific transporters and enzymes, this is not a fundamental limitation on growth rate, at least for certain 'poor' substrates. We show that it is possible to turn mannose, one of the 'poorest' substrates of E. coli, into one of the 'best' substrates by reengineering chromosomal promoters of the mannose transporter and metabolic enzymes required for mannose degradation. This result falls in line with previous observations of more subtle growth rate improvement for many other carbon sources. However, we show that this faster growth rate comes at the cost of diverse cellular capabilities, reflected in longer lag phases, worse starvation survival and lower motility. We show that addition of cAMP to the medium can rescue these phenotypes but imposes a corresponding growth cost. Based on these data, we propose that nutrient quality is largely a self-determined, plastic property that can be modulated by the fraction of proteomic resources devoted to a specific substrate in the much larger proteome sector of catabolically activated genes. Rather than a fundamental biochemical limitation, nutrient quality reflects resource allocation decisions that are shaped by evolution in specific ecological niches and can be quickly adapted if necessary.

Extracellular vesicle surface display enhances the therapeutic efficacy and safety profile of cancer immunotherapy.

Immunotherapy has emerged as a mainstay in cancer therapy, yet its efficacy is constrained by the risk of immune-related adverse events. In this study, we present a nanoparticle-based delivery system that enhances the therapeutic efficacy of immunomodulatory ligands while concurrently limiting systemic toxicity. We demonstrate that extracellular vesicles (EVs), lipid bilayer enclosed particles released by cells, can be efficiently engineered via inverse electron demand Diels-Alder (iEDDA)-mediated conjugation to display multiple immunomodulatory ligands on their surface. Display of immunomodulatory ligands on the EV surface conferred substantial enhancements in signaling efficacy, particularly for tumor necrosis factor receptor superfamily (TNFRSF) agonists, where the EV surface display served as an alternative FcγR-independent approach to induce ligand multimerization and efficient receptor crosslinking. EVs displaying a complementary combination of immunotherapeutic ligands were able to shift the tumor immune milieu toward an anti-tumorigenic phenotype and significantly suppress tumor burden and increase survival in multiple models of metastatic cancer to a greater extent than an equivalent dose of free ligands. In summary, we present an EV-based delivery platform for cancer immunotherapeutic ligands that facilitates superior anti-tumor responses at significantly lower doses with fewer side effects than is possible with conventional delivery approaches.