Tumor-specific activation of folate receptor beta enables reprogramming of immune cells in the tumor microenvironment.

Folate receptors can perform folate transport, cell adhesion, and/or transcription factor functions. The beta isoform of the folate receptor (FRß) has attracted considerable attention as a biomarker for immunosuppressive macrophages and myeloid-derived suppressor cells, however, its role in immunosuppression remains uncharacterized. We demonstrate here that FRß cannot bind folate on healthy tissue macrophages, but does bind folate after macrophage incubation in anti-inflammatory cytokines or cancer cell-conditioned media. We further show that FRß becomes functionally active following macrophage infiltration into solid tumors, and we exploit this tumor-induced activation to target a toll-like receptor 7 agonist specifically to immunosuppressive myeloid cells in solid tumors without altering myeloid cells in healthy tissues. We then use single-cell RNA-seq to characterize the changes in gene expression induced by the targeted repolarization of tumor-associated macrophages and finally show that their repolarization not only changes their own phenotype, but also induces a proinflammatory shift in all other immune cells of the same tumor mass, leading to potent suppression of tumor growth. Because this selective reprogramming of tumor myeloid cells is accompanied by no systemic toxicity, we propose that it should constitute a safe method to reprogram the tumor microenvironment.

Engineering metal-carbide hydrogen traps in steels.

Hydrogen embrittlement reduces the durability of the structural steels required for the hydrogen economy. Understanding how hydrogen interacts with the materials plays a crucial role in managing the embrittlement problems. Theoretical models have indicated that carbon vacancies in metal carbide precipitates are effective hydrogen traps in steels. Increasing the number of carbon vacancies in individual metal carbides is important since the overall hydrogen trapping capacity can be leveraged by introducing abundant metal carbides in steels. To verify this concept, we compare a reference steel containing titanium carbides (TiCs), which lack carbon vacancies, with an experimental steel added with molybdenum (Mo), which form Ti-Mo carbides comprising more carbon vacancies than TiCs. We employ theoretical and experimental techniques to examine the hydrogen trapping behavior of the carbides, demonstrating adding Mo alters the hydrogen trapping mechanism, enabling hydrogen to access carbon vacancy traps within the carbides, leading to an increase in trapping capacity.

MetaTiME integrates single-cell gene expression to characterize the meta-components of the tumor immune microenvironment.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation.

Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

Emotion recognition based on multi-modal physiological signals and transfer learning.

In emotion recognition based on physiological signals, collecting enough labeled data of a single subject for training is time-consuming and expensive. The physiological signals' individual differences and the inherent noise will significantly affect emotion recognition accuracy. To overcome the difference in subject physiological signals, we propose a joint probability domain adaptation with the bi-projection matrix algorithm (JPDA-BPM). The bi-projection matrix method fully considers the source and target domain's different feature distributions. It can better project the source and target domains into the feature space, thereby increasing the algorithm's performance. We propose a substructure-based joint probability domain adaptation algorithm (SSJPDA) to overcome physiological signals' noise effect. This method can avoid the shortcomings that the domain level matching is too rough and the sample level matching is susceptible to noise. In order to verify the effectiveness of the proposed transfer learning algorithm in emotion recognition based on physiological signals, we verified it on the database for emotion analysis using physiological signals (DEAP dataset). The experimental results show that the average recognition accuracy of the proposed SSJPDA-BPM algorithm in the multimodal fusion physiological data from the DEAP dataset is 63.6 and 64.4% in valence and arousal, respectively. Compared with joint probability domain adaptation (JPDA), the performance of valence and arousal recognition accuracy increased by 17.6 and 13.4%, respectively.

Simulation of Water Flow in a Nanochannel with a Sudden Contraction or Expansion.

Water flow in a nanoscale channel is known to be affected by strong water-wall interactions; as a result, the flow considerably deviates from the conventional continuum flow. Nanochannel with a sudden contraction or expansion is the most fundamental morphological nanostructure in many nanoporous systems such as shale matrix, mudrock, membrane, etc. However, the nanoconfinement effects of water flow in nanoporous systems and its effect on macroscopic flow behavior are still evolving research topics. In this work, our recently developed pore-scale lattice Boltzmann method (LBM) considering the nanoscale effects is extended to directly simulate water flow in nanoporous systems. The results show that the flow rate is dramatically decreased in hydrophobic nanopores because of additional flow resistances at the contraction and expansion junctions. This indicates that the bundle of capillary models or the permeability averaging method overestimates the water flow rate in nanoporous media if the contraction/expansion effects between different nanopores are ignored. This work highlights the importance of wettability of the nanochannel in the determination of dynamic water flow behaviors in the contraction/expansion nanosystem. Other important aspects, including velocity distribution, flow patterns, and vortex characteristics as well as pressure variation along the flow direction, are for the first time revealed and quantified. Large differences can be found comparing gas or larger-scale water flows through the same system. A new type of pressure variation curve along the axis of flow direction is found in the hydrophobic nanochannel with a sudden contraction/expansion. This work provides the fundamental understanding of water transport through the nanoscale system with contraction and expansion effects, giving implications to a wide range of industry applications.

Targeted Rejuvenation of Exhausted Chimeric Antigen Receptor T Cells Regresses Refractory Solid Tumors.

Chimeric antigen receptor (CAR) T-cell therapies have proven to be effective in treating hematologic malignancies but demonstrate only marginal efficacy in eradicating solid tumors. Although several mechanisms can account for these differences, a major cause is thought to derive from CAR T-cell exhaustion, where chronic exposure to tumor antigen can activate feedback pathways that suppress CAR T-cell cytotoxicity. We describe here a strategy to reverse this CAR T-cell exhaustion using a universal anti-fluorescein CAR that concurrently serves as (i) a cancer recognition receptor that enables engagement of multiple cancer cell clones upon addition of a cocktail of bispecific fluorescein-linked tumor-targeting ligands, and (ii) a drug-internalizing receptor that mediates uptake of a CAR T-cell activator comprised of fluorescein linked to an immune stimulant. By attaching a Toll-like receptor 7 agonist (TLR7-1A) to fluorescein, we enable the anti-fluorescein CAR to bind and internalize TLR7-1A, leading to both downregulation of exhaustion markers (i.e., PD-1, TIM3, LAG3) and reactivation of exhausted CAR-T cells without causing the toxicities commonly associated with systemic administration of TLR7 agonists. The resulting rejuvenated CAR-T cells are observed to regress otherwise refractory solid tumors. Moreover, because no other immune cells are altered by this treatment, the data demonstrate that the exhaustion state of the CAR-T cells constitutes a major property that determines the efficacies of CAR T-cell therapies in solid tumors. IMPLICATIONS: A novel strategy for rejuvenating exhausted CAR-T cells is described previously that promotes downregulation of exhaustion markers and renewed eradication of cancer cells in a tumor mass.

Design, Synthesis, and Targeted Delivery of an Immune Stimulant that Selectively Reactivates Exhausted CAR T Cells.

Although chimeric antigen receptor (CAR) T cells have demonstrated significant promise in suppressing hematopoietic cancers, their applications in treating solid tumors have been limited by onset of CAR T cell exhaustion that accompanies continuous CAR T cell exposure to tumor antigen. To address this limitation, we have exploited the abilities of recently designed universal CARs to bind fluorescein and internalize a fluorescein-TLR7 agonist conjugate by CAR-mediated endocytosis. We demonstrate here that anti-fluorescein CAR-mediated uptake of a fluorescein-TLR7-3 conjugate can reactivate exhausted CAR T cells, leading to dramatic reduction in T cell exhaustion markers (PD-1+ Tim-3+ ) and shrinkage of otherwise resistant tumors without inducing systemic activation of the immune system. We conclude that CAR T cell exhaustion can be reversed by administration of a CAR-targeted TLR7 agonist, thereby enabling the CAR T cells to successfully treat solid tumors without incurring the systemic toxicity that commonly accompanies administration of nontargeted TLR7 agonists.

TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response.

Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.

In vivo CRISPR screens identify the E3 ligase Cop1 as a modulator of macrophage infiltration and cancer immunotherapy target.

Despite remarkable clinical efficacy of immune checkpoint blockade (ICB) in cancer treatment, ICB benefits for triple-negative breast cancer (TNBC) remain limited. Through pooled in vivo CRISPR knockout (KO) screens in syngeneic TNBC mouse models, we found that deletion of the E3 ubiquitin ligase Cop1 in cancer cells decreases secretion of macrophage-associated chemokines, reduces tumor macrophage infiltration, enhances anti-tumor immunity, and strengthens ICB response. Transcriptomics, epigenomics, and proteomics analyses revealed that Cop1 functions through proteasomal degradation of the C/ebpδ protein. The Cop1 substrate Trib2 functions as a scaffold linking Cop1 and C/ebpδ, which leads to polyubiquitination of C/ebpδ. In addition, deletion of the E3 ubiquitin ligase Cop1 in cancer cells stabilizes C/ebpδ to suppress expression of macrophage chemoattractant genes. Our integrated approach implicates Cop1 as a target for improving cancer immunotherapy efficacy in TNBC by regulating chemokine secretion and macrophage infiltration in the tumor microenvironment.

Design of Neuraminidase-Targeted Imaging and Therapeutic Agents for the Diagnosis and Treatment of Influenza Virus Infections.

The last step in influenza virus replication involves the assembly of viral components on the infected cell's plasma membrane followed by budding of intact virus from the host cell surface. Because viral neuraminidase and hemagglutinin are both inserted into the host cell's membrane during this process, influenza virus-infected cells are distinguished from uninfected cells by the presence of viral neuraminidase and hemagglutinin on their cell surfaces. In an effort to exploit this difference in cell surface markers for development of diagnostic and therapeutic agents, we have modified an influenza neuraminidase inhibitor, zanamivir, for targeting of attached imaging and therapeutic agents selectively to influenza viruses and virus-infected cells. We have designed here a zanamivir-conjugated rhodamine dye that allows visual monitoring of binding, internalization, and intracellular trafficking of the fluorescence-labeled neuraminidase in virus-infected cells. We also synthesize a zanamivir-99mTc radioimaging conjugate that permits whole body imaging of the virus's biodistribution and abundance in infected mice. Finally, we create both a zanamivir-targeted cytotoxic drug (i.e., zanamivir-tubulysin B) and a viral neuraminidase-targeted CAR T cell and demonstrate that they are both able to kill viral neuraminidase-expressing cells without damaging healthy cells. Taken together, these data suggest that the influenza virus neuraminidase inhibitor, zanamivir, can be exploited to improve the diagnosis, imaging, and treatment of influenza virus infections.

Systematic characterization of mutations altering protein degradation in human cancers.

The ubiquitin-proteasome system (UPS) is the primary route for selective protein degradation in human cells. The UPS is an attractive target for novel cancer therapies, but the precise UPS genes and substrates important for cancer growth are incompletely understood. Leveraging multi-omics data across more than 9,000 human tumors and 33 cancer types, we found that over 19% of all cancer driver genes affect UPS function. We implicate transcription factors as important substrates and show that c-Myc stability is modulated by CUL3. Moreover, we developed a deep learning model (deepDegron) to identify mutations that result in degron loss and experimentally validated the prediction that gain-of-function truncating mutations in GATA3 and PPM1D result in increased protein stability. Last, we identified UPS driver genes associated with prognosis and the tumor microenvironment. This study demonstrates the important role of UPS dysregulation in human cancer and underscores the potential therapeutic utility of targeting the UPS.

A universal dual mechanism immunotherapy for the treatment of influenza virus infections.

Seasonal influenza epidemics lead to 3-5 million severe infections and 290,000-650,000 annual global deaths. With deaths from the 1918 influenza pandemic estimated at >50,000,000 and future pandemics anticipated, the need for a potent influenza treatment is critical. In this study, we design and synthesize a bifunctional small molecule by conjugating the neuraminidase inhibitor, zanamivir, with the highly immunogenic hapten, dinitrophenyl (DNP), which specifically targets the surface of free virus and viral-infected cells. We show that this leads to simultaneous inhibition of virus release, and immune-mediated elimination of both free virus and virus-infected cells. Intranasal or intraperitoneal administration of a single dose of drug to mice infected with 100x MLD50 virus is shown to eradicate advanced infections from representative strains of both influenza A and B viruses. Since treatments of severe infections remain effective up to three days post lethal inoculation, our approach may successfully treat infections refractory to current therapies.

Sensitive manipulation of CAR T cell activity using a chimeric endocytosing receptor.

BACKGROUND: Most adoptive cell therapies (ACTs) suffer from an inability to control the therapeutic cell's behavior following its transplantation into a patient. Thus, efforts to inhibit, activate, differentiate or terminate an ACT after patient reinfusion can be futile, because the required drug adversely affects other cells in the patient. METHODS: We describe here a two domain fusion receptor composed of a ligand-binding domain linked to a recycling domain that allows constitutive internalization and trafficking of the fusion receptor back to the cell surface. Because the ligand-binding domain is designed to bind a ligand not normally present in humans, any drug conjugated to this ligand will bind and endocytose selectively into the ACT. RESULTS: In two embodiments of our strategy, we fuse the chronically endocytosing domain of human folate receptor alpha to either a murine scFv that binds fluorescein or human FK506 binding protein that binds FK506, thereby creating a fusion receptor composed of largely human components. We then create the ligand-targeted drug by conjugating any desired drug to either fluorescein or FK506, thereby generating a ligand-drug conjugate with ~10-9 M affinity for its fusion receptor. Using these tools, we demonstrate that CAR T cell activities can be sensitively tuned down or turned off in vitro as well as tightly controlled following their reinfusion into tumor-bearing mice. CONCLUSIONS: We suggest this 'chimeric endocytosing receptor' can be exploited to manipulate not only CAR T cells but other ACTs following their reinfusion into patients. With efforts to develop ACTs to treat diseases including diabetes, heart failure, osteoarthritis, cancer and sickle cell anemia accelerating, we argue an ability to manipulate ACT activities postinfusion will be important.

ß-hydroxybutyrate antagonizes aortic endothelial injury by promoting generation of VEGF in diabetic rats.

Endothelial injury is regarded as the initial pathological process in diabetic vascular diseases, but effective therapy has not yet been identified. Although ß-hydroxybutyrate plays various protective roles in the cardiovascular system, its ability to antagonize diabetic endothelial injury is unclear. ß-hydroxybutyrate reportedly causes histone H3K9 ß-hydroxybutyrylation (H3K9bhb), which activates gene expression; however, there has been no report regarding the role of H3K9bhb in up-regulation of vascular endothelial growth factor (VEGF), a crucial factor in endothelial integrity and function. Here, male Sprague-Dawley rats were intraperitoneally injected with streptozotocin to induce diabetes, and then treated with different concentrations of ß-hydroxybutyrate. After 10 weeks, body weight, blood glucose, morphological changes and serum nitric oxide concentration were examined. Moreover, the mRNA expression level, protein content and distribution of VEGF in the aorta were investigated, as were total protein ß-hydroxybutyrylation and H3K9bhb contents. The results showed injury of aortic endothelium, along with reductions of the concentration of nitric oxide and generation of VEGF in diabetic rats. However, ß-hydroxybutyrate treatment attenuated diabetic injury of the endothelium and up-regulated the generation of VEGF. Furthermore, ß-hydroxybutyrate treatment caused marked total protein ß-hydroxybutyrylation and significant elevation of H3K9bhb content in the aorta of diabetic rats. The ability of ß-hydroxybutyrate to protect against diabetic injury of the aortic endothelium was greatest for its intermediate concentration. In conclusion, moderately elevated ß-hydroxybutyrate could antagonize aortic endothelial injury, potentially by causing H3K9bhb to promote generation of VEGF in diabetic rats.

Nurses' Interest, Readiness and Absorptive Capacity to Information Technology: A Survey in China.

To investigate nurses' interest, readiness and absorptive capacity to information technology, 261 nurses were investigated using anonymous questionnaires. This study showed: 1) the top 3 information technologies were personal digital assistant (PDA), Hospital Information System (HIS) and wireless mobile nursing trolley; and 2) the mean scores of interest, readiness and absorptive capacity to information technology were 16.3, 56.7 and 46.8, respectively. Further educational programs should be provided for nurses.