Improving dermal fibroblast-to-epidermis communications and aging wound repair through extracellular vesicle-mediated delivery of Gstm2 mRNA.

Skin aging is characterized by the disruption of skin homeostasis and impaired skin injury repair. Treatment of aging skin has long been limited by the unclear intervention targets and delivery techniques. Engineering extracellular vesicles (EVs) as an upgraded version of natural EVs holds great potential in regenerative medicine. In this study, we found that the expression of the critical antioxidant and detoxification gene Gstm2 was significantly reduced in aging skin. Thus, we constructed the skin primary fibroblasts-derived EVs encapsulating Gstm2 mRNA (EVsGstm2), and found that EVsGstm2 could significantly improve skin homeostasis and accelerate wound healing in aged mice. Mechanistically, we found that EVsGstm2 alleviated oxidative stress damage of aging dermal fibroblasts by modulating mitochondrial oxidative phosphorylation, and promoted dermal fibroblasts to regulate skin epidermal cell function by paracrine secretion of Nascent Polypeptide-Associated Complex Alpha subunit (NACA). Furthermore, we confirmed that NACA is a novel skin epidermal cell protective molecule that regulates skin epidermal cell turnover through the ROS-ERK-ETS-Cyclin D pathway. Our findings demonstrate the feasibility and efficacy of EVs-mediated delivery of Gstm2 for aged skin treatment and unveil novel roles of GSTM2 and NACA for improving aging skin.

Aging and comprehensive molecular profiling in acute myeloid leukemia.

Acute myeloid leukemia (AML) is an aging-related and heterogeneous hematopoietic malignancy. In this study, a total of 1,474 newly diagnosed AML patients with RNA sequencing data were enrolled, and targeted or whole exome sequencing data were obtained in 94% cases. The correlation of aging-related factors including age and clonal hematopoiesis (CH), gender, and genomic/transcriptomic profiles (gene fusions, genetic mutations, and gene expression networks or pathways) was systematically analyzed. Overall, AML patients aged 60 y and older showed an apparently dismal prognosis. Alongside age, the frequency of gene fusions defined in the World Health Organization classification decreased, while the positive rate of gene mutations, especially CH-related ones, increased. Additionally, the number of genetic mutations was higher in gene fusion-negative (GF-) patients than those with GF. Based on the status of CH- and myelodysplastic syndromes (MDS)-related mutations, three mutant subgroups were identified among the GF- AML cohort, namely, CH-AML, CH-MDS-AML, and other GF- AML. Notably, CH-MDS-AML demonstrated a predominance of elderly and male cases, cytopenia, and significantly adverse clinical outcomes. Besides, gene expression networks including HOXA/B, platelet factors, and inflammatory responses were most striking features associated with aging and poor prognosis in AML. Our work has thus unraveled the intricate regulatory circuitry of interactions among different age, gender, and molecular groups of AML.

Cellular hierarchy insights reveal leukemic stem-like cells and early death risk in acute promyelocytic leukemia.

Acute promyelocytic leukemia (APL) represents a paradigm for targeted differentiation therapy, with a minority of patients experiencing treatment failure and even early death. We here report a comprehensive single-cell analysis of 16 APL patients, uncovering cellular compositions and their impact on all-trans retinoic acid (ATRA) response in vivo and early death. We unveil a cellular differentiation hierarchy within APL blasts, rooted in leukemic stem-like cells. The oncogenic PML/RARα fusion protein exerts branch-specific regulation in the APL trajectory, including stem-like cells. APL cohort analysis establishes an association of leukemic stemness with elevated white blood cell counts and FLT3-ITD mutations. Furthermore, we construct an APL-specific stemness score, which proves effective in assessing early death risk. Finally, we show that ATRA induces differentiation of primitive blasts and patients with early death exhibit distinct stemness-associated transcriptional programs. Our work provides a thorough survey of APL cellular hierarchies, offering insights into cellular dynamics during targeted therapy.

Mutant U2AF1-Induced Mis-Splicing of mRNA Translation Genes Confers Resistance to Chemotherapy in Acute Myeloid Leukemia.

Patients with primary refractory acute myeloid leukemia (AML) have a dismal long-term prognosis. Elucidating the resistance mechanisms to induction chemotherapy could help identify strategies to improve AML patient outcomes. Herein, we retrospectively analyzed the multiomics data of more than 1,500 AML cases and found that patients with spliceosome mutations had a higher risk of developing refractory disease. RNA splicing analysis revealed that the mis-spliced genes in refractory patients converged on translation-associated pathways, promoted mainly by U2AF1 mutations. Integrative analyses of binding and splicing in AML cell lines substantiated that the splicing perturbations of mRNA translation genes originated from both the loss and gain of mutant U2AF1 binding. In particular, the U2AF1S34F and U2AF1Q157R mutants orchestrated the inclusion of exon 11 (encoding a premature termination codon) in the eukaryotic translation initiation factor 4A2 (EIF4A2). This aberrant inclusion led to reduced eIF4A2 protein expression via nonsense-mediated mRNA decay. Consequently, U2AF1 mutations caused a net decrease in global mRNA translation that induced the integrated stress response (ISR) in AML cells, which was confirmed by single-cell RNA sequencing. The induction of ISR enhanced the ability of AML cells to respond and adapt to stress, contributing to chemoresistance. A pharmacologic inhibitor of ISR, ISRIB, sensitized U2AF1 mutant cells to chemotherapy. These findings highlight a resistance mechanism by which U2AF1 mutations drive chemoresistance and provide a therapeutic approach for AML through targeting the ISR pathway. SIGNIFICANCE: U2AF1 mutations induce the integrated stress response by disrupting splicing of mRNA translation genes that improves AML cell fitness to enable resistance to chemotherapy, which can be targeted to improve AML treatment.

Cross-sectional network analysis of plasma proteins/metabolites correlated with pathogenesis and therapeutic response in acute promyelocytic leukemia.

The treatment of PML/RARA+ acute promyelocytic leukemia (APL) with all-trans-retinoic acid and arsenic trioxide (ATRA/ATO) has been recognized as a model for translational medicine research. Though an altered microenvironment is a general cancer hallmark, how APL blasts shape their plasma composition is poorly understood. Here, we reported a cross-sectional correlation network to interpret multilayered datasets on clinical parameters, proteomes, and metabolomes of paired plasma samples from patients with APL before or after ATRA/ATO induction therapy. Our study revealed the two prominent features of the APL plasma, suggesting a possible involvement of APL blasts in modulating plasma composition. One was characterized by altered secretory protein and metabolite profiles correlating with heightened proliferation and energy consumption in APL blasts, and the other featured APL plasma-enriched proteins or enzymes catalyzing plasma-altered metabolites that were potential trans-regulatory targets of PML/RARA. Furthermore, results indicated heightened interferon-gamma signaling characterizing a tumor-suppressing function of the immune system at the first hematological complete remission stage, which likely resulted from therapy-induced cell death or senescence and ensuing supraphysiological levels of intracellular proteins. Overall, our work sheds new light on the pathophysiology and treatment of APL and provides an information-rich reference data cohort for the exploratory and translational study of leukemia microenvironment.

Predicting anticancer synergistic drug combinations based on multi-task learning.

BACKGROUND: The discovery of anticancer drug combinations is a crucial work of anticancer treatment. In recent years, pre-screening drug combinations with synergistic effects in a large-scale search space adopting computational methods, especially deep learning methods, is increasingly popular with researchers. Although achievements have been made to predict anticancer synergistic drug combinations based on deep learning, the application of multi-task learning in this field is relatively rare. The successful practice of multi-task learning in various fields shows that it can effectively learn multiple tasks jointly and improve the performance of all the tasks. METHODS: In this paper, we propose MTLSynergy which is based on multi-task learning and deep neural networks to predict synergistic anticancer drug combinations. It simultaneously learns two crucial prediction tasks in anticancer treatment, which are synergy prediction of drug combinations and sensitivity prediction of monotherapy. And MTLSynergy integrates the classification and regression of prediction tasks into the same model. Moreover, autoencoders are employed to reduce the dimensions of input features. RESULTS: Compared with the previous methods listed in this paper, MTLSynergy achieves the lowest mean square error of 216.47 and the highest Pearson correlation coefficient of 0.76 on the drug synergy prediction task. On the corresponding classification task, the area under the receiver operator characteristics curve and the area under the precision-recall curve are 0.90 and 0.62, respectively, which are equivalent to the comparison methods. Through the ablation study, we verify that multi-task learning and autoencoder both have a positive effect on prediction performance. In addition, the prediction results of MTLSynergy in many cases are also consistent with previous studies. CONCLUSION: Our study suggests that multi-task learning is significantly beneficial for both drug synergy prediction and monotherapy sensitivity prediction when combining these two tasks into one model. The ability of MTLSynergy to discover new anticancer synergistic drug combinations noteworthily outperforms other state-of-the-art methods. MTLSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

FAT4 overexpression promotes antitumor immunity by regulating the ß-catenin/STT3/PD-L1 axis in cervical cancer.

BACKGROUND: FAT4 (FAT Atypical Cadherin 4) is a member of the cadherin-associated protein family, which has been shown to function as a tumor suppressor by inhibiting proliferation and metastasis. The Wnt/ß-catenin pathway activation is highly associated with PD-L1-associated tumor immune escape. Here, we report the mechanism by which FAT4 overexpression regulates anti-tumor immunity in cervical cancer by inhibiting PD-L1 N-glycosylation and cell membrane localization in a ß-catenin-dependent manner. METHODS: FAT4 expression was first detected in cervical cancer tissues and cell lines. Cell proliferation, clone formation, and immunofluorescence were used to determine the tumor suppressive impact of FAT4 overexpression in vitro, and the findings were confirmed in immunodeficient and immunocomplete mice xenografts. Through functional and mechanistic experiments in vivo and in vitro, we investigated how FAT4 overexpression affects the antitumor immunity via the ß-catenin/STT3/PD-L1 axis. RESULTS: FAT4 is downregulated in cervical cancer tissues and cell lines. We determined that FAT4 binds to ß-catenin and antagonizes its nuclear localization, promotes phosphorylation and degradation of ß-catenin by the degradation complexes (AXIN1, APC, GSK3ß, CK1). FAT4 overexpression decreases programmed death-ligand 1 (PD-L1) mRNA expression at the transcriptional level, and causes aberrant glycosylation of PD-L1 via STT3A at the post-translational modifications (PTMs) level, leading to its endoplasmic reticulum (ER) accumulation and polyubiquitination-dependent degradation. We found that FAT4 overexpression promotes aberrant PD-L1 glycosylation and degradation in a ß-catenin-dependent manner, thereby increasing cytotoxic T lymphocyte (CTL) activity in immunoreactive mouse models. CONCLUSIONS: These findings address the basis of Wnt/ß-catenin pathway activation in cervical cancer and provide combination immunotherapy options for targeting the FAT4/ß-catenin/STT3/PD-L1 axis. Schematic cartoons showing the antitumor immunity mechanism of FAT4. (left) when Wnts bind to their receptors, which are made up of Frizzled proteins and LRP5/6, the cytoplasmic protein DVL is activated, inducing the aggregation of degradation complexes (AXIN, GSK3ß, CK1, APC) to the receptor. Subsequently, stable ß-catenin translocates into the nucleus and binds to TCF/LEF and TCF7L2 transcription factors, leading to target genes transcription. The catalytically active subunit of oligosaccharyltransferase, STT3A, enhances PD-L1 glycosylation, and N-glycosylated PD-L1 translocates to the cell membrane via the ER-to-Golgi pathway, resulting in immune evasion. (Right) FAT4 exerts antitumor immunity mainly through following mechanisms: (i) FAT4 binds to ß-catenin and antagonizes its nuclear localization, promotes phosphorylation and degradation of ß-catenin by the degradation complexes (AXIN1, APC, GSK3ß, CK1); (ii) FAT4 inhibits PD-L1 and STT3A transcription in a ß-catenin-dependent manner and induces aberrant PD-L1 glycosylation and ubiquitination-dependent degradation; (iii) Promotes activation of cytotoxic T lymphocytes (CTL) and infiltration into the tumor microenvironment.

Semi-supervised heterogeneous graph contrastive learning for drug-target interaction prediction.

Identification of drug-target interactions (DTIs) is an important step in drug discovery and drug repositioning. In recent years, graph-based methods have attracted great attention and show advantages on predicting potential DTIs. However, these methods face the problem that the known DTIs are very limited and expensive to obtain, which decreases the generalization ability of the methods. Self-supervised contrastive learning is independent of labeled DTIs, which can mitigate the impact of the problem. Therefore, we propose a framework SHGCL-DTI for predicting DTIs, which supplements the classical semi-supervised DTI prediction task with an auxiliary graph contrastive learning module. Specifically, we generate representations for the nodes through the neighbor view and meta-path view, and define positive and negative pairs to maximize the similarity between positive pairs from different views. Subsequently, SHGCL-DTI reconstructs the original heterogeneous network to predict the potential DTIs. The experiments on the public dataset show that SHGCL-DTI has significant improvement in different scenarios, compared with existing state-of-the-art methods. We also demonstrate that the contrastive learning module improves the prediction performance and generalization ability of SHGCL-DTI through ablation study. In addition, we have found several novel predicted DTIs supported by the biological literature. The data and source code are available at: https://github.com/TOJSSE-iData/SHGCL-DTI.

Human PBMC scRNA-seq-based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity.

Quantifying aging rate is important for evaluating age-associated decline and mortality. A blood single-cell RNA sequencing dataset for seven supercentenarians (SCs) was recently generated. Here, we generate a reference 28-sample aging cohort to compute a single-cell level aging clock and to determine the biological age of SCs. Our clock model placed the SCs at a blood biological age to between 80.43 and 102.67 years. Compared to the model-expected aging trajectory, SCs display increased naive CD8+ T cells, decreased cytotoxic CD8+ T cells, memory CD4+ T cells, and megakaryocytes. As the most prominent molecular hallmarks at the single-cell level, SCs contain more cells and cell types with high ribosome level, which is associated with and, according to Bayesian network inference, contributes to a low inflammation state and slow aging of SCs. Inhibiting ribosomal activity or translation in monocytes validates such translation against inflammation balance revealed by our single-cell aging clock.

A complete graph-based approach with multi-task learning for predicting synergistic drug combinations.

MOTIVATION: Drug combination therapy shows significant advantages over monotherapy in cancer treatment. Since the combinational space is difficult to be traversed experimentally, identifying novel synergistic drug combinations based on computational methods has become a powerful tool for pre-screening. Among them, methods based on deep learning have far outperformed other methods. However, most deep learning-based methods are unstable and will give inconsistent predictions even by simply changing the input order of drugs. In addition, the insufficient experimental data of drug combination screening limits the generalization ability of existing models. These problems prevent the deep learning-based models from being in service. RESULTS: In this article, we propose CGMS to address the above problems. CGMS models a drug combination and a cell line as a heterogeneous complete graph, and generates the whole-graph embedding to characterize their interaction by leveraging the heterogeneous graph attention network. Based on the whole-graph embedding, CGMS can make a stable, order-independent prediction. To enhance the generalization ability of CGMS, we apply the multi-task learning technique to train the model on drug synergy prediction task and drug sensitivity prediction task simultaneously. We compare CGMS's generalization ability with six state-of-the-art methods on a public dataset, and CGMS significantly outperforms other methods in the leave-drug combination-out scenario, as well as in the leave-cell line-out and leave-drug-out scenarios. We further present the benefit of eliminating the order dependency and the discrimination power of whole-graph embeddings, interpret the rationality of the attention mechanism, and verify the contribution of multi-task learning. AVAILABILITY AND IMPLEMENTATION: The code of CGMS is available via https://github.com/TOJSSE-iData/CGMS.

Lenalidomide overcomes the resistance to third-generation CD19-CAR-T cell therapy in preclinical models of diffuse large B-cell lymphoma.

PURPOSE: Chimeric antigen receptor (CAR)-T cells against CD19 have been proven to be effective in treating B-cell hematological malignancies. However, the efficacy of this promising therapy is limited by many factors. METHODS: In this study, the germinal center B-cell-like diffuse large B-cell lymphoma (GCB-DLBCL) cell line OCI-Ly1, and patient-derived xenografted (PDX) mice (CY-DLBCL) were used as the CAR-T cell-resistant model. Meanwhile, the activated B-cell-like (ABC) DLBCL cell line OCI-Ly3 and PDX mice (ZML-DLBCL) were defined as the CAR-T sensitive model. The enhancement of CAR-T cell function by lenalidomide (LEN) was examined in vitro and in vivo. RESULTS: Lenalidomide effectively enhanced the function of third-generation CD19-CAR-T cells by polarizing CD8+ CAR-T cells to CD8 early-differentiated stage and Th1 type, reducing CAR-T cell exhaustion and improving cell expansion. It was further demonstrated that CAR-T cells combined with LEN substantially reduce the tumor burden and prolong the survival time in various DLBCL mouse models. LEN was also found to promote the infiltration of CD19-CAR-T cells into the tumor site by modulating the tumor microenvironment. CONCLUSION: In summary, the results of the present study suggest that LEN can improve the function of CD19-CAR-T cells, providing a basis for clinical trials using this combination therapy against DLBCL.

The Ubiquitin-specific Protease USP36 Associates with the Microprocessor Complex and Regulates miRNA Biogenesis by SUMOylating DGCR8.

miRNA biogenesis is a cellular process that produces mature miRNAs from their primary transcripts, pri-miRNAs, via two RNAse III enzyme complexes: the Drosha-DGCR8 microprocessor complex in the nucleus and the Dicer-TRBP complex in the cytoplasm. Emerging evidence suggests that miRNA biogenesis is tightly regulated by posttranscriptional and posttranslational modifications and aberrant miRNA biogenesis is associated with various human diseases including cancer. DGCR8 has been shown to be modified by SUMOylation. Yet, the SUMO ligase mediating DGCR8 SUMOylation is currently unknown. Here, we report that USP36, a nucleolar ubiquitin-specific protease essential for ribosome biogenesis, is a novel regulator of DGCR8. USP36 interacts with the microprocessor complex and promotes DGCR8 SUMOylation, specifically modified by SUMO2. USP36-mediated SUMOylation does not affect the levels of DGCR8 and the formation of the Drosha-DGCR8 complex, but promotes the binding of DGCR8 to pri-miRNAs. Consistently, abolishing DGCR8 SUMOylation significantly attenuates its binding to pri-miRNAs and knockdown of USP36 attenuates pri-miRNA processing, resulting in marked reduction of tested mature miRNAs. Induced expression of a SUMOylation-defective mutant of DGCR8 inhibits cell proliferation. Together, these results suggest that USP36 plays an important role in regulating miRNA biogenesis by SUMOylating DGCR8. Significance: This study identifies that USP36 mediates DGCR8 SUMOylation by SUMO2 and is critical for miRNA biogenesis. As USP36 is frequently overexpressed in various human cancers, our study suggests that deregulated USP36-miRNA biogenesis pathway may contribute to tumorigenesis.

Loading Nanoceria Improves Extracellular Vesicle Membrane Integrity and Therapy to Wounds in Aged Mice.

Wound healing is a programmed process through which tissue restores its integrity after an injury. Advancing age is a risk factor for delayed cutaneous wound healing; however, ideal therapeutic approaches for aged wound have not been developed yet. By dissecting the harsh microenvironment of aged wound, we propose an integrated chemical and biological strategy to mitigate two main hostile factors including oxidative stress and ischemia. Mesenchymal stem cell-derived extracellular vesicles (EVs) are a rising star in regenerative medicine due to their powerful facilitation in tissue repair and regeneration. However, the fragile lipid membrane limits their function under the oxidative stress microenvironment. Nanoceria is an antioxidative nanozyme; here, we reveal that nanoceria-loaded EVs derived from mesenchymal stem cells facilitate cutaneous wound healing in aged mice. DG-CeO2 was prepared via coating CeO2 covalently with d-glucose to promote their cellular endocytosis. DG-CeO2 was packaged into EVs under optimized hypoxic conditions (DG-CeO2 EVsHyp). We further demonstrated that DG-CeO2 EVsHyp had favorable biocompatibility and antioxidative and proangiogenic effects during the cutaneous wound healing in both young and aged mice. Further evidence revealed that DG-CeO2 EVsHyp-transferred miR-92a-3p/125b-5p and their targets associated with aging degeneration may be the potential mechanisms. Collectively, these findings highlight that nanoceria-loaded EVs released by engineered stem cells may represent a potential therapeutic approach for tissue regeneration in aged population.

Extracellular vesicle: A magic lamp to treat skin aging, refractory wound, and pigmented dermatosis?

Exposure of the skin to an external stimulus may lead to a series of irreversible dysfunctions, such as skin aging, refractory wounds, and pigmented dermatosis. Nowadays, many cutaneous treatments have failed to strike a balance between cosmetic needs and medical recovery. Extracellular vesicles (EVs) are one of the most promising therapeutic tools. EVs are cell-derived nanoparticles that can carry a variety of cargoes, such as nucleic acids, lipids, and proteins. They also have the ability to communicate with neighboring or distant cells. A growing body of evidence suggests that EVs play a significant role in skin repair. We summarize the current findings of EV therapy in skin aging, refractory wound, and pigmented dermatosis and also describe the novel engineering strategies for optimizing EV function and therapeutic outcomes.

An analysis of the trickle-down effect of supervisor knowledge hiding on subordinate knowledge hiding based on displaced aggression theory.

The harm of horizontal knowledge hiding behavior (colleague-colleague) to individuals and organizations has been discussed and confirmed by many studies. The negative consequences of top-down (supervisor-subordinate) knowledge hiding have now emerged as a new focus of research. This study aims to enrich the understanding of the consequences of supervisor knowledge hiding by exploring its trickle-down effect and mechanism. Based on the displaced aggression theory in psychology, this paper analyses and examines the cognitive psychological process and mechanism informing employee knowledge hiding from colleagues when faced with their supervisor's malicious knowledge hiding behavior. Using a three-stage time-lag questionnaire survey strategy, we collect 233 valid samples of full-time employees from representative provinces and cities in China, covering multiple industries. The following findings are observed: (1) Supervisor knowledge hiding from subordinates (SKHS) positively affects subordinate knowledge hiding from colleagues (SKHC); (2) Revenge motivation plays a mediating role; (3) Traditionality weakens the influence of supervisor knowledge hiding on a subordinates' revenge motivation. This study confirms the trickle-down effects of supervisor knowledge hiding behavior, extends research on the consequences of top-down knowledge hiding and its mechanism and provides new insights for organizational practice.

Case Report: Successful therapy with all-trans retinoic acid combined with chemotherapy followed by hematopoietic stem cell transplantation for acute promyelocytic leukemia carrying the BCOR-RARA fusion gene.

Acute promyelocytic leukemia (APL) is characterized by the balanced translocation of chromosomes 15 and 17, resulting in the formation of PML-RARA fusion gene. More than 98% of APL have PML-RARA fusion, and less than 2% have other types of RARA gene partners, which named variant APL (vAPL). In the present study, we reported a vAPL with BCOR-RARA, which was the third case of BCOR-RARA APL published. The patient achieved complete remission (CR) with all-trans retinoic acid (ATRA) monotherapy, and molecular CR with ATRA plus standard chemotherapy. After that, he underwent allogeneic hematopoietic stem cell transplantation (allo-HSCT) and ATRA maintenance and maintained a molecular CR status. This case provided valuable insights into the accurate identification of vAPL. Moreover, ATRA combined with chemotherapy followed by allo-HSCT was suggested as an optimal choice for those vAPL patients who had a high risk of relapse.

Predictive values of plasma TNFα and IL-8 for intracranial hemorrhage in patients with acute promyelocytic leukemia.

In patients with acute promyelocytic leukemia (APL), intracranial hemorrhage (ICH), if not identified promptly, could be fatal. It is the leading cause of failure of induction and early death. Thus, biomarkers that could promptly predict severe complications are critical. Here, cytokine differences between patients with APL with and without ICH were investigated to develop predictive models for this complication. The initial cytokine profiling using plasma samples from 39 patients and 18 healthy donors found a series of cytokines that were remarkedly different between patients with APL and healthy controls. The APL patients were subsequently divided into high and low white blood cell count groups. Results showed that tumor necrosis factor a and interleukin 8 (IL-8) were vital in distinguishing patients with APL who did or did not develop ICH. In addition, verification in 81 patients with APL demonstrated that the two cytokines were positively correlated with the cumulative incidence of ICH. Finally, in-vitro and in-vivo experimental evidence were provided to show that IL-8 influenced the migration of APL-derived NB4 cells and impaired the blood-brain barrier in PML/RARα positive blast-transplanted FVB/NJ mice. These assessments may facilitate the early warning of ICH and reduce future mortality levels in APL.