BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs.

Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug-ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model's exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, our method would provide a promising tool for ADR prediction and drug safety assessment in drug discovery and development.

Atox1 protects hippocampal neurons after traumatic brain injury via DJ-1 mediated anti-oxidative stress and mitophagy.

Regulation of the oxidative stress response is crucial for the management and prognosis of traumatic brain injury (TBI). The copper chaperone Antioxidant 1 (Atox1) plays a crucial role in regulating intracellular copper ion balance and impacting the antioxidant capacity of mitochondria, as well as the oxidative stress state of cells. However, it remains unknown whether Atox1 is involved in modulating oxidative stress following TBI. Here, we investigated the regulatory role of Atox1 in oxidative stress on neurons both in vivo and in vitro, and elucidated the underlying mechanism through culturing hippocampal HT-22 cells with Atox1 mutation. The expression of Atox1 was significantly diminished following TBI, while mice with overexpressed Atox1 exhibited a more preserved hippocampal structure and reduced levels of oxidative stress post-TBI. Furthermore, the mice displayed notable impairments in learning and memory functions after TBI, which were ameliorated by the overexpression of Atox1. In the stretch injury model of HT-22 cells, overexpression of Atox1 mitigated oxidative stress by preserving the normal morphology and network connectivity of mitochondria, as well as facilitating the elimination of damaged mitochondria. Mechanistically, co-immunoprecipitation and mass spectrometry revealed the binding of Atox1 to DJ-1. Knockdown of DJ-1 in HT-22 cells significantly impaired the antioxidant capacity of Atox1. Mutations in the copper-binding motif or sequestration of free copper led to a substantial decrease in the interaction between Atox1 and DJ-1, with overexpression of DJ-1 failing to restore the antioxidant capacity of Atox1 mutants. The findings suggest that DJ-1 mediates the ability of Atox1 to withstand oxidative stress. And targeting Atox1 could be a potential therapeutic approach for addressing post-traumatic neurological dysfunction.

Meningioma achieves malignancy and erastin-induced ferroptosis resistance through FOXM1-AURKA-NRF2 axis.

The oncogene Aurora kinase A (AURKA) has been implicated in various tumor, yet its role in meningioma remains unexplored. Recent studies have suggested a potential link between AURKA and ferroptosis, although the underlying mechanisms are unclear. This study presented evidence of AURKA upregulation in high grade meningioma and its ability to enhance malignant characteristics. We identified AURKA as a suppressor of erastin-induced ferroptosis in meningioma. Mechanistically, AURKA directly interacted with and phosphorylated kelch-like ECH-associated protein 1 (KEAP1), thereby activating nuclear factor erythroid 2 related factor 2 (NFE2L2/NRF2) and target genes transcription. Additionally, forkhead box protein M1 (FOXM1) facilitated the transcription of AURKA. Suppression of AURKA, in conjunction with erastin, yields significant enhancements in the prognosis of a murine model of meningioma. Our study elucidates an unidentified mechanism by which AURKA governs ferroptosis, and strongly suggests that the combination of AURKA inhibition and ferroptosis-inducing agents could potentially provide therapeutic benefits for meningioma treatment.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

Resveratrol modulates the Nrf2/NF-kB pathway and inhibits TSLP-mediated atopic march

Background: Resveratrol has been found to have anti-inflammatory and anti-allergic proper-ties. The effects of resveratrol on thymic stromal lymphopoietin (TSLP)-mediated atopic march remain unclear. Purpose: To explore the potential role of resveratrol in TSLP-mediated atopic march.Methods: The atopic march mouse model was established by topical application of MC903 (a vitamin D3 analog). Following the treatment with resveratrol, airway resistance in mice was discovered by pulmonary function apparatus, and the number of total cells, neutrophils, and eosinophils in bronchoalveolar lavage fluid was counted. The histopathological features of pul-monary and ear skin tissues, inflammation, and cell infiltration were determined by hematoxy-lin and eosin staining. The messenger RNA (mRNA) levels of TSLP, immunoglobulin E, interleukin (IL)-4, IL-5, and IL-13 were measured by real-time quantitative polymerase chain reaction. The protein expression of nuclear factor kappa B (NF-κB)/nuclear factor erythroid 2-related factor 2 (Nrf2) signaling-associated molecules (p-p65, p65, p-I kappa B kinase alpha (IκBα), IκBα, Nrf2, and TSLP) in lung and ear skin tissues were assessed by Western blot analysis.Results: Resveratrol attenuated airway resistance and infiltration of total cells, eosinophils, and neutrophils in both lung and ear skin tissues. Resveratrol ameliorates serum inflammatory markers in allergic mice. Moreover, the phosphorylation levels of NF-κB pathway-related pro-teins were significantly reduced by administration of resveratrol in allergic lung and ear skin tissues. Similarly, the protein expression of TSLP in both lung and ear skin tissues was reduced by resveratrol, and Nrf2, a protector molecule, was increased with resveratrol treatment (AU)

Resveratrol modulates the Nrf2/NF-κB pathway and inhibits TSLP-mediated atopic march.

BACKGROUND: Resveratrol has been found to have anti-inflammatory and anti-allergic properties. The effects of resveratrol on thymic stromal lymphopoietin (TSLP)-mediated atopic march remain unclear. PURPOSE: To explore the potential role of resveratrol in TSLP-mediated atopic march. METHODS: The atopic march mouse model was established by topical application of MC903 (a vitamin D3 analog). Following the treatment with resveratrol, airway resistance in mice was discovered by pulmonary function apparatus, and the number of total cells, neutrophils, and eosinophils in bronchoalveolar lavage fluid was counted. The histopathological features of pulmonary and ear skin tissues, inflammation, and cell infiltration were determined by hematoxylin and eosin staining. The messenger RNA (mRNA) levels of TSLP, immunoglobulin E, interleukin (IL)-4, IL-5, and IL-13 were measured by real-time quantitative polymerase chain reaction. The protein expression of nuclear factor kappa B (NF-κB)/nuclear factor erythroid 2-related factor 2 (Nrf2) signaling-associated molecules (p-p65, p65, p-I kappa B kinase alpha (IκBα), IκBα, Nrf2, and TSLP) in lung and ear skin tissues were assessed by Western blot analysis. RESULTS: Resveratrol attenuated airway resistance and infiltration of total cells, eosinophils, and neutrophils in both lung and ear skin tissues. Resveratrol ameliorates serum inflammatory markers in allergic mice. Moreover, the phosphorylation levels of NF-κB pathway-related proteins were significantly reduced by administration of resveratrol in allergic lung and ear skin tissues. Similarly, the protein expression of TSLP in both lung and ear skin tissues was reduced by resveratrol, and Nrf2, a protector molecule, was increased with resveratrol treatment. CONCLUSION: Resveratrol attenuates TSLP-reduced atopic march through ameliorating inflammation and cell infiltration in pulmonary and ear skin tissues by inhibiting the abnormal activation of NF-κB signaling pathway.

Identifying survival of pan-cancer patients under immunotherapy using genomic mutation signature with large sample cohorts.

Although immune checkpoint inhibitors have led to durable clinical response in multiple cancers, only a small proportion of patients respond to this treatment. Therefore, we aim to develop a predictive model that utilizes gene mutation profiles to accurately identify the survival of pan-cancer patients with immunotherapy. Here, we develop and evaluate three different nomograms using two cohorts containing 1,594 cancer patients whose mutation profiles are obtained by MSK-IMPACT sequencing and 230 cancer patients receiving whole-exome sequencing, respectively. Using eighteen genes (SETD2, BRAF, NCOA3, LATS1, IL7R, CREBBP, TET1, EPHA7, KDM5C, MET, KMT2D, RET, PAK7, CSF1R, JAK2, FAT1, ASXL1 and SPEN), the first nomogram stratifies patients from both cohorts into High-Risk and Low-Risk groups. Pan-cancer patients in the High-Risk group exhibit significantly shorter overall survival and progression-free survival than patients in the Low-Risk group in both cohorts. Meanwhile, the first nomogram also accurately identifies the survival of patients with melanoma or lung cancer undergoing immunotherapy, or pan-cancer patients treated with anti-PD-1/PD-L1 inhibitor or anti-CTLA-4 inhibitor. The model proposed is not a prognostic model for the survival of pan-cancer patients without immunotherapy, but a simple, effective and robust predictive model for pan-cancer patients' survival under immunotherapy, and could provide valuable assistance for clinical practice.

O-GlcNAcylation of melanophilin enhances radiation resistance in glioblastoma via suppressing TRIM21 mediated ubiquitination.

The molecular mechanism of glioblastoma (GBM) radiation resistance remains poorly understood. The aim of this study was to elucidate the potential role of Melanophilin (MLPH) O-GlcNAcylation and the specific mechanism through which it regulates GBM radiotherapy resistance. We found that MLPH was significantly upregulated in recurrent GBM tumor tissues after ionizing radiation (IR). MLPH induced radiotherapy resistance in GBM cells and xenotransplanted human tumors through regulating the NF-κB pathway. MLPH was O-GlcNAcylated at the conserved serine 510, and radiation-resistant GBM cells showed higher levels of O-GlcNAcylation of MLPH. O-GlcNAcylation of MLPH protected its protein stability and tripartite motif containing 21(TRIM21) was identified as an E3 ubiquitin ligase promoting MLPH degradation whose interaction with MLPH was affected by O-GlcNAcylation. Our data demonstrate that MLPH exerts regulatory functions in GBM radiation resistance by promoting the NF-κB signaling pathway and that O-GlcNAcylation of MLPH both stabilizes and protects it from TRIM21-mediated ubiquitination. These results identify a potential mechanism of GBM radiation resistance and suggest a potential therapeutic strategy for GBM treatment.

MicroRNA-34a Mediates High-Fat-Induced Hepatic Insulin Resistance by Targeting ENO3.

The etiology of numerous metabolic disorders is characterized by hepatic insulin resistance (IR). Uncertainty surrounds miR-34a's contribution to high-fat-induced hepatic IR and its probable mechanism. The role and mechanism of miR-34a and its target gene ENO3 in high-fat-induced hepatic IR were explored by overexpressing/suppressing miR-34a and ENO3 levels in in vivo and in vitro experiments. Moreover, as a human hepatic IR model, the miR-34a/ENO3 pathway was validated in patients with non-alcoholic fatty liver disease (NAFLD). The overexpression of hepatic miR-34a lowered insulin signaling and altered glucose metabolism in hepatocytes. In contrast, reducing miR-34a expression significantly reversed hepatic IR indices induced by palmitic acid (PA)/HFD. ENO3 was identified as a direct target gene of miR-34a. Overexpression of ENO3 effectively inhibited high-fat-induced hepatic IR-related indices both in vitro and in vivo. Moreover, the expression patterns of members of the miR-34a/ENO3 pathway in the liver tissues of NAFLD patients was in line with the findings of both cellular and animal studies. A high-fat-induced increase in hepatic miR-34a levels attenuates insulin signaling and impairs glucose metabolism by suppressing the expression of its target gene ENO3, ultimately leading to hepatic IR. The miR-34a/ENO3 pathway may be a potential therapeutic target for hepatic IR and related metabolic diseases.

A Novel Acetylation-Immune Subtyping for the Identification of a BET Inhibitor-Sensitive Subgroup in Melanoma.

BACKGROUND: There have been significant advancements in melanoma therapies. BET inhibitors (BETis) show promise in impairing melanoma growth. However, identifying BETi-sensitive melanoma subtypes is challenging. METHODS AND RESULTS: We analyzed 48 melanoma cell lines and 104 patients and identified two acetylation-immune subtypes (ALISs) in the cell lines and three ALISs in the patients. ALIS I, with high HAT1 and low KAT2A expression, showed a higher sensitivity to the BETi JQ-1 than ALIS II. ALIS III had low HAT1 expression. The TAD2B expression was low in ALIS I and II. KAT2A and HAT1 expressions were negatively correlated with the methylation levels of their CG sites (p = 0.0004 and 0.0003). Immunological gene sets, including B cell metagenes, activated stroma-related genes, fibroblast TGF response signatures (TBRS), and T cell TBRS-related genes, were up-regulated in ALIS I. Furthermore, KAT2A played a key role in regulating BETi sensitivity. CONCLUSIONS: The sensitivity of ALIS I to the BETi JQ-1 may be due to the inhibition of BETi resistance pathways and genes by low KAT2A expression and the dysregulation of the immune microenvironment by high HAT1 expression resulting from the absence of immune cells. ALIS I had the worst progression but showed sensitivity to BETi and B-cell-related immunotherapy, despite not responding to BRAF inhibitors.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.886 on testing set and 0.803 on an unseen evaluation set when predicting responders (CR/PR/SD with PFS ≥6 mo) versus non-responders (PD/SD with PFS <6 mo) in anti-CTLA-4 treated melanoma patients. The model also achieves an AUROC of 0.917 and 0.833 in predicting CR/PR versus PD, respectively. Meanwhile, the AUROC is 0.913 when predicting responders versus non-responders in anti-PD-1/PD-L1 melanomas. Moreover, the KP-NET reveals some genes and pathways associated with response to anti-CTLA-4 treatment, such as genes PIK3CA, AOX1 and CBLB, and ErbB signaling pathway, T cell receptor signaling pathway, et al. In conclusion, the KP-NET can accurately predict the response of melanomas to immunotherapy and screen related biomarkers pre-clinically, which can contribute to precision medicine of melanoma.

E2EFP-MIL: End-to-end and high-generalizability weakly supervised deep convolutional network for lung cancer classification from whole slide image.

Efficient and accurate distinction of histopathological subtype of lung cancer is quite critical for the individualized treatment. So far, artificial intelligence techniques have been developed, whose performance yet remained debatable on more heterogenous data, hindering their clinical deployment. Here, we propose an end-to-end, well-generalized and data-efficient weakly supervised deep learning-based method. The method, end-to-end feature pyramid deep multi-instance learning model (E2EFP-MIL), contains an iterative sampling module, a trainable feature pyramid module and a robust feature aggregation module. E2EFP-MIL uses end-to-end learning to extract generalized morphological features automatically and identify discriminative histomorphological patterns. This method is trained with 1007 whole slide images (WSIs) of lung cancer from TCGA, with AUCs of 0.95-0.97 in test sets. We validated E2EFP-MIL in 5 real-world external heterogenous cohorts including nearly 1600 WSIs from both United States and China with AUCs of 0.94-0.97, and found that 100-200 training images are enough to achieve an AUC of >0.9. E2EFP-MIL overperforms multiple state-of-the-art MIL-based methods with high accuracy and low hardware requirements. Excellent and robust results prove generalizability and effectiveness of E2EFP-MIL in clinical practice. Our code is available at https://github.com/raycaohmu/E2EFP-MIL.

Associations between the compositional patterns of blood volatile organic compounds and chronic respiratory diseases and ages at onset in NHANES 2003-2012.

BACKGROUND: and Purpose Volatile organic compounds (VOCs) pose a serious respiratory hazard. This study evaluated the relationship between the compositional patterns of blood VOCs and the risk and age at onset of chronic respiratory diseases (CRDs), including asthma, emphysema and chronic bronchitis, with the objective of preventing or delaying CRDs. METHODS: Participants from five cycles of the NHANES survey were included. Blood VOCs were clustered using k-means clustering. Differences in VOCs and age at onset between multiple groups were compared with the Kruskal‒Wallis test. Logistic regression and a generalized linear model were applied to examine the associations between different compositional patterns of blood VOCs and risk and age at onset of CRDs. RESULTS: 12,386 participants were enrolled in this study. Three VOC compositional patterns were identified after clustering nine species of blood VOCs. The concentration of VOCs in pattern 2 was relatively low and stable. The concentrations of benzene, ethylbenzene, o-xylene, styrene, toluene and m-p-xylene in pattern 3 and the concentrations of 1,4-dichlorobenzene and MTBE in pattern 1 were significantly higher than those in pattern 2. After adjustment for covariates, the participants with VOC pattern 3 had an increased risk of asthma (OR = 1.23, 95% CI: 1.02, 1.49), emphysema (OR = 3.37, 95% CI: 2.24, 5.06) and chronic bronchitis (OR = 1.79, 95% CI: 1.30, 2.45). Meanwhile, VOC pattern 3 was negatively correlated with the age at onset of asthma (ß = -5.61, 95% CI: 9.69, -1.52) and chronic bronchitis (ß = -9.17, 95% CI: 13.96, -4.39). VOC pattern 1 was not associated with either risk or age at onset of the three CRDs after adjustment. CONCLUSIONS: Changing the compositional pattern of blood VOCs by reducing certain species of VOCs may be a new strategy to lengthen the ages at onset of CRDs and effectively prevent them.

HiRAND: A novel GCN semi-supervised deep learning-based framework for classification and feature selection in drug research and development.

The prediction of response to drugs before initiating therapy based on transcriptome data is a major challenge. However, identifying effective drug response label data costs time and resources. Methods available often predict poorly and fail to identify robust biomarkers due to the curse of dimensionality: high dimensionality and low sample size. Therefore, this necessitates the development of predictive models to effectively predict the response to drugs using limited labeled data while being interpretable. In this study, we report a novel Hierarchical Graph Random Neural Networks (HiRAND) framework to predict the drug response using transcriptome data of few labeled data and additional unlabeled data. HiRAND completes the information integration of the gene graph and sample graph by graph convolutional network (GCN). The innovation of our model is leveraging data augmentation strategy to solve the dilemma of limited labeled data and using consistency regularization to optimize the prediction consistency of unlabeled data across different data augmentations. The results showed that HiRAND achieved better performance than competitive methods in various prediction scenarios, including both simulation data and multiple drug response data. We found that the prediction ability of HiRAND in the drug vorinostat showed the best results across all 62 drugs. In addition, HiRAND was interpreted to identify the key genes most important to vorinostat response, highlighting critical roles for ribosomal protein-related genes in the response to histone deacetylase inhibition. Our HiRAND could be utilized as an efficient framework for improving the drug response prediction performance using few labeled data.

Single-Cell Sequencing of Malignant Ascites Reveals Transcriptomic Remodeling of the Tumor Microenvironment during the Progression of Epithelial Ovarian Cancer.

Epithelial ovarian cancer (EOC) is the main cause of mortality among gynecological malignancies worldwide. Although patients with EOC undergo aggregate treatment, the prognosis is often poor. Peritoneal malignant ascites is a distinguishable clinical feature in EOC patients and plays a pivotal role in tumor progression and recurrence. The mechanisms of the tumor microenvironment (TME) in ascites in the regulation of tumor progression need to be explored. We comprehensively analyzed the transcriptomes of 4680 single cells from five EOC patients (three diagnostic samples and two recurrent samples) derived from Gene Expression Omnibus (GEO) databases. Batch effects between different samples were removed using an unsupervised deep embedding single-cell cluster algorithm. Subcluster analysis identified the different phenotypes of cells. The transition of a malignant cell state was confirmed using pseudotime analysis. The landscape of TME in malignant ascites was profiled during EOC progression. The transformation of epithelial cancer cells into mesenchymal cells was observed to lead to the emergence of related anti-chemotherapy and immune escape phenotypes. We found the activation of multiple biological pathways with the transition of tumor-associated macrophages and fibroblasts, and we identified the infiltration of CD4+CD25+ T regulatory cells in recurrent samples. The cell adhesion molecules mediated by integrin might be associated with the formation of the tumorsphere. Our study provides novel insights into the remodeling of the TME heterogeneity in malignant ascites during EOC progression, which provides evidence for identifying novel therapeutic targets and promotes the development of ovarian cancer treatment.