Shared genetic architecture highlights the bidirectional association between major depressive disorder and fracture risk.

Background: There is limited evidence suggesting that osteoporosis might exacerbate depressive symptoms, while more studies demonstrate that depression negatively affects bone density and increases fracture risk. Aims: To explore the relationship between major depressive disorder (MDD) and fracture risk. Methods: We conducted a nested case-control analysis (32 670 patients with fracture and 397 017 individuals without fracture) and a matched cohort analysis (16 496 patients with MDD and 435 492 individuals without MDD) in the same prospective UK Biobank data set. Further, we investigated the shared genetic architecture between MDD and fracture with linkage disequilibrium score regression and the MiXeR statistical tools. We used the conditional/conjunctional false discovery rate approach to identify the specific shared loci. We calculated the weighted genetic risk score for individuals in the UK Biobank and logistic regression was used to confirm the association observed in the prospective study. Results: We found that MDD was associated with a 14% increase in fracture risk (hazard ratio (HR) 1.14, 95% CI 1.14 to 1.15, p<0.001) in the nested case-control analysis, while fracture was associated with a 72% increase in MDD risk (HR 1.72, 95% CI 1.64 to 1.79, p<0.001) in the matched cohort analysis, suggesting a longitudinal and bidirectional relationship. Further, genetic summary data suggested a genetic overlap between MDD and fracture. Specifically, we identified four shared genomic loci, with the top signal (rs7554101) near SGIP1. The protein encoded by SGIP1 is involved in cannabinoid receptor type 1 signalling. We found that genetically predicted MDD was associated with a higher risk of fracture and vice versa. In addition, we found that the higher expression level of SGIP1 in the spinal cord and muscle was associated with an increased risk of fracture and MDD. Conclusions: The genetic pleiotropy between MDD and fracture highlights the bidirectional association observed in the epidemiological analysis. The shared genetic components (such as SGIP1) between the diseases suggest that modulating the endocannabinoid system could be a potential therapeutic strategy for both MDD and bone loss.

CHES1 modulated tumorigenesis and senescence of pancreas cancer cells through repressing AKR1B10.

Pancreatic ductal adenocarcinoma (PDAC), is characteristic by a heterogeneous tumor microenvironment and gene mutations, conveys a dismal prognosis and low response to chemotherapy and immunotherapy. Here, we found that checkpoint suppressor 1 (CHES1) served as a tumor repressor in PDAC and was associated with patient prognosis. Functional experiments indicated that CHES1 suppressed the proliferation and invasion of PDAC by modulating cellular senescence. To further identify the downstream factor of CHES1 in PDAC, label-free quantitative proteomics analysis was conducted, which showed that the oncogenic Aldo-keto reductase 1B10 (AKR1B10) was transcriptionally repressed by CHES1 in PDAC. And AKR1B10 facilitated the malignant activity and repressed senescent phenotype of PDAC cells. Moreover, pharmaceutical inhibition of AKR1B10 with Oleanolic acid (OA) significantly induced tumor regression and sensitized PDAC cells to gemcitabine, and this combined therapy did not cause obvious side effects. Rescued experiments revealed that CHES1 regulated the tumorigenesis and gemcitabine sensitivity through AKR1B10-mediated senescence in PDAC. In summary, this study revealed that the CHES1/AKR1B10 axis modulated the progression and cellular senescence in PDAC, which might provide revenues for drug-targeting and senescence-inducing therapies for PDAC.

The value of dynamic changes in FT3 level for predicting 90-day prognosis of HBV-ACLF patients.

OBJECTIVE: To explore the effect of dynamic changes in free triiodothyronine (FT3) level for predicting the 90 day prognosis of patients with hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). METHODS: The clinical data of 122 hospitalised patients with HBV-ACLF between September 2018 and January 2020 were collected and divided into a survival group (77 cases) and a death group (45 cases) according to the 90 day prognosis. We statistically analysed the characteristics of FT3 changes in the two groups of patients. Binary logistic regression one-way analysis was used to assess the degree of influence of each factor. The Kaplan-Meier survival curve and receiver operating characteristic curve were used to evaluate the effect of a single change in FT3 level difference (single △FT3) and the FT3 level change range (△FT3 range) in predicting the 90-day prognosis of patients. RESULTS: There were only three types of changes in FT3 levels, which included 19 (15.6%) cases of continuous normal type, 35 (28.7%) cases of continuous decrease type and 68 (55.7%) cases of U-shaped change type. The difference in survival curves between the three types of patients was statistically significant (P < 0.001). CONCLUSION: The dynamic change type of FT3 is related to the disease severity and 90-day prognosis of patients with HBV-ACLF. The single FT3 value and FT3 range could be used as a predictive factor for the 90-day prognosis of patients with HBV-ACLF. These results have a degree of research value and are worth further exploration in the future.

Multifunctional Gold Nanozyme-Engineered Amphotericin B for Enhanced Antifungal Infection Therapy.

Chronic wounds of significant severity and acute injuries are highly vulnerable to fungal infections, drastically impeding the expected wound healing trajectory. The clinical use of antifungal therapeutic drug is hampered by poor solubility, high toxicity and adverse reactions, thereby necessitating the urgent development of novel antifungal therapy strategy. Herein, this study proposes a new strategy to enhance the bioactivity of small-molecule antifungal drugs based on multifunctional metal nanozyme engineering, using amphotericin B (AmB) as an example. AmB-decorated gold nanoparticles (AmB@AuNPs) are synthesized by a facile one-pot reaction strategy, and the AmB@AuNPs exhibit superior peroxidase (POD)-like enzyme activity, with maximal reaction rates (Vmax ) 3.4 times higher than that of AuNPs for the catalytic reaction of H2 O2 . Importantly, the enzyme-like activity of AuNPs significantly enhanced the antifungal properties of AmB, and the minimum inhibitory concentrations of AmB@AuNPs against Candida albicans (C. albicans) and Saccharomyces cerevisiae (S. cerevisiae) W303 are reduced by 1.6-fold and 50-fold, respectively, as compared with AmB alone. Concurrent in vivo studies conducted on fungal-infected wounds in mice underscored the fundamentally superior antifungal ability and biosafety of AmB@AuNPs. The proposed strategy of engineering antifungal drugs with nanozymes has great potential for enhanced therapy of fungal infections and related diseases.

Explainable ensemble learning method for OCT detection with transfer learning.

The accuracy and interpretability of artificial intelligence (AI) are crucial for the advancement of optical coherence tomography (OCT) image detection, as it can greatly reduce the manual labor required by clinicians. By prioritizing these aspects during development and application, we can make significant progress towards streamlining the clinical workflow. In this paper, we propose an explainable ensemble approach that utilizes transfer learning to detect fundus lesion diseases through OCT imaging. Our study utilized a publicly available OCT dataset consisting of normal subjects, patients with dry age-related macular degeneration (AMD), and patients with diabetic macular edema (DME), each with 15 samples. The impact of pre-trained weights on the performance of individual networks was first compared, and then these networks were ensemble using majority soft polling. Finally, the features learned by the networks were visualized using Grad-CAM and CAM. The use of pre-trained ImageNet weights improved the performance from 68.17% to 92.89%. The ensemble model consisting of the three CNN models with pre-trained parameters loaded performed best, correctly distinguishing between AMD patients, DME patients and normal subjects 100% of the time. Visualization results showed that Grad-CAM could display the lesion area more accurately. It is demonstrated that the proposed approach could have good performance of both accuracy and interpretability in retinal OCT image detection.

TEAD1, MYO7A and NDUFC2 are novel functional genes associated with glucose metabolism in BXD recombinant inbred population.

AIM: The liver is an important metabolic organ that governs glucolipid metabolism, and its dysfunction may cause non-alcoholic fatty liver disease, type 2 diabetes mellitus, dyslipidaemia, etc. We aimed to systematic investigate the key factors related to hepatic glucose metabolism, which may be beneficial for understanding the underlying pathogenic mechanisms for obesity and diabetes mellitus. MATERIALS AND METHODS: Oral glucose tolerance test (OGTT) phenotypes and liver transcriptomes of BXD mice under chow and high-fat diet conditions were collected from GeneNetwork. QTL mapping was conducted to pinpoint genomic regions associated with glucose homeostasis. Candidate genes were further nominated using a multi-criteria approach and validated to confirm their functional relevance in vitro. RESULTS: Our results demonstrated that plasma glucose levels in OGTT were significantly affected by both diet and genetic background, with six genetic regulating loci were mapped on chromosomes 1, 4, and 7. Moreover, TEAD1, MYO7A and NDUFC2 were identified as the candidate genes. Functionally, siRNA-mediated TEAD1, MYO7A and NDUFC2 knockdown significantly decreased the glucose uptake and inhibited the transcription of genes related to insulin and glucose metabolism pathways. CONCLUSIONS: Our study contributes novel insights to the understanding of hepatic glucose metabolism, demonstrating the impact of TEAD1, MYO7A and NDUFC2 on mitochondrial function in the liver and their regulatory role in maintaining in glucose homeostasis.

A homologous-targeting cGAS-STING agonist multimodally activates dendritic cells for enhanced cancer immunotherapy.

Herein, we developed a doxorubicin (Dox)-loaded and 4T1 cancer cell membrane-modified hydrogenated manganese oxide nanoparticles (mHMnO-Dox) to elicit systemic antitumor immune responses. The results revealed that mHMnO-Dox actively recognized tumor cells and then effectively delivered Dox into the cells. Upon entering tumor cells, the mHMnO-Dox underwent rapid degradation and abundant release of Mn2+ and chemotherapeutic drugs. The released Mn2+ not only catalysed a Fenton-type reaction to produce excessive reactive oxygen species (ROS) but also activated the cGAS-STING pathway to boost dendritic cell (DC) maturation. This process increased cytotoxic T lymphocyte infiltration as well as natural killer cell recruitment into the tumor site. In addition, the released Dox could contribute to a chemotherapeutic effect, while activating DC cells and subsequently intensifying immune responses through immunogenic cell death (ICD) of tumor cells. Consequently, the mHMnO-Dox suppressed the primary and distal tumor growth and inhibited tumor relapse and metastasis, as well as prolonged the lifespan of tumor-bearing mice. Thus, the mHMnO-Dox multimodally activated DC cells to demonstrate synergistic antitumor activity, which was mediated via the activation of the cGAS-STING signalling pathway to regulate tumor microenvironment, ICD-mediated immunotherapy and ROS-mediated CDT. These findings suggest the therapeutic potential of mHMnO-Dox in cancer immunotherapy. STATEMENT OF SIGNIFICANCE: A cancer cell membrane-camouflaged hydrogenated mesoporous manganese oxide (mHMnO) has been developed as a cGAS-STING agonist and ICD inducer. The mHMnO effectively induced abundance of ROS production in cancer cells, which caused cancer cell death and then promoted DC maturation via tumour-associated antigen presentation. Meanwhile, the mHMnO significantly activated cGAS-STING pathway to facilitate DC maturation and cytotoxic T lymphocyte infiltration as well as natural killer cell recruitment, which further enhanced tumour immune response. In addition, the combination of the mHMnO and Dox could synergistically promote tumour ICD and then multimodally induce DC maturation, achieving an enhanced CIT. Overall, this study provides a potential strategy to design novel immunologic adjuvant for enhanced CIT.

Pathological image profiling identifies onco-microbial, tumor immune microenvironment, and prognostic subtypes of colorectal cancer.

Histology slide, tissue microbes, and the host gene expression can be independent prognostic factors of colorectal cancer (CRC), but the underlying associations and biological significance of these multimodal omics remain unknown. Here, we comprehensively profiled the matched pathological images, intratumoral microbes, and host gene expression characteristics in 527 patients with CRC. By clustering these patients based on histology slide features, we classified the patients into two histology slide subtypes (HSS). Onco-microbial community and tumor immune microenvironment (TIME) were also significantly different between the two subtypes (HSS1 and HSS2) of patients. Furthermore, variation in intratumoral microbes-host interaction was associated with the prognostic heterogeneity between HSS1 and HSS2. This study proposes a new CRC classification based on pathological image features and elucidates the process by which tumor microbes-host interactions are reflected in pathological images through the TIME.

NIR-IIb fluorescence antiangiogenesis copper nano-reaper for enhanced synergistic cancer therapy.

The formation of blood vessel system under a relatively higher Cu2+ ion level is an indispensable precondition for tumor proliferation and migration, which was assisted in forming the tumor immune microenvironment. Herein, a copper ions nano-reaper (LMDFP) is rationally designed not only for chelating copper ions in tumors, but also for combination with photothermal therapy (PTT) to improve antitumor efficiency. Under 808 nm laser irradiation, the fabricated nano-reaper converts light energy into thermal energy to kill tumor cells and promotes the release of D-penicillamine (DPA) in LMDFP. Photothermal properties of LMDFP can cause tumor ablation in situ, which further induces immunogenic cell death (ICD) to promote systematic antitumor immunity. The released DPA exerts an anti-angiogenesis effect on the tumor through chelating copper ions, and inhibits the expression of programmed death ligand 1 (PD-L1), which synergizes with PTT to enhance antitumor immunity and inhibit tumor metastasis. Meanwhile, the nanoplatform can emit near-infrared-IIb (NIR-IIb) fluorescence under 980 nm excitation, which can be used to track the nano-reaper and determine the optimal time point for PTT. Thus, the fabricated nano-reaper shows powerful potential in inhibiting tumor growth and metastasis, and holds great promise for the application of copper nanochelator in precise tumor treatment.

Predictable regulation of survival by intratumoral microbe-immune crosstalk in patients with lung adenocarcinoma.

Intratumoral microbiota can regulate the tumor immune microenvironment (TIME) and mediate tumor prognosis by promoting inflammatory response or inhibiting anti-tumor effects. Recent studies have elucidated the potential role of local tumor microbiota in the development and progression of lung adenocarcinoma (LUAD). However, whether intratumoral microbes are involved in the TIME that mediates the prognosis of LUAD remains unknown. Here, we obtained the matched tumor microbiome and host transcriptome and survival data of 478 patients with LUAD in The Cancer Genome Atlas (TCGA). Machine learning models based on immune cell marker genes can predict 1- to 5-year survival with relative accuracy. Patients were stratified into high- and low-survival-risk groups based on immune cell marker genes, with significant differences in intratumoral microbial communities. Specifically, patients in the high-risk group had significantly higher alpha diversity (p < 0.05) and were characterized by an enrichment of lung cancer-related genera such as Streptococcus. However, network analysis highlighted a more active pattern of dominant bacteria and immune cell crosstalk in TIME in the low-risk group compared to the high-risk group. Our study demonstrated that intratumoral microbiota-immune crosstalk was strongly associated with prognosis in LUAD patients, which would provide new targets for the development of precise therapeutic strategies.

The crustacean DNA virus tegument protein VP26 binds to SNAP29 to inhibit SNARE complex assembly and autophagic degradation.

Autophagy generally functions as a cellular surveillance mechanism to combat invading viruses, but viruses have evolved various strategies to block autophagic degradation and even subvert it to promote viral propagation. White spot syndrome virus (WSSV) is the most highly pathogenic crustacean virus, but little is currently known about whether crustacean viruses such as WSSV can subvert autophagic degradation for escape. Here, we show that even though WSSV proliferation triggers the accumulation of autophagosomes, autophagic degradation is blocked in the crustacean species red claw crayfish. Interestingly, the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex including CqSNAP29, CqVAMP7, and the novel autophagosome SNARE protein CqSyx12 is required for autophagic flux to restrict WSSV replication, as revealed by gene silencing experiments. Simultaneously, the expressed WSSV tegument protein VP26, which likely localizes on autophagic membrane mediated by its transmembrane region, binds the Qb-SNARE domain of CqSNAP29 to competitively inhibit the binding of CqSyx12-Qa-SNARE with CqSNAP29-Qb-SNARE; this in turn disrupts the assembly of the CqSyx12-SNAP29-VAMP7 SNARE complex, which is indispensable for the proposed fusion of autophagosomes and lysosomes. Consequently, the autophagic degradation of WSSV is likely suppressed by the expressed VP26 protein in vivo in crayfish, thus probably protecting WSSV components from degradation via the autophagosome-lysosome pathway, resulting in evasion by WSSV. Collectively, these findings highlight how a DNA virus can subvert autophagic degradation by impairing the assembly of the SNARE complex to achieve evasion, paving the way for understanding host-DNA virus interactions from an evolutionary point of view, from crustaceans to mammals.IMPORTANCEWhite spot syndrome virus (WSSV) is one of the largest animal DNA viruses in terms of its genome size and has caused huge economic losses in the farming of crustaceans such as shrimp and crayfish. Detailed knowledge of WSSV-host interactions is still lacking, particularly regarding viral escape from host immune clearance. Intriguingly, we found that the presence of WSSV-VP26 might inhibit the autophagic degradation of WSSV in vivo in the crustacean species red claw crayfish. Importantly, this study is the first to show that viral protein VP26 functions as a core factor to benefit WSSV escape by disrupting the assembly of the soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) complex, which is necessary for the proposed fusion of autophagosomes with lysosomes for subsequent degradation. These findings highlight a novel mechanism of DNA virus evasion by blocking SNARE complex assembly and identify viral VP26 as a key candidate for anti-WSSV targeting.

Serum Elabela expression is decreased in hypertensive patients and could be associated with the progression of hypertensive renal damage.

BACKGROUND: Elabela, a recently discovered hormonal peptide containing 32 amino acids, is a ligand for the apelin receptor. It can lower blood pressure and attenuate renal fibrosis. However, the clinicopathological relationship between Elabela level and renal damage caused by benign hypertension (BHT) and malignant hypertension (MHT) has not been elucidated. Therefore, we investigated the clinicopathological correlation between serum Elabela level and renal damage caused by BHT and MHT. METHODS: The participants comprised 50 patients and 25 age-matched healthy adults. The 50 patients were separated into two groups: MHT (n = 25) and BHT groups (n = 25). We analyzed their medical histories, demographics, and clinical examinations, including physical and laboratory tests. RESULTS: The results showed that serum Elabela level decreased gradually with a continuous increase in blood pressure from the healthy control group, BHT, to MHT. Moreover, Elabela levels negatively correlated with BMI (R = - 0.27, P = 0.02), SBP (r = - 0.64, P < 0.01), DBP (r = - 0.58, P < 0.01), uric acid (r = - 0.39, P < 0.01), bun (r = - 0.53, P < 0.01), and Scr (r = - 0.53 P < 0.01) but positively correlated with eGFR (r = 0.54, P < 0.01). Stepwise multivariate linear regression analysis showed that SBP was the variable most related to Elabela (t = - 5.592, P < 0.01). CONCLUSIONS: Serum Elabela levels decreased in patients with hypertension, especially malignant hypertension, and has the potential to be a marker of hypertension-related kidney damage.

Glucagon Enhances Chemotherapy Efficacy By Inhibition of Tumor Vessels in Colorectal Cancer.

Chemotherapy is widely used to treat colorectal cancer (CRC). Despite its substantial benefits, the development of drug resistance and adverse effects remain challenging. This study aimed to elucidate a novel role of glucagon in anti-cancer therapy. In a series of in vitro experiments, glucagon inhibited cell migration and tube formation in both endothelial and tumor cells. In vivo studies demonstrated decreased tumor blood vessels and fewer pseudo-vessels in mice treated with glucagon. The combination of glucagon and chemotherapy exhibited enhanced tumor inhibition. Mechanistic studies demonstrated that glucagon increased the permeability of blood vessels, leading to a pronounced disruption of vessel morphology. Signaling pathway analysis identified a VEGF/VEGFR-dependent mechanism whereby glucagon attenuated angiogenesis through its receptor. Clinical data analysis revealed a positive correlation between elevated glucagon expression and chemotherapy response. This is the first study to reveal a role for glucagon in inhibiting angiogenesis and vascular mimicry. Additionally, the delivery of glucagon-encapsulated PEGylated liposomes to tumor-bearing mice amplified the inhibition of angiogenesis and vascular mimicry, consequently reinforcing chemotherapy efficacy. Collectively, the findings demonstrate the role of glucagon in inhibiting tumor vessel network and suggest the potential utility of glucagon as a promising predictive marker for patients with CRC receiving chemotherapy.

Rational design of proteasome inhibitors based on the structure of the endogenous inhibitor PI31/Fub1.

Proteasome inhibitors are widely used anticancer drugs. The three clinically approved agents are modified small peptides that preferentially target one of the proteasome's three active sites (ß5) at physiologic concentrations. In addition to these drugs, there is also an endogenous proteasome inhibitor, PI31/Fub1, that enters the proteasome's interior to simultaneously yet specifically inhibit all three active sites. Here, we have used PI31's evolutionarily optimized inhibitory mechanisms to develop a suite of potent and specific ß2 inhibitors. The lead compound strongly inhibited growth of multiple myeloma cells as a standalone agent, indicating the compound's cell permeability and establishing ß2 as a potential therapeutic target in multiple myeloma. The lead compound also showed strong synergy with the existing ß5 inhibitor bortezomib; such combination therapies might help with existing challenges of resistance and severe side effects. These results represent an effective method for rational structure-guided development of proteasome inhibitors.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine.

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

A novel c-Met/TRK inhibitor 1D228 efficiently inhibits tumor growth by targeting angiogenesis and tumor cell proliferation.

Multiple tumors are synergistically promoted by c-Met and TRK, and blocking their cross-signalling pathway may give better effects. In this study, we developed a tyrosine kinase inhibitor 1D228, which exhibited excellent anti-tumor activity by targeting c-Met and TRK. Models in vitro, 1D228 showed a significant better inhibition on cancer cell proliferation and migration than the positive drug Tepotinib. Models in vivo, 1D228 showed robust anti-tumor effect on gastric and liver tumor growth with 94.8% and 93.4% of the TGI, respectively, comparing 67.61% and 63.9% of Tepotinib. Importantly, compared with the combination of Larotrectinib and Tepotinib, 1D228 monotherapy in MKN45 xenograft tumor models showed stronger antitumor activity and lower toxicity. Mechanistic studies showed that 1D228 can largely inhibit the phosphorylation of TRKB and c-Met. Interestingly, both kinases, TRKs and c-Met, have been found to be co-expressed at high levels in patients with gastric cancer through IHC. Furthermore, bioinformatics analysis has revealed that both genes are abnormally co-expressed in multiple types of cancer. Cell cycle analysis found that 1D228 induced G0/G1 arrest by inhibiting cyclin D1. Additionally, vascular endothelial cells also showed a pronounced response to 1D228 due to its expression of TRKB and c-Met. 1D228 suppressed the migration and tube formation of endothelial cells, which are the key functions of tumor angiogenesis. Taken together, compound 1D228 may be a promising candidate for the next generation of c-Met and TRK inhibitors for cancer treatment, and offers a novel potential treatment strategy for cancer patients with abnormal expressions of c-Met or NTRK, or simultaneous of them.

Drug repositioning based on weighted local information augmented graph neural network.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.

Enhancing Drug Repositioning through Local Interactive Learning with Bilinear Attention Networks.

Drug repositioning has emerged as a promising strategy for identifying new therapeutic applications for existing drugs. In this study, we present DRGBCN, a novel computational method that integrates heterogeneous information through a deep bilinear attention network to infer potential drugs for specific diseases. DRGBCN involves constructing a comprehensive drug-disease network by incorporating multiple similarity networks for drugs and diseases. Firstly, we introduce a layer attention mechanism to effectively learn the embeddings of graph convolutional layers from these networks. Subsequently, a bilinear attention network is constructed to capture pairwise local interactions between drugs and diseases. This combined approach enhances the accuracy and reliability of predictions. Finally, a multi-layer perceptron module is employed to evaluate potential drugs. Through extensive experiments on three publicly available datasets, DRGBCN demonstrates better performance over baseline methods in 10-fold cross-validation, achieving an average area under the receiver operating characteristic curve (AUROC) of 0.9399. Furthermore, case studies on bladder cancer and acute lymphoblastic leukemia confirm the practical application of DRGBCN in real-world drug repositioning scenarios. Importantly, our experimental results from the drug-disease network analysis reveal the successful clustering of similar drugs within the same community, providing valuable insights into drug-disease interactions. In conclusion, DRGBCN holds significant promise for uncovering new therapeutic applications of existing drugs, thereby contributing to the advancement of precision medicine.

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network.

Background: Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive. Methods: We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs. Results: GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation. Conclusion: The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

Pan-cancer analyses reveal the stratification of patient prognosis by viral composition in tumor tissues.

The associations between cancer and bacteria/fungi have been extensively studied, but the implications of cancer-associated viruses have not been thoroughly examined. In this study, we comprehensively characterized the cancer virome of tissue samples across 31 cancer types, as well as blood samples from 23 cancer types. Our findings demonstrated the presence of viral DNA at low abundances in both tissue and blood across major human cancers, with significant differences in viral community composition observed among various cancer types. Furthermore, Cox regression analyses conducted on four cancers, including Head and Neck squamous cell carcinoma (HNSC), Kidney renal clear cell carcinoma (KIRC), Stomach adenocarcinoma (STAD), and Uterine Corpus Endometrial Carcinoma (UCEC), revealed strong correlation between viral composition/abundance in tissues and patient survival. Additionally, we identified virus-associated prognostic signatures (VAPS) for these four cancers, and discerned differences in the interplay between VAPS and dominant bacteria in tissues among patients with varying survival risks. Notably, clinically relevant analyses revealed prognostic capacities of the VAPS in these four cancers. Taken together, our study provides novel insights into the role of viruses in tissue in the prognosis of multiple cancers and offers guidance on the use of tissue viruses to stratify prognosis for patients with cancer.