An interpretable survival model for diffuse large B-cell lymphoma patients using a biologically informed visible neural network.

Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma (NHL) and is characterized by high heterogeneity. Assessment of its prognosis and genetic subtyping hold significant clinical implications. However, existing DLBCL prognostic models are mainly based on transcriptomic profiles, while genetic variation detection is more commonly used in clinical practice. In addition, current clustering-based subtyping methods mostly focus on genes with high mutation frequencies, providing insufficient explanations for the heterogeneity of DLBCL. Here, we proposed VNNSurv (https://bio-web1.nscc-gz.cn/app/VNNSurv), a survival model for DLBCL patients based on a biologically informed visible neural network (VNN). VNNSurv achieved an average C-index of 0.72 on the cross-validation set (HMRN cohort, n = 928), outperforming the baseline methods. The remarkable interpretability of VNNSurv facilitated the identification of the most impactful genes and the underlying pathways through which they act on patient outcomes. When only the 30 highest-impact genes were used as genetic input, the overall performance of VNNSurv improved, and a C-index of 0.70 was achieved on the external TCGA cohort (n = 48). Leveraging these high-impact genes, including 16 genes with low (<5 %) alteration frequencies, we devised a genetic-based prognostic index (GPI) for risk stratification and a subtype identification method. We stratified the patient group according to the International Prognostic Index (IPI) into three risk grades with significant prognostic differences. Furthermore, the defined subtypes exhibited greater prognostic consistency than clustering-based methods. Broadly, VNNSurv is a valuable DLBCL survival model. Its high interpretability has significant value for precision medicine, and its framework is scalable to other diseases.

Structure-aware dual-target drug design through collaborative learning of pharmacophore combination and molecular simulation.

Dual-target drug design has gained significant attention in the treatment of complex diseases, such as cancers and autoimmune disorders. A widely employed design strategy is combining pharmacophores to leverage the knowledge of structure-activity relationships of both targets. Unfortunately, pharmacophore combination often struggles with long and expensive trial and error, because the protein pockets of the two targets impose complex structural constraints. In this study, we propose AIxFuse, a structure-aware dual-target drug design method that learns pharmacophore fusion patterns to satisfy the dual-target structural constraints simulated by molecular docking. AIxFuse employs two self-play reinforcement learning (RL) agents to learn pharmacophore selection and fusion by comprehensive feedback including dual-target molecular docking scores. Collaboratively, the molecular docking scores are learned by active learning (AL). Through collaborative RL and AL, AIxFuse learns to generate molecules with multiple desired properties. AIxFuse is shown to outperform state-of-the-art methods in generating dual-target drugs against glycogen synthase kinase-3 beta (GSK3ß) and c-Jun N-terminal kinase 3 (JNK3). When applied to another task against retinoic acid receptor-related orphan receptor γ-t (RORγt) and dihydroorotate dehydrogenase (DHODH), AIxFuse exhibits consistent performance while compared methods suffer from performance drops, leading to a 5 times higher performance in success rate. Docking studies demonstrate that AIxFuse can generate molecules concurrently satisfying the binding mode required by both targets. Further free energy perturbation calculation indicates that the generated candidates have promising binding free energies against both targets.

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge.

Recent advances in microfluidics and sequencing technologies allow researchers to explore cellular heterogeneity at single-cell resolution. In recent years, deep learning frameworks, such as generative models, have brought great changes to the analysis of transcriptomic data. Nevertheless, relying on the potential space of these generative models alone is insufficient to generate biological explanations. In addition, most of the previous work based on generative models is limited to shallow neural networks with one to three layers of latent variables, which may limit the capabilities of the models. Here, we propose a deep interpretable generative model called d-scIGM for single-cell data analysis. d-scIGM combines sawtooth connectivity techniques and residual networks, thereby constructing a deep generative framework. In addition, d-scIGM incorporates hierarchical prior knowledge of biological domains to enhance the interpretability of the model. We show that d-scIGM achieves excellent performance in a variety of fundamental tasks, including clustering, visualization, and pseudo-temporal inference. Through topic pathway studies, we found that d-scIGM-learned topics are better enriched for biologically meaningful pathways compared to the baseline models. Furthermore, the analysis of drug response data shows that d-scIGM can capture drug response patterns in large-scale experiments, which provides a promising way to elucidate the underlying biological mechanisms. Lastly, in the melanoma dataset, d-scIGM accurately identified different cell types and revealed multiple melanin-related driver genes and key pathways, which are critical for understanding disease mechanisms and drug development.

Study on the Antifungal Activity of Gallic Acid and Its Azole Derivatives against Fusarium graminearum.

The wheat scab caused by Fusarium graminearum (F. graminearum) has seriously affected the yield and quality of wheat in China. In this study, gallic acid (GA), a natural polyphenol, was used to synthesize three azole-modified gallic acid derivatives (AGAs1-3). The antifungal activity of GA and its derivatives against F. graminearum was studied through mycelial growth rate experiments and field efficacy experiments. The results of the mycelial growth rate test showed that the EC50 of AGAs-2 was 0.49 mg/mL, and that of AGAs-3 was 0.42 mg/mL. The biological activity of AGAs-3 on F. graminearum is significantly better than that of GA. The results of field efficacy tests showed that AGAs-2 and AGAs-3 significantly reduced the incidence rate and disease index of wheat scab, and the control effect reached 68.86% and 72.11%, respectively. In addition, preliminary investigation was performed on the possible interaction between AGAs-3 and F. graminearum using density functional theory (DFT). These results indicate that compound AGAs-3, because of its characteristic of imidazolium salts, has potential for use as a green and environmentally friendly plant-derived antifungal agent for plant pathogenic fungi.

Deciphering cell types by integrating scATAC-seq data with genome sequences.

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

Genome-scale annotation of protein binding sites via language model and geometric deep learning.

Revealing protein binding sites with other molecules, such as nucleic acids, peptides, or small ligands, sheds light on disease mechanism elucidation and novel drug design. With the explosive growth of proteins in sequence databases, how to accurately and efficiently identify these binding sites from sequences becomes essential. However, current methods mostly rely on expensive multiple sequence alignments or experimental protein structures, limiting their genome-scale applications. Besides, these methods haven't fully explored the geometry of the protein structures. Here, we propose GPSite, a multi-task network for simultaneously predicting binding residues of DNA, RNA, peptide, protein, ATP, HEM, and metal ions on proteins. GPSite was trained on informative sequence embeddings and predicted structures from protein language models, while comprehensively extracting residual and relational geometric contexts in an end-to-end manner. Experiments demonstrate that GPSite substantially surpasses state-of-the-art sequence-based and structure-based approaches on various benchmark datasets, even when the structures are not well-predicted. The low computational cost of GPSite enables rapid genome-scale binding residue annotations for over 568,000 sequences, providing opportunities to unveil unexplored associations of binding sites with molecular functions, biological processes, and genetic variants. The GPSite webserver and annotation database can be freely accessed at https://bio-web1.nscc-gz.cn/app/GPSite.

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma.

BACKGROUND: Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients. METHODS: We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC). RESULTS: In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction. CONCLUSIONS: Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

TCR signaling induces STAT3 phosphorylation to promote TH17 cell differentiation.

TH17 differentiation is critically controlled by "signal 3" of cytokines (IL-6/IL-23) through STAT3. However, cytokines alone induced only a moderate level of STAT3 phosphorylation. Surprisingly, TCR stimulation alone induced STAT3 phosphorylation through Lck/Fyn, and synergistically with IL-6/IL-23 induced robust and optimal STAT3 phosphorylation at Y705. Inhibition of Lck/Fyn kinase activity by Srci1 or disrupting the interaction between Lck/Fyn and STAT3 by disease-causing STAT3 mutations selectively impaired TCR stimulation, but not cytokine-induced STAT3 phosphorylation, which consequently abolished TH17 differentiation and converted them to FOXP3+ Treg cells. Srci1 administration or disrupting the interaction between Lck/Fyn and STAT3 significantly ameliorated TH17 cell-mediated EAE disease. These findings uncover an unexpected deterministic role of TCR signaling in fate determination between TH17 and Treg cells through Lck/Fyn-dependent phosphorylation of STAT3, which can be exploited to develop therapeutics selectively against TH17-related autoimmune diseases. Our study thus provides insight into how TCR signaling could integrate with cytokine signal to direct T cell differentiation.

An uncertainty-based interpretable deep learning framework for predicting breast cancer outcome.

BACKGROUND: Predicting outcome of breast cancer is important for selecting appropriate treatments and prolonging the survival periods of patients. Recently, different deep learning-based methods have been carefully designed for cancer outcome prediction. However, the application of these methods is still challenged by interpretability. In this study, we proposed a novel multitask deep neural network called UISNet to predict the outcome of breast cancer. The UISNet is able to interpret the importance of features for the prediction model via an uncertainty-based integrated gradients algorithm. UISNet improved the prediction by introducing prior biological pathway knowledge and utilizing patient heterogeneity information. RESULTS: The model was tested in seven public datasets of breast cancer, and showed better performance (average C-index = 0.691) than the state-of-the-art methods (average C-index = 0.650, ranged from 0.619 to 0.677). Importantly, the UISNet identified 20 genes as associated with breast cancer, among which 11 have been proven to be associated with breast cancer by previous studies, and others are novel findings of this study. CONCLUSIONS: Our proposed method is accurate and robust in predicting breast cancer outcomes, and it is an effective way to identify breast cancer-associated genes. The method codes are available at: https://github.com/chh171/UISNet .

Tri©DB: an integrated platform of knowledgebase and reporting system for cancer precision medicine.

BACKGROUND: With the development of cancer precision medicine, a huge amount of high-dimensional cancer information has rapidly accumulated regarding gene alterations, diseases, therapeutic interventions and various annotations. The information is highly fragmented across multiple different sources, making it highly challenging to effectively utilize and exchange the information. Therefore, it is essential to create a resource platform containing well-aggregated, carefully mined, and easily accessible data for effective knowledge sharing. METHODS: In this study, we have developed "Consensus Cancer Core" (Tri©DB), a new integrative cancer precision medicine knowledgebase and reporting system by mining and harmonizing multifaceted cancer data sources, and presenting them in a centralized platform with enhanced functionalities for accessibility, annotation and analysis. RESULTS: The knowledgebase provides the currently most comprehensive information on cancer precision medicine covering more than 40 annotation entities, many of which are novel and have never been explored previously. Tri©DB offers several unique features: (i) harmonizing the cancer-related information from more than 30 data sources into one integrative platform for easy access; (ii) utilizing a variety of data analysis and graphical tools for enhanced user interaction with the high-dimensional data; (iii) containing a newly developed reporting system for automated annotation and therapy matching for external patient genomic data. Benchmark test indicated that Tri©DB is able to annotate 46% more treatments than two officially recognized resources, oncoKB and MCG. Tri©DB was further shown to have achieved 94.9% concordance with administered treatments in a real clinical trial. CONCLUSIONS: The novel features and rich functionalities of the new platform will facilitate full access to cancer precision medicine data in one single platform and accommodate the needs of a broad range of researchers not only in translational medicine, but also in basic biomedical research. We believe that it will help to promote knowledge sharing in cancer precision medicine. Tri©DB is freely available at www.biomeddb.org , and is hosted on a cutting-edge technology architecture supporting all major browsers and mobile handsets.

Biological informed graph neural network for tumor mutation burden prediction and immunotherapy-related pathway analysis in gastric cancer.

Tumor mutation burden (TMB) has emerged as an essential biomarker for assessing the efficacy of cancer immunotherapy. However, due to the inherent complexity of tumors, TMB is not always correlated with the responsiveness of immune checkpoint inhibitors (ICIs). Thus, refining the interpretation and contextualization of TMB is a requisite for enhancing clinical outcomes. In this study, we conducted a comprehensive investigation of the relationship between TMB and multi-omics data across 33 human cancer types. Our analysis revealed distinct biological changes associated with varying TMB statuses in STAD, COAD, and UCEC. While multi-omics data offer an opportunity to dissect the intricacies of tumors, extracting meaningful biological insights from such massive information remains a formidable challenge. To address this, we developed and implemented the PGLCN, a biologically informed graph neural network based on pathway interaction information. This model facilitates the stratification of patients into subgroups with distinct TMB statuses and enables the evaluation of driver biological processes through enhanced interpretability. By integrating multi-omics data for TMB prediction, our PGLCN model outperformed previous traditional machine learning methodologies, demonstrating superior TMB status prediction accuracy (STAD AUC: 0.976 ± 0.007; COAD AUC: 0.994 ± 0.007; UCEC AUC: 0.947 ± 0.023) and enhanced interpretability (BA-House: 1.0; BA-Community: 0.999; BA-Grid: 0.994; Tree-Cycles: 0.917; Tree-Grids: 0.867). Furthermore, the biological interpretability inherent to PGLCN identified the Toll-like receptor family and DNA repair pathways as potential combined biomarkers in conjunction with TMB status in gastric cancer. This finding suggests a potential synergistic targeting strategy with immunotherapy for gastric cancer, thus advancing the field of precision oncology.

Effect of Chestnut (Castanea Mollissima Blume) Bur Polyphenol Extract on Shigella dysenteriae: Antibacterial Activity and the Mechanism.

Shigella dysenteriae is a highly pathogenic microorganism that can cause human bacillary dysentery by contaminating food and drinking water. This study investigated the antibacterial activity of chestnut bur polyphenol extract (CBPE) on S. dysenteriae and the underlying mechanism. The results showed that the minimum inhibitory concentration (MIC) of CBPE for S. dysenteriae was 0.4 mg/mL, and the minimum bactericidal concentration (MBC) was 1.6 mg/mL. CBPE treatment irreversibly disrupted cell morphology, decreased cell activity, and increased cell membrane permeability, cell membrane depolarization, and cell content leakage of S. dysenteriae, indicating that CBPE has obvious destructive effects on the cell membrane and cell wall of S. dysenteriae. Combined transcriptomic and metabolomics analysis revealed that CBPE inhibits S. dysenteriae by interfering with ABC protein transport, sulfur metabolism, purine metabolism, amino acid metabolism, glycerophospholipid metabolism, and some other pathways. These findings provide a theoretical basis for the prevention and treatment of S. dysenteriae infection with extract from chestnut burs.

Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.

Pyrus ussuriensis Maxim 70% ethanol eluted fraction ameliorates inflammation and oxidative stress in LPS-induced inflammation in vitro and in vivo.

Pyrus ussuriensis Maxim (PUM) is a popular fruit among consumers, and also used as medical diet for dissolving phlegm and arresting cough. The present study aims to investigate the potential protective effect of P. ussuriensis Maxim 70% ethanol eluted fraction (PUM70) on lipopolysaccharide (LPS)-induced alveolar macrophages and acute lung injury (ALI) in mice. A total of 18 polyphenol compounds were tentatively identified in PUM70 by mass spectrometry (MS) analysis. The results in vivo suggested that PUM70 treatment could effectively alleviate the histological changes, and significantly inhibit the activity of myeloperoxidase (MPO) and the expression of pro-inflammatory cytokines (tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), and interleukin-6 (IL-6)). The cell test results show that PUM70 exerted its protective effect by suppressing the messenger RNA (mRNA) expression levels (inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) and decreasing nitric oxide (NO) and prostaglandin 2 (PGE2) contents. In addition, it also inhibited the overproduction of pro-inflammatory cytokines (TNF-α, IL-1ß, and IL-6). Furthermore, PUM70 induced the production of heme oxygenase 1 (HO-1) protein and nuclear translocation of Nrf2 (nuclear factor erythroid 2-related factor 2), indicating that PUM70 could mitigate oxidative injury via the Nrf2/HO-1 pathway. Moreover, PUM70 inhibited LPS-induced inflammation by blocking the phosphorylation of mitogen-activated protein kinases (MAPKs). The above results indicate that PUM70 has protective effects on LPS-induced ALI, possibly be related to the inhibition of MAPK and Nrf2/HO-1 signaling pathways.

Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy.

OBJECTIVES: Recent studies have revealed the change of molecular subtypes in breast cancer (BC) after neoadjuvant therapy (NAT). This study aims to construct a non-invasive model for predicting molecular subtype alteration in breast cancer after NAT. METHODS: Eighty-two estrogen receptor (ER)-negative/ human epidermal growth factor receptor 2 (HER2)-negative or ER-low-positive/HER2-negative breast cancer patients who underwent NAT and completed baseline MRI were retrospectively recruited between July 2010 and November 2020. Subtype alteration was observed in 21 cases after NAT. A 2D-DenseUNet machine-learning model was built to perform automatic segmentation of breast cancer. 851 radiomic features were extracted from each MRI sequence (T2-weighted imaging, ADC, DCE, and contrast-enhanced T1-weighted imaging), both in the manual and auto-segmentation masks. All samples were divided into a training set (n = 66) and a test set (n = 16). XGBoost model with 5-fold cross-validation was performed to predict molecular subtype alterations in breast cancer patients after NAT. The predictive ability of these models was subsequently evaluated by the AUC of the ROC curve, sensitivity, and specificity. RESULTS: A model consisting of three radiomics features from the manual segmentation of multi-sequence MRI achieved favorable predictive efficacy in identifying molecular subtype alteration in BC after NAT (cross-validation set: AUC = 0.908, independent test set: AUC = 0.864); whereas an automatic segmentation approach of BC lesions on the DCE sequence produced good segmentation results (Dice similarity coefficient = 0.720). CONCLUSIONS: A machine learning model based on baseline MRI is proven useful for predicting molecular subtype alterations in breast cancer after NAT. KEY POINTS: • Machine learning models using MRI-based radiomics signature have the ability to predict molecular subtype alterations in breast cancer after neoadjuvant therapy, which subsequently affect treatment protocols. • The application of deep learning in the automatic segmentation of breast cancer lesions from MRI images shows the potential to replace manual segmentation..

Enhancing anti-tumor effect of ultrasensitive bimetallic RuCu nanoparticles as radiosensitizers with dual enzyme-like activities.

Radiotherapy (RT), through the generation of reactive oxygen species (ROS) and DNA damage to tumor cells caused by high-energy irradiation, has been a widely applied cancer treatment strategy in clinic. However, the therapeutic effect of traditional RT is restricted by the insufficient radiation energy deposition and the side effects on normal tissues. Recently, multifunctional nano-formulations and synergistic therapy has been developed as attractive strategies for used to enhancing the efficacy and safety of RT. Herein, we show that a bimetallic nanozyme (copper-modified ruthenium nanoparticles, RuCu NPs), containing the high atomic number (Z) element Ru as a novel radiosensitizer, offers an ideal solution to RT sensitization, with ultrasensitive peroxidase (POD)-like activity and catalase (CAT)-like activity. Density functional theory (DFT) calculations also clarified the optimal POD-like catalytic ratio of RuCu NPs and further revealed the mechanism of its supper catalytic activity. Under X-ray exposure, RuCu NPs coated with poly(ethylene glycol) (PEG) exhibited simultaneously improved the ROS production and relieved tumor hypoxia in the acid tumor microenvironment (TME), and demonstrated remarkable therapeutic efficacy in the MDA-MB-231 breast cancer model. Our results provide a proof-of-concept for a RT sensitization strategy, which combine the intrinsic nature of high-Z element and the advantages of nanozymes to overcome the tricky drawbacks existed in radiotherapy, and further open a new direction of exploring novel nanozyme-based strategies for tumor catalytic therapy and synergistic radiotherapy.

Genetic evidence for a causal relationship between type 2 diabetes and peripheral artery disease in both Europeans and East Asians.

BACKGROUND: Observational studies have revealed that type 2 diabetes (T2D) is associated with an increased risk of peripheral artery disease (PAD). However, whether the two diseases share a genetic basis and whether the relationship is causal remain unclear. It is also unclear as to whether these relationships differ between ethnic groups. METHODS: By leveraging large-scale genome-wide association study (GWAS) summary statistics of T2D (European-based: Ncase = 21,926, Ncontrol = 342,747; East Asian-based: Ncase = 36,614, Ncontrol = 155,150) and PAD (European-based: Ncase = 5673, Ncontrol = 359,551; East Asian-based: Ncase = 3593, Ncontrol = 208,860), we explored the genetic correlation and putative causal relationship between T2D and PAD in both Europeans and East Asians using linkage disequilibrium score regression and seven Mendelian randomization (MR) models. We also performed multi-trait analysis of GWAS and two gene-based analyses to reveal candidate variants and risk genes involved in the shared genetic basis between T2D and PAD. RESULTS: We observed a strong genetic correlation (rg) between T2D and PAD in both Europeans (rg = 0.51; p-value = 9.34 × 10-15) and East Asians (rg = 0.46; p-value = 1.67 × 10-12). The MR analyses provided consistent evidence for a causal effect of T2D on PAD in both ethnicities (odds ratio [OR] = 1.05 to 1.28 for Europeans and 1.15 to 1.27 for East Asians) but not PAD on T2D. This putative causal effect was not influenced by total cholesterol, body mass index, systolic blood pressure, or smoking initiation according to multivariable MR analysis, and the genetic overlap between T2D and PAD was further explored employing an independent European sample through polygenic risk score regression. Multi-trait analysis of GWAS revealed two novel European-specific single nucleotide polymorphisms (rs927742 and rs1734409) associated with the shared genetic basis of T2D and PAD. Gene-based analyses consistently identified one gene ANKFY1 and gene-gene interactions (e.g., STARD10 [European-specific] to AP3S2 [East Asian-specific]; KCNJ11 [European-specific] to KCNQ1 [East Asian-specific]) associated with the trans-ethnic genetic overlap between T2D and PAD, reflecting a common genetic basis for the co-occurrence of T2D and PAD in both Europeans and East Asians. CONCLUSIONS: Our study provides the first evidence for a genetically causal effect of T2D on PAD in both Europeans and East Asians. Several candidate variants and risk genes were identified as being associated with this genetic overlap. Our findings emphasize the importance of monitoring PAD status in T2D patients and suggest new genetic biomarkers for screening PAD risk among patients with T2D.

Spatial transcriptomics prediction from histology jointly through Transformer and graph neural networks.

The rapid development of spatial transcriptomics allows the measurement of RNA abundance at a high spatial resolution, making it possible to simultaneously profile gene expression, spatial locations of cells or spots, and the corresponding hematoxylin and eosin-stained histology images. It turns promising to predict gene expression from histology images that are relatively easy and cheap to obtain. For this purpose, several methods are devised, but they have not fully captured the internal relations of the 2D vision features or spatial dependency between spots. Here, we developed Hist2ST, a deep learning-based model to predict RNA-seq expression from histology images. Around each sequenced spot, the corresponding histology image is cropped into an image patch and fed into a convolutional module to extract 2D vision features. Meanwhile, the spatial relations with the whole image and neighbored patches are captured through Transformer and graph neural network modules, respectively. These learned features are then used to predict the gene expression by following the zero-inflated negative binomial distribution. To alleviate the impact by the small spatial transcriptomics data, a self-distillation mechanism is employed for efficient learning of the model. By comprehensive tests on cancer and normal datasets, Hist2ST was shown to outperform existing methods in terms of both gene expression prediction and spatial region identification. Further pathway analyses indicated that our model could reserve biological information. Thus, Hist2ST enables generating spatial transcriptomics data from histology images for elucidating molecular signatures of tissues.

Anti-inflammatory effect of flavonoids from chestnut flowers in lipopolysaccharide-stimulated RAW 264.7 macrophages and acute lung injury in mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Chestnut flowers were one of the by-products during chestnut industrial processing. Chestnut (Castanea mollissima Blume) flower is rich in flavonoids and has been used as a traditional medicine to treat a variety of diseases including respiratory disorders for a long history. AIM OF THE STUDY: The present study aims to investigate the potential anti-inflammatory effect of flavonoids from chestnut flower (FCF) in lipopolysaccharide (LPS)-treated RAW 264.7 cells and stimulated acute lung injury (ALI) in mice. MATERIALS AND METHODS: HPLC-ESI-MS/MS was applied to identify flavonoids from Chestnut flower. The ROS content in cells and lung tissue was measured by flow cytometry. The malondialdehyde (MDA) content, superoxide dismutase (SOD) activity and glutathione (GSH) content in cells and bronchoalveolar lavage fluid (BALF) was analyzed by photometry. Furthermore, the level of pro-inflammatory factors was analyzed by ELISA, and the expression of inflammatory gene mRNA by fluorescence quantitative PCR. H&E staining was used to evaluate the degree of lung tissue injury in mice. MPO activity was used to measure the degree of neutrophil infiltration. Total protein content was detected by BCA method. RESULTS: A total of forty-nine flavonoids compounds were tentatively identified in FCF by mass spectrometry analysis. The results of cell experiment suggested that FCF could alleviate oxidative injury via increasing SOD activity and GSH content, as well as inhibiting the production of intracellular ROS and MDA. FCF exerted its protective effect by suppressing the expression of both inducible nitric oxide synthase (iNOS) and cycooxygenase 2 (COX-2) to inhibit the synthesis of pro-inflammatory factors and cytokines, including NO, PGE2, TNF-α, IL-6 and IL-1ß. Besides, FCF treatment could alleviate the thickening of alveolar wall and pulmonary congestion in LPS-treated ALI mice, and significantly inhibit the activity of myeloperoxidas (MPO) and the expression of cytokines in BALF. CONCLUSIONS: FCF could ameliorate inflammation and oxidative stress in LPS-treated inflammation, resulting in an overall improvement in both macroscopic and histological parameters.

Effect of Ultrasonic Irradiation on the Physicochemical and Structural Properties of Laminaria japonica Polysaccharides and Their Performance in Biological Activities.

Due to the large molecular weight and complex structure of Laminaria japonica polysaccharides (LJP), which limit their absorption and utilization by the body, methods to effectively degrade polysaccharides had received more and more attention. In the present research, hot water extraction coupled with three-phase partitioning (TPP) was developed to extract and isolate LJP. Ultrasonic L. japonica polysaccharides (ULJP) were obtained by ultrasonic degradation. In addition, their physicochemical characteristics and in vitro biological activities were investigated. Results indicated that ULJP had lower weight-average molecular weight (153 kDa) and looser surface morphology than the LJP. The primary structures of LJP and ULJP were basically unchanged, both contained α-hexo-pyranoses and were mainly connected by 1,4-glycosidic bonds. Compared with LJP, ULJP had stronger antioxidant activity, α-amylase inhibitory effect and anti-inflammatory effect on RAW264.7 macrophages. The scavenging rate of DPPH free radicals by ULJP is 35.85%. Therefore, ultrasonic degradation could effectively degrade LJP and significantly improve the biological activity of LJP, which provided a theoretical basis for the in-depth utilization and research and development of L. japonica in the fields of medicine and food.