GPDRP: a multimodal framework for drug response prediction with graph transformer.

BACKGROUND: In the field of computational personalized medicine, drug response prediction (DRP) is a critical issue. However, existing studies often characterize drugs as strings, a representation that does not align with the natural description of molecules. Additionally, they ignore gene pathway-specific combinatorial implication. RESULTS: In this study, we propose drug Graph and gene Pathway based Drug response prediction method (GPDRP), a new multimodal deep learning model for predicting drug responses based on drug molecular graphs and gene pathway activity. In GPDRP, drugs are represented by molecular graphs, while cell lines are described by gene pathway activity scores. The model separately learns these two types of data using Graph Neural Networks (GNN) with Graph Transformers and deep neural networks. Predictions are subsequently made through fully connected layers. CONCLUSIONS: Our results indicate that Graph Transformer-based model delivers superior performance. We apply GPDRP on hundreds of cancer cell lines' bulk RNA-sequencing data, and it outperforms some recently published models. Furthermore, the generalizability and applicability of GPDRP are demonstrated through its predictions on unknown drug-cell line pairs and xenografts. This underscores the interpretability achieved by incorporating gene pathways.

Detecting the Critical States of Type 2 Diabetes Mellitus Based on Degree Matrix Network Entropy by Cross-Tissue Analysis.

Type 2 diabetes mellitus (T2DM) is a metabolic disease caused by multiple etiologies, the development of which can be divided into three states: normal state, critical state/pre-disease state, and disease state. To avoid irreversible development, it is important to detect the early warning signals before the onset of T2DM. However, detecting critical states of complex diseases based on high-throughput and strongly noisy data remains a challenging task. In this study, we developed a new method, i.e., degree matrix network entropy (DMNE), to detect the critical states of T2DM based on a sample-specific network (SSN). By applying the method to the datasets of three different tissues for experiments involving T2DM in rats, the critical states were detected, and the dynamic network biomarkers (DNBs) were successfully identified. Specifically, for liver and muscle, the critical transitions occur at 4 and 16 weeks. For adipose, the critical transition is at 8 weeks. In addition, we found some "dark genes" that did not exhibit differential expression but displayed sensitivity in terms of their DMNE score, which is closely related to the progression of T2DM. The information uncovered in our study not only provides further evidence regarding the molecular mechanisms of T2DM but may also assist in the development of strategies to prevent this disease.

Significance of density functional theory (DFT) calculations for electrocatalysis of N2 and CO2 reduction reactions.

Density functional theory (DFT) based computational methods have shown great significance in developing high-performance electrocatalysts. In this perspective, we briefly summarized the state-of-the-art research progress of electrocatalysts for the nitrogen reduction reaction (NRR) and CO2 reduction reaction (CO2RR), which are important processes for the conversion of common molecules into value-added products. With the help of DFT calculations, various modulation strategies are employed to improve the catalytic activity and performance of NRR and CO2RR electrocatalysts. DFT calculations are performed to confirm the surface catalytic sites, evaluate the catalytic activity, reveal the possible reaction mechanisms, and design novel structures with high catalytic performance. By discussing the currently applied computational methods and conditions during the calculations, we outlined our concerns on the prospects and future challenges of DFT calculations in electrocatalysis studies.

Lovastatin Inhibits Cancer Stem Cells and Sensitizes to Chemo- and Photodynamic Therapy in Nasopharyngeal Carcinoma.

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus-associated malignancy occurring at high incidence in Southeast Asia and southern China. In spite of the good response to radio- and chemo-therapy at the early stage, resistance and recurrence develop in NPC patients in the advanced setting. Cancer stem cells (CSCs) play an important role in drug resistance and cancer recurrence. Here we report that lovastatin, a natural compound and a lipophilic statin that has already been used in the clinic to treat hypercholesterolemia, inhibited the CSC properties and induced apoptosis and cell cycle arrest in sphere-forming cells derived from the 5-8F and 6-10B NPC cell lines. Furthermore, lovastatin conferred enhanced sensitivity to the chemotherapeutic and photodynamic agents in NPC CSCs. Together our findings suggest that targeting CSCs by lovastatin in combination with routine chemotherapeutic drugs or photodynamic therapy might be a promising approach to the treatment of NPC.

Diet- and Genetically-induced Obesity Produces Alterations in the Microbiome, Inflammation and Wnt Pathway in the Intestine of Apc+/1638N Mice: Comparisons and Contrasts.

Obesity is an established risk factor for colorectal cancer (CRC). Our previous study indicated that obesity increases activity of the pro-tumorigenic Wnt-signaling. Presently, we sought to further advance our understanding of the mechanisms by which obesity promotes CRC by examining associations between microbiome, inflammation and Wnt-signaling in Apc+/1638N mice whose obesity was induced by one of two modalities, diet- or genetically-induced obesity. Three groups were employed: Apc+/1638NLepr+/+ fed a low fat diet (10% fat), Apc+/1638NLepr+/+ fed a high fat diet (60% fat, diet-induced obesity), and Apc+/1638NLeprdb/db fed a low fat diet (genetically-induced obesity). All animals received diets for 16 weeks from 8 to 24 weeks of age. The abundance of 19 bowel cancer-associated bacterial taxa were examined by real-time PCR. The abundance of Turicibacter and Desulfovibrio decreased, but F. prausnitizii increased, in diet-induced obese mice (p < 0.05). In contrast, in genetically-induced obese mice, Bifidobacterium, A. muciniphila and E. rectale decreased, but Peptostrptococcus, and E. coli increased (p < 0.05). Both diet- and genetically-induced obesity altered the expression of genes involved in bacterial recognition (MyD88) and increased inflammation as indicated by elevated levels of cytokines (IFNγ and TNF-α for genetically-induced obesity, and IL-6 for diet-induced obesity). The elevated inflammation was associated with altered expression of genes that are integral components of the Wnt-signaling cascade in a fashion indicating its activation. These findings demonstrate that the composition of the small intestinal microbiome is affected differently in diet- and genetically-induced obesity, but both are associated with elevated intestinal inflammation and alterations of the Wnt pathway towards enhancing tumorigenesis.

Effects of Lovastatin on MDA-MB-231 Breast Cancer Cells: An Antibody Microarray Analysis.

Despite the tremendous improvement in cancer therapeutics, treatment of late-stage breast cancer remains a challenge for both basic scientists and clinicians. Lovastatin, a natural product derived from Aspergillus terreus or Monascus ruber, has been widely used as cholesterol-lowing drug in the clinic. It also has anti-cancer properties through poorly defined molecular mechanisms. In the present study, we employed a novel antibody microarray technology to investigate the molecular mechanisms through which lovastatin inhibits breast cancer. We found that lovastatin up-regulated 17 proteins and down-regulated 20 proteins in MDA-MB-231 breast cancer cells. These included proteins that modulate apoptosis, cell proliferation, differentiation, signal transduction, epithelial-to-mesenchymal transition and tumor metastasis. Modulation of these pathways may mediate, in part, the inhibitory activity of lovastatin on breast cancer.

Embryonic stem cells attenuate viral myocarditis in murine model.

We used mice to test our hypothesis that in response to viral invasion, stem cells may migrate into the heart and attenuate the effect of viral myocarditis. Male BALB/c mice were divided into three groups: mouse embryonic stem (ES) cell control, encephalomyocarditis virus (EMCV), and EMCV + ES cells. After administration of ES cells via tail vein, mice were immediately inoculated with EMCV. Mice were sacrificed at different days after EMCV inoculation. Mortality was recorded. Inflammatory cell infiltration and necrosis (major pathological changes of viral myocarditis) were evaluated by hematoxylin-eosin staining. ES cell migration and differentiation were identified by immunofluorescence. The survival rate in the EMCV + ES cell group (80%) was significantly increased (p < 0.05) over the EMCV-alone group (64%). Also, the incidence of inflammatory cell infiltration and myocardial lesions was lower in the EMCV + ES cell mice. Furthermore, the result of green fluorescent protein (GFP) and alpha-actinin analysis indicated that ES cells migrated into the heart and differentiated into myocytes after virus inoculation. In conclusion, ES cells significantly increased the survival of viral myocarditis mice and also decreased the necrosis and infiltration of inflammatory cells. These results demonstrated the ability of stem cells to mitigate the effects of viral infection on the heart and illustrated their potential therapeutic application to other mammalian species, including humans.