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Na(v)1.5 sodium channels contribute to the generation of the rapid upstroke of the myocardial action potential and thereby play a central role in the excitability of myocardial cells. At present, the patch clamp method is the gold standard for ion channel inhibitor screening. However, this method has disadvantages such as high technical difficulty, high cost and low speed. In this study, novel machine learning models to screen chemical blockers were developed to overcome the above shortage. The data from the ChEMBL Database were employed to establish the machine learning models. Firstly, six molecular fingerprints together with five machine learning algorithms were used to develop 30 classification models to predict effective inhibitors. A validation and a test set were used to evaluate the performance of the models. Subsequently, the privileged substructures tightly associated with the inhibition of the Na(v)1.5 ion channel were extracted using the bioalerts Python package. In the validation set, the RF-Graph model performed best. Similarly, RF-Graph produced the best result in the test set in which the Prediction Accuracy (Q) was 0.9309 and Matthew's correlation coefficient was 0.8627, further indicating the model had high classification ability. The results of the privileged substructures indicated Sulfa structures and fragments with large Steric hindrance tend to block Na(v)1.5. In the unsupervised learning task of identifying sulfa drugs, MACCS and Graph fingerprints had good results. In summary, effective machine learning models have been constructed which help to screen potential inhibitors of the Na(v)1.5 ion channel and key privileged substructures with high affinity were also extracted.

The occurrence of obesity is associated with age. But their interplay remains mysterious. Here, we discovered that rotundic acid (RA), a plant-derived pentacyclic triterpene, was a powerful agent for both anti-aging and treating obesity. Considering that obese individuals decrease the appetite-suppressing and energy-expenditure-enhancing functions of leptin leading to obesity, we found RA was a leptin sensitizer, evidenced by observations that RA enhanced the leptin sensitivity to normal diet-induced obese (DIO) mice, and had minimal or no use to normal lean mice, leptin receptor-deficient (db/db) mice, and leptin-deficient (ob/ob) mice. Simultaneously, RA significantly increased energy expenditure, BAT thermogenesis, and glucose metabolism in DIO mice, as the results of enhancing leptin sensitivity. Regarding mode of action, we demonstrated that RA is a noncompetitive inhibitor of leptin negative regulators protein tyrosine phosphatase 1B (PTP1B) and T-cell PTP through interaction with their C-terminus, thus leading to weight loss through enhancing leptin sensitivity. Besides, we showed that deletion of yPTP1 in yeast completely abolished the lifespan extension effect of RA, celstrol, and withaferin A, while these compounds exhibited PTP1B inhibition activity. Furthermore, PTP1B knockdown extend lifespan in yeast and human cells, indicating PTP1B is an important factor regulating cellular aging.

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Treatment responses of patients with acute myeloid leukemia (AML) are known to be heterogeneous, posing challenges for risk scoring and treatment stratification. In this retrospective multi-cohort study, we investigated whether combining pyroptosis- and immune-related genes improves prognostic classification of AML patients. Using a robust gene pairing approach, which effectively eliminates batch effects across heterogeneous patient cohorts and transcriptomic data, we developed an immunity and pyroptosis-related prognostic (IPRP) signature that consists of 15 genes. Using 5 AML cohorts (n = 1327 patients total), we demonstrate that the IPRP score leads to more consistent and accurate survival prediction performance, compared with 10 existing signatures, and that IPRP scoring is widely applicable to various patient cohorts, treatment procedures and transcriptomic technologies. Compared to current standards for AML patient stratification, such as age or ELN2017 risk classification, we demonstrate an added prognostic value of the IPRP risk score for providing improved prediction of AML patients. Our web-tool implementation of the IPRP score and a simple 4-factor nomogram enables practical and robust risk scoring for AML patients. Even though developed for AML patients, our pan-cancer analyses demonstrate a wider application of the IPRP signature for prognostic prediction and analysis of tumor-immune interplay also in multiple solid tumors.

Immune infiltration of ovarian cancer (OV) is a critical factor in determining patient's prognosis. Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, 46 genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on 3 immune genes and 3 types of clinical information. The IGCI score has been verified by K-M curves, ROC curves and C-index on test set. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, p < 0.05) and CIN25 (C-index = 0.571, p < 0.05). In addition, we identified key mutations to analyse prognosis of patients and the process related to immunity. Chi-squared tests revealed that 6 mutations are significantly (p < 0.05) related to immune infiltration: BRCA1, ZNF462, VWF, RBAK, RB1 and ADGRV1. According to mutation survival analysis, we found 5 key mutations significantly related to patient prognosis (p < 0.05): CSMD3, FLG2, HMCN1, TOP2A and TRRAP. RB1 and CSMD3 mutations had small p-value (p < 0.1) in both chi-squared tests and survival analysis. The drug sensitivity analysis of key mutation showed when RB1 mutation occurs, the efficacy of six anti-tumour drugs has changed significantly (p < 0.05).

Globally, liver hepatocellular carcinoma (LIHC) has a high mortality and recurrence rate, leading to poor prognosis. The recurrence of LIHC is closely related to two aspects: degree of immune infiltration and content of tumor stem cells. Hence, this study aimed to used RNA-seq and clinical data of LIHC from The Cancer Genome Atlas, Estimation of Stromal and Immune cells in Malignant Tumours, mRNA stemness index score, and weighted gene correlation network analysis methods to find genes significantly linked to the aforementioned two aspects. Key genes and clinical factors were used as input. Lasso regression and multivariate Cox regression were conducted to build an effective prognostic model for patients with liver cancer. Finally, four key genes (KLHL30, PLN, LYVE1, and TIMD4) and four clinical factors (Asian, age, grade, and bilirubin) were included in the prognostic model, namely Immunity and Cancer-stem-cell Related Prognosis (ICRP) score. The ICRP score achieved a great performance in test set. The area under the curve value of the ICRP score in test set for 1, 3, and 5 years was 0.708, 0.723, and 0.765, respectively, which was better than that of other prognostic prediction methods for LIHC. The C-index evaluation method also reached the same conclusion.

The molecular landscape in breast cancer is characterized by large biological heterogeneity and variable clinical outcomes. Here, we performed an integrative multi-omics analysis of patients diagnosed with breast cancer. Using transcriptomic analysis, we identified three subtypes (cluster A, cluster B and cluster C) of breast cancer with distinct prognosis, clinical features, and genomic alterations: Cluster A was asso-ciated with higher genomic instability, immune suppression and worst prognosis outcome; cluster B was associated with high activation of immune-pathway, increased mutations and middle prognosis out-come; cluster C was linked to Luminal A subtype patients, moderate immune cell infiltration and best prognosis outcome. Combination of the three newly identified clusters with PAM50 subtypes, we pro-posed potential new precision strategies for 15 subtypes using L1000 database. Then, we developed a robust gene pair (RGP) score for prognosis outcome prediction of patients with breast cancer. The RGP score is based on a novel gene-pairing approach to eliminate batch effects caused by differences in heterogeneous patient cohorts and transcriptomic data distributions, and it was validated in ten cohorts of patients with breast cancer. Finally, we developed a user-friendly web-tool (https://sujiezhulab.shi-nyapps.io/BRCA/) to predict subtype, treatment strategies and prognosis states for patients with breast cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).

Ferroptosis is a new type of programmed cell death, which occurs with iron dependence. Previous studies have showed that ferroptosis plays an important regulatory role in the occurrence and development of tumors. Colon cancer is one of the major morbidities and causes of mortality in the world. This study used RNA-seq and colon cancer clinical data to explore the relationship between ferroptosis-related genes and colon cancer. Based on the fifteen prognostic ferroptosis-related genes, two molecular subgroups of colon cancer were identified. Surprisingly, we also found cluster2 was characterized by lower mutation burden and expression of checkpoint genes, better survival, and higher expression of NOX1. Moreover, cluster2 has fewer BRAF mutations. We also found the expression of NOX1 is related to the status of BRAF. Finally, using 15 ferroptosis-related genes from The Cancer Genome Atlas cohort, we constructed a prognosis model, and this model may be used to predict the prognosis of patients in clinics.

Hyperuricemia (HUA) is a risk factor for chronic kidney disease (CKD). Serum uric acid (SUA) levels in CKD stage 3&ndash, 4 patients closely correlate with hyperuricemic nephropathy (HN) morbidity. New uric acid (UA)-lowering strategies are required to prevent CKD. The multiple-purpose connectivity map (CMAP) was used to discover potential molecules against HUA and renal fibrosis. We used HUA and unilateral ureteral occlusion (UUO) model mice to verify renoprotective effects of molecules and explore related mechanisms. In vitro experiments were performed in HepG2 and NRK-52E cells induced by UA. Esculetin was the top scoring compound and lowered serum uric acid (SUA) levels with dual functions on UA excretion. Esculetin exerted these effects by inhibiting expression and activity of xanthine oxidase (XO) in liver, and modulating UA transporters in kidney. The mechanism by which esculetin suppressed XO was related to inhibiting the nuclear translocation of hexokinase 2 (HK2). Esculetin was anti-fibrotic in HUA and UUO mice through inhibiting TGF-&beta, 1-activated profibrotic signals. The renoprotection effects of esculetin in HUA mice were associated with lower SUA, alleviation of oxidative stress, and inhibition of fibrosis. Esculetin is a candidate urate-lowering drug with renoprotective activity and the ability to inhibit XO, promote excretion of UA, protect oxidative stress injury, and reduce renal fibrosis.

Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning-based efficacy prediction system (DLEPS) that identifies drug candidates using a change in the gene expression profile in the diseased state as input. DLEPS was trained using chemically induced changes in transcriptional profiles from the L1000 project. We found that the changes in transcriptional profiles for previously unexamined molecules were predicted with a Pearson correlation coefficient of 0.74. We examined three disorders and experimentally tested the top drug candidates in mouse disease models. Validation showed that perillen, chikusetsusaponin IV and trametinib confer disease-relevant impacts against obesity, hyperuricemia and nonalcoholic steatohepatitis, respectively. DLEPS can generate insights into pathogenic mechanisms, and we demonstrate that the MEK-ERK signaling pathway is a target for developing agents against nonalcoholic steatohepatitis. Our findings suggest that DLEPS is an effective tool for drug repurposing and discovery.

With the advances in the field of expanded genetic code, the application of non-canonical amino acid (ncAA) is considered an effective strategy for protein engineering. However, cumbersome and complicated selection schemes limit the extensive application of this technology in Saccharomyces cerevisiae. To address this issue, a simplified selection scheme with confident results was developed and tested in this study. Based on a mutation library derived from Escherichia coli tyrosyl-tRNA synthetase (EcTyrRS), a logic gate in synthetic biology was used to optimize screening procedures. We found that an "and" gate was more suitable than an "or" gate for isolating aminoacyl-tRNA synthetase from S. cerevisiae. The successful incorporation of O-methyltyrosine (OMeY) proved the utility and efficiency of this new selection scheme. After a round of positive selection, several new OMeY-tRNA synthetase (OMeYRS) mutants were screened, and their incorporation efficiency was improved. Furthermore, we characterized the insertion of several tyrosine analogs into Herceptine Fab and discovered that OMeYRS and its mutants were polyspecific. One of these mutants showed an optimal performance to incorporate different ncAAs into recombinant proteins in S. cerevisiae; this mutant was cloned and transfected into mammalian cells, and the results proved its functionality in HEK293 cells. This study could expand the application of ncAA in S. cerevisiae to construct efficient yeast cell factories for producing natural and synthetic products.

Background Ovarian cancer is one of the most lethal gynecological cancers,and is in the top five cancer types associated with death in women. Immune infiltration of ovarian cancer is a critical factor in determining patient's prognosis. In addition to immune infiltration, key mutations also have a greater impact on the development and prognosis of OV.Methods Using data from TCGA and GTEx database combined with WGCNA and ESTIMATE methods, genes related to OV occurrence and immune infiltration were identified. Lasso and multivariate Cox regression were applied to define a prognostic score (IGCI score) based on immune genes and clinical information. The IGCI score has been verified by K-M curves, ROC curves, C-index on test set. Based on mutation data from TCGA, we identified key mutations related to immune infiltration by Chi-squared tests and we also did survival analysis of these mutations.Results In this section, we found 46 genes related to disease occurrence and immune infiltration, IGCI score was established based on six factors: Age, White, Pharmaceutical, FGF7, CCR1 and CD14. In test set, IGCI score (C-index = 0.630) is significantly better than AJCC stage (C-index = 0.541, P P P P P P Conclusion Based on immune-related genes and clinical information, we have established an ovarian cancer prognosis score that is superior to other methods. At the same time, RB1 and CSMD3 gene mutations are considered to be closely related to ovarian cancer immunity and prognosis.

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Lung squamous cell carcinoma (LUSC) is often diagnosed at the advanced stage with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished good prediction accuracy. Three hundred and thirty&ndash, seven up&ndash, regulated and 119 down-regulated genes were identified, in which four genes have been found to play vital roles in LUSC development, namely CCNA2, AURKA, AURKB, and FEN1. The prognostic model contained 5 genes, which were all detrimental to prognosis. The AUC of the established prognostic model for predicting the survival of patients at 1, 3, and 5 years was 0.692, 0.722, and 0.651 in the test data, respectively. In conclusion, this study identified several biomarkers of significant interest for additional investigation of the therapies and methods of prognosis of lung squamous cell carcinoma.

Epilepsy is the second largest disease after headache in the neurology department of the hospital. Previous studies have shown that Xiao Chai Hu Tang (XCHT) has a certain therapeutic effect on epilepsy. We used the gene expression array data of epilepsy patients, and the relevant data of the Chinese medicine components in the TCMSP database and DrugBank database, combined with bioinformatics analysis methods, to construct herbs-compounds-targets (HCT) network and protein-protein interaction (PPI) core network of XCHT. The result suggests that Caveolin 1 (CAV1) may be a key target in the treatment of epilepsy, and β-carotene may have potential antiepileptic activity. In addition, the PPI core function network is enriched in the process of transcription factor regulation, and the related protein is located on the nuclear chromosome and closely related to the immune process, which provides a reference for other researchers.

We analyze the impact of climate change to different extent of country’s fragility, using Central African Republic and Mexico as an example of top ten most fragile country and a moderate fragile country respectively. Through our dynamic model, a strong intervention of government is proved to have a significant effect on alleviating the fragility of country. The strengths and weaknesses of our models are discussed and we also propose solution for the fragility of the country. In this paper, we conclude the relationship between climate change and country’s fragility together with the effect of interventions based on our models, which may also give an inspiration for the later dynamic prediction.