PathwayTMB: A pathway-based tumor mutational burden analysis method for predicting the clinical outcome of cancer immunotherapy.

Immunotherapy has become one of the most promising therapy methods for cancer, but only a small number of patients are responsive to it, indicating that more effective biomarkers are urgently needed. This study developed a pathway analysis method, named PathwayTMB, to identify genomic mutation pathways that serve as potential biomarkers for predicting the clinical outcome of immunotherapy. PathwayTMB first calculates the patient-specific pathway-based tumor mutational burden (PTMB) to reflect the cumulative extent of mutations for each pathway. It then screens mutated survival benefit-related pathways to construct an immune-related prognostic signature based on PTMB (IPSP). In a melanoma training set, IPSP-high patients presented a longer overall survival and a higher response rate than IPSP-low patients. Moreover, the IPSP showed a superior predictive effect compared with TMB. In addition, the prognostic and predictive value of the IPSP was consistently validated in two independent validation sets. Finally, in a multi-cancer dataset, PathwayTMB also exhibited good performance. Our results indicate that PathwayTMB could identify the mutation pathways for predicting immunotherapeutic survival, and their combination may serve as a potential predictive biomarker for immune checkpoint inhibitor therapy.

A pathway-based mutation signature to predict the clinical outcomes and response to CTLA-4 inhibitors in melanoma.

Immune checkpoint inhibitor (ICI) therapy has become a powerful clinical strategy for treating melanoma. The relationship between somatic mutations and the clinical benefits of immunotherapy has been widely recognized. However, the gene-based predictive biomarkers are less stable due to the heterogeneity of cancer at the individual gene level. Recent studies have suggested that the accumulation of gene mutations in biological pathways may activate antitumor immune responses. Herein, a novel pathway mutation signature (PMS) was constructed to predict the survival and efficacy of ICI therapy. In a dataset of melanoma patients treated with anti-CTLA-4, we mapped the mutated genes into the pathways and then identified seven significant mutation pathways associated with survival and immunotherapy response, which were used to construct the PMS model. According to the PMS model, the patients in the PMS-high group showed better overall survival (hazard ratio (HR) = 0.37; log-rank test, p < 0.0001) and progression-free survival (HR = 0.52; log-rank test, p = 0.014) than those in the PMS-low group. The PMS-high patients also showed a significantly higher objective response rate to anti-CTLA-4 therapy than the PMS-low patients (Fisher's exact test, p = 0.0055), and the predictive power of the PMS model was superior to that of TMB. Finally, the prognostic and predictive value of the PMS model was validated in two independent validation sets. Our study demonstrated that the PMS model can be considered a potential biomarker to predict the clinical outcomes and response to anti-CTLA-4 therapy in melanoma patients.

iATMEcell: identification of abnormal tumor microenvironment cells to predict the clinical outcomes in cancer based on cell-cell crosstalk network.

Interactions between Tumor microenvironment (TME) cells shape the unique growth environment, sustaining tumor growth and causing the immune escape of tumor cells. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the identification of cancer-related TME cells. Here, we proposed a novel network-based computational method, named as iATMEcell, to identify the abnormal TME cells associated with the biological outcome of interest based on a cell-cell crosstalk network. In the method, iATMEcell first manually collected TME cell types from multiple published studies and obtained their corresponding gene signatures. Then, a weighted cell-cell crosstalk network was constructed in the context of a specific cancer bulk tissue transcriptome data, where the weight between cells reflects both their biological function similarity and the transcriptional dysregulated activities of gene signatures shared by them. Finally, it used a network propagation algorithm to identify significantly dysregulated TME cells. Using the cancer genome atlas (TCGA) Bladder Urothelial Carcinoma training set and two independent validation sets, we illustrated that iATMEcell could identify significant abnormal cells associated with patient survival and immunotherapy response. iATMEcell was further applied to a pan-cancer analysis, which revealed that four common abnormal immune cells play important roles in the patient prognosis across multiple cancer types. Collectively, we demonstrated that iATMEcell could identify potentially abnormal TME cells based on a cell-cell crosstalk network, which provided a new insight into understanding the effect of TME cells in cancer. iATMEcell is developed as an R package, which is freely available on GitHub (https://github.com/hanjunwei-lab/iATMEcell).

Identification of comutation in signaling pathways to predict the clinical outcomes of immunotherapy.

BACKGROUND: Immune checkpoint blockades (ICBs) have emerged as a promising treatment for cancer. Recently, tumour mutational burden (TMB) and neoantigen load (NAL) have been proposed to be potential biomarkers to predict the efficacy of ICB; however, they were limited by difficulties in defining the cut-off values and inconsistent detection platforms. Therefore, it is critical to identify more effective predictive biomarkers for screening patients who will potentially benefit from immunotherapy. In this study, we aimed to identify comutated signaling pathways to predict the clinical outcomes of immunotherapy. METHODS: Here, we comprehensively analysed the signaling pathway mutation status of 9763 samples across 33 different cancer types from The Cancer Genome Atlas (TCGA) by mapping the somatic mutations to the pathways. We then explored the comutated pathways that were associated with increased TMB and NAL by using receiver operating characteristic (ROC) curve analysis and multiple linear regressions. RESULTS: Our results revealed that comutation of the Spliceosome (Sp) pathway and Hedgehog (He) signaling pathway (defined as SpHe-comut+) could be used as a predictor of increased TMB and NAL and was associated with increased levels of immune-related signatures. In seven independent immunotherapy cohorts, we validated that SpHe-comut+ patients exhibited a longer overall survival (OS) or progression-free survival (PFS) and a higher objective response rate (ORR) than SpHe-comut- patients. Moreover, a combination of SpHe-comut status with PD-L1 expression further improved the predictive value for ICB therapy. CONCLUSION: Overall, SpHe-comut+ was demonstrated to be an effective predictor of immunotherapeutic benefit in seven independent immunotherapy cohorts and may serve as a potential and convenient biomarker for the clinical application of ICB therapy.

Tumor Microenvironment Characterization in Breast Cancer Identifies Prognostic Pathway Signatures.

Breast cancer is one of the most common female malignancies worldwide. Due to its early metastases formation and a high degree of malignancy, the 10 year-survival rate of metastatic breast cancer does not exceed 30%. Thus, more precise biomarkers are urgently needed. In our study, we first estimated the tumor microenvironment (TME) infiltration using the xCell algorithm. Based on TME infiltration, the three main TME clusters were identified using consensus clustering. Our results showed that the three main TME clusters cause significant differences in survival rates and TME infiltration patterns (log-rank test, p = 0.006). Then, multiple machine learning algorithms were used to develop a nine-pathway-based TME-related risk model to predict the prognosis of breast cancer (BRCA) patients (the immune-related pathway-based risk score, defined as IPRS). Based on the IPRS, BRCA patients were divided into two subgroups, and patients in the IPRS-low group presented significantly better overall survival (OS) rates than the IPRS-high group (log-rank test, p < 0.0001). Correlation analysis revealed that the IPRS-low group was characterized by increases in immune-related scores (cytolytic activity (CYT), major histocompatibility complex (MHC), T cell-inflamed immune gene expression profile (GEP), ESTIMATE, immune, and stromal scores) while exhibiting decreases in tumor purity, suggesting IPRS-low patients may have a strong immune response. Additionally, the gene-set enrichment analysis (GSEA) result confirmed that the IPRS-low patients were significantly enriched in several immune-associated signaling pathways. Furthermore, multivariate Cox analysis revealed that the IPRS was an independent prognostic biomarker after adjustment by clinicopathologic characteristics. The prognostic value of the IPRS model was further validated in three external validation cohorts. Altogether, our findings demonstrated that the IPRS was a powerful predictor to screen out certain populations with better prognosis in breast cancer and may serve as a potential biomarker guiding clinical treatment decisions.

A novel pathway mutation perturbation score predicts the clinical outcomes of immunotherapy.

The link between tumor genetic variations and immunotherapy benefits has been widely recognized. Recent studies suggested that the key biological pathways activated by accumulated genetic mutations may act as an effective biomarker for predicting the efficacy of immune checkpoint inhibitor (ICI) therapy. Here, we developed a novel individual Pathway Mutation Perturbation (iPMP) method that measures the pathway mutation perturbation level by combining evidence of the cumulative effect of mutated genes with the position of mutated genes in the pathways. In iPMP, somatic mutations on a single sample were first mapped to genes in a single pathway to infer the pathway mutation perturbation score (PMPscore), and then, an integrated PMPscore profile was produced, which can be used in place of the original mutation dataset to identify associations with clinical outcomes. To illustrate the effect of iPMP, we applied it to a melanoma cohort treated with ICIs and identified seven significant perturbation pathways, which jointly constructed a pathway-based signature. With the signature, patients were classified into two subgroups with significant distinctive overall survival and objective response rate to immunotherapy. Moreover, the pathway-based signature was consistently validated in two independent melanoma cohorts. We further applied iPMP to two non-small cell lung cancer cohorts and also obtained good performance. Altogether, the iPMP method could be used to identify the significant mutation perturbation pathways for constructing the pathway-based biomarker to predict the clinical outcomes of immunotherapy. The iPMP method has been implemented as a freely available R-based package (https://CRAN.R-project.org/package=PMAPscore).

DRviaSPCN: a software package for drug repurposing in cancer via a subpathway crosstalk network.

SUMMARY: Drug repurposing is an approach used to discover new indications for existing drugs. Recently, several computational approaches have been developed for drug repurposing in cancer. Nevertheless, no approaches have reported a systematic analysis of pathway crosstalk. Pathway crosstalk, which refers to the phenomenon of interaction or cooperation between pathways, is a critical aspect of tumor pathways that allows cancer cells to survive and acquire resistance to drug therapy. Here, we innovatively developed a system biology R-based software package, DRviaSPCN, to repurpose drugs for cancer via a subpathway (SP) crosstalk network. This package provides a novel approach to prioritize cancer candidate drugs by considering drug-induced SPs and their crosstalk effects. The operation modes mainly include construction of the SP network and calculation of the centrality scores of SPs to reflect the influence of SP crosstalk, calculation of enrichment scores of drug- and disease-induced dysfunctional SPs and weighted them by the centrality scores of SPs, evaluation of the drug-disease reverse association at the weighted SP level, identification of cancer candidate drugs and visualization of the results. Its capabilities enable DRviaSPCN to find cancer candidate drugs, which will complement the recent tools which did not consider crosstalk among pathways/SPs. DRviaSPCN may help to facilitate the development of drug discovery. AVAILABILITY AND IMPLEMENTATION: The package is implemented in R and available under GPL-2 license from the CRAN website (https://CRAN.R-project.org/package=DRviaSPCN). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Pan-cancer analysis reveals sex-specific signatures in the tumor microenvironment.

The processes of cancer initiation, progression, and response to therapy are affected by the sex of cancer patients. Immunotherapy responses largely depend on the tumor microenvironment (TME), but how sex may shape some TME features, remains unknown. Here, we analyzed immune infiltration signatures across 19 cancer types from 1771 male and 1137 female patients in The Cancer Genome Atlas to evaluate how sex may affect the tumor mutational burden (TMB), immune scores, stromal scores, tumor purity, immune cells, immune checkpoint genes, and functional pathways in the TME. Pan-cancer analyses showed higher TMB and tumor purity scores, as well as lower immune and stromal scores in male patients as compared to female patients. Lung adenocarcinoma, lung squamous carcinoma, kidney papillary carcinoma, and head and neck squamous carcinoma showed the most significant sex biases in terms of infiltrating immune cells, immune checkpoint gene expression, and functional pathways. We further focused on lung adenocarcinoma samples in order to identify and validate sex-specific immune cell biomarkers with prognostic potential. Overall, sex may affect the tumor microenvironment, and sex-specific TME biomarkers may help tailor cancer immunotherapy in certain cancer types.

Identification of Somatic Mutation-Driven Immune Cells by Integrating Genomic and Transcriptome Data.

Tumor somatic mutations in protein-coding regions may generate neoantigens which may trigger antitumor immune cell response. Increasing evidence supports that immune cell response may profoundly influence tumor progression. However, there are no calculated tools to systematically identify immune cells driven by specific somatic mutations. It is urgent to develop a calculated method to comprehensively detect tumor-infiltrating immune cells driven by the specific somatic mutations in cancer. We developed a novel software package (SMDIC) that enables the automated identification of somatic mutation-driven immune cell. SMDIC provides a novel pipeline to discover mutation-specific immune cells by integrating genomic and transcriptome data. The operation modes include inference of the relative abundance matrix of tumor-infiltrating immune cells, detection of differential abundance immune cells with respect to the gene mutation status, conversion of the abundance matrix of significantly dysregulated cells into two binary matrices (one for upregulated and one for downregulated cells), identification of somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and visualization of immune cell abundance of samples in different mutation status for each gene. SMDIC provides a user-friendly tool to identify somatic mutation-specific immune cell response. SMDIC may contribute to understand the mechanisms underlying anticancer immune response and find targets for cancer immunotherapy. The SMDIC was implemented as an R-based tool which was freely available from the CRAN website https://CRAN.R-project.org/package=SMDIC.

Fabrication of Enhanced Mechanical Properties and Intrinsic Flame-Retardant Polyurethane Elastomer Containing 4-(Phenylethynyl) Di(Ethylene Glycol) Phthalate.

In this study, the aromatic acetylene compound 4-(phenylethynyl) di(ethylene glycol) phthalate (PEPE) was used as a chain extender, partially replacing 1,4-butanediol. To synthesize an intrinsic flame-retardant thermoplastic polyurethane elastomer (TPU) with an aromatic acetylene structure, PEPE was synthesized by a two-step polymerization. The flame retardancy, thermal stability, and mechanical properties of TPU were studied. The microstructure of TPU char was investigated by scanning electron microscopy to analyze the flame-retardant mechanism. The tensile strength of TPU containing 1.35 wt% PEPE was 39.2 MPa, which was almost twice as much as neat TPU, showed a dramatic decrease in the peak heat release rate and total heat release, and declined by 46.2% and 24.5%, respectively. After the flame-retardant TPU burned, a cross-linked network foaming char structure was formed. The results showed that PEPE improved the mechanical properties of TPU and conferred good stability that promoted the formation of charcoal and reduced heat release during the combustion of TPU.

Polystyrene Microsphere-Based Immunochromatographic Assay for Detection of Aflatoxin B1 in Maize.

Aflatoxin B1 (AFB1), a mycotoxin, is hepatotoxic, carcinogenic, and nephrotoxic in humans and animals, and contaminate a wide range of maize. In this study, an immunochromatographic assay (ICA) based on polystyrene microspheres (PMs) was developed for sensitive and quantitative detection of AFB1 in maize. The amounts of PMs, the condition for activating carboxyl groups of PMs, the amount of monoclonal antibody (mAb), and the volume of the immune probe were optimized to enhance the performance PMs-ICA for point-of-care testing of AFB1 in maize. The PMs-ICA showed the cut-off value of 1 ng/mL in phosphate buffer (PB) and 6 µg/kg in maize samples, respectively. The quantitative limit of detection (qLOD) was 0.27 and 1.43 µg/kg in PB and maize samples, respectively. The accuracy and precision of the PMs-ICA were evaluated by analysis of spiked maize samples with recoveries of 96.0% to 107.6% with coefficients of variation below 10%. In addition, the reliability of PMs-ICA was confirmed by the liquid chromatography-tandem mass spectrometry method. The results indicated that the PMs-ICA could be used as a sensitive, simple, rapid point-of-care testing of AFB1 in maize.

CNA2Subpathway: identification of dysregulated subpathway driven by copy number alterations in cancer.

Biological pathways reflect the key cellular mechanisms that dictate disease states, drug response and altered cellular function. The local areas of pathways are defined as subpathways (SPs), whose dysfunction has been reported to be associated with the occurrence and development of cancer. With the development of high-throughput sequencing technology, identifying dysfunctional SPs by using multi-omics data has become possible. Moreover, the SPs are not isolated in the biological system but interact with each other. Here, we propose a network-based calculated method, CNA2Subpathway, to identify dysfunctional SPs is driven by somatic copy number alterations (CNAs) in cancer through integrating pathway topology information, multi-omics data and SP crosstalk. This provides a novel way of SP analysis by using the SP interactions in the system biological level. Using data sets from breast cancer and head and neck cancer, we validate the effectiveness of CNA2Subpathway in identifying cancer-relevant SPs driven by the somatic CNAs, which are also shown to be associated with cancer immune and prognosis of patients. We further compare our results with five pathway or SP analysis methods based on CNA and gene expression data without considering SP crosstalk. With these analyses, we show that CNA2Subpathway could help to uncover dysfunctional SPs underlying cancer via the use of SP crosstalk. CNA2Subpathway is developed as an R-based tool, which is freely available on GitHub (https://github.com/hanjunwei-lab/CNA2Subpathway).

Development and Validation of a Three-Gene Prognostic Signature Based on Tumor Microenvironment for Gastric Cancer.

Gastric cancer (GC), which has high morbidity and low survival rate, is one of the most common malignant tumors in the world. The increasing evidences show that the tumor microenvironment (TME) is related to the occurrence and progression of tumors and the prognosis of patients. In this study, we aimed to develop a TME-based prognostic signature for GC. We first identified the differentially expressed genes (DEGs) related to the TME using the Wilcoxon rank-sum test in a training set of GC. Univariate Cox regression analysis was used to identify prognostic-related DEGs. To decrease the overfitting, we performed the least absolute shrinkage and selection operator (LASSO) regression to reduce the number of signature genes and obtained three genes (LPPR4, ADAM12, NOX4). Next, the multivariate Cox regression was performed to construct the risk score model, and a three-gene prognostic signature was developed. According to the signature, patients were classified into high-risk and low-risk groups with significantly different survival. The signature was then applied to three independent validated sets and obtained the same results. We conducted the time-dependent Receiver Operating Characteristic (ROC) curve analysis to evaluate our signature. We further evaluated the differential immune characters between high-risk and low-risk patients to reveal the potential immune mechanism of the impact on the prognosis of the model. Overall, we identified a three-gene prognostic signature based on TME to predict the prognosis of patients with GC and facilitate the development of a precise treatment strategy.

Prevalence of overweight and obesity and associated risk factors among adult residents of northwest China: a cross-sectional study.

OBJECTIVE: Overweight and obesity have been shown to be related to multiple chronic conditions, leading to a heavy economic burden on society throughout the world. This study aims to estimate the prevalence of overweight and obesity and determine potential influencing factors among adults in Xinjiang, northwest China. DESIGN: A community-based observational study. SETTING: The First Affiliated Hospital of Xinjiang Medical University. METHODS: In total, 14 618 adult participants (7799 males; 6819 females) aged over 35 years were recruited from the Cardiovascular Risk Survey conducted in 2010. Data were obtained from face-to-face interviews and physical examinations. The sample was used to estimate the prevalence of overweight (body mass index (BMI) 24-28 kg/m2) and obesity (BMI ≥28 kg/m2) in Xinjiang Province. Influencing factors were analysed based on statistical methods. RESULTS: In Xinjiang Province, the overall prevalence of overweight was 36.5% (male 40.1%; female 33.4%), and the prevalence of obesity was 26.5% (male 27.2%; female 25.8%). The prevalence of both overweight and obesity were higher in women than in men (p<0.001). The main influencing factors for overweight and obesity were sex, age, race, marital status, education level, occupation, smoking, drinking, hypertension, diabetes and dyslipidaemia (p<0.05). CONCLUSIONS: This study estimated that the prevalence of overweight and obesity among adult residents of Xinjiang Province, northwest China, was high. These data suggest that efforts related to the prevention and control of overweight and obesity should be a public health priority in northwest China.

Expression profiles and potential functions of long non-coding RNA in stable angina pectoris patients from Uyghur population of China.

Long non-coding RNAs (lncRNAs) are transcripts longer than 200 nt that are involved in cardiovascular diseases (CVDs). To determine whether lncRNAs are involved in stable angina pectoris (SAP), we analysed the expression profile of lncRNAs and mRNAs on a genome-wide scale in SAP of Uyghur population. Five pairs of SAP patients and healthy controls were screened by an Agilent microarray (human lncRNA + mRNA Array V4.0). Quantitative real-time polymerase chain reaction (qRT-PCR) was used to validate the lncRNA expression levels in 50 SAP and 50 controls. Data analyses were performed using R and Bioconductor. A total of 1871 up- and 231 down-regulated lncRNAs were identified to be differentially expressed in the peripheral blood mononuclear cells (PBMCs). Microarray analysis results identified the lncRNAs NR_037652.1, ENST00000607654.1, ENST00000589524.1 and uc004bhb.3, which were confirmed by qRT-PCR. Among screened lncRNAs, the annotation result of their co-expressed mRNAs showed that the most significantly related pathways were the NF-κB signalling pathway, apoptosis and the p53 signalling pathway, while the main significantly related diseases were the cholesterol, calcium and coronary disease. Our study indicated that clusters of lncRNAs were significantly differentially expressed between SAP patients and matched controls. These lncRNAs may play a significant role in SAP development and could serve as biomarkers and potential targets for the future treatment of SAP.

Prenylated 2-Phenoxychromones and Flavonoids from Epimedium brevicornum and Revised Structures of Epimedonins A and B.

Six hitherto unknown prenylated 2-phenoxychromones, epimedonin G (1), 7-O-methylepimedonin G (2), and epimedonins H-K (3-6), and two new prenylflavonoids, epimedonin L (7) and 3″-O-desmethylspinorhamnoside (8), were isolated from an ethanol extract of the aerial parts of Epimedium brevicornum. Their structures were established on the basis of spectroscopic data interpretation. Compound 7 exhibited cytotoxic activity when evaluated against four human cancer cell lines (HL-60, A-549, MCF-7, and SW-480), with IC50 values of <10 µM. In addition, the structures of epimedonins A and B (9a, 10a), recently isolated from E. koreanum, were corrected by reanalysis of the published NMR data.

Polysubstituted Isoflavonoids from Spatholobus suberectus, Flemingia macrophylla, and Cudrania cochinchinensis.

Four hitherto unknown polysubstituted isoflavonoids, including three isoflavans: 7,4'-dihydroxy-8,2',3'-trimethoxyisoflavan (1), 7,2',4'-trihydroxy-8,3'-dimethoxyisoflavan (2), and 7,2',4'-trihydroxy-5-methoxyisoflavan (3), and one prenylated isoflavone cudraisoflavone M (4) were isolated from the ethanol extracts of Spatholobus suberectus (for 1 and 2), Flemingia macrophylla (for 3), and Cudrania cochinchinensis (for 4), respectively. Their structures were established on the basis of extensive spectroscopic analysis. Compounds 1 and 4 exhibited weak cytotoxic activity against five human cancer cell lines (HL-60, A-549, SMMC-7721, MCF-7, and SW-480).

Prenylated Coumarins from Heracleum stenopterum, Peucedanum praeruptorum, Clausena lansium, and Murraya paniculata.

Four hitherto unknown prenylated coumarins, namely 6″-O-ß-D-apiofuranosylapterin (1), 4'-O-isobutyroylpeguangxienin (2), 6-(3-methyl-2-oxobutyroyl)-7-methoxycoumarin (3), and 6-hydroxycoumurrayin (4), were isolated from the ethanol extract of Heracleum stenopterum, Peucedanum praeruptorum, Clausena lansium, and Murraya paniculata, respectively. Their chemical structures were established on the basis of extensive spectroscopic analysis. Compound 2 exhibited in vitro cytotoxic activity against five human cancer cell lines (HL-60, A-549, SMMC-7721, MCF-7, and SW-480) with IC50 values ranging from 15.9 to 23.2 µM.

Antidiabetic and Antinephritic Activities of Aqueous Extract of Cordyceps militaris Fruit Body in Diet-Streptozotocin-Induced Diabetic Sprague Dawley Rats.

Cordyceps militaris has long been used as a crude drug and folk tonic food in East Asia. The present study aims to evaluate the antidiabetic and antinephritic effects of the aqueous extract of the Cordyceps militaris fruit body (CM) in diet-streptozotocin- (STZ-) induced diabetic rats. During four weeks of continuous oral administration of CM at doses of 0.5, 1.0, and 2.0 g/kg and metformin at 100 mg/kg, the fasting blood glucose and bodyweight of each rat were monitored. Hypoglycemic effects of CM on diabetic rats were indicated by decreases in plasma glucose, food and water intake, and urine output. The hypolipidemic activity of CM was confirmed by the normalization of total cholesterol, triglycerides, and low- and high-density lipoprotein cholesterol in diabetic rats. Inhibitory effects on albuminuria, creatinine, urea nitrogen, and n-acetyl-ß-d-glucosaminidase verified CM's renal protective activity in diabetic rats. Furthermore, CM exerted beneficial modulation of inflammatory factors and oxidative enzymes. Compared with untreated diabetic rats, CM decreased the expression of phosphor-AKT and phosphor-GSK-3ß in the kidneys. Altogether, via attenuating oxidative stress, CM displayed antidiabetic and antinephritic activities in diet-STZ-induced diabetic rats.

Activation of BNGR-A24 by direct interaction with tachykinin-related peptides from the silkworm Bombyx mori leads to the Gq- and Gs-coupled signaling cascades.

Tachykinins constitute one of the largest peptide families in the animal kingdom and exert their diverse actions via G protein-coupled receptors (GPCRs). In this study, the Bombyx tachykinin-related peptides (TKRPs) were identified as specific endogenous ligands for the Bombyx neuropeptide GPCR A24 (BNGR-A24) and thus designated BNGR-A24 as BmTKRPR. Using both mammalian cell line HEK293 and insect cell line Sf21, further characterization demonstrated that BmTKRPR was activated, thus resulting in intracellular accumulation of cAMP, Ca(2+) mobilization, and ERK1/2 phosphorylation in a Gs and Gq inhibitor-sensitive manner. Moreover, quantitative reverse transcriptase polymerase chain reaction analysis and dsRNA-mediated knockdown experiments suggested a possible role for BmTKRPR in the regulation of feeding and growth. Our findings enhance the understanding of the Bombyx TKRP system in the regulation of fundamental physiological processes.