The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study

BackgroundType 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.MethodsThe summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.ResultsThe outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10−3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10−3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10−3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.ConclusionThis MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM

Dynamic Security Risk Evaluation via Hybrid Bayesian Risk Graph in Cyber-Physical Social Systems

© 2014 IEEE. Cyber-physical social system (CPSS) plays an important role in both the modern lifestyle and business models, which significantly changes the way we interact with the physical world. The increasing influence of cyber systems and social networks is also a high risk for security threats. The objective of this paper is to investigate associated risks in CPSS, and a hybrid Bayesian risk graph (HBRG) model is proposed to analyze the temporal attack activity patterns in dynamic cyber-physical social networks. In the proposed approach, a hidden Markov model is introduced to model the dynamic influence of activities, which then be mapped into a Bayesian risks graph (BRG) model that can evaluate the risk propagation in a layered risk architecture. Our numerical studies demonstrate that the framework can model and evaluate risks of user activity patterns that expose to CPSSs

Associated Clustering Strategy for Wireless Sensor Network

We consider the soil moisture monitoring problem and propose a WSN associated clustering strategy based on spatiotemporal data correlation, which ensures that the nodes within each cluster can share a good data correlation and consequently makes the cluster head do the data fusion more efficiently. As a result, the energy of each node will be saved and the lifetime of the whole sensor network will be extended. In the associated clustering strategy, the different clusters can be divided by the correlation characteristics of nodes data, which is based on a dynamic model and a correlation characteristics model after the correlation coefficient analysis. Simulation results show that our proposed associated clustering strategy works very well in soil moisture measurement. Moreover, as compared with the traditional random clustering, the associated clustering strategy based on data correlation achieves better performance for each cluster, and will be more efficient in data fusion at the cluster heads

Data_Sheet_1_The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study.zip

BackgroundType 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.MethodsThe summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.ResultsThe outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10−3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10−3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10−3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.ConclusionThis MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.</p

Table_1_The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study.xlsx

BackgroundType 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.MethodsThe summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.ResultsThe outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10−3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10−3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10−3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.ConclusionThis MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.</p

Data_Sheet_2_The causal relationship between gut microbiota and type 2 diabetes: a two-sample Mendelian randomized study.zip

BackgroundType 2 diabetes mellitus (T2DM) is a commonly observed metabolic anomaly globally, and as of the present time, there's no recognized solution. There is an increasing body of evidence from numerous observational studies indicating a significant correlation between gut flora and metabolic disease progression, particularly in relation to T2DM. Despite this, the direct impact of gut microbiota on T2DM isn't fully understood yet.MethodsThe summary statistical figures for intestinal microbiota were sourced from the MiBioGen consortium, while the summary statistical data for T2DM were gathered from the Genome-Wide Association Studies (GWAS) database. These datasets were used to execute a two-sample Mendelian randomization (MR) investigation. The Inverse Variance Weighted (IVW), Maximum Likelihood, MR-Egger, Weighted Median, and Weighted Models strategies were employed to assess the impact of gut microbiota on T2DM. Findings were primarily obtained using the IVW technique. Techniques like MR-Egger were employed to identify the occurrence of horizontal pleiotropy among instrumental variables. Meanwhile, Cochran's Q statistical measures were utilized to assess the variability or heterogeneity within these instrumental variables.ResultsThe outcomes from the IVW analysis demonstrated that the genus Alistipes (OR = 0.998, 95% confidence interval: 0.996–1.000, and P = 0.038), genus Allisonella (OR = 0.998, 95% confidence interval: 0.997-0.999, P = 0.033), genus Flavonifractor (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 3.78 × 10−3), and genus Haemophilus (OR = 0.995, 95% confidence interval: 0.993–0.998, P = 8.08 × 10−3) all acted as defense elements against type 2 diabetes. Family Clostridiaceae1 (OR = 1.003, 95% confidence interval: 1.001–1.005, P = 0.012), family Coriobacteriaceae (OR = 1.0025, 95% confidence interval: 1.000–1.005, P = 0.043), genus Actinomyces (OR = 1.003,95% confidence interval: 1.001–1.005, P = 4.38 × 10−3), genus Candidatus Soleaferrea (OR = 1.001,95% confidence interval: 1.000–1.002 P = 0.012) were risk factors for type 2 diabetes. False Discovery Rate correction was performed with finding that genus.Allisonella, genus.Alistipes, family Coriobacteriaceaeand T2DM no longer displayed a significant causal association. In addition, no significant heterogeneity or horizontal pleiotropy was found for instrumental variable.ConclusionThis MR study relies on genetic variation tools to confirm the causal effect of genus Flavonifractor, genus Haemophilus, family Clostridiaceae1, genus Actinomyces and genus Candidatus Soleaferrea on T2DM in the gut microbiome, providing new directions and strategies for the treatment and early screening of T2DM, which carries significant clinical relevance. To develop new biomarkers and better understand targeted prevention strategies for T2DM, further comprehensive investigations are required into the protective and detrimental mechanisms exerted by these five genera against T2DM.</p

In Vitro Assessment of Cadmium Bioavailability in Chinese Cabbage Grown on Different Soils and Its Toxic Effects on Human Health

The minimum concentration of cadmium (Cd), by Chinese cabbage grown on Cd contaminated soils that can initiate toxicity in human liver cells using in vitro digestion coupled with Caco-2/HL-7702 cell models was studied. Cadmium bioaccessibility in the gastric phase for yellow soil (YS) cabbage (40.84%) and calcareous soil (CS) cabbage (21.54%) was significantly higher than small intestinal phase with the corresponding values of 21.2% and 11.11%, respectively. Cadmium bioavailability was higher in YS cabbage (5.27%–14.66%) than in CS cabbage (1.12%–9.64%). Cadmium concentrations (>0.74 μg) transported from YS and CS cabbage were able to induce oxidative (MDA, H2O2) stress by inhibiting antioxidant (SOD, GPx) enzyme activities in human liver cells (HL-7702). Additionally the study revealed that the ingestion of Cd contaminated Chinese cabbage grown in acidic soil (yellow soil) weakened the antioxidant defense system under all levels of contamination (2, 6, and 9 mg·kg−1) which ultimately escalated the oxidative stress in liver cells; however, in case of CS cabbage, a marked oxidative stress was observed only at 9 mg kg−1 Cd level of soil. Therefore, it is necessary to monitor Cd concentrations in leafy vegetables grown on acidic soils to minimize human health risk

Molecular subtype identification and prognosis stratification based on lysosome-related genes in breast cancer

Background: Lysosomes are known to have a significant impact on the development and recurrence of breast cancer. However, the association between lysosome-related genes (LRGs) and breast cancer remains unclear. This study aims to explore the potential role of LRGs in predicting the prognosis and treatment response of breast cancer. Methods: Breast cancer gene expression profile data and clinical information were downloaded from TCGA and GEO databases, and prognosis-related LRGs were screened for consensus clustering analysis. Lasso Cox regression analysis was used to construct risk features derived from LRGs, and immune cell infiltration, immune therapy response, drug sensitivity, and clinical pathological feature differences were evaluated for different molecular subtypes and risk groups. A nomogram based on risk features derived from LRGs was constructed and evaluated. Results: Our study identified 176 differentially expressed LRGs that are associated with breast cancer prognosis. Based on these genes, we divided breast cancer into two molecular subtypes with significant prognostic differences. We also found significant differences in immune cell infiltration between these subtypes. Furthermore, we constructed a prognostic risk model consisting of 7 LRGs, which effectively divides breast cancer patients into high-risk and low-risk groups. Patients in the low-risk group have better prognostic characteristics, respond better to immunotherapy, and have lower sensitivity to chemotherapy drugs, indicating that the low-risk group is more likely to benefit from immunotherapy and chemotherapy. Additionally, the risk score based on LRGs is significantly correlated with immune cell infiltration, including CD8 T cells and macrophages. This risk score model, along with age, chemotherapy, clinical stage, and N stage, is an independent prognostic factor for breast cancer. Finally, the nomogram composed of these factors has excellent performance in predicting overall survival of breast cancer. Conclusions: In conclusion, this study has constructed a novel LRG-derived breast cancer risk feature, which performs well in prognostic prediction when combined with clinical pathological features

Clitocine reversal of P-glycoprotein associated multi-drug resistance through down-regulation of transcription factor NF-κB in R-HepG2 cell line.

Multidrug resistance (MDR) is one of the major reasons for failure in cancer chemotherapy and its suppression may increase the efficacy of therapy. The human multidrug resistance 1 (MDR1) gene encodes the plasma membrane P-glycoprotein (P-gp) that pumps various anti-cancer agents out of the cancer cell. R-HepG2 and MES-SA/Dx5 cells are doxorubicin induced P-gp over-expressed MDR sublines of human hepatocellular carcinoma HepG2 cells and human uterine carcinoma MES-SA cells respectively. Herein, we observed that clitocine, a natural compound extracted from Leucopaxillus giganteus, presented similar cytotoxicity in multidrug resistant cell lines compared with their parental cell lines and significantly suppressed the expression of P-gp in R-HepG2 and MES-SA/Dx5 cells. Further study showed that the clitocine increased the sensitivity and intracellular accumulation of doxorubicin in R-HepG2 cells accompanying down-regulated MDR1 mRNA level and promoter activity, indicating the reversal effect of MDR by clitocine. A 5'-serial truncation analysis of the MDR1 promoter defined a region from position -450 to -193 to be critical for clitocine suppression of MDR1. Mutation of a consensus NF-κB binding site in the defined region and overexpression of NF-κB p65 could offset the suppression effect of clitocine on MDR1 promoter. By immunohistochemistry, clitocine was confirmed to suppress the protein levels of both P-gp and NF-κB p65 in R-HepG2 cells and tumors. Clitocine also inhibited the expression of NF-κB p65 in MES-SA/Dx5. More importantly, clitocine could suppress the NF-κB activation even in presence of doxorubicin. Taken together; our results suggested that clitocine could reverse P-gp associated MDR via down-regulation of NF-κB