Inferred regulons are consistent with regulator binding sequences in E. coli

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC > = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery

Interaction Mechanisms Between the NOX4/ROS and RhoA/ROCK1 Signaling Pathways as New Anti- fibrosis Targets of Ursolic Acid in Hepatic Stellate Cells

BackgroundStudies have shown that both NOX4 and RhoA play essential roles in fibrosis and that they regulate each other. In lung fibrosis, NOX4/ROS is located upstream of the RhoA/ROCK1 signaling pathway, and the two molecules are oppositely located in renal fibrosis. Currently, no reports have indicated whether the above mechanisms or other regulatory mechanisms exist in liver fibrosis.ObjectivesTo investigate the effects of the NOX4/ROS and RhoA/ROCK1 signaling pathways on hepatic stellate cell (HSC)-T6 cells, the interaction mechanisms of the two pathways, and the impact of UA on the two pathways to elucidate the role of UA in the reduction of hepatic fibrosis and potential mechanisms of HSC-T6 cell proliferation, migration, and activation.MethodsStable cell lines were constructed using the lentiviral transduction technique. Cell proliferation, apoptosis, migration, and invasion were examined using the MTS, TdT-mediated dUTP nick-end labeling, cell scratch, and Transwell invasion assays, respectively. The DCFH-DA method was used to investigate the ROS levels in each group. RT-qPCR and western blotting techniques were utilized to assess the mRNA and protein expression in each group. CoIP and the Biacore protein interaction analysis systems were used to evaluate protein interactions.ResultsThe NOX4/ROS and RhoA/ROCK1 signaling pathways promoted the proliferation, migration, and activation of HSCs. UA inhibited cell proliferation, migration, and activation by inhibiting the activation of the two signaling pathways, but the mechanism of apoptosis was independent of these two pathways. The NOX4/ROS pathway was upstream of and positively regulated the RhoA/ROCK1 pathway in HSCs. No direct interaction between the NOX4 and RhoA proteins was detected.ConclusionThe NOX4/ROS and RhoA/ROCK1 signaling pathways are two critical signaling pathways in a series of behavioral processes in HSCs, and NOX4/ROS regulates RhoA/ROCK1 through an indirect pathway to control the activation of HSCs. Additionally, NOX4/ROS and RhoA/ROCK1 constitute a new target for UA antifibrosis treatment

Ursolic acid improves the bacterial community mapping of the intestinal tract in liver fibrosis mice

Liver fibrosis often appears in chronic liver disease, with extracellular matrix (ECM) deposition as the main feature. Due to the presence of the liver-gut axis, the destruction of intestinal homeostasis is often accompanied by the development of liver fibrosis. The inconsistent ecological environment of different intestinal sites may lead to differences in the microbiota. The traditional Chinese medicine ursolic acid (UA) has been proven to protect the liver from fibrosis. We investigated the changes in the microbiota of different parts of the intestine during liver fibrosis and the effect of UA on these changes based on high-throughput sequencing technology. Sequencing results suggest that the diversity and abundance of intestinal microbiota decline and the composition of the microbiota is disordered, the potentially beneficial Firmicutes bacteria are reduced, and the pathways for functional prediction are changed in the ilea and anal faeces of liver fibrosis mice compared with normal mice. However, in UA-treated liver fibrosis mice, these disorders improved. It is worth noting that the bacterial changes in the ilea and anal faeces are not consistent. In conclusion, in liver fibrosis, the microbiota of different parts of the intestines have different degrees of disorder, and UA can improve this disorder. This may be a potential mechanism for UA to achieve anti-fibrosis. This study provides theoretical guidance for the UA targeting of intestinal microbiota for the treatment of liver fibrosis

Inferred regulons are consistent with regulator binding sequences in E. coli

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC &gt; = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery.</p

Gender difference in the associations between health literacy and problematic mobile phone use in Chinese middle school students

Abstract Background Problematic mobile phone use (PMPU) is becoming increasingly popular and has serious harmful effects on physical and mental health among adolescents. Inadequate health literacy (HL) is related to some risky behaviors and mental health problems in adolescents. Nevertheless, few studies have explored the relationship between HL and PMPU and the gender difference in the relationship among Chinese adolescents. The aim of this study was to examine the associations between HL and PMPU and explore gender difference in the associations. Methods A total of 22,628 junior and senior high school students (10,990 males and 11,638 females) in 6 regions of China participated in this study. HL and PMPU were measured by self-report validated questionnaires. Chi-square tests and logistic regression analysis were conducted in the study. Results Logistic regression analysis showed that students with inadequate HL are likely to have PMPU (OR = 2.013, 95% CI: 1.840–2.202), and different degrees of association can be seen in six dimensions. Besides, in both males and females, students with inadequate HL had a higher risk of PMPU (OR male = 1.607, 95% CI: 1.428–1.807; OR female = 2.602, 95% CI: 2.261–2.994). Regarding the gender difference, the results showed that males had more PMPU than females, and the difference was more significant for students with adequate HL than those with inadequate HL (OR inadequate = 1.085, 95% CI: 1.016–1.159; OR adequate = 1.770, 95% CI: 1.490–2.101). Similarly, there were associations in the six dimensions. Conclusions HL decreases PMPU, and males have a higher risk of PMPU than females. These findings suggest a reasonable strategy to reduce PMPU by improving the HL level of adolescents