Codon usage pattern of the ancestor of green plants revealed through Rhodophyta.

Rhodophyta are among the closest known relatives of green plants. Studying the codons of their genomes can help us understand the codon usage pattern and characteristics of the ancestor of green plants. By studying the codon usage pattern of all available red algae, it was found that although there are some differences among species, high-bias genes in most red algae prefer codons ending with GC. Correlation analysis, Nc-GC3s plots, parity rule 2 plots, neutrality plot analysis, differential protein region analysis and comparison of the nucleotide content of introns and flanking sequences showed that the bias phenomenon is likely to be influenced by local mutation pressure and natural selection, the latter of which is the dominant factor in terms of translation accuracy and efficiency. It is worth noting that selection on translation accuracy could even be detected in the low-bias genes of individual species. In addition, we identified 15 common optimal codons in seven red algae except for G. sulphuraria for the first time, most of which were found to be complementary and bound to the tRNA genes with the highest copy number. Interestingly, tRNA modification was found for the highly degenerate amino acids of all multicellular red algae and individual unicellular red algae, which indicates that highly biased genes tend to use modified tRNA in translation. Our research not only lays a foundation for exploring the characteristics of codon usage of the red algae as green plant ancestors, but will also facilitate the design and performance of transgenic work in some economic red algae in the future.

The Anti-inflammatory Effects of Isoflavonoids from Radix Astragali in Hepatoprotective Potential against LPS/D-gal-induced Acute Liver Injury.

Radix Astragali (RA) is an important Traditional Chinese Medicine widely used in the treatment of various diseases, such as pneumonia, atherosclerosis, diabetes, kidney and liver fibrosis. The role of isoflavonoids from RA in the treatment of liver injury remains unclear. The study aimed to explore hepatoprotective and anti-inflammatory effects of isoflavonoids from Astragalus mongholicus. Network pharmacological analysis showed that RA had a multi-target regulating effect on alleviating liver injury and inhibiting inflammation through its active ingredients, among which isoflavones were closely related to its key molecular targets. The anti-inflammatory and liver protection effects of isoflavonoids of RA were investigated using lipopolysaccharide (LPS)-induced RAW 264.7 cells in vitro and LPS/D-galactosamine (D-gal)-induced acute liver injury mice in vivo. The experimental results showed that methylnissolin (ML) and methylnissolin-3-O-ß-D-glucoside (MLG) presented more notable anti-inflammatory effects. Both of them suppressed the release of pro-inflammatory cytokines, such as iNOS, COX-2, IL-1ß, IL-6, and TNF-α in LPS-stimulated RAW 264.7 cells. In vivo investigation demonstrated that ML markedly meliorated liver injury in LPS/D-gal-induced mice. Western blot results revealed that ML and MLG down-regulated the expression of proinflammatory cytokines via NF-κB signaling pathway. The isoflavonoids, methylnissolin (ML), and methylnissolin-3-O-ß-D-glucoside (MLG), play a vital role in the hepatoprotective and anti-inflammatory effects of RA.

The development and validation of a predictive model for neonatal phototherapy outcome using admission indicators.

Delayed exchange transfusion therapy (ETT) after phototherapy failure for newborns with severe hyperbilirubinemia could lead to serious complications such as bilirubin encephalopathy (BE). In this current manuscript we developed and validated a model using admission data for early prediction of phototherapy failure. We retrospectively examined the medical records of 292 newborns with severe hyperbilirubinemia as the training cohort and another 52 neonates as the validation cohort. Logistic regression modeling was employed to create a predictive model with seven significant admission indicators, i.e., age, past medical history, presence of hemolysis, hemoglobin, neutrophil proportion, albumin (ALB), and total serum bilirubin (TSB). To validate the model, two other models with conventional indicators were created, one incorporating the admission indicators and phototherapy failure outcome and the other using TSB decrease after phototherapy failure as a variable and phototherapy outcome as an outcome indicator. The area under the curve (AUC) of the predictive model was 0.958 [95% confidence interval (CI): 0.924-0.993] and 0.961 (95% CI: 0.914-1.000) in the training and validation cohorts, respectively. Compared with the conventional models, the new model had better predictive power and greater value for clinical decision-making by providing a possibly earlier and more accurate prediction of phototherapy failure. More rapid clinical decision-making and interventions may potentially minimize occurrence of serious complications of severe neonatal hyperbilirubinemia.

An enhanced analytical strategy integrating offline two-dimensional liquid chromatography with high-resolution accurate mass spectrometry and molecular networking: Comprehensive characterization of HuangLian JieDu Decoction as a case study.

Comprehensive ingredient research is of great significance for understanding the effective material basis of herbal medicines, but due to the diversity and complexity of their phytochemicals, such research is challenging. Here, a multifaceted strategy was proposed to analyze and identify the composition of HuangLian JieDu Decoction based on offline two-dimensional liquid chromatography combined with ultraviolet detection and high-resolution mass spectrometry. Multiple components were separated by two-dimensional liquid chromatography, which consisted of hydrophilic interaction chromatography and reversed-phase liquid chromatography, and then further characterized by high-resolution mass spectrometry with a full mass spectrometry/precursor ion list/data-dependent secondary scan data acquisition method. For data processing, database screening and molecular networking were used to identify the components in HuangLian JieDu Decoction. The offline two-dimensional liquid chromatography combined with ultraviolet detection and a high-resolution mass spectrometry system showed good orthogonality of 76.35% and a high peak capacity of 5175, effectively separating multiple components. Finally, 527 compounds, including 164 alkaloids, 133 terpenoids, 88 flavonoids, 60 phenylpropanoids, 38 organic acids, and 44 other compounds, were characterized. This integrated approach is suitable for the comprehensive characterization of herbal medicines and other complex chemical systems.

Riligustilide alleviates hepatic insulin resistance and gluconeogenesis in T2DM mice through multitarget actions.

Riligustilide (RG), one of the dimeric phthalides of Angelica sinensis and Ligusticum chuanxiong, was confirmed effective against many diseases. However, its effects on type 2 diabetes mellitus (T2DM) and the underlying molecular mechanisms have not been clearly elucidated yet. The current study was designed to investigate the hypoglycemic potential by which RG affects the pathogenesis of T2DM. Comprehensive insights into the effects and underlying molecular mechanisms of RG on attenuating aberrant metabolism of glucose were determined in high-fat diet-induced T2DM mice and insulin-resistant (IR) HepG2 cells. In high-fat diet-induced C57BL/6J mice, RG administration significantly reduced hyperglycemia, decreased hyperinsulinemia, and ameliorated glucose intolerance. Mechanistically, RG activated PPARγ and insulin signaling pathway to improve insulin sensitivity, and increase glucose uptake as well as glycogenesis. In addition, RG also upregulated AMPK-TORC2-FoxO1 axis to attenuate gluconeogenesis in vivo and in vitro. According to the findings, RG may be a promising candidate for the treatment of T2DM.