PGM1 suppresses colorectal cancer cell migration and invasion by regulating the PI3K/AKT pathway

Novel findings from arsenic‑lead combined exposure in mouse testicular TM4 Sertoli cells based on transcriptomics

Arsenic (As) and lead (Pb) exist widespread in daily life, and they are common harmful substances in the environment. As and Pb pollute the environment more often in combination than in isolation. The TM4 Sertoli cell line is one of the most common normal mouse testicular Sertoli cell lines. In vitro, we found that the type of combined action of As and Pb on TM4 Sertoli cells was additive action by using the isobologram analysis. To further investigate the combined toxicity of As and Pb, we performed mRNA and miRNA sequencing on TM4 Sertoli cells exposed to As alone (4 μM NaAsO2) and AsPb combined (4 μM NaAsO2 and 150 μM PbAc), respectively. Compared with the control group, 1391 differentially expressed genes (DEGs) and 6 differentially expressed miRNAs (DEMs) were identified in the As group. Compared with the control group, 2384 DEGs and 44 DEMs were identified in the AsPb group. Compared with the As group, 387 DEGs and 4 DEMs were identified in the AsPb group. Through data analysis, we discovered for the first time that As caused the dysfunction of cholesterol synthesis and energy metabolism, and disrupted cyclic adenosine monophosphate signaling pathway and wingless/integrated (Wnt) signaling pathway in TM4 Sertoli cells. In addition to affecting cholesterol synthesis and energy metabolism, AsPb combined exposure also up-regulated the antioxidant reaction level of TM4 Sertoli cells. Meanwhile, the Wnt signaling pathway of TM4 Sertoli cells was relatively normal when exposed to AsPb. In conclusion, at the transcription level, the combined action of AsPb is not merely additive effect, but involves synergistic and antagonistic effects. The new discovery of the joint toxic mechanism of As and Pb breaks the stereotype of the combined action and provides a good theoretical basis and research clue for future study of the combined-exposure of harmful materials.

Functional identification of PGM1 in the regulating development and depositing of inosine monophosphate specific for myoblasts

Background

Inosine monophosphate (IMP) is naturally present in poultry muscle and plays a key role in improving meat flavour. However, IMP deposition is regulated by numerous genes and complex molecular networks. In order to excavate key candidate genes that may regulate IMP synthesis, we performed proteome and metabolome analyses on the leg muscle, compared to the breast muscle control of 180-day-old Jingyuan chickens (hens), which had different IMP content. The key candidate genes identified by a differential analysis were verified to be associated with regulation of IMP-specific deposition.

Results

The results showed that the differentially expressed (DE) proteins and metabolites jointly involve 14 metabolic pathways, among which the purine metabolic pathway closely related to IMP synthesis and metabolism is enriched with four DE proteins downregulated (with higher expression in breast muscles than in leg muscles), including adenylate kinase 1 (AK1), adenosine monophosphate deaminase 1 (AMPD1), pyruvate kinase muscle isoenzyme 2 (PKM2) and phosphoglucomutase 1 (PGM1), six DE metabolites, Hypoxanthine, Guanosine, L-Glutamine, AICAR, AMP and Adenylsuccinic acid. Analysis of PGM1 gene showed that the high expression of PGM1 promoted the proliferation and differentiation of myoblasts and inhibited the apoptosis of myoblasts. ELISA tests have shown that PGM1 reduced adenosine triphosphate (ATP) and IMP and uric acid (UA), while enhancing the biosynthesis of hypoxanthine (HX). In addition, up-regulation of PGM1 inhibited the expression of purine metabolism pathway related genes, and promoted the IMP de novo and salvage synthesis pathways.

Conclusion

This study preliminarily explored the mechanism of action of PGM1 in regulating the growth and development of myoblasts and specific IMP deposition in Jingyuan chickens, which provided certain theoretical basis for the development and utilization of excellent traits in Jingyuan chickens.

In Vitro Cytotoxic Effect of Aqueous Extracts from Leaves and Rhizomes of the Seagrass Posidonia oceanica (L.) Delile on HepG2 Liver Cancer Cells: Focus on Autophagy and Apoptosis

Aqueous extracts from Posidonia oceanica's green and brown (beached) leaves and rhizomes were prepared, submitted to phenolic compound and proteomic analysis, and examined for their potential cytotoxic effect on HepG2 liver cancer cells in culture. The chosen endpoints related to survival and death were cell viability and locomotory behavior, cell-cycle analysis, apoptosis and autophagy, mitochondrial membrane polarization, and cell redox state. Here, we show that 24 h exposure to both green-leaf- and rhizome-derived extracts decreased tumor cell number in a dose-response manner, with a mean half maximal inhibitory concentration (IC₅₀) estimated at 83 and 11.5 μg of dry extract/mL, respectively. Exposure to the IC₅₀ of the extracts appeared to inhibit cell motility and long-term cell replicating capacity, with a more pronounced effect exerted by the rhizome-derived preparation. The underlying death-promoting mechanisms identified involved the down-regulation of autophagy, the onset of apoptosis, the decrease in the generation of reactive oxygen species, and the dissipation of mitochondrial transmembrane potential, although, at the molecular level, the two extracts appeared to elicit partially differentiating effects, conceivably due to their diverse composition. In conclusion, P. oceanica extracts merit further investigation to develop novel promising prevention and/or treatment agents, as well as beneficial supplements for the formulation of functional foods and food-packaging material with antioxidant and anticancer properties.

A hybrid deep forest-based method for predicting synergistic drug combinations

Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.