Background: Xanthohumol may be beneficial for obesity-related conditions, but mechanisms are unknown. Results: XN lowers ROS and dysfunctional lipid metabolism in animals, and XN uncouples respiration and induces oxidant defense systems in myocytes. Conclusion: XN may ameliorate metabolic syndrome by these mechanisms. Significance: Metabolomics helped reveal potential XN anti-obesity mechanisms.

The advent of large-scale microbiome studies affords newfound analytical opportunities to understand how these communities of microbes operate and relate to their environment. However, the analytical methodology needed to model microbiome data and integrate them with other data constructs remains nascent. This emergent analytical toolset frequently ports over techniques developed in other multi-omics investigations, especially the growing array of statistical and computational techniques for integrating and representing data through networks. While network analysis has emerged as a powerful approach to modeling microbiome data, oftentimes by integrating these data with other types of omics data to discern their functional linkages, it is not always evident if the statistical details of the approach being applied are consistent with the assumptions of microbiome data or how they impact data interpretation. In this review, we overview some of the most important network methods for integrative analysis, with an emphasis on methods that have been applied or have great potential to be applied to the analysis of multi-omics integration of microbiome data. We compare advantages and disadvantages of various statistical tools, assess their applicability to microbiome data, and discuss their biological interpretability. We also highlight on-going statistical challenges and opportunities for integrative network analysis of microbiome data.

Two diametric paradigms have been proposed to model the molecular co-evolution of microbial mutualists and their eukaryotic hosts. In one, mutualist and host exhibit an antagonistic arms race and each partner evolves rapidly to maximize their own fitness from the interaction at potential expense of the other. In the opposing model, conflicts between mutualist and host are largely resolved and the interaction is characterized by evolutionary stasis. We tested these opposing frameworks in two lineages of mutualistic rhizobia, Sinorhizobium fredii and Bradyrhizobium japonicum. To examine genes demonstrably important for host-interactions we coupled the mining of genome sequences to a comprehensive functional screen for type III effector genes, which are necessary for many Gram-negative pathogens to infect their hosts. We demonstrate that the rhizobial type III effector genes exhibit a surprisingly high degree of conservation in content and sequence that is in contrast to those of a well characterized plant pathogenic species. This type III effector gene conservation is particularly striking in the context of the relatively high genome-wide diversity of rhizobia. The evolution of rhizobial type III effectors is inconsistent with the molecular arms race paradigm. Instead, our results reveal that these loci are relatively static in rhizobial lineages and suggest that fitness conflicts between rhizobia mutualists and their host plants have been largely resolved.

Lactate has been observed to fuel TCA cycle and is associated with cancer progression in human lung cancer, the leading cause of cancer deaths worldwide, but the effect of lactate on lung cancer metabolism is rarely reported. In this study, disordered metabolism in non-small cell lung cancer was demonstrated by increased G6PD and SDHA protein levels via immunofluorescence, and up-regulated lactate dehydrogenase was found to be associated with poor prognosis. Then flow cytometry and Seahorse XFe analyzer were utilized to detect the effect of lactate on glycolysis and mitochondrial function in non-small cell lung cancer cells. The results show that in non-small cell lung cancer cells lactate attenuates glucose uptake and glycolysis while maintaining mitochondrial homeostasis as indicated by improved mitochondrial membrane potential. Further exploration found that mRNA levels of glycolytic enzymes (HK-1, PKM) and TCA cycle enzymes (SDHA, IDH3G) are respectively down-regulated and up-regulated by lactate, and increased histone lactylation was observed in promoters of HK-1 and IDH3G via chromatin immunoprecipitation assay. Taken together, the above results indicate that lactate modulates cellular metabolism at least in part through histone lactylation-mediated gene expression in non-small cell lung cancer.

Foodborne outbreaks are a serious public health and food safety concern worldwide. There is a great demand for rapid, sensitive, specific, and accurate methods to detect microbial pathogens in foods. Conventional methods based on cultivation of pathogens have been the gold standard protocols; however, they take up to a week to complete. Molecular assays such as polymerase chain reaction (PCR), sequencing, microarray technologies have been widely used in detection of foodborne pathogens. Among molecular assays, PCR technology [conventional and real-time PCR (qPCR)] is most commonly used in the foodborne pathogen detection because of its high sensitivity and specificity. However, a major drawback of PCR is its inability to differentiate the DNA from dead and viable cells, and this is a critical factor for the food industry, regulatory agencies and the consumer. To remedy this shortcoming, researchers have used biological dyes such as ethidium monoazide and propidium monoazide (PMA) to pretreat samples before DNA extraction to intercalate the DNA of dead cells in food samples, and then proceed with regular DNA preparation and qPCR. By combining PMA treatment with qPCR (PMA-qPCR), scientists have applied this technology to detect viable cells of various bacterial pathogens in foods. The incorporation of PMA into PCR-based assays for viability detection of pathogens in foods has increased significantly in the last decade. On the other hand, some downsides with this approach have been noted, particularly to achieve complete suppression of signal of DNA from the dead cells present in some particular food matrix. Nowadays, there is a tendency of more and more researchers adapting this approach for viability detection; and a few commercial kits based on PMA are available in the market. As time goes on, more scientists apply this approach to a broader range of pathogen detections, this viability approach (PMA or other chemicals such as platinum compound) may eventually become a common methodology for the rapid, sensitive, and accurate detection of foodborne pathogens. In this review, we summarize the development in the field including progress and challenges and give our perspective in this area.