With the development of human genome sequencing and techniques such as intestinal microbial culture and fecal microbial transplantation, newly discovered microorganisms have been isolated, cultured, and researched. Consequently, many beneficial probiotics have emerged as next-generation probiotics (NGPs). Currently, “safety,” “individualized treatment,” and “internal interaction within the flora” are requirements of a potential NGPs. Furthermore, in the complex ecosystem of humans and microbes, it is challenging to identify the relationship between specific strains, specific flora, and hosts to warrant a therapeutic intervention in case of a disease. Thus, this review focuses on the progress made in NGPs and human health research by elucidating the limitations of traditional probiotics; summarizing the functions and strengths of Akkermansia muciniphila, Faecalibacterium prausnitzii, Bacteroides fragilis, Eubacterium hallii, and Roseburia spp. as NGPs; and determining the role of their intervention in treatment of certain diseases. Finally, we aim to provide a reference for developing new probiotics in the future.
Renewable energy power prediction plays a crucial role in the development of renewable energy generation, and it also faces a challenging issue because of the uncertainty and complex fluctuation caused by environmental and climatic factors. In recent years, deep learning has been increasingly applied in the time series prediction of new energy, where Deep Belief Networks (DBN) can perform outstandingly for learning of nonlinear features. In this paper, we employed the DBN as the prediction model to forecast wind power and PV power. A novel metaheuristic optimization algorithm, called swarm spider optimization (SSO), was utilized to optimize the parameters of the DBN so as to improve its performance. The SSO is a novel swarm spider behavior based optimization algorithm, and it can be employed for addressing complex optimization and engineering problems. Considering that the prediction performance of the DBN is affected by the number of the nodes in the hidden layer, the SSO is used to optimize this parameter during the training stage of DBN (called SSO-DBN), which can significantly enhance the DBN prediction performance. Two datasets, including wind power and PV power with their influencing factors, were used to evaluate the forecasting performance of the proposed SSO-DBN. We also compared the proposed model with several well-known methods, and the experiment results demonstrate that the proposed prediction model has better stability and higher prediction accuracy in comparison to other methods.
Metabolites of probiotics that are beneficial to human health have been isolated from the intestinal tract and natural dairy products. However, many studies on probiotics and prebiotics are limited to the observation of human cohorts and animal phenotypes. The molecular mechanisms by which metabolites of probiotics regulate health are still need further exploration. In this work, we isolated a strain of Lactobacillus Paracasei from human milk samples. We numbered it as Lactobacillus Paracasei BD5115. The mouse model of high-fat diet confirmed that the metabolites of this strain also promotes intestinal epithelial cells (IECs) proliferation. Single-cell sequencing showed that a bZIP transcription factor MAFF was specifically expressed in some IECs. We found that MAFF interacted with MBP1 to regulate the expression of MYC. Analysis of the active components in BD5115 metabolites confirmed that 2-hydroxy-3-methylbutyric acid promotes the expression of the MYC gene. This promotes the proliferation of IECs. Our findings indicate that 2-hydroxy-3-methylbutyric acid regulate MYC gene expression mediated by MAFF/MBP1 interaction. This study not only screened a strain with promoted IECs proliferation, but also discovered a new signal pathway that regulates MYC gene expression.
Chronic low-grade inflammation is widely involved in the development and progression of metabolic syndrome, which can lead to type 2 diabetes mellitus (T2DM). Dysregulation of proinflammatory and anti-inflammatory cytokines not only impairs insulin secretion by pancreatic β-cells but also results in systemic complications in late diabetes. In our previous work, metabolites produced by Paenibacillus bovis sp. nov. BD3526, which were isolated from Tibetan yak milk, demonstrated antidiabetic effects in Goto–Kakizaki (GK) rats. In this work, we used single-cell RNA sequencing (scRNA-seq) to further explore the impact of BD3526 metabolites on the intestinal cell composition of GK rats. Oral administration of the metabolites significantly reduced the number of adipocytes in the colon tissue of GK rats. In addition, cluster analysis of immune cells confirmed that the metabolites reduced the expression of interleukin (IL)-1β in macrophages in the colon and increased the numbers of dendritic cells (DCs) and regulatory T (Treg) cells. Further mechanistic studies of DCs confirmed that activation of the WNT/β-catenin pathway in DCs promoted the expression of IL-10 and transforming growth factor (TGF)-β, thereby increasing the number of Treg cells.
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