Detoxification and underlying mechanisms towards toxic alkaloids by Traditional Chinese Medicine processing: A comprehensive review.

BACKGROUND: Alkaloids have attracted enduring interest worldwide due to their remarkable therapeutic effects, including analgesic, anti-inflammatory, and anti-tumor properties, thus offering a rich source for lead compound design and new drug discovery. However, some of these alkaloids possess intrinsic toxicity. Processing (Paozhi) is a pre-treatment step before the application of herbal medicines in traditional Chinese medicine (TCM) clinics, which has been employed for centuries to mitigate the toxicity of alkaloid-rich TCMs. PURPOSE: To explore the toxicity phenotypes, chemical basis, mode of action, detoxification processing methods, and underlying mechanisms, we can gain crucial insights into the safe and rational use of these toxic alkaloid-rich herbs. Such insights have the great potential to offer new strategies for drug discovery and development, ultimately improving the quality of life for millions of people. METHODS: Literatures published or early accessed until December 31, 2023, were retrieved from databases including PubMed, Web of Science, and CNKI. The following keywords, such as "toxicity", "alkaloid", "detoxification", "processing", "traditional Chinese medicine", "medicinal plant", and "plant", were used in combination or separately for screening. RESULTS: Toxicity of alkaloids in TCM includes hepatotoxicity, nephrotoxicity, neurotoxicity, cardiotoxicity, and other forms of toxicity, primarily induced by pyrrolizidines, quinolizidines, isoquinolines, indoles, pyridines, terpenoids, and amines. Factors such as whether the toxic-alkaloid enriched part is limited or heat-sensitive, and whether toxic alkaloids are also therapeutic components, are critical for choosing appropriate detoxification processing methods. Mechanisms of alkaloid detoxification includes physical removal, chemical decomposition or transformation, as well as biological modifications. CONCLUSION: Through this exploration, we review toxic alkaloids and the mechanisms underlying their toxicity, discuss methods to reduce toxicity, and unravel the intricate mechanisms behind detoxification. These offers insights into the quality control of herbs containing toxic alkaloids, safe and rational use of alkaloid-rich TCMs in clinics, new strategies for drug discovery and development, and ultimately helping improve the quality of life for millions of people.

MultiModRLBP: A Deep Learning Approach for Multi-Modal RNA-Small Molecule Ligand Binding Sites Prediction.

This study aims to tackle the intricate challenge of predicting RNA-small molecule binding sites to explore the potential value in the field of RNA drug targets. To address this challenge, we propose the MultiModRLBP method, which integrates multi-modal features using deep learning algorithms. These features include 3D structural properties at the nucleotide base level of the RNA molecule, relational graphs based on overall RNA structure, and rich RNA semantic information. In our investigation, we gathered 851 interactions between RNA and small molecule ligand from the RNAglib dataset and RLBind training set. Unlike conventional training sets, this collection broadened its scope by including RNA complexes that have the same RNA sequence but change their respective binding sites due to structural differences or the presence of different ligands. This enhancement enables the MultiModRLBP model to more accurately capture subtle changes at the structural level, ultimately improving its ability to discern nuances among similar RNA conformations. Furthermore, we evaluated MultiModRLBP on two classic test sets, Test18 and Test3, highlighting its performance disparities on small molecules based on metal and non-metal ions. Additionally, we conducted a structural sensitivity analysis on specific complex categories, considering RNA instances with varying degrees of structural changes and whether they share the same ligands. The research results indicate that MultiModRLBP outperforms the current state-of-the-art methods on multiple classic test sets, particularly excelling in predicting binding sites for non-metal ions and instances where the binding sites are widely distributed along the sequence. MultiModRLBP also can be used as a potential tool when the RNA structure is perturbed or the RNA experimental tertiary structure is not available. Most importantly, MultiModRLBP exhibits the capability to distinguish binding characteristics of RNA that are structurally diverse yet exhibit sequence similarity. These advancements hold promise in reducing the costs associated with the development of RNA-targeted drugs.

Enhanced water treatment performance of ceramic-based forward osmosis membranes via MOF interlayer.

Forward osmosis (FO) membrane processes could operate without hydraulic pressures, enabling the efficient treatment of wastewaters with mitigated membrane fouling and enhanced efficiency. Designing a high-performance polyamide (PA) layer on ceramic substrates remains a challenge for FO desalination applications. Herein, we report the enhanced water treatment performance of thin-film nanocomposite ceramic-based FO membranes via an in situ grown Zr-MOF (UiO-66-NH2) interlayer. With the Zr-MOF interlayer, the ceramic-based FO membranes exhibit lower thickness, higher cross-linking degree, and increased surface roughness, leading to higher water flux of 27.38 L m-2 h-1 and lower reverse salt flux of 3.45 g m-2 h-1. The ceramic-based FO membranes with Zr-MOF interlayer not only have an application potential in harsh environments such as acidic solution (pH 3) and alkaline solution (pH 11), but also exhibit promising water and reverse salt transport properties, which are better than most MOF-incorporated PA membranes. Furthermore, the membranes could reject major species (ions, oil and organics) with rejections ＞94 % and water flux of 22.62-14.35 L m-2 h-1 in the treatment of actual alkaline industrial wastewater (pH 8.6). This rational design proposed in this study is not only applicable for the development of a high-quality ceramic-based FO membrane with enhanced performance but also can be potentially extended to more challenging water treatment applications.

Bifidobacterium alleviate metabolic disorders via converting methionine to 5'-methylthioadenosine.

Dietary patterns and corresponding gut microbiota profiles are associated with various health conditions. A diet rich in polyphenols, primarily plant-based, has been shown to promote the growth of probiotic bacteria in the gastrointestinal tract, subsequently reducing the risk of metabolic disorders in the host. The beneficial effects of these bacteria are largely due to the specific metabolites they produce, such as short-chain fatty acids and membrane proteins. In this study, we employed a metabolomics-guided bioactive metabolite identification platform that included bioactivity testing using in vitro and in vivo assays to discover a bioactive metabolite produced from probiotic bacteria. Through this approach, we identified 5'-methylthioadenosine (MTA) as a probiotic bacterial-derived metabolite with anti-obesity properties. Furthermore, our findings indicate that MTA administration has several regulatory impacts on liver functions, including modulating fatty acid synthesis and glucose metabolism. The present study elucidates the intricate interplay between dietary habits, gut microbiota, and their resultant metabolites.

Proteomic analysis of multiple organ dysfunction induced by rhabdomyolysis.

Rhabdomyolysis (RM) leads to dysfunction in the core organs of kidney, lung and heart, which is an important reason for the high mortality and disability rate of this disease. However, there is a lack of systematic research on the characteristics of rhabdomyolysis-induced injury in various organs and the underlying pathogenetic mechanisms, and especially the interaction between organs. We established a rhabdomyolysis model, observed the structural and functional changes in kidney, heart, and lung. It is observed that rhabdomyolysis results in significant damage in kidney, lung and heart of rats, among which the pathological damage of kidney and lung was significant, and of heart was relatively light. Meanwhile, we analyzed the differentially expressed proteins (DEPs) in the kidney, heart and lung between the RM group and the sham group based on liquid chromatography-tandem mass spectrometry (LC-MS/MS). In our study, Serpina3n was significantly up-regulated in the kidney, heart and lung. Serpina3n is a secreted protein and specifically inhibits a variety of proteases and participates in multiple physiological processes such as complement activation, inflammatory responses, apoptosis pathways, and extracellular matrix metabolism. It is inferred that Serpina3n may play an important role in multiple organ damage caused by rhabdomyolysis and could be used as a potential biomarker. This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis. SIGNIFICANCE: This study comprehensively describes the functional and structural changes of kidney, heart and lung in rats after rhabdomyolysis, analyzes the DEPs of kidney, heart and lung, and determines the key role of Serpina3n in multiple organ injury caused by rhabdomyolysis.

Intermittent short-duration reoxygenation relieves high-altitude pulmonary hypertension via NOX4/H2O2/PPAR-γ axis.

High-altitude pulmonary hypertension (HAPH) is a severe and progressive disease that can lead to right heart failure. Intermittent short-duration reoxygenation at high altitude is effective in alleviating HAPH; however, the underlying mechanisms are unclear. In the present study, a simulated 5,000-m hypoxia rat model and hypoxic cultured pulmonary artery smooth muscle cells (PASMCs) were used to evaluate the effect and mechanisms of intermittent short-duration reoxygenation. The results showed that intermittent 3-h/per day reoxygenation (I3) effectively attenuated chronic hypoxia-induced pulmonary hypertension and reduced the content of H2O2 and the expression of NADPH oxidase 4 (NOX4) in lung tissues. In combination with I3, while the NOX inhibitor apocynin did not further alleviate HAPH, the mitochondrial antioxidant MitoQ did. Furthermore, in PASMCs, I3 attenuated hypoxia-induced PASMCs proliferation and reversed the activated HIF-1α/NOX4/PPAR-γ axis under hypoxia. Targeting this axis offset the protective effect of I3 on hypoxia-induced PASMCs proliferation. The present study is novel in revealing a new mechanism for preventing HAPH and provides insights into the optimization of intermittent short-duration reoxygenation.

RPEMHC: improved prediction of MHC-peptide binding affinity by a deep learning approach based on residue-residue pair encoding.

MOTIVATION: Binding of peptides to major histocompatibility complex (MHC) molecules plays a crucial role in triggering T cell recognition mechanisms essential for immune response. Accurate prediction of MHC-peptide binding is vital for the development of cancer therapeutic vaccines. While recent deep learning-based methods have achieved significant performance in predicting MHC-peptide binding affinity, most of them separately encode MHC molecules and peptides as inputs, potentially overlooking critical interaction information between the two. RESULTS: In this work, we propose RPEMHC, a new deep learning approach based on residue-residue pair encoding to predict the binding affinity between peptides and MHC, which encode an MHC molecule and a peptide as a residue-residue pair map. We evaluate the performance of RPEMHC on various MHC-II-related datasets for MHC-peptide binding prediction, demonstrating that RPEMHC achieves better or comparable performance against other state-of-the-art baselines. Moreover, we further construct experiments on MHC-I-related datasets, and experimental results demonstrate that our method can work on both two MHC classes. These extensive validations have manifested that RPEMHC is an effective tool for studying MHC-peptide interactions and can potentially facilitate the vaccine development. AVAILABILITY: The source code of the method along with trained models is freely available at https://github.com/lennylv/RPEMHC.