A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering.

BACKGROUND: As one of the world's most important beverage crops, tea plants (Camellia sinensis) are renowned for their unique flavors and numerous beneficial secondary metabolites, attracting researchers to investigate the formation of tea quality. With the increasing availability of transcriptome data on tea plants in public databases, conducting large-scale co-expression analyses has become feasible to meet the demand for functional characterization of tea plant genes. However, as the multidimensional noise increases, larger-scale co-expression analyses are not always effective. Analyzing a subset of samples generated by effectively downsampling and reorganizing the global sample set often leads to more accurate results in co-expression analysis. Meanwhile, global-based co-expression analyses are more likely to overlook condition-specific gene interactions, which may be more important and worthy of exploration and research. RESULTS: Here, we employed the k-means clustering method to organize and classify the global samples of tea plants, resulting in clustered samples. Metadata annotations were then performed on these clustered samples to determine the "conditions" represented by each cluster. Subsequently, we conducted gene co-expression network analysis (WGCNA) separately on the global samples and the clustered samples, resulting in global modules and cluster-specific modules. Comparative analyses of global modules and cluster-specific modules have demonstrated that cluster-specific modules exhibit higher accuracy in co-expression analysis. To measure the degree of condition specificity of genes within condition-specific clusters, we introduced the correlation difference value (CDV). By incorporating the CDV into co-expression analyses, we can assess the condition specificity of genes. This approach proved instrumental in identifying a series of high CDV transcription factor encoding genes upregulated during sustained cold treatment in Camellia sinensis leaves and buds, and pinpointing a pair of genes that participate in the antioxidant defense system of tea plants under sustained cold stress. CONCLUSIONS: To summarize, downsampling and reorganizing the sample set improved the accuracy of co-expression analysis. Cluster-specific modules were more accurate in capturing condition-specific gene interactions. The introduction of CDV allowed for the assessment of condition specificity in gene co-expression analyses. Using this approach, we identified a series of high CDV transcription factor encoding genes related to sustained cold stress in Camellia sinensis. This study highlights the importance of considering condition specificity in co-expression analysis and provides insights into the regulation of the cold stress in Camellia sinensis.

Fungal fermentation of Fuzhuan brick tea: A comprehensive evaluation of sensory properties using chemometrics, visible near-infrared spectroscopy, and electronic nose.

Fuzhuan brick tea (FBT) fungal fermentation is a key factor in achieving its unique dark color, aroma, and taste. Therefore, it is essential to develop a rapid and reliable method that could assess its quality during FBT fermentation process. This study focused on using electronic nose (e-nose) and spectroscopy combination with sensory evaluations and physicochemical measurements for building machine learning (ML) models of FBT. The results showed that the fused data achieved 100 % accuracy in classifying the FBT fermentation process. The SPA-MLR method was the best prediction model for FBT quality (R2 = 0.95, RMSEP = 0.07, RPD = 4.23), and the fermentation process was visualized. Where, it was effectively detecting the degree of fermentation relationship with the quality characteristics. In conclusion, the current study's novelty comes from the established real-time method that could sensitively detect the unique post-fermentation quality components based on the integration of spectral, and e-nose and ML approaches.

Development of a paper-based transcription aptasensor for convenient urinary uric acid self-testing.

The abnormal uric acid (UA) level in urine can serve as warning signals of many diseases, such as gout and metabolic cardiovascular diseases. The current methods for detecting UA face limitations of instrument dependence and the requirement for non-invasiveness, making it challenging to fulfill the need for home-based application. In this study, we designed an aptasensor that combined UA-specific transcriptional regulation and a fluorescent RNA aptamer for convenient urinary UA testing. The concentration of UA can be translated into the intensity of fluorescent signals. The aptasensor showed higher sensitivity and more robust anti-interference performance. UA levels in the urine of different volunteers could be accurately tested using this method. In addition, a paper-based aptasensor for UA self-testing was manufactured, in which the urinary UA levels could be determined using a smartphone-based colorimetric approach. This work not only demonstrates a new approach for the design of disease-associated aptasensor, but also offers promising ideas for home-based detection of UA.

Antimicrobial therapy based on self-assembling peptides.

The emergence of drug-resistant microorganisms has threatened global health, and microbial infections have severely limited the use of medical materials. For example, the attachment and colonization of pathogenic bacteria to medical implant materials can lead to wound infections, inflammation and complications, as well as implant failure, shortening their lifespan and even resulting in patient death. In the era of antibiotic resistance, antimicrobial drug discovery needs to prioritize unconventional therapies that act on new targets or adopt new mechanisms. In this regard, supramolecular antimicrobial peptides have emerged as attractive therapeutic platforms, both as bactericides for combination antibiotics and as delivery vehicles. By taking advantage of their programmable intermolecular and intramolecular interactions, peptides can be modified to form higher-order structures (including nanofibers and nanoparticles) with unique functionality. This paper begins with an analysis of the relationship between peptide self-assembly and antimicrobial activity, describes in detail the research and development of various self-assembled antimicrobial peptides in recent years, and finally explores different combinatorial strategies for self-assembling antimicrobial peptides.

Compounds identification and mechanism prediction of YuXueBi capsule in the treatment of arthritis by integrating UPLC/IM-QTOF-MS and network pharmacology.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease that seriously affects the life quality of patients. As a patent medicine of Chinese traditional medicine, YuXueBi capsule (YXBC) is widely used for treating RA with significant effects. However, its active compounds and therapeutic mechanisms are not fully illuminated, encumbering the satisfactory clinical application. In this study, we developed a method for identifying the chemical compounds of YXBC and the absorbed compounds into blood of rats using ultra performance liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (UPLC/IM-QTOF-MS) combined with UNIFI analysis software. A total of 58 compounds in YXBC were unambiguously or tentatively identified, 16 compounds from which were found in serum of rats after administration of YXBC. By network pharmacology, these prototype compounds identified in serum were predicted to regulate 30 main pathways (including HIF-1 signaling pathway, neuroactive ligand-receptor interaction, IL-17 signaling pathway, and so on) through 146 targets, resulting in promoting blood circulation and removing blood stasis, analgesia, and anti-inflammatory activities. This study provides a scientific basis for the clinical efficacy of YXBC in the treatment of RA.

Dan-shen Yin promotes bile acid metabolism and excretion to prevent atherosclerosis via activating FXR/BSEP signaling pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: Dan-shen Yin (DSY), a traditional prescription, has been demonstrated to be effective in decreasing hyperlipidemia and preventing atherosclerosis (AS), but its mechanism remains unknown. We hypothesized that DSY activates farnesoid X receptor (FXR) to promote bile acid metabolism and excretion, thereby alleviating AS. AIM OF THE STUDY: This study was designed to explore whether DSY reduces liver lipid accumulation and prevents AS by activating FXR and increasing cholesterol metabolism and bile acid excretion. MATERIALS AND METHODS: The comprehensive chemical characterization of DSY was analyzed by UHPLC-MS/MS. The AS models of ApoE-/- mice and SD rats was established by high-fat diet and high-fat diet combined with intraperitoneal injection of vitamin D3, respectively. The aortic plaque and pathological changes were used to evaluate AS. Lipid levels, H&E staining and oil red O staining were used to evaluate liver lipid accumulation. The cholesterol metabolism and bile acid excretion were evaluated by enzyme-linked immunosorbent assay, UPLC-QQQ/MS. In vitro, the lipid and FXR/bile salt export pump (BSEP) levels were evaluated by oil red O staining, real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting. RESULTS: A total of 36 ingredients in DSY were identified by UPLC-MS/MS analysis. In vivo, high-dose DSY significantly inhibited aortic intimal thickening, improved arrangement disorder, tortuosity, and rupture of elastic fibers, decreased lipid levels, and reduced the number of fat vacuoles and lipid droplets in liver tissue in SD rats and ApoE-/- mice. Further studies found that high-dose DSY significantly reduced liver lipid and total bile acids levels, increased liver ursodeoxycholic acid (UDCA) and other non-conjugated bile acids levels, increased fecal total cholesterol (TC) levels, and augmented FXR, BSEP, cholesterol 7-alpha hydroxylase (CYP7A1), ATP binding cassette subfamily G5/G8 (ABCG5/8) expression levels, while decreasing ASBT expression levels. In vitro studies showed that DSY significantly reduced TC and TG levels, as well as lipid droplets, while also increasing the expression of ABCG5/8, FXR, and BSEP in both HepG2 and Nr1h4 knockdown HepG2 cells. CONCLUSION: This study demonstrated that DSY promotes bile acid metabolism and excretion to prevent AS by activating FXR. For the prevent of AS and drug discovery provided experimental basis.

Identifying the temporal contributors and their interactions during dynamic formation of black tea cream.

Tea cream formed in hot and strong tea infusion while cooling deteriorates quality and health benefits of tea. However, the interactions among temporal contributors during dynamic formation of tea cream are still elusive. Here, by deletional recombination experiments and molecular dynamics simulation, it was found that proteins, caffeine (CAF), and phenolics played a dominant role throughout the cream formation, and the contribution of amino acids was highlighted in the early stage. Furthermore, CAF was prominent due to its extensive binding capacity and the filling complex voids property, and caffeine-theaflavins (TFs) complexation may be the core skeleton of the growing particles in black tea infusion. In addition to TFs, the unidentified phenolic oxidation-derived products (PODP) were confirmed to contribute greatly to the cream formation.

Overcoming Endosomal Escape Barriers in Gene Drug Delivery Using De Novo Designed pH-Responsive Peptides.

A major challenge in using nanocarriers for intracellular drug delivery is their restricted capacity to escape from endosomes into the cytosol. Here, we significantly enhance the drug delivery efficiency by accurately predicting and regulating the transition pH (pH0) of peptides to modulate their endosomal escape capability. Moreover, by inverting the chirality of the peptide carriers, we could further enhance their ability to deliver nucleic acid drugs as well as antitumor drugs. The resulting peptide carriers exhibit versatility in transfecting various cell types with a high efficiency of up to 90% by using siRNA, pDNA, and mRNA. In vivo antitumor experiments demonstrate a tumor growth inhibition of 83.4% using the peptide. This research offers a potent method for the rapid development of peptide vectors with exceptional transfection efficiencies for diverse pathophysiological indications.

A novel maximum power point tracking with hybrid control algorithm for automotive thermoelectric generator system.

To dynamically track the maximum power of an automotive thermoelectric generator (ATEG) system in real-time, this study introduces a novel maximum power point tracking (MPPT) algorithm that integrates Kalman filtering and fuzzy control. Employing a two-phase interleaved parallel DC-DC boost converter in the MPPT controller effectively reduces current ripple and switch loss. Results demonstrated a significant improvement in tracking time compared to the traditional incremental conductance algorithm, attributed to the elimination of high-frequency components in output power by the Kalman filter. The novel algorithm exhibits enhanced tracking stability through the application of fuzzy control. Ultimately, the tracking accuracy of the novel algorithm surpasses that of the incremental conductance algorithm by 5.2%, achieving an impressive 94.9%. This study, therefore, presents a valuable contribution to a novel MPPT algorithm for precisely and rapidly tracking the global maximum power points of the ATEG system throughout the entire vehicle driving cycle.

Exploration of the Dynamic Variations of the Characteristic Constituents and the Degradation Products of Catalpol during the Process of Radix Rehmanniae.

Radix Rehmanniae (RR), a famous traditional Chinese medicine (TCM) widely employed in nourishing Yin and invigorating the kidney, has three common processing forms in clinical practice, including fresh Radix Rehmanniae (FRR), raw Radix Rehmanniae (RRR), and processed Radix Rehmanniae (PRR). However, until now, there has been less exploration of the dynamic variations in the characteristic constituents and degradation products of catalpol as a representative iridoid glycoside with the highest content in RR during the process from FRR to PRR. In this study, an ultra-performance liquid chromatography coupled with photodiode array detector (UPLC-PDA) method was successfully established for the simultaneous determination of ten characteristic components to explore their dynamic variations in different processed products of RR. Among them, iridoid glycosides, especially catalpol, exhibited a sharp decrease from RRR to PRR. Then, three degradation products of catalpol were detected under simulated processing conditions (100 °C, pH 4.8 acetate buffer solution), which were isolated and identified as jiofuraldehyde, cataldehyde, and norviburtinal, respectively. Cataldehyde was first reported as a new compound. Moreover, the specificity of norviburtinal in self-made PRR samples was discovered and validated, which was further confirmed by testing in commercially available PRR samples. In conclusion, our study revealed the decrease in iridoid glycosides and the production of new degradation substances during the process from FRR to PRR, which is critical for unveiling the processing mechanism of RR.

MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks.

Clinical metabolomics is growing as an essential tool for precision medicine. However, classical machine learning algorithms struggle to comprehensively encode and analyze the metabolomics data due to their high dimensionality and complex intercorrelations. This article introduces a new method called MetDIT, designed to analyze intricate metabolomics data effectively using deep convolutional neural networks (CNN). MetDIT comprises two components: TransOmics and NetOmics. Since CNN models have difficulty in processing one-dimensional (1D) sequence data efficiently, we developed TransOmics, a framework that transforms sequence data into two-dimensional (2D) images while maintaining a one-to-one correspondence between the sequences and images. NetOmics, the second component, leverages a CNN architecture to extract more discriminative representations from the transformed samples. To overcome the overfitting due to the small sample size and class imbalance, we introduced a feature augmentation module (FAM) and a loss function to improve the model performance. Furthermore, we systematically optimized the model backbone and image resolution to balance the model parameters and computational costs. To demonstrate the performance of the proposed MetDIT, we conducted extensive experiments using three different clinical metabolomics data sets and achieved better classification performance than classical machine learning methods used in metabolomics, including Random Forest, SVM, XGBoost, and LightGBM. The source code is available at the GitHub repository at https://github.com/Li-OmicsLab/MetDIT, and the WebApp can be found at http://metdit.bioinformatics.vip/.

Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases.

The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.

An integrated strategy of UPLC-Q-TOF-MS analysis, network pharmacology, and molecular docking to explore the chemical constituents and mechanism of Zixue Powder against febrile seizures.

Febrile seizures (FS) are the most common type of seizures for children. As a commonly used representative cold formula for resuscitation, Zixue Powder (ZP) has shown great efficacy for the treatment of FS in clinic, while its active ingredients and underlying mechanism remain largely unclear. This study aimed to preliminarily elucidate the material basis of ZP and the potential mechanism for the treatment of FS through ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), network pharmacology, and molecular docking. UPLC-Q-TOF-MS was firstly applied to characterize the ingredients in ZP, followed by network pharmacology to explore the potential bioactive ingredients and pathways of ZP against FS. Furthermore, molecular docking technique was employed to verify the binding affinity between the screened active ingredients and targets. As a result, 75 ingredients were identified, containing flavonoids, chromogenic ketones, triterpenes and their saponins, organic acids, etc. Through the current study, we focused on 13 potential active ingredients and 14 key potential anti-FS targets of ZP, such as IL6, STAT3, TNF, and MMP9. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that inflammatory response, EGFR tyrosine kinase inhibitor resistance, AGE-RAGE signaling pathway in diabetic complications, and neuroactive ligand-receptor interaction were the main anti-FS signaling pathways. Licochalcones A and B, 26-deoxycimicifugoside, and hederagenin were screened as the main potential active ingredients by molecular docking. In conclusion, this study provides an effective in-depth investigation of the chemical composition, potential bioactive components, and possible anti-FS mechanism of ZP, which lays the foundation for pharmacodynamic studies and clinical applications of ZP.

Analysis of the key aroma components of Pu'er tea by synergistic fermentation with three beneficial microorganisms.

Aroma is a key indicator of the quality and value of Pu'er tea. A total of 36 aroma components were detected,which Saccharomyces, Rhizopus, and Aspergillus niger, were in the ratios of 2:1:2, 2:2:2, and 2:3:2 inoculated to ferment Pu'er tea, comparing with natural fermentation. In addition, 12 key aroma compounds were identified by analysing ROAVs. Methoxyphenyl compounds and ß-ionone were the primary contributors to the formation of aged and woody aroma when fermenting Pu'er tea naturally or using Rhizopus, while linalool and its oxides, benzyl alcohol, hexanal, and limonene were the primary contributors to the formation of floral and fruity aroma when fermenting Pu'er tea using synergistic fermentation with Saccharomyces, Rhizopus, and Aspergillus niger. This study identified the key aroma components of the Pu'er tea fermented using five methods, which revealed and demonstrated the potential application of synergistic effects of different microorganisms in the changes of aroma of Pu'er tea.

Computational Design of Peptide Assemblies.

With the ongoing development of peptide self-assembling materials, there is growing interest in exploring novel functional peptide sequences. From short peptides to long polypeptides, as the functionality increases, the sequence space is also expanding exponentially. Consequently, attempting to explore all functional sequences comprehensively through experience and experiments alone has become impractical. By utilizing computational methods, especially artificial intelligence enhanced molecular dynamics (MD) simulation and de novo peptide design, there has been a significant expansion in the exploration of sequence space. Through these methods, a variety of supramolecular functional materials, including fibers, two-dimensional arrays, nanocages, etc., have been designed by meticulously controlling the inter- and intramolecular interactions. In this review, we first provide a brief overview of the current main computational methods and then focus on the computational design methods for various self-assembled peptide materials. Additionally, we introduce some representative protein self-assemblies to offer guidance for the design of self-assembling peptides.

Comprehensive metabolome characterization and comparison between two sources of Dragon's blood by integrating liquid chromatography/mass spectrometry and chemometrics.

Dragon's Blood (DB) serves as a precious Chinese medicine facilitating blood circulation and stasis dispersion. Daemonorops draco (D. draco; Qi-Lin-Jie) and Dracaena cochinchinensis (D. cochinchinenesis; Long-Xue-Jie) are two reputable plant sources for preparing DB. This work was designed to comprehensively characterize and compare the metabolome differences between D. draco and D. cochinchinenesis, by integrating liquid chromatography/mass spectrometry and untargeted metabolomics analysis. Offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), by utilizing a powerful hybrid scan approach, was elaborated for multicomponent characterization. Configuration of an XBridge Amide column and an HSS T3 column in offline mode exhibited high orthogonality (A0 0.80) in separating the complex components in DB. Particularly, the hybrid high-definition MSE-high definition data-dependent acquisition (HDMSE-HDDDA) in both positive and negative ion modes was applied for data acquisition. Streamlined intelligent data processing facilitated by the UNIFI™ (Waters) bioinformatics platform and searching against an in-house chemical library (recording 223 known compounds) enabled efficient structural elucidation. We could characterize 285 components, including 143 from D. draco and 174 from D. cochinchinensis. Holistic comparison of the metabolomes among 21 batches of DB samples by the untargeted metabolomics workflows unveiled 43 significantly differential components. Separately, four and three components were considered as the marker compounds for identifying D. draco and D. cochinchinenesis, respectively. Conclusively, the chemical composition and metabolomic differences of two DB resources were investigated by a dimension-enhanced analytical approach, with the results being beneficial to quality control and the differentiated clinical application of DB.

Crude Tieguanyin oolong tea polysaccharides regulate intestinal immune and gut microflora in dextran sulfate sodium-induced mice colitis.

BACKGROUND: The global incidence and prevalence of inflammatory bowel diseases (IBDs) have been increasing. Epidemiological studies, clinical trials, and animal experiments have indicated a negative association between the consumption of tea and IBD. This study aims to investigate the protective effects of crude Tieguanyin oolong tea polysaccharides (CTPS) on experimental colitis, while also exploring the underlying mechanisms. RESULTS: The administration of CTPS significantly alleviated IBD in the mouse model, and was found to regulate T-cell mediated immune responses in the colon by modulating cytokine production associated with T cells. Furthermore, CTPS demonstrated a positive impact on the gut microbiota, reversing the increase in pathogenic Helicobacter and enhancing the relative abundances of beneficial bacteria such as Akkermansia, Lachnospiraceae, and Odoribacter. Oral administration of CTPS also led to an improvement in intestinal metabolism, specifically by increasing the levels of short-chain fatty acids. CONCLUSION: This study provides the first in vivo evidence of the protective effects of CTPS on colitis in mice. The effects are likely facilitated through the regulation of T cell-mediated responses and modulation of the gut microbiota, suggesting that CTPS may be a potential preventive and therapeutic approach for IBD. © 2023 Society of Chemical Industry.

Recent Advances in Room-Temperature Phosphorescence Metal-Organic Hybrids: Structures, Properties, and Applications.

Metal-organic hybrid (MOH) materials with room-temperature phosphorescence (RTP) have drawn attention in recent years due to their superior RTP properties of high phosphorescence efficiency and ultralong emission lifetime. Great achievement has been realized in developing MOH materials with high-performance RTP, but a systematic study on MOH materials with RTP feature is lacking. This review highlights recent advances in metal-organic hybrid RTP materials. The molecular packing, the photophysical properties, and their applications of metal-organic hybrid RTP materials are discussed in detail. Metal-organic hybrid RTP materials can be divided into six parts: coordination polymers, metal-organic frameworks (MOFs), metal-halide hybrids, organic ionic crystals, organic ionic polymers, and organic-inorganic hybrid perovskites. These RTP materials have been successfully applied in time-resolved data encryption, fingerprint recognition, information logic gates, X-ray imaging, and photomemory. This review not only provides the basic principles of designing RTP metal-organic hybrids, but also propounds the future research prospects of RTP metal-organic hybrids. This review offers many effective strategies for developing metal-organic hybrids with excellent RTP properties, thus satisfying practical applications.

Guest adaptative supramolecular sensing strategy for warning the risky aflatoxins in contaminated cereals.

In the face of diversified analytes, it is a great challenge and infeasible task to design and synthesize corresponding macrocyclic hosts to realize the ideal supramolecular sensing. Herein, we proposed a novel supramolecular sensing strategy, guest adaptative assay (GAA), in which analyte was quantitatively transformed under mild conditions to perfectly adapt to macrocyclic host. As a health-threatening "landmine" in cereals, aflatoxins were converted by the aid of alkali hydrolysis to satisfactorily obtain aflatoxins transformants in ionic state, resulting in sensitive response by the guanidinocalix[5]arene•fluorescein reporter pair. Surprisingly, the established strategy not only exhibited effective practicality in screening out high-risk cereals contaminated with aflatoxins, but also relieved the laborious task of macrocycle design and screening in supramolecular sensing.