Metagenomics reveals the characteristics and potential spread of microbiomes and virulence factor genes in the dairy cattle production system.

Virulence factor genes (VFGs) pose a potential threat to ecological security and animal health, and have attracted increasing attention in the livestock industry. As one of the primary livestock types, dairy cattle may be an important source of VFG transmission. However, the distribution, transmission, and evolution of VFGs in the gastrointestinal tract and surrounding environment of dairy cattle remain unclear. In the present study, a total of 263 samples were collected from cows, calves, colostrum, farm wastewater, and soil. Metagenomics was conducted to analyze changes in the microbiome and VFGs characteristics in these ecological niches. The VFGs of the cows showed distinct differences between the rumen and feces, and were influenced by the region. The dominant VFG hosts was regulated by their microbial structure. Colostrum administration of cows increased VFG abundance in their newborn calf feces sharply and Enterobacteriaceae became the primary host. While diet was the primary driving force for the temporal variation in calf VFGs. For samples of the surrounding environment, water and soil had higher VFG concentrations and were more structurally stable. Moreover, extensive interactions between the mobile genetic elements and VFGs and gene mobile analysis map based on metagenomic binning both displayed the potential horizontal transfer ability of VFGs in the cows and environment. Our study revealed the prevalence, diffusion, and regulatory factors of VFGs in dairy cattle production systems, providing novel insights into reducing livestock VFGs and limiting their spread.

The Anti-Hypoxic Mechanism of Sesamoside Determined Using Network Pharmacology.

This study aims to elucidate the anti-hypoxia mechanism of sesamoside, an active component of Phlomis younghusbandii Mukerjee, through a network pharmacology approach. Sesamoside has demonstrated potential anti-oxidant and antiglycation activities. The hypoxia-related disease targets were collected from databases like GeneCards and OMIM. Protein-protein interaction (PPI) networks were constructed using the STRING database. GO/KEGG enrichment analysis was performed using the Metascape database to identify biological processes and signaling pathways. Our results indicate that sesamoside interacts with multiple targets related to glucose and lipid metabolism, nucleotide metabolism, and inflammatory, and we find that AKR1B1 (AR) plays a crucial role in sesamoside responses to hypoxia. Molecular docking studies were performed using Autodock software, revealing good binding activity between sesamoside and AR. We then use CCK-8 assay, qPCR, WB, and ELISA analysis to validate the role of sesamoside in regulating AR and participating in anti-hypoxia through cell experiments. The results show that compared with the hypoxia group, sesamoside treatment significantly improves the expression of AR and inflammation cytokines. In summary, this study sheds light on the anti-hypoxia mechanism of sesamoside using a network pharmacology approach, providing a theoretical basis and experimental foundation for its application in the prevention and treatment of hypoxic diseases.

Serum Medium-Chain Fatty Acids and the Risk of Incident Diabetes: Findings from the 4C Study.

CONTEXT: Emerging studies have revealed associations between dietary medium-chain fatty acids (MCFAs) and glucose homeostasis. However, the relationship between serum MCFAs and the incidence of diabetes, and potential interactions with genetic predisposition, remains unclear in prospective cohort studies. OBJECTIVE: To investigate associations and genetic susceptibility between serum MCFAs and diabetes risk. METHODS: We investigated baseline serum MCFAs (n=5) in a nested case-control study comprising incident diabetes cases (n=1,707) and matched normoglycemic control subjects (n=1,707) from the China Cardiometabolic Disease and Cancer Cohort Study. Associations between MCFAs and type 2 diabetes mellitus (T2DM) were examined, both overall and stratified by diabetes genetic susceptibility. Genetic risk scores (GRS) were calculated based on 86 T2DM-associated genetic variants. RESULTS: In the fully adjusted conditional logistic regression model, serum octanoic acid and nonanoic acid exhibited inverse dose-response relationships with diabetes risk, showing odds ratios (95% confidence intervals) of 0.90 (0.82-0.98) and 0.84 (0.74-0.95), respectively. Subgroup analysis demonstrated that inverse associations between MCFAs and incident diabetes were more pronounced among individuals with physical inactivity (Pinteraction = 0.042, 0.034, and 0.037, for octanoic, nonanoic and decanoic acid, respectively). Moreover, inverse associations of octanoic acid with diabetes risk were notably enhanced among individuals with high genetic risk compared to those with low genetic risk. Significant interactions were observed between octanoic acid and GRS on T2DM risk (Pinteraction = 0.003). CONCLUSIONS: These findings provide evidence supporting inverse associations between serum MCFAs and T2DM risk, and reveal potential interplay between genetic susceptibility and circulating octanoic acid in modulating diabetes risk.

The Bifidobacterium-dominated fecal microbiome in dairy calves shapes the characteristic growth phenotype of host.

The dominant bacteria in the hindgut of calves play an important role in their growth and health, which could even lead to lifelong consequences. However, the identification of core probiotics in the hindgut and its mechanism regulating host growth remain unclear. Here, a total of 1045 fecal samples were analyzed by 16S rRNA gene sequencing from the 408 Holstein dairy calves at the age of 0, 14, 28, 42, 56, and 70 days to characterize the dynamic changes of core taxa. Moreover, the mechanisms of nutrient metabolism of calf growth regulated by core bacteria were investigated using multi-omics analyses. Finally, fecal microbiota transplantation (FMT) in mice were conducted to illustrate the potential beneficial effects of core bacteria. Four calf enterotypes were identified and enterotypes dominated by Bifidobacterium and Oscillospiraceae_UCG-005 were representative. The frequency of enterotype conversion shifted from variable to stable. The close relationship observed between phenotype and enterotype, revealing a potential pro-growth effect of Bifidobacterium, might be implemented by promoting the use of carbohydrate, activating the synthesis of volatile fatty acids, amino acids and vitamin B6, and inhibiting methane production in the hindgut. The FMT results indicated the beneficial effect of Bifidobacterium on host growth and hindgut development. These results support the notion that the Bifidobacterium-dominated fecal microbiome would be an important driving force for promoting the host growth in the early life. Our findings provide new insights into the potential probiotic mining and application strategies to promote the growth of young animals or improve their growth retardation.

A qualitative exploration of e-cigarette prevention advertisements' effectiveness among college students in China.

INTRODUCTION: The rapid growth of e-cigarette usage among youth and young people has emerged as a significant public health concern. It is imperative to initiate effective vaping prevention campaigns and undertake relevant research to address this pressing issue. This research seeks to identify effective video advertisements to deter young people from starting to use e-cigarettes. It aims to offer evidence-based insights and recommendations for creating communication materials and designing messages for youth e-cigarette prevention efforts. METHODS: College students aged 18-24 years (n=40) participated in focus groups within this qualitative study. After viewing four stimulus videos, participants discussed what they perceived as effective and ineffective video characteristics, as well as suggestions for future videos. RESULTS: Effective video characteristics included the use of real-life testimonials, displaying specific health hazards, revealing harmful chemical ingredients and the deceptive nature of flavors, and positively perceived effectiveness. Participants generally found that videos with strong visual impact and graphics were more engaging and that approaches using fear and emotion were more effective. Ineffective characteristics included complex and exaggerated information, lack of empathy and irrelevance, insufficiently specific information, extreme and death-themed content, industry messages, as well as preachy tones, animations, metaphors, dull formats, excessive length, and scenes of e-cigarette use. CONCLUSIONS: Developing anti-e-cigarette campaign materials for youth necessitates target audience-focused qualitative research. This helps in deeply exploring and identifying effective themes and messages, as well as video characteristics and details while avoiding ineffective or even misleading messages and themes from young people's perspectives outside the United States. Future development of e-cigarette prevention videos for Chinese college students may consider incorporating localized real-life testimonial cases to convey specific harms, including self-efficacy information, and utilizing fear and emotional appeals.

Association of circulating long-chain free fatty acids and incident diabetes risk among normoglycemic Chinese adults: a prospective nested case-control study.

BACKGROUND: Long-chain free fatty acids (FFAs) are associated with risk of incident diabetes. However, a comprehensive assessment of the associations in normoglycemic populations is lacking. OBJECTIVES: Our study aimed to comprehensively investigate the prospective associations and patterns of FFA profiles with diabetes risk among normoglycemic Chinese adults. METHODS: This is a prospective nested case-control study from the China Cardiometabolic Disease and Cancer Cohort (4C) study. We quantitatively measured 53 serum FFAs using a targeted metabolomics approach in 1707 incident diabetes subjects and 1707 propensity score-matched normoglycemic controls. Conditional logistic regression models were employed to estimate odds ratios (ORs) for associations. Least Absolute Shrinkage and Selection Operator (LASSO) penalty regression and quantile g-computation (qg-comp) analyses were implemented to estimate the association between multi-FFA exposures and incident diabetes. RESULTS: The majority of odd-chain FFAs exhibited an inverse association with incident diabetes, wherein the ORs per SD increment of all 7 saturated fatty acids (SFAs), monounsaturated fatty acid (MUFA) 15:1, and polyunsaturated fatty acid (PUFA) 25:2 were ranging from 0.79 to 0.88 (95% CIs ranging between 0.71 and 0.97). Even-chain FFAs comprised 99.3% of total FFAs and displayed heterogeneity with incident diabetes. SFAs with 18-26 carbon atoms are inversely linked to incident diabetes, with ORs ranging from 0.81 to 0.86 (95% CIs ranging between 0.73 and 0.94). MUFAs 26:1 (OR: 0.85; 95% CI: 0.76, 0.94), PUFAs 20:4 (OR: 0.84; 95% CI: 0.75, 0.94), and 24:2 (OR: 0.87; 95% CI: 0.78, 0.97) demonstrated significant associations. In multi-FFA exposure model, 24 FFAs were significantly associated with incident diabetes, most of which were consistent with univariate results. The mixture OR was 0.78 (95% CI: 0.61, 0.99; P = 0.04159). Differential correlation network analysis revealed pre-existing perturbations in intraclass and interclass FFA coregulation before diabetes onset. CONCLUSIONS: These findings underscore the variations in diabetes risk associated with FFAs across chain length and unsaturation degree, highlighting the importance of recognizing FFA subtypes in the pathogenesis of diabetes.

OsLCD3 interacts with OsSAMS1 to regulate grain size via ethylene/polyamine homeostasis control.

A fundamental question in developmental biology is how to regulate grain size to improve crop yields. Despite this, little is still known about the genetics and molecular mechanisms regulating grain size in crops. Here, we provide evidence that a putative protein kinase-like (OsLCD3) interacts with the S-adenosyl-L-methionine synthetase 1 (OsSAMS1) and determines the size and weight of grains. OsLCD3 mutation (lcd3) significantly increased grain size and weight by promoting cell expansion in spikelet hull, whereas its overexpression caused negative effects, suggesting that grain size was negatively regulated by OsLCD3. Importantly, lcd3 and OsSAMS1 overexpression (SAM1OE) led to large and heavy grains, with increased ethylene and decreased polyamines production. Based on genetic analyses, it appears that OsLCD3 and OsSAMS1 control rice grain size in part by ethylene/polyamine homeostasis. The results of this study provide a genetic and molecular understanding of how the OsLCD3-OsSAMS1 regulatory module regulates grain size, suggesting that ethylene/polyamine homeostasis is an appropriate target for improving grain size and weight.

Ru-Mg promoted reductive cross-coupling of allyl bromides and alkenes to synthesize 1,7-octadienes with an all-carbon quaternary center.

A Ru-promoted reductive cross-coupling of allyl bromides and electron-deficient alkenes to provide terminal 1,7-octadienes with magnesium as a reductant is reported herein. This approach enables the facile construction of a series of complex terminal 1,7-octadienes with an all-carbon quaternary center under mild reaction conditions, and the synthetic utility of the current method has been demonstrated by a gram scale synthesis. Preliminary mechanism investigations suggested that a radical pathway might not be involved in this transformation.

Unlocking the power of postbiotics: A revolutionary approach to nutrition for humans and animals.

Postbiotics, which comprise inanimate microorganisms or their constituents, have recently gained significant attention for their potential health benefits. Extensive research on postbiotics has uncovered many beneficial effects on hosts, including antioxidant activity, immunomodulatory effects, gut microbiota modulation, and enhancement of epithelial barrier function. Although these features resemble those of probiotics, the stability and safety of postbiotics make them an appealing alternative. In this review, we provide a comprehensive summary of the latest research on postbiotics, emphasizing their positive impacts on both human and animal health. As our understanding of the influence of postbiotics on living organisms continues to grow, their application in clinical and nutritional settings, as well as animal husbandry, is expected to expand. Moreover, by substituting postbiotics for antibiotics, we can promote health and productivity while minimizing adverse effects. This alternative approach holds immense potential for improving health outcomes and revolutionizing the food and animal products industries.

Adiponectin protects against myocardial ischemia-reperfusion injury: a systematic review and meta-analysis of preclinical animal studies.

BACKGROUND: Myocardial ischemia-reperfusion injury (MIRI) is widespread in the treatment of ischemic heart disease, and its treatment options are currently limited. Adiponectin (APN) is an adipocytokine with cardioprotective properties; however, the mechanisms of APN in MIRI are unclear. Therefore, based on preclinical (animal model) evidence, the cardioprotective effects of APN and the underlying mechanisms were explored. METHODS: The literature was searched for the protective effect of APN on MIRI in six databases until 16 November 2023, and data were extracted according to selection criteria. The outcomes were the size of the myocardial necrosis area and hemodynamics. Markers of oxidation, apoptosis, and inflammation were secondary outcome indicators. The quality evaluation was performed using the animal study evaluation scale recommended by the Systematic Review Center for Laboratory animal Experimentation statement. Stata/MP 14.0 software was used for the summary analysis. RESULTS: In total, 20 papers with 426 animals were included in this study. The pooled analysis revealed that APN significantly reduced myocardial infarct size [weighted mean difference (WMD) = 16.67 (95% confidence interval (CI) = 13.18 to 20.16, P < 0.001)] and improved hemodynamics compared to the MIRI group [Left ventricular end-diastolic pressure: WMD = 5.96 (95% CI = 4.23 to 7.70, P < 0.001); + dP/dtmax: WMD = 1393.59 (95% CI = 972.57 to 1814.60, P < 0.001); -dP/dtmax: WMD = 850.06 (95% CI = 541.22 to 1158.90, P < 0.001); Left ventricular ejection fraction: WMD = 9.96 (95% CI = 7.29 to 12.63, P < 0.001)]. Apoptosis indicators [caspase-3: standardized mean difference (SMD) = 3.86 (95% CI = 2.97 to 4.76, P < 0.001); TUNEL-positive cells: WMD = 13.10 (95% CI = 8.15 to 18.05, P < 0.001)], inflammatory factor levels [TNF-α: SMD = 4.23 (95% CI = 2.48 to 5.98, P < 0.001)], oxidative stress indicators [Superoxide production: SMD = 4.53 (95% CI = 2.39 to 6.67, P < 0.001)], and lactate dehydrogenase levels [SMD = 2.82 (95% CI = 1.60 to 4.04, P < 0.001)] were significantly reduced. However, the superoxide dismutase content was significantly increased [SMD = 1.91 (95% CI = 1.17 to 2.65, P < 0.001)]. CONCLUSION: APN protects against MIRI via anti-inflammatory, antiapoptotic, and antioxidant effects, and this effect is achieved by activating different signaling pathways.

Fine-Scale Characterization of Plant Diterpene Glycosides Using Energy-Resolved Untargeted LC-MS/MS Metabolomics Analysis.

Plant diterpene glycosides are essential for diverse physiological processes. Comprehensive structural characterization proved to be a challenge due to variations in glycosylation patterns, diverse aglycone structures, and the absence of comprehensive reference databases. In this study, a method for fine-scale characterization was proposed based on energy-resolved (ER) untargeted LC-MS/MS metabolomics analysis using steviol glycosides as a demonstration. Energy-dependent fragmentation patterns were unveiled by a series of model compounds. Distinct glycosylation sites were discerned by leveraging varying fragmentation energies for the precursor ions. The sugar moiety linkage at C19OOH (R1) exhibited facile and intact cleavage at low collision energies, while the sugar moiety at C13-OH (R2) demonstrated consecutive cleavage with increasing energy. Aglycone ions exhibited a higher relative intensity at NCE 50, with relative intensities ranging from 95% to 100%. Subsequently, aglycone candidates, R1 sugar composition, and R2 sugar sequence were deduced through ER-MS/MS analysis. The developed method was applied to Stevia rebaudiana leaves. A total of 91 diterpene glycosides were unambiguously identified, including 16 steviol glycosides with novel acetylglycosylation patterns. This method offers a rapid alternative for glycan analysis and the structural differentiation of isomers. The developed method enhances the understanding of diterpene glycosides in plants, providing a reliable tool for the in-depth characterization of complex metabolite profiles.

Leveraging Unidentified Metabolic Features for Key Pathway Discovery: Chemical Classification-driven Network Analysis in Untargeted Metabolomics.

Untargeted metabolomics using liquid chromatography-electrospray ionization-high-resolution tandem mass spectrometry (UPLC-ESI-MS/MS) provides comprehensive insights into the dynamic changes of metabolites in biological systems. However, numerous unidentified metabolic features limit its utilization. In this study, a novel approach, the Chemical Classification-driven Molecular Network (CCMN), was proposed to unveil key metabolic pathways by leveraging hidden information within unidentified metabolic features. The method was demonstrated by using the herbivore-induced metabolic response in corn silk as a case study. Untargeted metabolomics analysis using UPLC-MS/MS was performed on wild corn silk and two genetically modified lines (pre- and postinsect treatment). Global annotation initially identified 256 (ESI-) and 327 (ESI+) metabolites. MS/MS-based classifications predicted 1939 (ESI-) and 1985 (ESI+) metabolic features into the chemical classes. CCMNs were then constructed using metabolic features shared classes, which facilitated the structure- or class annotation for completely unknown metabolic features. Next, 844/713 significantly decreased and 1593/1378 increased metabolites in ESI-/ESI+ modes were defined in response to insect herbivory, respectively. Method validation on a spiked maize sample demonstrated an overall class prediction accuracy rate of 95.7%. Potential key pathways were prescreened by a hypergeometric test using both structure- and class-annotated differential metabolites. Subsequently, CCMN was used to deeply amend and uncover the pathway metabolites deeply. Finally, 8 key pathways were defined, including phenylpropanoid (C6-C3), flavonoid, octadecanoid, diterpenoid, lignan, steroid, amino acid/small peptide, and monoterpenoid. This study highlights the effectiveness of leveraging unidentified metabolic features. CCMN-based key pathway analysis reduced the bias in conventional pathway enrichment analysis. It provides valuable insights into complex biological processes.

Quantified Metabolomics and Lipidomics Profiles Reveal Serum Metabolic Alterations and Distinguished Metabolites of Seven Chronic Metabolic Diseases.

The co-occurrence of multiple chronic metabolic diseases is highly prevalent, posing a huge health threat. Clarifying the metabolic associations between them, as well as identifying metabolites which allow discrimination between diseases, will provide new biological insights into their co-occurrence. Herein, we utilized targeted serum metabolomics and lipidomics covering over 700 metabolites to characterize metabolic alterations and associations related to seven chronic metabolic diseases (obesity, hypertension, hyperuricemia, hyperglycemia, hypercholesterolemia, hypertriglyceridemia, fatty liver) from 1626 participants. We identified 454 metabolites were shared among at least two chronic metabolic diseases, accounting for 73.3% of all 619 significant metabolite-disease associations. We found amino acids, lactic acid, 2-hydroxybutyric acid, triacylglycerols (TGs), and diacylglycerols (DGs) showed connectivity across multiple chronic metabolic diseases. Many carnitines were specifically associated with hyperuricemia. The hypercholesterolemia group showed obvious lipid metabolism disorder. Using logistic regression models, we further identified distinguished metabolites of seven chronic metabolic diseases, which exhibited satisfactory area under curve (AUC) values ranging from 0.848 to 1 in discovery and validation sets. Overall, quantitative metabolome and lipidome data sets revealed widespread and interconnected metabolic disorders among seven chronic metabolic diseases. The distinguished metabolites are useful for diagnosing chronic metabolic diseases and provide a reference value for further clinical intervention and management based on metabolomics strategy.

WRKY6 transcription factor modulates root potassium acquisition through promoting expression of AKT1 in Arabidopsis.

Potassium (K+), being an essential macronutrient in plants, plays a central role in many aspects. Root growth is highly plastic and is affected by many different abiotic stresses including nutrient deficiency. The Shaker-type K+ channel Arabidopsis (Arabidopsis thaliana) K+ Transporter 1 (AKT1) is responsible for K+ uptake under both low and high external K+ conditions. However, the upstream transcription factor of AKT1 is not clear. Here, we demonstrated that the WRKY6 transcription factor modulates root growth to low potassium (LK) stress in Arabidopsis. WRKY6 showed a quick response to LK stress and also to many other abiotic stress treatments. The two wrky6 T-DNA insertion mutants were highly sensitive to LK treatment, whose primary root lengths were much shorter, less biomass and lower K+ content in roots than those of wild-type plants, while WRKY6-overexpression lines showed opposite phenotypes. A further investigation showed that WRKY6 regulated the expression of the AKT1 gene via directly binding to the W-box elements in its promoter through EMSA and ChIP-qPCR assays. A dual luciferase reporter analysis further demonstrated that WRKY6 enhanced the transcription of AKT1. Genetic analysis further revealed that the overexpression of AKT1 greatly rescued the short root phenotype of the wrky6 mutant under LK stress, suggesting AKT1 is epistatic to WRKY6 in the control of LK response. Further transcriptome profiling suggested that WRKY6 modulates LK response through a complex regulatory network. Thus, this study unveils a transcription factor that modulates root growth under potassium deficiency conditions by affecting the potassium channel gene AKT1 expression.

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases.

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artificial intelligence (AI) in pulmonary infectious disease diagnosis and public health management. The computer tomography (CT) images of 200 patients with pulmonary infectious disease are collected and input into the AI-assisted diagnosis software based on the deep learning (DL) model, "UAI, pulmonary infectious disease intelligent auxiliary analysis system", for lesion detection. By analyzing the principles of convolutional neural networks (CNN) in deep learning (DL), the study selects the AlexNet model for the recognition and classification of pulmonary infection CT images. The software automatically detects the pneumonia lesions, marks them in batches, and calculates the lesion volume. The result shows that the CT manifestations of the patients are mainly involved in multiple lobes and density, the most common shadow is the ground-glass opacity. The detection rate of the manual method is 95.30%, the misdetection rate is 0.20% and missed diagnosis rate is 4.50%; the detection rate of the DL-based AI-assisted lesion method is 99.76%, the misdetection rate is 0.08%, and the missed diagnosis rate is 0.08%. Therefore, the proposed model can effectively identify pulmonary infectious disease lesions and provide relevant data information to objectively diagnose pulmonary infectious disease and manage public health.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking.

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

The effect of intercropping leguminous green manure on theanine accumulation in the tea plant: A metagenomic analysis.

Intercropping is a widely recognised technique that contributes to agricultural sustainability. While intercropping leguminous green manure offers advantages for soil health and tea plants growth, the impact on the accumulation of theanine and soil nitrogen cycle are largely unknown. The levels of theanine, epigallocatechin gallate and soluble sugar in tea leaves increased by 52.87% and 40.98%, 22.80% and 6.17%, 22.22% and 29.04% in intercropping with soybean-Chinese milk vetch rotation and soybean alone, respectively. Additionally, intercropping significantly increased soil amino acidnitrogen content, enhanced extracellular enzyme activities, particularly ß-glucosidase and N-acetyl-glucosaminidase, as well as soil multifunctionality. Metagenomics analysis revealed that intercropping positively influenced the relative abundances of several potentially beneficial microorganisms, including Burkholderia, Mycolicibacterium and Paraburkholderia. Intercropping resulted in lower expression levels of nitrification genes, reducing soil mineral nitrogen loss and N2 O emissions. The expression of nrfA/H significantly increased in intercropping with soybean-Chinese milk vetch rotation. Structural equation model analysis demonstrated that the accumulation of theanine in tea leaves was directly influenced by the number of intercropping leguminous green manure species, soil ammonium nitrogen and amino acid nitrogen. In summary, the intercropping strategy, particularly intercropping with soybean-Chinese milk vetch rotation, could be a novel way for theanine accumulation.

A New Biomarker Profiling Strategy for Gut Microbiome Research: Valid Association of Metabolites to Metabolism of Microbiota Detected by Non-Targeted Metabolomics in Human Urine.

The gut microbiome is of tremendous relevance to human health and disease, so it is a hot topic of omics-driven biomedical research. However, a valid identification of gut microbiota-associated molecules in human blood or urine is difficult to achieve. We hypothesize that bowel evacuation is an easy-to-use approach to reveal such metabolites. A non-targeted and modifying group-assisted metabolomics approach (covering 40 types of modifications) was applied to investigate urine samples collected in two independent experiments at various time points before and after laxative use. Fasting over the same time period served as the control condition. As a result, depletion of the fecal microbiome significantly affected the levels of 331 metabolite ions in urine, including 100 modified metabolites. Dominating modifications were glucuronidations, carboxylations, sulfations, adenine conjugations, butyrylations, malonylations, and acetylations. A total of 32 compounds, including common, but also unexpected fecal microbiota-associated metabolites, were annotated. The applied strategy has potential to generate a microbiome-associated metabolite map (M3) of urine from healthy humans, and presumably also other body fluids. Comparative analyses of M3 vs. disease-related metabolite profiles, or therapy-dependent changes may open promising perspectives for human gut microbiome research and diagnostics beyond analyzing feces.

Redox state and altered pyruvate metabolism contribute to a dose-dependent metformin-induced lactate production of human myotubes.

Metformin-induced glycolysis and lactate production can lead to acidosis as a life-threatening side effect, but slight increases in blood lactate levels in a physiological range were also reported in metformin-treated patients. However, how metformin increases systemic lactate concentrations is only partly understood. Because human skeletal muscle has a high capacity to produce lactate, the aim was to elucidate the dose-dependent regulation of metformin-induced lactate production and the potential contribution of skeletal muscle to blood lactate levels under metformin treatment. This was examined by using metformin treatment (16-776 µM) of primary human myotubes and by 17 days of metformin treatment in humans. As from 78 µM, metformin induced lactate production and secretion and glucose consumption. Investigating the cellular redox state by mitochondrial respirometry, we found metformin to inhibit the respiratory chain complex I (776 µM, P < 0.01) along with decreasing the [NAD+]:[NADH] ratio (776 µM, P < 0.001). RNA sequencing and phospho-immunoblot data indicate inhibition of pyruvate oxidation mediated through phosphorylation of the pyruvate dehydrogenase (PDH) complex (39 µM, P < 0.01). On the other hand, in human skeletal muscle, phosphorylation of PDH was not altered by metformin. Nonetheless, blood lactate levels were increased under metformin treatment (P < 0.05). In conclusion, the findings suggest that metformin-induced inhibition of pyruvate oxidation combined with altered cellular redox state shifts the equilibrium of the lactate dehydrogenase (LDH) reaction leading to a dose-dependent lactate production in primary human myotubes.NEW & NOTEWORTHY Metformin shifts the equilibrium of lactate dehydrogenase (LDH) reaction by low dose-induced phosphorylation of pyruvate dehydrogenase (PDH) resulting in inhibition of pyruvate oxidation and high dose-induced increase in NADH, which explains the dose-dependent lactate production of differentiated human skeletal muscle cells.

Network Pharmacology-Based Approach to Investigate the Active Ingredients and Therapeutic Mechanisms of Jingu Tongxiao Pill against Osteoarthritis.

This study aimed to investigate the active ingredients and therapeutic mechanisms of Jingu Tongxiao Pill (JGTXP), a commonly used Chinese patent medicine, in treating osteoarthritis (OA) via network pharmacology analysis combined with experimental validation. First, we administered JGTXP to rat plasma and identified the candidate active compounds. Next, target prediction, protein-protein interaction, compound-target network construction, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were conducted for JGTXP. Lastly, the network-derived key targets and pathways were validated in vitro and in vivo. Finally, we identified 106 compounds in JGTXP and 24 absorbed compounds in the rat plasma. Network analysis revealed that JGTXP interferes with OA mainly via regulating the inflammatory response, collagen catabolic process, and osteoclast differentiation, and the nuclear factor kappa B (NF-κB) signaling pathway plays a pivotal role in these processes. Experimentally, JGTXP exerted potential protective effects on articular cartilage and inhibited expression of inflammatory mediators and collagen catabolism-related proteins, including interleukin 1 beta (IL-1ß), interleukin 6, tumor necrosis factor alpha (TNF-α), and matrix metalloproteinase (MMP) 3 and MMP13, in a papain-induced OA rat model. Consistently, mRNA expression levels of these factors and nitric oxide release were suppressed by JGTXP in an LPS-induced RAW 264.7 inflammation model. The reporter gene assay showed that JGTXP could reduce the transcriptional activity of NF-κB. Consecutive western blot analysis demonstrated that nuclear NF-κB p65, inducible nitric oxide synthase (iNOS), and cyclooxygenase 2 (COX-2) expression were inhibited while cytoplasmic NF-κB p65 was upregulated by JGTXP. Using a combination of chemical profiling, network pharmacology analysis, and experimental validation, we preliminarily clarified the active ingredients of JGTXP intervention for OA and demonstrated that JGTXP ameliorates OA, at least partially, by regulating the NF-κB signaling pathway.