Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.e., FunTrans and FunRep), structured databases, and supporting resources for achieving highly accurate (Accuracy > 0.99, F1-score > 0.97) and fine-grained classification of antibiotic resistance genes (ARGs) and virulence factor genes. Using an experimentally confirmed dataset of ARGs comprising remote homologous sequences as the test set, our framework achieves by-far-the-best performance in the discovery of new ARGs from human gut (F1-score: 0.6948), wastewater (0.6072), and soil (0.5445) microbiomes, beating the state-of-the-art bioinformatics tools and sequence alignment-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) annotation approaches. Furthermore, our framework is implemented as a lightweight, privacy-preserving, and plug-and-play neural network module, facilitating its versatility and accessibility to developers and users worldwide. We anticipate widespread utilization of FunGeneTyper (https://github.com/emblab-westlake/FunGeneTyper) for precise classification of protein-coding gene functions and the discovery of numerous valuable enzymes. This advancement will have a significant impact on various fields, including microbiome research, biotechnology, metagenomics, and bioinformatics.

Sevoflurane impedes neuropathic pain by maintaining endoplasmic reticulum stress and oxidative stress homeostasis through inhibiting the activation of the PLCγ/CaMKII/IP3R signaling pathway.

OBJECTIVE: To investigate the effect of sevoflurane on neuropathic pain induced by chronic constriction injury (CCI) of sciatic nerve in mice, and to elucidate its mechanism by animal experiments. METHODS AND RESULTS: Thirty-two C57BL/6 mice were randomly divided into four groups: Sham group, Model group, Control group and Sevoflurane group. First, a mouse model of neuropathic pain was established. Then, the mice in each group were killed on Day 14 after operation to harvest the enlarged lumbosacral spinal cord. In contrast with the Model group, the Sevoflurane group displayed a significantly increased paw withdrawal mechanical threshold (PWMT) and significantly prolonged paw withdrawal thermal latency (PWTL) from Day 5 after operation. The morphological changes of lumbosacral spinal cord were observed by hematoxylin-eosin (HE) staining and transmission electron microscopy. Pathological results showed that sevoflurane reduced nuclear pyknosis in lumbosacral spinal cord tissue, with a large number of mitochondrial crista disappearance and mitochondrial swelling. The results of Western blotting showed that sevoflurane significantly decreased the protein expressions of phosphorylated phospholipase Cγ (p-PLCγ), phosphorylated calcium/calmodulin-dependent protein kinase II (p-CaMKII) and phosphorylated inositol 1,4,5-triphosphate receptor (p-IP3R), and reduced the protein expressions of endoplasmic reticulum (ER) stress proteins glucose-regulated protein 78 (GRP78) and GRP94, oxidative stress-related proteins P22 and P47 and inflammatory factors nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3), interleukin-1 ß (IL-1ß), and tumor necrosis factor-α (TNF-α). CONCLUSIONS: Sevoflurane inhibits neuropathic pain by maintaining ER stress and oxidative stress homeostasis through inhibiting the activation of the PLCγ/CaMKII/IP3R signaling pathway.

Corrigendum: Advanced lung cancer inflammation index predicts survival outcomes of hepatocellular carcinoma patients receiving immunotherapy.

[This corrects the article DOI: 10.3389/fonc.2023.997314.].

Metabolomics-based estimation of activated sludge microbial composition and prediction of filamentous bulking.

Understanding the structure and activity of activated sludge (AS) microbiome is key to ensuring optimal operation of wastewater treatment processes. While high-throughput metagenomics offers a comprehensive view of AS microbiome, its cost and time demands warrant alternative approaches. This study employed machine learning methods to integrate metabolomic and metagenomic data, enabling predictions of selected microbial abundances from metabolite profiling. Model training relied on rich microbial and metabolite abundance data collected in an intensively sampled AS system, including a period of filamentous bulking, as well as a few other AS systems. Multiple linear regression out-competed other three algorithms in achieving relatively high prediction accuracy (R2 = 0.70±0.02) for the abundances of 10 selected, either keystone or core metagenome-assembled genomes (MAGs). The model predicted the abundances of filamentous Microtrichaceae and Thiotrichaceae during bulking with an error range of 14-17.8 %. This predictive power extends beyond the specific system studied, showcasing potentials for broader applications across other AS systems. Aspartate, glycine, and folate were the most influential metabolite features contributing to model performance, which were also effective indicators for filamentous bulking, with up to one week of early warning potential. This study pioneers the application of metabolomics for fast, relatively accurate and cost-effective prediction of AS community composition, enabling proactive management of AS systems towards improved efficiency and stability.

The Microbiome Protocols eBook initiative: Building a bridge to microbiome research.

The Microbiome Protocols eBook (MPB) serves as a crucial bridge, filling gaps in microbiome protocols for both wet experiments and data analysis. The first edition, launched in 2020, featured 152 meticulously curated protocols, garnering widespread acclaim. We now extend a sincere invitation to researchers to participate in the upcoming 2nd version of MPB, contributing their valuable protocols to advance microbiome research.

Comparison of gasless transaxillary endoscopic thyroidectomy, endoscopic thyroidectomy via areola approach and conventional open thyroidectomy in patients with unilateral papillary thyroid carcinoma.

BACKGROUND: Gasless transaxillary endoscopic thyroidectomy (GTET) and endoscopic thyroidectomy via the areola approach (ETA) have emerged as minimally invasive surgical techniques for managing papillary thyroid carcinoma (PTC). This study aimed to assess the surgical efficacy of endoscopic thyroidectomy (ET) as compared to conventional open thyroidectomy (COT) in PTC patients. METHODS: Between 2020 and 2022, 571 PTC patients underwent unilateral thyroidectomy accompanied by ipsilateral central lymph node dissection. This cohort comprised 72 patients who underwent GTET, 105 ETA, and 394 COT. The analysis encompassed a comprehensive examination of patient clinicopathologic characteristics and postoperative complaints. Furthermore, the learning curve of GTET was evaluated using the cumulative summation (CUSUM) method. RESULTS: Patients in the ET group exhibited a lower mean age and a higher proportion of female individuals. Operation time in the ET group was significantly longer. No significant differences were observed in the incidence of postoperative complications among the three groups. With regard to postoperative complaints reported three months after surgery, GTET demonstrated superior alleviation of anterior chest discomfort and swallowing difficulties. Patients who underwent ET reported significantly higher cosmetic satisfaction levels. Additionally, the learning curve of GTET was 27 cases, and the operation time during the mature phase of the learning curve exhibited a significant reduction when compared to ETA. CONCLUSIONS: The findings of this study affirm the safety and feasibility of employing GTET and ETA for the surgical management of PTC. GTET presents an attractive surgical option, particularly for patients with unilateral PTC who place a premium on cosmetic outcomes.

Assessing mortality risk in Type 2 Diabetes patients with prolonged ASCVD risk factors: the inclusive Poh-Ai predictive scoring system with CAC Score integration.

PURPOSE: To enhance the predictive risk model for all-cause mortality in individuals with Type 2 Diabetes (T2DM) and prolonged Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Despite the utility of the Coronary Artery Calcium (CAC) score in assessing cardiovascular risk, its capacity to predict all-cause mortality remains limited. METHODS: A retrospective cohort study included 1929 asymptomatic T2DM patients with ASCVD risk factors, aged 40-80. Variables encompassed demographic attributes, clinical parameters, CAC scores, comorbidities, and medication usage. Factors predicting all-cause mortality were selected to create a predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups. RESULTS: In our analysis of all-cause mortality in T2DM patients with extended ASCVD risk factors over 5 years, we identified significant risk factors, their adjusted hazard ratios (aHR), and scores: e.g., CAC score > 1000 (aHR: 1.57, score: 2), CAC score 401-1000 (aHR: 2.05, score: 2), and more. These factors strongly predict all-cause mortality, with varying risk groups (e.g., very low-risk: 2.0%, very high-risk: 24.0%). Significant differences in 5-year overall survival rates were observed among these groups (log-rank test < 0.001). CONCLUSION: The Poh-Ai Predictive Scoring System excels in forecasting mortality and cardiovascular events in individuals with Type 2 Diabetes Mellitus and extended ASCVD risk factors.

Microbiome dynamics and products profiles of biowaste fermentation under different organic loads and additives.

Biowaste fermentation is a promising technology for low-carbon print bioenergy and biochemical production. Although it is believed that the microbiome determines both the fermentation efficiency and the product profiles of biowastes, the explicit mechanisms of how microbial activity controls fermentation processes remained to be unexplored. The current study investigated the microbiome dynamics and fermentation product profiles of biowaste fermentation under different organic loads (5, 20, and 40 g-VS/L) and with additives that potentially modulate the fermentation process via methanogenesis inhibition (2-bromoethanesulfonate) or electron transfer promotion (i.e., reduced iron, magnetite iron, and activated carbon). The overall fermentation products yields were 440, 373 and 208 CH4-eq/g-VS for low-, medium- and high-load fermentation. For low- and medium-load fermentation, volatile fatty acids (VFAs) were first accumulated and were gradually converted to methane. For high-load fermentation, VFAs were the main fermentation products during the entire fermentation period, accounting for 62% of all products. 16S rRNA-based analyses showed that both 2-bromoethanesulfonate addition and increase of organic loads inhibited the activity of methanogens and promoted the activity of distinct VFA-producing bacterial microbiomes. Moreover, the addition of activated carbon promoted the activity of H2-producing Bacteroides, homoacetogenic Eubacteriaceae and methanogenic Methanosarcinaceae, whose activity dynamics during the fermentation led to changes in acetate and methane production. The current results unveiled mechanisms of microbiome activity dynamics shaping the biowaste fermentation product profiles and provided the fundamental basis for the development of microbiome-guided engineering approaches to modulate biowaste fermentation toward high-value product recovery.

A review on the pharmacology, pharmacokinetics and toxicity of sophocarpine.

Sophocarpine is a natural compound that belongs to the quinolizidine alkaloid family, and has a long history of use and widespread distribution in traditional Chinese herbal medicines such as Sophora alopecuroides L., Sophora flavescens Ait., and Sophora subprostrata. This article aims to summarize the pharmacology, pharmacokinetics, and toxicity of sophocarpine, evaluate its potential pharmacological effects in various diseases, and propose the necessity for further research and evaluation to promote its clinical application. A large number of studies have shown that it has anti-inflammatory, analgesic, antiviral, antiparasitic, anticancer, endocrine regulatory, and organ-protective effects as it modulates various signaling pathways, such as the NF-κB, MAPK, PI3K/AKT, and AMPK pathways. The distribution of sophocarpine in the body conforms to a two-compartment model, and sophocarpine can be detected in various tissues with a relatively short half-life. Although the pharmacological effects of sophocarpine have been confirmed, toxicity and safety assessments and reports on molecular mechanisms of its pharmacological actions have been limited. Given its significant pharmacological effects and potential clinical value, further research and evaluation are needed to promote the clinical application of sophocarpine.

Metagenomic Insights into Potential Impacts of Antibacterial Biosynthesis and Anthropogenic Activity on Nationwide Soil Resistome.

The presence of antibiotic resistance genes (ARGs) in soils has received extensive attention regarding its impacts on environmental, animal, and human systems under One Health. However, the health risks of soil ARGs and microbial determinants of soil resistomes remain poorly understood. Here, a nationwide metagenomic investigation of ARGs in cropland and forest soils in China was conducted. The findings indicated that the abundance and richness of high-risk (i.e., mobilizable, pathogen-carriable and clinically relevant) ARGs in cropland soils were 25.7 times and 8.4 times higher, respectively, compared to those identified in forest soils, suggesting the contribution of agricultural practices to the elevated risk level of soil resistomes. The biosynthetic potential of antibacterials best explained the total ARG abundance (Mantel's r = 0.52, p < 0.001) when compared with environmental variables and anthropogenic disturbance. Both microbial producers' self-resistance and antagonistic interactions contributed to the ARG abundance, of which self-resistance ARGs account for 14.1 %- 35.1 % in abundance. With the increased biosynthetic potential of antibacterials, the antagonistic interactions within the microbial community were greatly enhanced, leading to a significant increase in ARG abundance. Overall, these findings advance our understanding of the emergence and dissemination of soil ARGs and provide critical implications for the risk control of soil resistomes.

Enriched rhizospheric functional microbiome may enhance adaptability of Artemisia lavandulaefolia and Betula luminifera in antimony mining areas.

Dominant native plants are crucial for vegetation reconstruction and ecological restoration of mining areas, though their adaptation mechanisms in stressful environments are unclear. This study focuses on the interactions between dominant indigenous species in antimony (Sb) mining area, Artemisia lavandulaefolia and Betula luminifera, and the microbes in their rhizosphere. The rhizosphere microbial diversity and potential functions of both plants were analyzed through the utilization of 16S, ITS sequencing, and metabarcoding analysis. The results revealed that soil environmental factors, rather than plant species, had a more significant impact on the composition of the rhizosphere microbial community. Soil pH and moisture significantly affected microbial biomarkers and keystone species. Actinobacteria, Proteobacteria and Acidobacteriota, exhibited high resistance to Sb and As, and played a crucial role in the cycling of carbon, nitrogen (N), phosphorus (P), and sulfur (S). The genes participating in N, P, and S cycling exhibited metabolic coupling with those genes associated with Sb and As resistance, which might have enhanced the rhizosphere microbes' capacity to endure environmental stressors. The enrichment of these rhizosphere functional microbes is the combined result of dispersal limitations and deterministic assembly processes. Notably, the genes related to quorum sensing, the type III secretion system, and chemotaxis systems were significantly enriched in the rhizosphere of plants, especially in B. luminifera, in the mining area. The phylogenetic tree derived from the evolutionary relationships among rhizosphere microbial and chloroplast whole-genome resequencing results, infers both species especially B. luminifera, may have undergone co-evolution with rhizosphere microorganisms in mining areas. These findings offer valuable insights into the dominant native rhizosphere microorganisms that facilitate plant adaptation to environmental stress in mining areas, thereby shedding light on potential strategies for ecological restoration in such environments.

Gut microbiome changes in mouse, Mongolian gerbil, and hamster models following Clostridioides difficile challenge.

Introduction: Clostridioides difficile infection (CDI), as well as its etiology and pathogenesis, have been extensively investigated. However, the absence of suitable CDI animal models that reflect CDI symptoms and the associated gut microbiome changes in humans has limited research progress in this field. Thus, we aimed to investigate whether Mongolian gerbils, which present a range of human pathological conditions, can been used in studies on CDI. Methods: In this study, we infected Mongolian gerbils and two existing CDI model animals, mice and hamsters, with the hypervirulent ribotype 027 C. difficile strain, and comparatively analyzed changes in their gut microbiome composition via 16S rRNA gene sequencing. Methods: In this study, we infected Mongolian gerbils and two existing CDI model animals, mice and hamsters, with the hypervirulent ribotype 027 C. difficile strain, and comparatively analyzed changes in their gut microbiome composition via 16S rRNA gene sequencing. Results: The results obtained showed that C. difficile colonized the gastrointestinal tracts of the three rodents, and after the C. difficile challenge, C57BL/6J mice did not manifest CDI symptoms and their intestines showed no significant pathological changes. However, the hamsters showed explosive intestinal bleeding and inflammation and the Mongolian gerbils presented diarrhea as well as increased infiltration of inflammatory cells, mucus secretion, and epithelial cell shedding in their intestinal tissue. Further, intestinal microbiome analysis revealed significant differences with respect to intestinal flora abundance and diversity. Specifically, after C. difficile challenge, the Firmicutes/Bacteroidetes ratio decreased for C57BL/6J mice, but increased significantly for Mongolian gerbils and hamsters. Furthermore, the abundance of Proteobacteria increased in all three models, especially in hamsters, while that of Verrucomicrobia only increased significantly in C57BL/6J mice and Mongolian gerbils. Our results also indicated that differences in the relative abundances of Lactobacillaceae and Akkermansia were primarily responsible for the observed differences in response to C. difficile challenge. Conclusion: Based on the observed responses to C. difficile challenge, we concluded for the first time that the Mongolian gerbil could be used as an animal model for CDI. Additionally, the taxa identified in this study may be used as biomarkers for further studies on CDI and to improve understanding regarding changes in gut microbiome in CDI-related diseases.

Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.

Background: Peripheral vascular disease (PVD) is a common complication in patients with type 2 diabetes mellitus (T2DM). Early detection or prediction the risk of developing PVD is important for clinical decision-making. Purpose: This study aims to establish and validate PVD risk prediction models and perform risk factor analysis for PVD in patients with T2DM using machine learning and Shapley Additive Explanation(SHAP) based on electronic health records. Methods: We retrospectively analyzed the data from 4,372 inpatients with diabetes in a hospital between January 1, 2021, and March 28, 2023. The data comprised demographic characteristics, discharge diagnoses and biochemical index test results. After data preprocessing and feature selection using Recursive Feature Elimination(RFE), the dataset was split into training and testing sets at a ratio of 8:2, with the Synthetic Minority Over-sampling Technique(SMOTE) employed to balance the training set. Six machine learning(ML) algorithms, including decision tree (DT), logistic regression (LR), random forest (RF), support vector machine(SVM),extreme gradient boosting (XGBoost) and Adaptive Boosting(AdaBoost) were applied to construct PVD prediction models. A grid search with 10-fold cross-validation was conducted to optimize the hyperparameters. Metrics such as accuracy, precision, recall, F1-score, G-mean, and the area under the receiver operating characteristic curve (AUC) assessed the models' effectiveness. The SHAP method interpreted the best-performing model. Results: RFE identified the optimal 12 predictors. The XGBoost model outperformed other five ML models, with an AUC of 0.945, G-mean of 0.843, accuracy of 0.890, precision of 0.930, recall of 0.927, and F1-score of 0.928. The feature importance of ML models and SHAP results indicated that Hemoglobin (Hb), age, total bile acids (TBA) and lipoprotein(a)(LP-a) are the top four important risk factors for PVD in T2DM. Conclusion: The machine learning approach successfully developed a PVD risk prediction model with good performance. The model identified the factors associated with PVD and offered physicians an intuitive understanding on the impact of key features in the model.

Comparison of continuous 24-hour and 14-day ECG monitoring for the detection of cardiac arrhythmias in patients with ischemic stroke or syncope.

BACKGROUND: Previous studies show that using 12-lead electrocardiogram (ECG) or 24-h ECG monitor for the detection of cardiac arrhythmia events in patients with stroke or syncope is ineffective. HYPOTHESIS: The 14-day continuous ECG patch has higher detection rates of arrhythmias compared with conventional 24-h ECG monitoring in patients with ischemic stroke or syncope. METHODS: This cross-sectional study of patients with newly diagnosed ischemic stroke or syncope received a 24-h ECG monitoring and 14-day continuous cardiac monitoring patch and the arrhythmia events were measured. RESULTS: This study enrolled 83 patients with ischemic stroke or syncope. The detection rate of composite cardiac arrhythmias was significantly higher for the 14-day ECG patch than 24-h Holter monitor (69.9% vs. 21.7%, p = .006). In patients with ischemic stroke, the detection rates of cardiac arrhythmias were 63.4% for supraventricular tachycardia (SVT), 7% for ventricular tachycardia (VT), 5.6% for atrial fibrillation (AF), 4.2% for atrioventricular block (AVB), and 1.4% for pause by 14-day ECG patch, respectively. The significant difference in arrhythmic detection rates were found for SVT (45.8%), AF (6%), pause (1.2%), AVB (2.4%), and VT (9.6%) by 14-day ECG patch but not by 24-h Holter monitor in patients with ischemic stroke or syncope. CONCLUSIONS: A 14-day ECG patch can be used on patients with ischemic stroke or syncope for the early detection of AF or other cardiac arrhythmia events. The patch can be helpful for physicians in planning medical or mechanical interventions of patients with ischemic stroke and occult AF.

The Effectiveness of Low-dose Esmketamine Versus Remifentanil Adjunct to Propofol for Sedation in Patients Undergoing Radiofrequency Thermocoagulation for Trigeminal Neuralgia.

Objectives: To access the effectiveness of propofol-esketamine versus propofol-remifentanyl in patients undergoing radiofrequency Thermocoagulation for Trigeminal Neuralgia of gasserian ganglion. Methods: In this clinical trial, 80 patients were candidates for RFT were randomly divided into two groups (n= 40). These patients aged from 21 to 81 years old. Before the start of the procedure, both groups received propofol TCI with a target level of 1.5 µgml-1. The intervention group (group E) received esketamine 0.15 mgkg-1, and the control group (group R) received remifentanyl 1.0 µgkg-1. The patients, the anesthetists and the surgeons were unaware of the medication regimen. Sedation level (based on a MOAA/S), blood pressure, oxygen saturation, the dosage of propofol, recovery time (based on Aldrete scores), postoperation pain (based on NRS), surgeons and patient satisfaction, and Pittsburgh Sleep Quality Index (PQSI) were recorded. Results: Data from 80 patients were analyzed. The sedative effects were equal in the two groups (P = .680) and the MOAA/s scores of both groups were basically maintained at or below 2 points, however, the dosage of propofol in group E was significantly less than that in group R [5.3mgkg-1h-1 (5.0 to 5.7) vs 5.8 mgkg-1h-1 ( 5.3 to 6.3), P = .000]. The group E had higher blood pressure levels during the procedure (PSBP = .002, PDBP = .023). Surgeons and patient satisfaction (Ps = .164, Pp = .580), recovery time (P = .228),The NRS values after 24hrs (P = .777)and PQSI showed no significant differences between the two groups (P = .133). Conclusions: Low-dose esketamine reduces the total amount of propofol necessary for sedation and incidence of respiratory depression during RFT of gasserian ganglion in American Society of Anesthesiologists I to III patients without affecting recovery time, satisfaction of surgeons and patients, cardiovascular adverse events, when compared with remifentanil.

Plastic particles affect N2O release via altering core microbial metabolisms in constructed wetlands.

Constructed wetlands (CWs) have been proven to effectively immobilize plastic particles. However, little is known about the differences in the impact of varying sized plastic particles on nitrous oxide (N2O) release, as well as the intervention mechanisms in CWs. Here, we built a lab-scale wetland model and introduced plastic particles of macro-, micro-, and nano-size at 100 µg/L for 370 days. The results showed that plastic particles of all sizes reduced N2O release in CWs, with the degrees being the strongest for the Nano group, followed by Micro and Macro groups. Meanwhile, 15N- and 18O-tracing experiment revealed that the ammoxidation process contributed the most N2O production, followed by denitrification. While for every N2O-releasing process, the contributing proportion of N2O in nitrification-coupled denitrification were most significantly cut down under exposing to macro-sized plastics and had an obvious increase in nitrifier denitrification in all groups, respectively. Finally, we revealed the three mechanism pathways of N2O release reduction with macro-, micro-, and nano-sized plastics by impacting carbon assimilation (RubisCO activity), ammonia oxidation (gene amo abundance and HAO activity), and N-ion transmembrane and reductase activities, respectively. Our findings thus provided novel insights into the potential effects of plastic particles in CWs as an eco-technology.

Diversity and distribution of biosynthetic gene clusters in agricultural soil microbiomes.

Bacterial secondary metabolites serve as an important source of molecules for drug discovery. They also play an important function in mediating the interactions of microbial producers with their living environment and surrounding organisms. However, little is known about the genetic novelty, distribution, and community-level impacts of soil bacterial biosynthetic potential on a large geographic scale. Here, we constructed the first catalog of 11,149 biosynthetic gene clusters (BGCs) from agricultural soils across China and unearthed hidden biosynthetic potential for new natural product discovery from the not-yet-cultivated soil bacteria. Notably, we revealed soil pH as the strongest environmental driver of BGC biogeography and predicted that soil acidification and global climate change could damage the biosynthetic potential of the soil microbiome. The co-occurrence network of bacterial genomes revealed two BGC-rich species, i.e., Nocardia niigatensis from Actinobacteriota and PSRF01 from Acidobacteriota, as the module hub and connector, respectively, indicating their keystone positions in the soil microbial communities. We also uncovered a dominant role of BGC-inferred biotic interactions over environmental drivers in structuring the soil microbiome. Overall, this study achieved novel insights into the BGC landscape in agricultural soils of China, substantially expanding our understanding of the diversity and novelty of bacterial secondary metabolism and the potential role of secondary metabolites in microbiota assembly.IMPORTANCEBacterial secondary metabolites not only serve as the foundation for numerous therapeutics (e.g., antibiotics and anticancer drugs), but they also play critical ecological roles in mediating microbial interactions (e.g., competition and communication). However, our knowledge of bacterial secondary metabolism is limited to only a small fraction of cultured strains, thus restricting our comprehensive understanding of their diversity, novelty, and potential ecological roles in soil ecosystems. Here, we used culture-independent metagenomics to explore biosynthetic potentials in agricultural soils of China. Our analyses revealed a high degree of genetic diversity and novelty within biosynthetic gene clusters in agricultural soil environments, offering valuable insights for biochemists seeking to synthesize novel bioactive products. Furthermore, we uncovered the pivotal role of BGC-rich species in microbial communities and the significant relationship between BGC richness and microbial phylogenetic turnover. This information emphasizes the importance of biosynthetic potential in the assembly of microbial communities.

Aloperine Alleviates Myocardial Injury Induced by Myocardial Ischemia and Reperfusion by Activating the ERK1/2/ß-catenin Signaling Pathway.

OBJECTIVE: Myocardial ischemia/reperfusion (I/R) injury can cause severe cardiac damage. Aloperine is a quinolizidine alkaloid found in the leaves and seeds of Sophora alopecuroides L. It has been recognized that aloperine has organ-protective properties; however, its role in cardioprotection is poorly characterized. This study aimed to evaluate the cardioprotective effects of aloperine against myocardial I/R injury in vivo. METHODS: Adult male Sprague‒Dawley rats were randomly divided into sham-operated, control, and aloperine groups. All rats except for the sham-operated rats were subjected to 45 min of myocardial ischemia (by left anterior descending ligation) followed by 3 h of reperfusion. Aloperine (10 mg/kg) was given intravenously at the onset of reperfusion. The cardioprotective effects of aloperine were evaluated by determining infarct size, hemodynamics, histological changes, cardiac biomarkers, and cardiac apoptosis. RESULTS: Aloperine limited infarct size; improved hemodynamics; attenuated myocardial I/R-induced histological deterioration; decreased serum LDH, CK-MB, and α-HBDH levels; and inhibited apoptosis after myocardial I/R injury. Moreover, aloperine stimulated the phosphorylation of ventricular ERK1/2, which is a major module of MAPK signaling pathways. Furthermore, aloperine increased the ventricular expression levels of ß-catenin. Pharmacological inhibition of ERK1/2 diminished aloperine-induced cardioprotection and blocked ERK1/2/ß-catenin signaling. CONCLUSIONS: These data support the cardioprotective effect of aloperine against myocardial I/R injury, which is mediated, at least in part, by the ERK1/2/ß-catenin signaling pathway.

Health risk ranking of antibiotic resistance genes in the Yangtze River.

Antibiotic resistance is an escalating global health concern, exacerbated by the pervasive presence of antibiotic resistance genes (ARGs) in natural environments. The Yangtze River, the world's third-longest river, traversing areas with intense human activities, presents a unique ecosystem for studying the impact of these genes on human health. Here, we explored ARGs in the Yangtze River, examining 204 samples from six distinct habitats of approximately 6000 km of the river, including free-living and particle-associated settings, surface and bottom sediments, and surface and bottom bank soils. Employing shotgun sequencing, we generated an average of 13.69 Gb reads per sample. Our findings revealed a significantly higher abundance and diversity of ARGs in water-borne bacteria compared to other habitats. A notable pattern of resistome coalescence was observed within similar habitat types. In addition, we developed a framework for ranking the risk of ARG and a corresponding method for calculating the risk index. Applying them, we identified water-borne bacteria as the highest contributors to health risks, and noted an increase in ARG risks in particle-associated bacteria correlating with heightened anthropogenic activities. Further analysis using a weighted ARG risk index pinpointed the Chengdu-Chongqing and Yangtze River Delta urban agglomerations as regions of elevated health risk. These insights provide a critical new perspective on ARG health risk assessment, highlighting the urgent need for strategies to mitigate the impact of ARGs on human health and to preserve the ecological and economic sustainability of the Yangtze River for future human use.