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Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health in the last decade. Many genes can confer resistance, but evaluating the relative health risks of ARGs is complex. Factors such as the abundance, propensity for lateral transmission and ability of ARGs to be expressed in pathogens are all important. Here, an analysis at the metagenomic level from various habitats (6 types of habitats, 4572 samples) detects 2561 ARGs that collectively conferred resistance to 24 classes of antibiotics. We quantitatively evaluate the health risk to humans, defined as the risk that ARGs will confound the clinical treatment for pathogens, of these 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. Our results demonstrate that 23.78% of the ARGs pose a health risk, especially those which confer multidrug resistance. We also calculate the antibiotic resistance risks of all samples in four main habitats, and with machine learning, successfully map the antibiotic resistance threats in global marine habitats with over 75% accuracy. Our novel method for quantitatively surveilling the health risk of ARGs will help to manage one of the most important threats to human and animal health. Antibiotic resistance genes (ARGs) have accelerated microbial threats to human health. Here, Zhang et al. analyze 4572 metagenomic samples to illustrate the global patterns of ARG distribution in diverse habitats. They quantitatively evaluate the health risk to humans of 2561 ARGs by integrating human accessibility, mobility, pathogenicity and clinical availability. With the machine learning, they map the antibiotic resistance threats in global marine habitats.

Obesity imposes a global health threat and calls for safe and effective therapeutic options. Here, we found that protein-rich diet significantly reduced body fat storage in fruit flies, which was largely attributed to dietary cysteine intake. Mechanistically, dietary cysteine increased the production of a neuropeptide FMRFamide (FMRFa). Enhanced FMRFa activity simultaneously promoted energy expenditure and suppressed food intake through its cognate receptor (FMRFaR), both contributing to the fat loss effect. In the fat body, FMRFa signaling promoted lipolysis by increasing PKA and lipase activity. In sweet-sensing gustatory neurons, FMRFa signaling suppressed appetitive perception and hence food intake. We also demonstrated that dietary cysteine worked in a similar way in mice via neuropeptide FF (NPFF) signaling, a mammalian RFamide peptide. In addition, dietary cysteine or FMRFa/NPFF administration provided protective effect against metabolic stress in flies and mice without behavioral abnormalities. Therefore, our study reveals a novel target for the development of safe and effective therapies against obesity and related metabolic diseases.

Microbial communities play a crucial role in ocean ecology and global biogeochemical processes. However, understanding the intricate interactions among diversity, taxonomical composition, functional traits, and how these factors respond to climate change remains a significant challenge. Here, we propose seven distinct ecological statuses by systematically considering the diversity, structure, and biogeochemical potential of the ocean microbiome to delineate their biogeography. Anthropogenic climate change is expected to alter the ecological status of the surface ocean by influencing environmental conditions, particularly nutrient and oxygen contents. Our predictive model, which utilizes machine learning, indicates that the ecological status of approximately 32.44% of the surface ocean may undergo changes from the present to the end of this century, assuming no policy interventions. These changes mainly include poleward shifts in the main taxa, increases in photosynthetic carbon fixation and decreases in nutrient metabolism. However, this proportion can decrease significantly with effective control of greenhouse gas emissions. Our study underscores the urgent necessity for implementing policies to mitigate climate change, particularly from an ecological perspective. Zhang et al. propose the ecological status of the ocean by considering microbial diversity, structure, and biogeochemical potential. Ecological status of 32.44% surface ocean will change due to climate change in 2100, assuming no policy intervention.

The vaginal microbiome is an immune defense against reproductive diseases and can serve as an important biomarker for cervical cancer. However, the intrinsic relationship between the recurrence and the vaginal microbiome in patients with cervical cancer before and after concurrent chemoradiotherapy is poorly understood. Here, we analyzed 125 vaginal microbial profiles from a patient cohort of stage IB–IVB cervical cancer using 16S metagenomic sequencing and deciphered the microbial composition and functional characteristics of the recurrent and non-recurrent both before and after chemoradiotherapy. We demonstrated that the abundance of beneficial bacteria and stability of the microbial community in the vagina decreased in the recurrence group, implying the unique characteristics of the vaginal microbiome for recurrent cervical cancer. Moreover, using machine learning, we identified Lactobacillus iners as the most important biomarker, combined with age and other biomarkers (such as Ndongobacter massiliensis , Corynebacterium pyruviciproducens ATCC BAA-1742, and Prevotella buccalis ), and could predict cancer recurrence phenotype before chemoradiotherapy. This study prospectively employed rigorous bioinformatics analysis and highlights the critical role of vaginal microbiota in post-treatment cervical cancer recurrence, identifying promising biomarkers with prognostic significance in the context of concurrent chemoradiotherapy for cervical cancer. The role of L. iners in determining chemoradiation resistance in cervical cancer warrants further detailed investigation. Our results expand our understanding of cervical cancer recurrence and help develop better strategies for prognosis prediction and personalized therapy.

The relative abundance of single-exon genes (SEGs) in higher plants is perplexing. Uncovering the synonymous codon usage pattern of SEGs will benefit for further understanding their underlying evolutionary mechanism in plants. Using internal correspondence analysis (ICA), we reveal a significant difference in synonymous codon usage between SEGs and multiple-exon genes (MEGs) in rice. But the effect is weak, accounting for only 2.61% of the total codon usage variability. SEGs and MEGs contain remarkably different base compositions, and are under clearly differential selective constraints, with the former having higher GC content, and evolving relatively faster during evolution. In the group of SEGs, the variability in synonymous codon usage among genes is partially due to the variations in GC content, gene function, and gene expression level, which accounts for 22.03%, 5.99%, and 3.32% of the total codon usage variability, respectively. Therefore, mutational bias and natural selection should work on affecting the synonymous codon usage of SEGs in rice. These findings may deepen our knowledge for the mechanisms of origination, differentiation and regulation of SEGs in plants.

Complex structural components with small concave surfaces are widely needed in aerospace, optics, and electronic industry. On account of the interference between grinding wheel and workpiece and small concave surfaces, traditional grinding machine cannot realize the grinding process; hence, a specialized grinding machine tool with on-machine electric discharge truing function is needed. After analyzing the structural characteristics and processing requirements of the component, the kinematic chain and configuration were designed. The structure of the machine tool was divided into four function modules, and each module was designed, analyzed, and optimized, respectively. Then the finite element analysis (FEA) of the whole machine tool was conducted including static, modal, and harmonic response analysis to verify the performance of the machine and identify the weak links of the structure loop. The error model was established by screw theory to study the quantitative relationship between the static deformations and processing accuracy. Both finite element analysis and error model can provide guidance for further optimization. Finally, the performance of the machine tool was evaluated by the grinding and on-machine truing experiments, achieving the profile accuracy (PV) of 0.339 μm, surface roughness (Ra) of 50.2 nm, and the grinding wheel surface with diamonds distributing homogeneously. The results indicate that the developed machine tool can well satisfy the processing requirements of the component.

The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile. The contribution of human gastrointestinal (GI) microbiota and metabolites to host health has recently become much clearer. However, many confounding factors can influence the accuracy of gut microbiome and metabolome studies, resulting in inconsistencies in published results. In this study, we systematically investigated the effects of fecal sampling regions and storage and retrieval conditions on gut microbiome and metabolite profiles from three healthy children. Our analysis indicated that compared to homogenized and snap-frozen samples (standard control [SC]), different sampling regions did not affect microbial community alpha diversity, while a total of 22 of 176 identified metabolites varied significantly across different sampling regions. In contrast, storage conditions significantly influenced the microbiome and metabolome. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles. Sample storage in RNALater showed a significant level of variation in both microbiome and metabolome profiles, independent of the storage or retrieval conditions. The effect of RNALater on the metabolome was stronger than the effect on the microbiome, and individual variability between study participants outweighed the effect of RNALater on the microbiome. We conclude that homogenizing stool samples was critical for metabolomic analysis but not necessary for microbiome analysis. Short-term room temperature storage had a minimal effect on the microbiome and metabolome profiles and is recommended for short-term fecal sample storage. In addition, our study indicates that the use of RNALater as a storage medium of stool samples for microbial and metabolomic analyses is not recommended. IMPORTANCE The gastrointestinal microbiome and metabolome can provide a new angle to understand the development of health and disease. Stool samples are most frequently used for large-scale cohort studies. Standardized procedures for stool sample handling and storage can be a determining factor for performing microbiome or metabolome studies. In this study, we focused on the effects of stool sampling regions and stool sample storage conditions on variations in the gut microbiome composition and metabolome profile.

The global crisis in antimicrobial resistance continues to grow. Estimating the risks of antibiotic resistance transmission across habitats is hindered by the lack of data on mobility and habitat‐specificity. Metagenomic samples of 6092 are analyzed to delineate the unique core resistomes from human feces and seven other habitats. This is found that most resistance genes (≈85%) are transmitted between external habitats and human feces. This suggests that human feces are broadly representative of the global resistome and are potentially a hub for accumulating and disseminating resistance genes. The analysis found that resistance genes with ancient horizontal gene transfer (HGT) events have a higher efficiency of transfer across habitats, suggesting that HGT may be the main driver for forming unique but partly shared resistomes in all habitats. Importantly, the human fecal resistome is historically different and influenced by HGT and age. The most important routes of cross‐transmission of resistance are from the atmosphere, buildings, and animals to humans. These habitats should receive more attention for future prevention of antimicrobial resistance. The study will disentangle transmission routes of resistance genes between humans and other habitats in a One Health framework and can identify strategies for controlling the ongoing dissemination and antibiotic resistance. The global crisis in antimicrobial resistance continues to grow. A novel analysis framework of resistance risk is built in all One‐Health components, to estimate the risks of transmission routes of resistance genes between humans and other habitats, which can help to identify strategies for controlling the ongoing dissemination and antibiotic resistance.

Zhe-Maidong ( Ophiopogon japonicus (L.f.) Ker-Gawl) is a traditional medicinal herb in the family Liliaceae that has significant pharmacological effects on immunity and cardiovascular disease. In this study, three different growth stages of Zhe-Maidong were investigated using RNA-seq, and a total of 16.4 Gb of raw data was obtained. After filtering and assembling, 96,738 unigenes with an average length of 605.3 bp were ultimately generated. A total of 77,300 unigenes were annotated using information from five databases, including the NT, NR, SwissProt, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) databases. Additionally, the mechanisms of flavonoid, saponin and polysaccharide biosynthesis and of accumulation at different stages of tuber development were also characterized. From the first to third years, the contents of flavonoids, saponins and polysaccharides all increased, whereas the expression levels of related genes decreased in the flavonoid and saponin pathways and first increased and then decreased in the polysaccharide pathway. The results of this study provide the most comprehensive expressed sequence resource for Zhe-Maidong and will expand the available O . japonicus gene library and facilitate further genome-wide research and analyses of this species.

Background Patients with pancreatic ductal adenocarcinoma (PDAC) display an altered oral, gastrointestinal, and intra-pancreatic microbiome compared to healthy individuals. However, knowledge regarding the bile microbiome and its potential impact on progression-free survival in PDACs remains limited. Methods Patients with PDAC ( n  = 45), including 20 matched pairs before and after surgery, and benign controls ( n  = 16) were included prospectively. The characteristics of the microbiomes of the total 81 bile were revealed by 16  S-rRNA gene sequencing. PDAC patients were divided into distinct groups based on tumor marker levels, disease staging, before and after surgery, as well as progression free survival (PFS) for further analysis. Disease diagnostic model was formulated utilizing the random forest algorithm. Results PDAC patients harbor a unique and diverse bile microbiome (PCoA, weighted Unifrac, p  = 0.038), and the increasing microbial diversity is correlated with dysbiosis according to key microbes and microbial functions. Aliihoeflea emerged as the genus displaying the most significant alteration among two groups ( p  < 0.01). Significant differences were found in beta diversity of the bile microbiome between long-term PFS and short-term PFS groups (PCoA, weighted Unifrac, p  = 0.005). Bacillota and Actinomycetota were identified as altered phylum between two groups associated with progression-free survival in all PDAC patients. Additionally, we identified three biomarkers as the most suitable set for the random forest model, which indicated a significantly elevated likelihood of disease occurrence in the PDAC group ( p  < 0.0001). The area under the receiver operating characteristic (ROC) curve reached 80.8% with a 95% confidence interval ranging from 55.0 to 100%. Due to the scarcity of bile samples, we were unable to conduct further external verification. Conclusion PDAC is characterized by an altered microbiome of bile ducts. Biliary dysbiosis is linked with progression-free survival in all PDACs. This study revealed the alteration of the bile microbiome in PDACs and successfully developed a diagnostic model for PDAC.