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Heavy metals are toxic and harmful pollutants that can affect the school environment and the exposed children's health. This study collected dust samples and children's fecal specimens, and performed gene sequencing. We used eXtreme Gradient Boosting to determine the impact of heavy metals on environmental microorganisms and gut microbiota, while using the relative length of the quadrant and Fourth-corner analysis to explore the relationship among the three components. We found heavy metal pollution existed in the classroom environment, with lead and copper significantly affecting environmental microorganisms' community structure. Although nonsignificant associations were observed between heavy metals and gut microbiota, Fourth-corner analysis revealed the associations were significantly mediated by environmental microorganisms. Both heavy metals and microorganisms in the environment can disrupt the microbial community structure in the intestines of exposed children.

The intricate bidirectional relationships among microbiota, microbial proteins, drugs, and diseases are essential for advancing precision medicine and minimizing adverse drug reactions. However, there are currently no data resources that comprehensively describe these valuable interactions. Therefore, the Microbiota-Drug Interaction and Disease Phenotype Interrelation Database (MDIPID) database was developed in this study. MDIPID is distinctive in its ability to elucidate the complex interactions among microbiota, microbial proteins, drugs/substances, and disease phenotypes, thereby providing a comprehensive interconnected network that facilitates the identification of microbial therapy targets and advances personalized medicine. This comprehensive resource is expected to become a popular repository for researchers aiming to identify microbial therapeutic targets, predict drug efficacy, and develop new therapies, thereby facilitating the advancement of personalized medicine. MDIPID can be accessed free without any login requirement at: https://idrblab.org/mdipid/.

This study assembled a high-quality chromosome-level genome of Prunus tomentosa, offering a vital resource for elucidating its genetic architecture, evolutionary relationships, and facilitating genome-assisted breeding efforts. Multi-omics integration revealed PtIMP3 and PtMIOX1L as key factors in cold tolerance of P. tomentosa. PtIMP3 drives the conversion of glucose-6-phosphate to myo-inositol, while PtMIOX1L catalyzes myo-inositol to d-glucuronic acid. Specifically, the high expression abundance of PtIMP3 and low expression abundance of PtMIOX1L resulted in high endogenous inositol levels in P. tomentosa. The application of myo-inositol enhanced the cold tolerance of cherry rootstocks by modulating reactive oxygen species concentrations and maintaining a stable relative water content. This finding supports the superior performance of P. tomentosa in adapting to extreme low-temperatures environmental conditions. These insights advance strategies for improving cold tolerance in horticultural crops, bridging fundamental research with practical applications in developing climate-resilient crops.

The gut microbiota–cancer interaction functions through multi-level biological mechanisms, forming the basis for both diagnostic and therapeutic applications. Current technical and biological challenges drive the field toward precision medicine approaches, aiming to integrate multi-dimensional data for optimized, personalized cancer treatments.

Code-based data visualization is a crucial tool for understanding and communicating experimental findings while ensuring scalability and reproducibility. However, complex programming interfaces pose a significant barrier for life scientists. To address this challenge, tidyplots provides a user-friendly code-based interface for creating customizable and insightful plots. With its consistent and intuitive syntax, tidyplots empowers researchers to leverage automated data visualization pipelines while minimizing required programming skills.

Maternal health, specifically changes in the gut microbiota, can profoundly impact offspring health; however, our understanding of how gut microbiota alterations during the preconception period influence the offspring remains limited. In this study, we investigated the impact and mechanisms of preconception maternal gut dysbiosis on the development of the enteric nervous system (ENS) in mice. We found that preconception maternal exposure to antibiotics led to the abnormal development of the ENS in offspring, increasing their susceptibility to water avoidance stress at the adult stage. Metagenomic, targeted metabolomic, and transcriptomic analyses revealed that preconception antibiotic exposure disrupted the expression of genes crucial for embryonic ENS development by altering maternal gut microbiota composition. Multi-omics analysis combined with Limosilactobacillus reuteri and propionate gestational supplementation demonstrated that the maternal gut microbiota and metabolites may influence embryonic ENS development via the GPR41–GDNF/RET/SOX10 signaling pathway. Our findings highlight the critical importance of maintaining a healthy maternal gut microbiota before conception to support normal ENS development in offspring.

Root System Architecture (RSA) plays an essential role in influencing maize yield by enhancing anchorage and nutrient uptake. Analyzing maize RSA dynamics holds potential for ideotype-based breeding and prediction, given the limited understanding of the genetic basis of RSA in maize. Here, we obtained 16 root morphology-related traits (R-traits), 7 weight-related traits (W-traits), and 108 slice-related microphenotypic traits (S-traits) from the meristem, elongation, and mature zones by cross-sectioning primary, crown, and lateral roots from 316 maize lines. Significant differences were observed in some root traits between tropical/subtropical and temperate lines, such as primary and total root diameters, root lengths, and root area. Additionally, root anatomy data were integrated with genome-wide association study (GWAS) to elucidate the genetic architecture of complex root traits. GWAS identified 809 genes associated with R-traits, 261 genes linked to W-traits, and 2577 key genes related to 108 slice-related traits. We confirm the function of a candidate gene, fucosyltransferase5 (FUT5), in regulating root development and heat tolerance in maize. The different FUT5 haplotypes found in tropical/subtropical and temperate lines are associated with primary root features and hold promising applications in molecular breeding. Furthermore, we performed machine learning prediction models of RSA using root slice traits, achieving high prediction accuracy. Collectively, our study offers a valuable tool for dissecting the genetic architecture of RSA, along with resources and predictive models beneficial for molecular design breeding and genetic enhancement.

Chimeric RNAs from chromosomal rearrangements have long been validated as cancer markers and therapeutic targets for many years. Recently, trans-splicing and cis-splicing between adjacent genes are also shown to generate chimeric RNAs. They influence tumor progression by coding fusion proteins, acting as long noncoding or circular RNAs, or altering parental gene expression. Here, we analyzed chimeric RNAs from The Cancer Genome Atlas and Chinese Prostate Cancer Genome and Epigenome Atlas, identifying similarities and differences between Western and Chinese prostate cancer (PCa) cohorts. We confirmed distinct chimeric RNA expression patterns among cancer epithelial cells, cancer-associated fibroblasts, tumor-associated macrophages, and T cells. We unraveled how these chimeras impact tumor cell growth, stromal cell transformation, and intercellular communication within the microenvironment. This comprehensive study establishes a chimeric transcriptome atlas for Chinese PCa patients, highlights population-specific disparities, and presents validated chimeric RNAs with diagnostic, prognostic, and therapeutic potential.

To address the substantial variability in immune checkpoint blockade (ICB) therapy effectiveness, we developed an innovative R package called integrated Machine Learning and Genetic Algorithm-driven Multiomics analysis (iMLGAM), which establishes a comprehensive scoring system for predicting treatment outcomes through advanced multi-omics data integration. Our research demonstrates that iMLGAM scores exhibit superior predictive performance across independent cohorts, with lower scores correlating significantly with enhanced therapeutic responses and outperforming existing clinical biomarkers. Detailed analysis revealed that tumors with low iMLGAM scores display distinctive immune microenvironment characteristics, including increased immune cell infiltration and amplified antitumor immune responses. Critically, through clustered regularly interspaced short palindromic repeats screening, we identified Centrosomal Protein 55 (CEP55) as a key molecule modulating tumor immune evasion, mechanistically confirming its role in regulating T cell-mediated antitumor immune responses. These findings not only validate iMLGAM as a powerful prognostic tool but also propose CEP55 as a promising therapeutic target, offering novel strategies to enhance ICB treatment efficacy. The iMLGAM package is freely available on GitHub (https://github.com/Yelab1994/iMLGAM), providing researchers with an innovative approach to personalized cancer immunotherapy prediction.

This study reports the first high-quality telomere-to-telomere (T2T) Rhododendron liliiflorum genome with 11 chromosomes that are gap free. The 24 telomeres and all 13 centromeres detected in this genome, which reached the highest quality gold level. In addition, other three Rhododendron species were sequenced and assembled to the chromosomal level. Based on 15 Rhododendron genomes, we conducted a pan-genome analysis of genus Rhododendron. Combining the genome and whole transcriptome sequencing, we identified several key genes and miRNAs related to the heat stress, which were further verified by transgenic experiments. Our findings provide rich resources for comparative and functional genomics studies of Rhododendron species.