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scRiskCell is an interpretable intelligent computational framework that leverages nearly 500,000 islet cell expression profiles from 106 donors across different continuous disease states. By calculating the intrinsic relationship between donor disease states and cell expression profiles, it assigns a pseudo-cell state index to each cell. Sorting the pseudo-indexes of cells enables the identification of risk cells truly disrupted by the disease. Importantly, scRiskCell reveals the dynamic aggregation pattern of risk cells during disease progression, providing mechanistic insights for early disease prediction and clinical dynamic monitoring of disease progression.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), remains a significant global health challenge. Recent advancements in gut microbiota (GM) research have shed light on the intricate relationship between GM and TB, suggesting that GM alterations may influence host susceptibility, disease progression, and response to antituberculosis drugs. This review systematically synthesizes and analyzes the current research progress on the relationship between GM and TB, focusing on six key aspects: (1) bidirectional effects between GM dynamics and TB progression; (2) the interaction between GM and anti-TB drugs; (3) GM and TB immune response; (4) GM as a potential target for diagnosis and treatment of TB; (5) multi-omics and artificial intelligence (AI) technologies in GM-TB research; (6) current challenges and future directions in GM-TB research. We highlight the bidirectional nature of the GM–TB interaction, where MTB infection can lead to GM dysbiosis, and changes can affect the host's immune response, contributing to TB onset and progression. Advanced molecular techniques, such as next-generation sequencing and metagenomics, along with AI, play pivotal roles in elucidating these complex interactions. Future research directions include investigating the relationship between GM and TB vaccine efficacy, exploring GM's potential in TB prevention, developing microbiome-based diagnostic and prognostic tools, and examining the role of GM in TB recurrence. By addressing these areas, we aim to provide a comprehensive perspective on the latest advancements in GM and TB research and offer insights for future studies and clinical applications. Ultimately, the development of novel microbiome-based strategies may offer new tools and insights for the effective control and management of TB, a disease that continues to pose a significant threat to public health.

In this study, we reveal that macrophage-derived reactive oxygen species (ROS) can trigger the rapid formation of Salmonella aggresomes, which substantially contribute to the increased frequency of persisters induced by phagocytosis. Salmonella containing aggresomes exhibited a dormant phenotype characterized by reduced adenosine triphosphate (ATP) levels and decreased metabolic activity. Furthermore, these dormant bacteria showed upregulated expression of Salmonella pathogenicity island 1 (SPI-1)-encoded type III secretion system (T3SS)-related genes, followed by later expression of SPI-2 T3SS-related genes when macrophages ROS production declined. Our results demonstrate that Salmonella containing aggresomes can enter a dormant state to escape antibiotic attack, while crucially maintaining the ability to resuscitate when the stress environment is improved. Research on bacterial aggresomes could potentially provide therapeutic strategies to combat bacterial antibiotic persistence.

Lin, Xu, Zuxiang Yu, Yang Liu, Changzhou Li, Hui Hu, Jia-Chun Hu, Mian Liu, et al. 2025. “Gut–X axis.” iMeta 4: e270. https://onlinelibrary.wiley.com/doi/abs/10.1002/imt2.270

1. In Figure 7 of the “Gut–Heart Axis” section, the word “myocarditis” was incorrect; this should be “myocardial injury.” The paragraph “myocardial injury” includes diseases or pathological injuries that fall under the category of myocardial injury and cannot be equated with the category of cardiomyopathy. This is a misuse of the noun here. Furthermore, in Figure 7 of the “Gut–Heart Axis” section, the word “PAGly” should be deleted. Because the paragraph “myocardial injury” didn't describe “PAGly.” The revised Figure 7 and figure legend are as follows.

2. In the “Gut–Heart Axis” section, the word “PAGIn” was incorrect, it should be “PAGln.”

3. The corresponding author's name “Leming, Zheng” is misspelled, it should be “Lemin, Zheng.”

We apologize for these errors.

Multi-omics approaches revealed how nanoplastics with different surface charges influence antibiotic resistance in Escherichia coli K12. Positively charged nanoplastics enhanced antibiotic resistance by upregulating genes and proteins linked to oxidative stress tolerance and efflux pumps, and promoted antibiotic resistance genes transfer via conjugation and transformation. In contrast, negatively charged nanoplastics disrupted biofilm formation and metabolism, potentially reducing antibiotic resistance. These findings highlight the critical role of nanoplastics' surface properties in shaping microbial resistance dynamics and highlight emerging risks posed by nanoplastics to public health through accelerated antibiotic resistance propagation.

METTL5 catalyzes the N⁶-methyladenosine (m⁶A) methylation at A₁₇₇₁ in 18S rRNA, a modification essential for its association with the ribosomal protein RPL24A, facilitating the assembly of 80S ribosome. This facilitates the translation of mRNAs encoding the detoxifying glutathione S-transferase (GST) enzymes, thereby maintaining normal reactive oxygen species (ROS) levels and ensuring proper abscisic acid (ABA) responses. In mettl5 mutants, the absence of m⁶A₁₇₇₁ compromises RPL24A incorporation and ribosome assembly, impairing the translation of GSTs. This results in ROS excessive accumulation and hypersensitivity to ABA.

Plant growth-promoting rhizobacteria (PGPR) represent a sustainable method to improve crop productivity. Synthetic microbial consortia have emerged as a powerful tool for engineering rhizosphere microbiomes. However, designing functionally stable consortia remains challenging due to an insufficient understanding of bacterial social interactions. In this study, we investigated the effects of Bacillus velezensis SQR9 (i.e., a commercially important PGPR) on social interactions within the rhizosphere community, particularly among Bacillus species. SQR9 inoculation significantly enhanced cucumber plant growth and altered the structure of rhizosphere Bacillus and its related bacterial communities. The results of swarm boundary and carbon utilization assays, revealed that phylogenetically closer Bacillus strains exhibited increased social cooperation and increased metabolic niche overlap. Building on these social interactions, we designed 30 consortia comprising both highly related (HR) and moderately related (MR) types across four richness levels (1, 2, 3, and 4 strains), with MR consortia demonstrating superior PGP effects through enhanced plant growth, root colonization, indole-3-acetic acid production, and siderophore production, than the HR consortia. Expanding these findings to 300 consortia across four richness levels (1, 2, 4, and 8 strains) confirmed enhanced PGP effects in MR consortia with increasing richness. These findings highlight the importance of bacterial interactions and phylogenetic relationships in shaping rhizosphere communities and designing synthetic microbial consortia. Specifically, this study provides a framework for assembling Bacillus consortia that enhance cooperation, which would aid in improving their stability and effectiveness in agricultural applications.

Early gastric cancer (EGC) represents a critical stage in preventing and controlling the progression from gastritis to advanced gastric cancer (AGC). Therefore, identifying the single-cell characteristics of EGC, particularly the cellular composition of the tumor microenvironment (TME), as well as identifying potential predictive markers and therapeutic targets, could significantly enhance the monitoring of gastric cancer and improve clinical cure rates. We constructed a comprehensive single-cell RNA sequencing atlas for 184,426 high-quality gastric cancer cells from various stages, utilizing clinical biopsies and surgical samples. Our single-cell atlas highlights the cellular and molecular characteristics of EGC. Eight distinct cell lineage states were identified, and it was observed that the number of epithelial cell meta-clusters gradually decreased, while the number of T&NK, B, plasma, fibroblast, myeloid, and endothelial cells increased with disease progression. Certain epithelial subclusters (metaplastic stem-like cells (MSCs), pit mucous-like cells (PMC-like), proliferating cells), T-cell subclusters (T_reg, CCR7⁺ naive, CH25H⁺ CD4⁺, T_EM CD8⁺, and GFPT2⁺ CD8⁺ T cells), and endothelial subclusters (IL-33⁺ Venous-1 and AMAMTSL2⁺ Artery-2) were found to be increased in EGC. The Venous-1 subcluster was found to express high levels of IL-33. Mechanistically, it was revealed that IL-33 enhances the survival and angiogenesis of endothelial cells by upregulating the expression of adhesion proteins CD34 and PECAM1. Patient-derived EGC and AGC organoids were subsequently generated, and it was demonstrated that endothelial-derived IL-33 promoted the growth of both EGC and AGC organoids ex vitro and in vivo. Furthermore, IL-33 was found to increase the expression of KRT17 in EGC organoids. Notably, we also found that high expression of IL-33 was positively correlated with the depth of invasion and malignancy of EGC. This study provides novel insights into the single-cell components involved in EGC and reveals the role of the IL-33⁺ endothelial subcluster in EGC progression.

A total of 2901 healthy Chinese children aged 0–12 years were enrolled in the reference group; 102 confirmed Phenylketonuria cases were included as validation individuals. Establishing the age-specific reference intervals of 42 plasma amino acids in Chinese pediatric populations. Profiling dynamic interactions between multiple nutrient intake and amino acid change patterns.

RepliChrom is an interpretable machine learning model that predicts enhancer-promoter interactions using DNA replication timing across multiple cell types. By integrating replication timing with chromatin interaction data from multiple experimental platforms, it accurately distinguishes true interactions and reveals promoter-region signals as key regulatory drivers. Importantly, the RepliChrom uncovers cancer-specific chromatin patterns in leukemia, offering mechanistic insights into how replication timing shapes long-range gene regulation in both normal and diseased genomes.