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The drug response phenotype is determined by a combination of genetic and environmental factors. The high clinical conversion failure rate of gene-targeted drugs might be attributed to the lack of emphasis on environmental factors and the inherent individual variability in drug response (IVDR). Current evidence suggests that environmental variables, rather than the disease itself, are the primary determinants of both gut microbiota composition and drug metabolism. Additionally, individual differences in gut microbiota create a unique metabolic environment that influences the in vivo processes underlying drug absorption, distribution, metabolism, and excretion (ADME). Here, we discuss how gut microbiota, shaped by both genetic and environmental factors, affects the host's ADME microenvironment within a new evaluation system for drug–microbiota interactions. Furthermore, we propose a new top-down research approach to investigate the intricate nature of drug–microbiota interactions in vivo. This approach utilizes germ-free animal models, providing foundation for the development of a new evaluation system for drug–microbiota interactions.

Akkermansia muciniphila pretreatment mitigated Listeria monocytogenes infection in mice. A. muciniphila improved gut microbiota disturbed by L. monocytogenes infection and significantly increased the level of intestinal linoleic acid in mice. Linoleic acid strengthened the intestinal epithelial barrier and reduced pathogen translocation partly by regulating NF-κB/MLCK pathway in a GPR40-dependent manner.

Gut Universe Database (GutUDB) provides a comprehensive, systematic, and practical platform for researchers, and is dedicated to the management, analysis, and visualization of knowledge related to intestinal diseases. Based on this database, eight major categories of omics data analyses are carried out to explore the genotype-phenotype characteristics of a certain intestinal disease. The first tool for comprehensive omics data research on intestinal diseases will help each researcher better understand intestinal diseases.

We investigated soil bacterial and fungal communities, constructed co-occurrence networks, and estimated bacterial traits along a gradient of nitrogen (N) input. The results showed that soil bacterial co-occurrence networks complexity decreased with increasing N input. The ratio of negative to positive cohesion decreased with increasing N input, suggesting the declined competitive but strengthened cooperative interactions. However, soil fungal network complexity did not change under N enrichment. In addition, N input stimulated the copiotroph/oligotroph ratio, ribosomal RNA operon (rrn) copy number, and guanine-cytosine (GC) content of soil bacteria, shifting bacterial life history strategy toward copiotroph with increased r-/K-strategy ratio. Piecewise structural equation modeling results further revealed that the reduction in bacterial co-occurrence network complexity was directly regulated by the increased bacterial r-/K-strategy ratio, rather than reduced bacterial richness. Our study reveals the mechanisms through which microbial traits regulate interactions and shape co-occurrence networks under global changes.

Venn diagrams serve as invaluable tools for visualizing set relationships due to their ease of interpretation. Widely applied across diverse disciplines such as metabolomics, genomics, transcriptomics, and proteomics, their utility is undeniable. However, the operational complexity has been compounded by the absence of standardized data formats and the need to switch between various platforms for generating different Venn diagrams. To address these challenges, we introduce the EVenn platform, a versatile tool offering a unified interface for efficient data exploration and visualization of diverse Venn diagrams. EVenn (http://www.ehbio.com/test/venn) streamlines the data upload process with a standardized format, enhancing the capabilities for multimodule analysis. This comprehensive protocol outlines various applications of EVenn, featuring representative results of multiple Venn diagrams, data uploads in the centralized data center, and step-by-step case demonstrations. Through these functionalities, EVenn emerges as a valuable and user-friendly tool for the in-depth exploration of multiomics data.

Recent studies have highlighted the biological significance of cuproptosis in disease occurrence and development. However, it remains unclear whether cuproptosis signaling also has potential impacts on tumor initiation and prognosis of gastric cancer (GC). In this study, 16 cuproptosis-related genes (CRGs) transcriptional profiles were harnessed to perform the regularized latent variable model-based clustering in GC. A cuproptosis signature risk scoring (CSRS) scheme, based on a weighted sum of principle components of the CRGs, was used to evaluate the prognosis and risk of individual tumors of GC. Four distinct cuproptosis signature-based clusters, characterized by differential expression patterns of CRGs, were identified among 1136 GC samples across three independent databases. The four clusters were also associated with different clinical outcomes and tumor immune contexture. Based on the CSRS, GC patients can be divided into CSRS-High and CSRS-Low subtypes. We found that DBT, MTF1, and ATP7A were significantly elevated in the CSRS-High subtype, while SLC31A1, GCSH, LIAS, DLAT, FDX1, DLD, and PDHA1 were increased in the CSRS-Low subtype. Patients with CSRS-Low score were characterized by prolonged survival time. Further analysis indicated that CSRS-Low score also correlated with greater tumor mutation burden (TMB) and higher mutation rates of significantly mutated genes (SMG) in GC. In addition, the CSRS-High subtype harbored more significantly amplified focal regions related to tumorigenesis (3q27.1, 12p12.1, 11q13.3, etc.) than the CSRS-Low tumors. Drug sensitivity analyses revealed the potential compounds for the treatment of gastric cancer with CSRS-High score, which were experimentally validated using GC cells. This study highlights that cuproptosis signature-based subtyping is significantly associated with different clinical features and molecular landscape of GC. Quantitative evaluation of the CSRS of individual tumors will strengthen our understanding of the occurrence and development of cuproptosis and the treatment progress of GC.

In this work, we introduced a siderophore information database (SIDERTE), a digitized siderophore information database containing 649 unique structures. Leveraging this digitalized data set, we gained a systematic overview of siderophores by their clustering patterns in the chemical space. Building upon this, we developed a functional group-based method for predicting new iron-binding molecules with experimental validation. Expanding our approach to the collection of open natural products (COCONUT) database, we predicted a staggering 3199 siderophore candidates, showcasing remarkable structure diversity that is largely unexplored. Our study provides a valuable resource for accelerating the discovery of novel iron-binding molecules and advancing our understanding of siderophores.

The assembly of two sorghum T2T genomes corrected the assembly errors in the current reference, uncovered centromere variation, boosted functional genomics research, and accelerated sorghum improvement.

In the era of ubiquitous high-throughput sequencing studies, there is a growing need for analysis tools that are not just performant but also comprehensive and user-friendly enough to cater to both novice and advanced users. This article introduces SeqKit2, the next iteration of the widely used sequence analysis tool SeqKit, featuring expanded functionality, performance optimizations, and support for additional compression methods. Retaining a pragmatic subcommand architecture, SeqKit2 represents substantial enhancement through the inclusion of 19 additional subcommands, expanding its overall repertoire to a total of 38 in eight categories. The new subcommands add functionality such as amplicon processing and robust, error-tolerant parsing of sequence records. In addition, three subcommands designed for real-time analysis are added for periodic monitoring of properties of FASTQ and Binary Alignment/Map alignment records and real-time streaming from multiple sequence files. The performance of SeqKit2 is benchmarked against the old version of SeqKit, Bioawk, Seqtk, and SeqFu tools. SeqKit2 consistently outperforms its predecessor, albeit with marginally higher memory usage, while maintaining competitive runtimes against other tools. With its broad functionality, proven usability, and ongoing development driven by user feedback, we hope that bioinformaticians will find SeqKit2 useful as a “Swiss army knife” of sequence and alignment processing—equally adept at facilitating ad hoc analyses and seamlessly integrating into larger pipelines.

Continuous cropping often results in severe “replant problem,” across various crops due to the autotoxins accumulation, soil acidification, pathogens proliferation, and microbial dysfunction. We unveiled a groundbreaking phenomenon that long-term continuous cropping (LTCC) can alleviate the tobacco replant problem. This mitigation occurs through the enrichment of autotoxin-degrading microbes, and the transformative impact is evident with even a modest application (10%) of LTCC soil to short-term continuous cropping (STCC) soil. Our investigation has pinpointed specific autotoxin-degrading bacteria, particularly the Pseudomonas and Burkholderia species, which exhibit the capacity to alleviate the tobacco replant problem in STCC soil. Their autotoxin-degrading mechanism using axenic culture and soil samples was also conducted via comprehensive analyses of microbiome and transcriptome approach. This research sheds light on the potential of LTCC as a strategic approach for sustainable agriculture, addressing replant problems and promoting the health of cropping systems. UV, ultraviolet; OD, optical density.