Tissue‐resident memory T (T_RM) cells are crucial components of the immune system that provide rapid, localized responses to recurrent pathogens at mucosal and epithelial barriers. Unlike circulating memory T cells, T_RM cells are located within peripheral tissues, and they play vital roles in antiviral, antibacterial, and antitumor immunity. Their unique retention and activation mechanisms, including interactions with local epithelial cells and the expression of adhesion molecules, enable their persistence and immediate functionality in diverse tissues. Recent advances have revealed their important roles in chronic inflammation, autoimmunity, and cancer, illuminating both their protective and their pathogenic potential. This review synthesizes current knowledge on T_RM cells’ molecular signatures, maintenance pathways, and functional dynamics across different tissues. We also explore the interactions of T_RM cells with other immune cells, such as B cells, macrophages, and dendritic cells, highlighting the complex network that underpins the efficacy of T_RM cells in immune surveillance and response. Understanding the nuanced regulation of T_RM cells is essential for developing targeted therapeutic strategies, including vaccines and immunotherapies, to enhance their protective roles while mitigating adverse effects. Insights into T_RM cells’ biology hold promise for innovative treatments for infectious diseases, cancer, and autoimmune conditions.

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.

Brucella spp. are Gram-negative bacteria causing brucellosis, a major zoonotic disease affecting animals and humans. Annually, over 500,000 human cases are reported globally, with many undiagnosed due to nonspecific symptoms and diagnostic challenges. Current methods for Brucella detection, such as culture and serology, are time-consuming and lack specificity, hindering effective disease control. This study aims to develop a novel dual priming oligonucleotide (DPO) system-based PCR method for the specific detection of Brucella spp. in slaughter-aged livestock. This approach provides a rapid, sensitive, and field-deployable tool to improve early diagnosis and control of brucellosis. We developed a DPO system-based PCR assay for the specific detection of Brucella spp. in slaughter-aged livestock. The method utilizes two sets of primers designed to specifically target unique regions of the Brucella genome. The assay was validated for specificity using a panel of 15 non-target bacterial species commonly found in livestock, including Escherichia coli, Salmonella spp., and Campylobacter spp. Sensitivity was evaluated using DNA extracted from a range of Brucella strains, with detection limits assessed using serially diluted samples. The assay’s performance was further tested on 500 samples from slaughter-aged sheep to assess its applicability in field conditions. The DPO-based PCR assay demonstrated excellent specificity, with no cross-reactivity observed in any of the 15 non-target bacterial species tested. The assay was able to detect Brucella spp. at low DNA concentrations, with a sensitivity limit of approximately 5.3 × 101 CFU/mL of the Brucella per reaction. In the field validation, 500 samples from slaughter-aged sheep were tested, and the assay successfully identified Brucella infections in animals with no false positives or negatives. When compared to conventional PCR, the DPO-PCR method exhibited improved specificity and faster results, with a significantly reduced time to diagnosis. The DPO-based PCR assay provides a highly specific, rapid, and cost-effective tool for the detection of Brucella spp. in slaughter-aged livestock. This method is suitable for routine surveillance in slaughterhouses, offering a promising solution for early detection and control of brucellosis in both livestock and public health contexts. The assay’s simplicity and robustness make it an ideal candidate for field deployment, particularly in resource-limited settings where timely disease control is crucial. Not applicable

Natural products (NPs) have long been an essential source of new bioactive compounds for drug discovery; however, traditional methods for screening and isolating these compounds can be slow and often yield diminishing returns. Fortunately, advanced multi-omics and computational approaches present powerful solutions to these challenges. This review highlights innovative methodologies that integrate metabolomics, genomics, transcriptomics, and proteomics with bioinformatics and analytical chemistry to accelerate NP discovery. For instance, untargeted metabolomics platforms like high-resolution liquid chromatography-tandem mass spectrometry (LC-MS/MS) and Global Natural Products Social (GNPS) molecular networking allow for comprehensive profiling of new compounds, while targeted isotope-labeling strategies enhance this process. Additionally, genome and metagenome mining tools such as antibiotics and secondary metabolite analysis shell (antiSMASH), Deep Biosynthetic Gene Cluster (DeepBGC), and Pipeline for Reconstructing Integrated Syntheses of Metabolites (PRISM) quickly identify biosynthetic gene clusters (BGCs) in both cultured and uncultured organisms, often using heterologous expression to validate products. Transcriptomic analyses, including RNA sequencing (RNA-seq), co-expression networks, and fluxomics, help clarify how pathways are regulated, while quantitative proteomics techniques like tandem mass tags/isobaric tags for relative and absolute quantitation (TMT/iTRAQ) and label-free methods, along with chemoproteomics approaches such as cellular thermal shift assay and thermal proteome profiling (TPP), uncover molecular targets and their mechanisms of action. This review also places significant emphasis on the role of artificial intelligence (AI) and machine learning (ML) in integrating multi-omics data, spanning activities from constructing gene-metabolite correlation networks to leveraging knowledge graphs and graph neural networks for data fusion and functional prediction. Finally, this review concludes by discussing the synergistic benefits of multi-omics for natural-product discovery, addressing current technical challenges, and exploring future directions toward high-throughput, intelligent data integration for next-generation NP research.