Jia Liu

Background/Objectives Coffee is widely consumed worldwide and is rich in bioactive compounds with potential metabolic benefits. Recently, lipid-enriched coffee formulations have gained popularity; however, their biological effects and underlying mechanisms remain poorly understood. Methods In this study, we employed an integrated multi-omics approach to investigate the impact of coffee and high-fat coffee on the plasma metabolome and gut microbiota of C57BL/6J mice. Eighteen male mice were randomly assigned to three groups (n = 6) and received water, coffee, or high-fat coffee by oral gavage daily for 14 days. The plasma metabolome was analyzed via UHPLC-MS/MS, and the gut microbiota was profiled u 16S rRNA gene sequencing. Results Metabolomic analysis revealed distinct clustering patterns among groups. A total of 200 metabolites were significantly altered in the coffee group compared with the water group, while 86 metabolites were altered in the high-fat coffee group compared with the coffee group, with 56 overlapping metabolites suggesting a core metabolic response. Microbiome analysis showed that coffee consumption increased the abundances of Akkermansia and Bifidobacterium and decreased the levels of Ligilactobacillus and Muribaculum. Coriobacteriaceae UCG-002 and Turicibacter were significantly enriched in the high-fat coffee group, whereas Lachnospiraceae NK4A136 group, Mucispirillum and unclassified Lactobacillaceae were reduced. Association analysis highlighted the top 20 metabolites with the highest degree of connection to gut microbial genera, two of which belong to the chlorogenic acid pathway. Conclusions Reduced levels of ferulic acid and 3-hydroxybenzoic acid, two metabolites potentially involved in antioxidant and anti-inflammatory activities, were observed in the high-fat coffee group, suggesting that dietary cream influences microbiota-associated chlorogenic acid metabolism.

Salmonella enterica subsp. enterica serovar typhimurium ( S. typhimurium ) is a facultative intracellular pathogen causing significant gastrointestinal infections in both humans and animals. The type VI secretion system (T6SS) plays a crucial role in its virulence, facilitating competition with host gut microbiota and promoting infection. While S. typhimurium possesses a single T6SS, it encodes three hcp genes, which are essential for its functionality. To investigate the non-redundant roles of each hcp gene, in this study, we used 16S rRNA sequencing to analyze gut microbiota in BALB/c mice after infection with wild-type (WT) S. typhimurium or three mutant strains (Δ hcp1 , Δ hcp2 , and Δ hcp3 ). Our findings revealed that S. typhimurium infection induced severe gut dysbiosis especially on the second day post-infection, which was characterized by a notable increase in Firmicutes and activation of the energy pathways. The deletion of each hcp gene led to distinct changes in bacterial taxa, underscoring the non-redundant function of each hcp. Lactobacillus showed significant effects in both strain main effects and strain-time interaction effects. Despite having only one T6SS, S. typhimurium achieves precise modulation of its functions through the divergent roles of its Hcp proteins, enabling efficient colonization and persistence in the host gut. These findings provide insights into the intricate mechanisms of bacterial adaptation and host-pathogen interactions, offering potential avenues for therapeutic interventions targeting T6SS-mediated dysbiosis. IMPORTANCE: Salmonella enterica subsp. enterica serovar typhimurium ( S. typhimurium ) is a facultative intracellular pathogen causing significant gastrointestinal infections in humans and animals. The type VI secretion system (T6SS) plays a crucial role in its virulence, facilitating competition with host gut microbiota and promoting infection. While S. typhimurium possesses a single T6SS, it encodes three hcp genes, which are crucial for its functionality and may exhibit non-redundant roles. In this study, we used 16S rRNA sequencing to analyze gut microbiota in BALB/c mice after infection with wild-type (WT) S. typhimurium or mutant strains (Δ hcp1 , Δ hcp2 , Δ hcp3 ). Our findings revealed that the deletion of individual hcp genes led to distinct bacterial taxa changes, underscoring the non-redundant functions of each hcp . Despite having only one T6SS, S. typhimurium achieves precise modulation of its functions through the divergent roles of its Hcp proteins, enabling efficient colonization and persistence in the host gut.

Background and Objectives: Avian influenza poses a continuous public health threat, particularly to individuals with occupational exposure to poultry such as farm workers, live animal market employees, and processing plant staff. This study aimed to investigate the systemic metabolic effects of such exposure and to identify potential biomarkers for early detection and health risk assessment. Materials and Methods: An untargeted liquid chromatography–mass spectrometry (LC-MS)-based metabolomics approach was applied to analyze serum samples from occupationally exposed individuals and healthy controls. Multivariate statistical analysis, pathway enrichment, and topology analysis were performed to identify significantly altered metabolites and metabolic pathways. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to select key metabolites. Results: Multivariate statistical analysis revealed a clear separation between the exposure group and control, suggesting distinct metabolic profiles between the two populations. Pathway analysis indicated significant alterations in alanine, aspartate, and glutamate metabolism, as well as tryptophan metabolism, which are closely linked to immune regulation, energy metabolism, and host–pathogen interactions. LASSO feature selection and subsequent manual verification identified 17 key metabolites with strong discriminative power. Furthermore, lipidomic profiling revealed a pronounced increase in lysophosphatidylcholine (LPC) levels and a concurrent decrease in phosphatidylcholine (PC) species in exposed individuals. Conclusions: This study reveals metabolic disruptions associated with occupational avian influenza exposure and identifies potential serum biomarkers related to immune and lipid metabolism. These findings provide novel insights into host responses to avian influenza exposure and may support early detection and health risk assessment in high-risk occupational populations.

The presence of sub-minimal inhibitory concentration (sub-MIC) antibiotics in our environment is widespread, and their ability to induce antibiotic resistance is inevitable. Acinetobacter baumannii, a pathogen known for its strong ability to acquire antibiotic resistance, has recently shown clinical resistance to the last-line antibiotic tigecycline. To unravel the complex mechanism of A. baumannii drug resistance, we subjected tigecycline-susceptible, -intermediate, and -mildly-resistant strains to successive increases in sub-MIC tigecycline and ultimately obtained tigecycline-resistant strains. The proteome of both key intermediate and final strains during the selection process was analyzed using nanoLC-MS/MS. Among the more than 2600 proteins detected in all strains, we found that RND efflux pump AdeABC was associated with the adaptability of A. baumannii to tigecycline under sub-MIC pressure. qRT-PCR analysis also revealed higher expression of AdeAB in strains that can quickly acquire tigecycline resistance compared with strains that displayed lower adaptability. To validate our findings, we added an efflux pump inhibitor, carbonyl cyanide m-chlorophenyl hydrazine (CCCP), to the medium and observed its ability to inhibit tigecycline resistance in A. baumannii strains with quick adaptability. This study contributes to a better understanding of the mechanisms underlying tigecycline resistance in A. baumannii under sub-MIC pressure.

As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography–Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to reduce the dimension of a dataset, the impact of phenotypic traits such as diet type and genotype on the plasma metabolome was evaluated quantitatively, and hub metabolites were found based on the network graph. Our work revealed that WGCNA is a very powerful tool to decipher, interpret, and visualize metabolomic datasets.