Antimicrobial resistance poses a major global health challenge, and infections caused by multidrug-resistant Gram-negative bacteria are associated with substantial morbidity and mortality. In contrast to many Gram-positive pathogens, Gram-negative bacteria combine intrinsic barriers with acquired determinants, including enzymatic drug inactivation, reduced outer membrane permeability, active efflux, and target modifications, which collectively compromise the efficacy of multiple antibiotic classes. Previous reviews have largely catalogued resistant pathogens or antimicrobial agents. This review provides a mechanism-focused overview of antimicrobial resistance in clinically important Gram-negative bacteria and explains how dominant resistance determinants translate into clinically relevant failure modes, such as delayed effective therapy, limited treatment options, and increased reliance on toxic last-line agents. Current and emerging therapeutic strategies are discussed through a mechanism-based lens, emphasizing newer β-lactam/β-lactamase inhibitor combinations and nontraditional approaches, including phages, antivirulence, and microbiome-based interventions. This review highlights the conceptual links between resistance mechanisms, clinical impact, and rational therapeutic choices and identifies priorities for future research aimed at mitigating antimicrobial-resistant Gram-negative infections.

While shotgun metagenomics is often used to profile antibiotic resistome in gut microbial communities, few studies have investigated if the choice of sequencing platform and assembly strategy affect what mobile genetic elements and antimicrobial resistance genes are recovered. In this study, we compared three platforms (Illumina, Oxford Nanopore, and PacBio HiFi) and seven assembly strategies on gut metagenomes from cattle, pig, and human as case studies. Long-read assemblies recovered 5- to 7-fold more plasmid sequence than Illumina in cattle and pig (mean 17.0 Mb vs. 3.1 Mb), while Illumina performed comparably in the less diverse human gut where high per-species coverage enabled effective short-read plasmid assembly. Long reads also detected more resistance genes on plasmid contigs. Hybrid assembly results depended on the algorithm: scaffolding-based OPERA-MS preserved long-read contiguity and recovered more plasmid-borne resistance genes, while the short-read-centric metaSPAdes hybrid mode produced fragmented assemblies. After collapsing haplotype redundancy, PacBio HiFi identified 2 and 49 unique multi-drug resistance plasmid lineages in cattle and pig, respectively. On the other hand, only 2 and 4 were identified from Illumina. Long reads also placed far more ARGs in a putative mobilization context (50–73%) compared to 14–21% for short reads. Platform and assembly strategy are thus key variables in mobilome and resistome characterization and should be accounted for in antimicrobial resistance surveillance.

Plastic waste in marine environments provides novel habitats for diverse organisms, forming distinct microbial ecosystems known as the ‘plastisphere’. Although bacteria of the plastisphere have been widely studied, the role of fungi in plastisphere formation and plastic degradation remains largely unexplored. Thus, we investigated temporal changes in culturable fungal community composition on three common plastic types—high-density polyethylene, low-density polyethylene, and polypropylene—across the early (seven days) and mature (30 days) plastisphere developmental stages through a marine mesocosm experiment. In total, 436 fungal strains were isolated and identified as belonging to 179 taxa, with Penicillium, Cladosporium, Trichoderma, Aspergillus, and Fusarium as the dominant genera. Temporal shifts in the species richness of the dominant genera were observed: Cladosporium showed higher species richness at the early stage, whereas that of Trichoderma increased at the mature stage. Plastic degradation assays revealed that 54.6% of the strains exhibited measurable degradation capacity, with patterns varying by plastic type rather than fungal developmental stage. Scanning electron microscope observations revealed surface damage patterns including cracks, pitting, and erosion on the plastic surfaces. These findings provide novel insights into the composition and functional heterogeneity of culturable fungal communities in the marine plastisphere and suggest that plastisphere fungi play diverse ecological roles beyond direct plastic degradation.

Mycobacterium tuberculosis (Mtb) encounters diverse and fluctuating microenvironments during infection, including changes in nutrient availability and pH. While adaptation to individual host-associated conditions has been extensively studied, the impact of repeated environmental fluctuations on bacterial physiology and antibiotic survival remains unclear. In this study, we investigated how long-term adaptation to stable or fluctuating environments influences Mtb growth and drug responses. Mtb populations were serially passaged for six consecutive passages under combinations of different carbon sources and pH conditions that were either maintained consistently or altered across passages. Environmental history significantly affected bacterial growth dynamics and antibiotic survival. Notably, under identical final condition with cholesterol as a sole carbon source, populations adapted to a stable environment exhibited higher survival following bedaquiline and rifampicin treatment than populations exposed to fluctuating environments. These findings suggest that stable environments promote the optimization of growth and stress-response programs, whereas environmental fluctuations limit such optimization despite potentially increasing phenotypic heterogeneity and a possibility of survival. Together, our results identify environmental history as an important determinant of antibiotic survival in Mtb and highlight the need to consider dynamic host-like environments when investigating tuberculosis physiology and drug responses.

Pathogenic Escherichia coli is a major cause of foodborne illness worldwide and includes strains capable of causing severe disease. To establish a genome-informed framework for foodborne outbreak surveillance, we analyzed 1,029 E. coli isolates from clinical, food, livestock, and environmental sources using whole-genome sequencing. Pathogenic isolates obtained from human clinical cases or linked to documented outbreaks were classified as epidemiologically defined high-risk (EpiHR), whereas the remaining pathogenic isolates were classified as non-EpiHR. Virulence-associated genomic features were extracted using a bioinformatics pipeline, and four machine learning (ML) algorithms, including gradient boosting machine, random forest (RF), and support vector machines with linear and radial basis function kernels, were evaluated. Among them, the RF model showed the best performance, achieving an area under the curve (AUC) of 0.98 and accuracy of 0.93 in 10-fold cross-validation. Additional leave-one-group-out validation showed retained discrimination across held-out sequence types and serotypes, although performance was reduced when isolates were grouped by isolation source. Evaluation using an independent test dataset of 1,908 publicly available pathogenic E. coli genomes showed an AUC of 0.97 and a sensitivity of 0.98. Feature importance analysis using Shapley additive explanations identified influential predictive features, including traT, etpB, and enterotoxin-associated genes. A reduced 10-feature model achieved an AUC of 0.79 in the independent test dataset, supporting its exploratory use for future simplified screening approaches. These results indicate that genome-based ML provides a sensitive framework for surveillance-oriented prioritization of EpiHR pathogenic E. coli isolates, with model predictions interpreted together with epidemiological information.

Hypervirulent Klebsiella pneumoniae (hvKp) is an emerging pathogen that causes severe community-acquired infections; however, the immune mechanisms controlling intracellular hvKp have yet to be clearly defined. In this study, we investigated the therapeutic potential of berberine against hvKp infection and elucidated the underlying molecular mechanisms using macrophage cell models and in vivo zebrafish models. Berberine significantly reduced intracellular hvKp survival in macrophages and improved survival in hvKp-infected zebrafish. Berberine markedly attenuated proinflammatory cytokine production and inhibited the c-Jun N-terminal kinase (JNK) and extracellular signal-regulated kinase (ERK) signaling pathways. Notably, we found that hvKp exploited host lipid droplets (LD) biosynthesis to support its intracellular survival, and berberine effectively suppressed LD accumulation. Mechanistically, berberine promoted the nuclear translocation of transcription factor EB (TFEB), thereby enhancing lipolysis. Although berberine upregulated autophagy-related gene expression during hvKp infection, it did not induce lipophagy, the selective autophagic degradation of LD. Collectively, these findings indicate that berberine has potential as a therapeutic agent against hvKp infection by modulating host lipid metabolism to restrict bacterial intracellular survival.