Bacterial cellulose (BC) hydrogels produced by Gluconacetobacter species hold considerable promise for a wide range of applications owing to their exceptional mechanical properties, biocompatibility, and biodegradability. Achieving precise control over their structural and mechanical characteristics is crucial for the engineering of BC-based materials. In this study, we investigated the formation dynamics and structural features of BC hydrogels, emphasizing the complex interplay between cellulose nanofibril secretion and bacterial motility. Comprehensive tracking of bacterial movement during hydrogel formation has validated mechanisms underlying the development of branching and merging junctions, which are key elements that define the network's physical properties. Additionally, we observed the emergence of vortex-lattice and chiral-nematic structures during hydrogel development, depending on bacterial and cellulose densities. These insights contribute to a fundamental understanding of bottom-up 3D fabrication of BC hydrogels that harness the collective behavior of cellulose-producing bacteria.

Transposon insertion sequencing (TIS) encompasses methods such as Transposon Directed Insertion sequencing (TraDIS) and Transposon-Sequencing (Tn-Seq); these methods are widely uses for genome scale screens of essential and conditionally essential genes. Early limitations centred on mutant generation, today the biggest factor is sequencing and data generation and the costs associated. Here, we present and evaluate four methods TIS sequencing library generation protocols compatible with Illumina whole genome sequencing (WGS) workflows to maximise sequence efficiency and minimise turnaround times and costs. Using E.coli BW25113 transposon mutagenesis libraries generated with Tn5 and mariner (Himar1) transposons, we compare the methods in terms of reagent cost, workflow complexity, and target enrichments for the recovery of unique insertion sites and essential genes identified. All methods generated TIS data suitable for gene essentiality analysis. Illumina Flex based protocols were 4-6 times cheaper than the traditional Illumina Nextera based approach with similar or superior Transposon-Chromosome (Tn-Chr) junction enrichment. Across all methods, sequencing depth and mutant library density were the dominant factor in useful biological insight. Subsampling demonstrated that for a good quality mutant library, five million reads were sufficient to identify essential genes in E.coli. Whereas deeper sequencing reduced the statistical power and included contaminating background noise, seen primarily with Tn5. We conclude that an Illumina Flex based approach, especially when integrating with routine WGS, provides an excellent balance of speed, cost and data quality. Assuming five million reads and a robust Illumina Flex approach, a TIS library can be sequenced for around &pound40.

In natural and engineered ecosystems, diverse species interact in complex ways to form highly efficient microecologies. One key orchestrator of these interactions is autoinducer-2 (AI-2), a signaling molecule that plays a crucial role in microbial community assembly, metabolic flux, and resilience to environmental disturbances. This review provides the systematic synthesis of AI-2’s dual structural dynamics (S-THMF-borate/R-THMF interconversion) and its context-dependent roles in mediating bacterial crosstalk. It also reveals the receptor diversity (such as LuxP and LsrB) of AI-2 in bacterial kingdom and the signal transduction mechanism. Systematically elaborated on AI-2’s regulation of cellular metabolic flux and its ability to autonomously exhibit a series of coordinated behaviors in response to environmental changes. The review explores the ramifications of AI-2 on bacterial community interactions in synthetic biology and natural ecosystems. The wide application of AI-2-mediated interspecific communication in various fields including host health, agriculture, industry and environmental ecology has also been widely discussed. Factors influencing AI-2 production are thoroughly examined, including internal factors such as strain specificity, cell density, growth form and the phenotypic heterogeneity. Additionally, external biological factors (such as nutritional status and environmental stress) and abiotic factors (aggregation, diffusion, and flow) are discussed in detail. By examining knowledge gaps in AI-2-mediated spatial heterogeneity and multi-QS system coordination, this work charts a roadmap for harnessing microbial communication in chemical engineering and environmental sustainability.

The escalating crisis of antimicrobial resistance poses a devastating and immediate threat to human life. Antimicrobial peptides (AMP) are a promising antibiotic substitute to combat antimicrobial resistance. Compared with the traditional wet-lab screening approaches, computational models have largely improved the efficiency of predicting antimicrobial peptide. However, most computational models overlook or underutilize the evolution and structural information of peptides, which is crucial for understanding the peptide functions. Here, we proposed a sophisticated deep learning model to predict AMPs, Antimicrobial Peptide Bilinear Attention Network (AMPBAN), which incorporates peptide evolution features from ESM3 protein language model, structure features from ESMFold predicted with equivariant graph neural network (EGNN), and the joint information from sequence and structure learned via Bilinear Attention Network. AMPBAN consistently demonstrated superior accuracy and generalization compared to nine state-of-the-art AMP prediction models across multiple independent benchmarks. Furthermore, an ablation study confirms that our multimodal fusion strategy significantly refines the integration of sequence and structural signals, yielding superior predictive balance over single-modality models. This framework provides a robust tool for the accelerated discovery of novel AMPs and the advancement of next-generation antimicrobial drug development. The datasets, source code and models are available at https://github.com/baiwenhuim/ampban.

Virus-induced genome editing (VIGE) using compact RNA-guided endonucleases is a transformational new approach in plant biotechnology, enabling tissue-culture-independent and transgene-free genome editing. We recently established a transgene-free VIGE approach for heritable editing at single loci in Arabidopsis by delivering ISYmu1 TnpB (Ymu1) and its guide RNA (gRNA) via Tobacco Rattle Virus (TRV). Here, we greatly improved this approach by devising a multiple gRNA expression system and by utilizing an engineered high-activity Ymu1 variant (Ymu1-WFR) to develop an efficient multiplexed genome editing approach.

The emergence of antibiotic-resistant bacteria has rendered conventional antibiotic treatments ineffective, necessitating the development of antibacterial agents with unique mechanisms of action. To address this challenge, researchers have increasingly resorted to synthetic and bioengineered nano-materials to augment the antibacterial activity of nonantibiotic antibacterials (nonantibiotic antibacterial agents), including antimicrobial peptides (AMPs), metallic nanoparticles (MNPs), bacteriophages (phages), and phage derivatives such as endolysins, which are under extensive investigation. In this review, we discuss how modifications and syntheses of these agents, leveraging advancements in nanoscience and nanotechnology, have and can significantly enhance their antibacterial properties and overcome limitations such as cytotoxicity, instability, and poor bioavailability for in vivo or clinical use. Furthermore, we highlight supramolecular strategies for improved delivery, including phage-based, AMP-based, and endolysin-based systems and their demonstrated efficacy against persistent bacterial infections. Additionally, we highlight how the integration of artificial intelligence and machine learning ultimately promises to revolutionize the design, optimization, and clinical translation of these precision antimicrobials, paving the way for targeted and highly effective treatments.

Metagenome-assembled genomes (MAGs) are routinely recovered from metagenomic studies, yet the population genetic information embedded within these datasets remains largely underutilized. Analyzing within-species genetic variation can reveal adaptive evolution, selection pressures, and ecological dynamics that are hidden when MAGs are treated as homogeneous entities. Existing tools address individual analysis steps in isolation, requiring manual integration and creating barriers for researchers without extensive bioinformatics expertise. Here we present PopMAG, a Nextflow pipeline and interactive Shiny application that automates population genetics analysis of MAGs. PopMAG integrates quality control, community profiling, competitive read mapping, functional annotation, and microdiversity estimation into a single reproducible workflow. The pipeline calculates key population genetics metrics including nucleotide diversity (π), pN/pS ratios, fixation index (FST), Levins index and SNVs counts with results consolidated into an interactive visualization platform for metadata-driven exploration. We demonstrate PopMAGs utility through analysis of longitudinal cystic fibrosis lung metagenomes, where we detect signatures of antibiotic-driven selection in Pseudomonas aeruginosa efflux pump genes coinciding with treatment intervention. Availability and implementation: PopMAG and corresponding documentation are publicly available at https://github.com/daasabogalro/PopMAG