Bacterial microcompartments (BMCs) are a diverse and widespread class of self-assembling protein-based organelles consisting of a semipermeable protein shell encapsulating an enzymatic core. Isolated BMC shell proteins have been shown to assemble into alternative superstructures such as flat sheets and nanotubes. The self-assembly and modularity of BMC shell proteins make them of great interest as modular platforms for applications involving scaffolding, immobilization, and compartmentalization. While the assembly of BMC shell proteins into higher-order structures has been well-studied, the design of controllable and modular cargo loading is underdeveloped in comparison. Recently, we reported the pH-controlled assembly of CcmK2, the major hexameric shell protein of the β-carboxysome BMC, into monodisperse mesh-like microscale particles. Here, we develop a suite of encapsulation strategies for stochastic or targeted loading of various cargos, as well as the direct conjugation of cargo to CcmK2 particles. Our systematic analysis demonstrates that cargo loading and particle assembly can be modulated by the choice of recruitment strategy and the order of cargo introduction. Our findings also reveal a cooperative cargo loading mechanism during assembly that influences particle sizing and apparent morphology. Our study serves as a blueprint for the rational design of tunable cargo loading into engineered BMC-derived microcompartment systems for diverse biotechnological applications.

Persistent organic pollutants (POPs), prevalent across diverse environmental matrices, are highly hazardous and recalcitrant compounds that can be transformed into low-toxicity compounds by diverse microorganisms. Many transformation processes of POPs could intricately interface with elemental biogeochemical cycles, which are fundamental drivers of ecosystem function. While microbial pathways of POPs transformation have been extensively studied, their integration into broader element turnover in the environment remains fragmented. Here, we review the relationship between POPs metabolism and biogeochemical cycles, spanning from single-species enzymatic coupling to multispecies syntrophic interactions. We contend that POPs transformation is not an isolated microbial event but is deeply embedded within elemental metabolism through direct mechanisms of electron transfer and cross-feeding, or indirect modulation of quorum sensing and mineral-interface interactions. Across levels from gene expression to community level-energy and material exchange, microorganisms in the environment mediate POPs transformation while maintaining elemental balance through dynamic metabolic regulation. Furthermore, we propose a strategic framework that leverages functional compensation and integrative strategies of native and engineered microbiomes to reinforce POPs degradation and coordinate element cycling. Future research should focus on integrating microbiome-based approaches with omics analyses, systems modeling, and ecological engineering. These efforts facilitate the predictable regulation of pollutant–element interactions, ultimately restoring ecosystem multifunctionality within POPs-contaminated sites.

The application of arbuscular mycorrhizal fungi represents a sustainable biotechnological approach to enhance plant anabolism, particularly the accumulation of bioactive metabolites that modulate the biological activities of plant-derived extracts. Despite the well-documented role of arbuscular mycorrhizal fungi in promoting secondary metabolite production, a comprehensive evaluation regarding the bioactivity of extracts from mycorrhizal plants remains lacking. This review consolidates 26 studies to identify trends in plant parts utilized, prevalent AMF inoculants, and assessed biological activities, including antifungal, antibacterial, antidiabetic, antiestrogenic, antityrosinase, cytotoxic, genotoxic, and larvicidal properties, as well as Sun Protection Factor enhancement. Considerations concerning the potential of mycorrhizal technology for generating plant raw material with outstanding biological activities, the relevance of establishing partnerships for research on this topic, and future assays to be carried out were addressed.

CRISPR-Cas systems, with their programmable nucleic acid-targeting capabilities, represent an ideal platform for constructing next-generation, highly sensitive biosensors. However, the clinical translation of these platforms is hindered by key limitations inherent to native single-guide RNAs (sgRNAs), including insufficient stability, potential immunogenicity, and off-target effects. To address these challenges, engineering sgRNAs has emerged as a central strategy to overcome such barriers and enhance overall biosensor performance. In this review, we provide a systematic overview of the field, beginning with the classification, molecular mechanisms, and structural features of representative CRISPR-Cas effector proteins to establish their foundational role as sensing elements. We then examine the specific limitations of native sgRNAs in biosensing applications. Building on this analysis, we highlight recent advances in sgRNA engineering strategies, which encompass three major approaches, including chemical modifications, structural remodeling, and modular functional integration. Furthermore, we review the integration of these engineered sgRNAs into advanced biosensor platforms, including microfluidic paper-based devices, centrifugal platforms, wearable patches, microneedles, and point-of-care testing (POCT) systems, and present a comparative table summarizing their performance in terms of detection signals, limits of detection, and other key metrics. Finally, we discuss persistent challenges such as the fine control of off-target effects, in vivo delivery bottlenecks, and system robustness in complex environments, and outline future directions toward amplification-free, multiplexed, and clinically translatable CRISPR-based biosensors. Overall, the engineering of sgRNAs offers a powerful means to systematically enhance the stability, specificity, and reliability of CRISPR-based biosensors, thereby accelerating their practical deployment in clinical diagnostics.

Bacteriophages are key regulators of bacterial populations and hold great promise for applications such as phage therapy, biocontrol, and industrial fermentation. The success of these applications depends on accurately determining phage host range, which is often specific at the strain level rather than the species level. However, existing computational approaches face major limitations: many rely on genus-specific features that do not generalize across taxa, while others require large amounts of training data that are unavailable for most bacterial lineages. These challenges create a critical need for methods that can accurately predict strain-level phage–host interactions across diverse bacterial genera, particularly under data-limited conditions.  We present PhageMind, a learning framework designed to address this challenge by enabling efficient transfer of knowledge across bacterial genera. PhageMind is trained to identify shared principles of phage–bacterium interactions from well-studied systems and to rapidly adapt these principles to new genera using only a small number of known interactions. To reflect the biological basis of infection, we represent phage–host relationships using a knowledge graph that explicitly incorporates phage tail fiber proteins and bacterial O-antigen biosynthesis gene clusters, and we use this representation to guide interaction prediction. Across four bacterial genera (Escherichia, Klebsiella, Vibrio, and Alteromonas), PhageMind achieves high prediction accuracy and shows strong adaptability to new lineages. In particular, in leave-one-genus-out evaluations, the model maintains robust performance when only limited reference data are available, demonstrating its potential as a scalable and practical tool for studying phage–host interactions across the global phageome.

D-amino acids (DAAs), once considered minor enantiomers, are now recognised as abundant and dynamic components of marine and terrestrial organic matter. While they do not participate in ribosomal protein synthesis, they play crucial roles in microbial physiology, particularly in bacterial cell wall structure. This review systematically synthesises the current understanding of DAA sources, distribution and fate, with a central focus on the microbial catabolic pathways that drive their recycling in marine and terrestrial environments. We show that, despite differences in DAA distribution and bioavailability between marine and terrestrial ecosystems, the core catabolic strategies adopted by bacteria—conversion to α-keto acids, L-amino acids (LAAs) or Gly—are largely conserved. We also evaluate limitations in current studies and major knowledge gaps, including the unclear role of marine fungi in DAA turnover and the relative lack of systematic studies on DAA-catabolising taxa and pathways in terrestrial microbial communities. This review highlights the ecological significance of microbial-mediated DAA recycling in marine and terrestrial environments, offering a better understanding of the global biogeochemical cycling of DAAs.

Transcriptional termination efficiency is considered an important parameter for fine tuning bacterial gene expression. Still, the design principles that determine transcription termination efficiency remain poorly understood. In this study, we aimed to investigate the impact of the 3′ untranslated region (3′UTR) on gene expression in Escherichia coli and other bacteria. First, 3′UTR variant sequences were generated, with randomized 30 bp sequences inserted between the STOP-codon and an intrinsic terminator, consisting of a GC-rich hairpin and a downstream poly(U)-tail. Using three reporter genes, it was found that different 3′UTR sequences resulted in an up to five-fold difference in protein production, independent of the upstream coding sequence. The highest protein production was achieved when an adenosine was present directly upstream of the terminator hairpin. This was consolidated by systematic substitution of key nucleotides of the terminator and assessing their effect on mRNA and protein levels. Subsequently, we developed a predictive random forest machine learning model trained on the termination efficiency of different natural and synthetic terminator sequences, revealing an important role for the nucleotides directly upstream of the terminator hairpin. Altogether, this study showed that an additional adenosine nucleotide upstream of the terminator hairpin leads to improved protein production while reducing terminator read-through.

Here we introduce OpenEvo, a fully open-source, low-cost turbidostat platform for automated continuous culture and directed evolution experiments. Existing tools are expensive, complex, or lack open-source hardware; OpenEvo addresses this gap. OpenEvo is a complete, fully automated evolution platform with detailed, illustrated construction instructions for beginners, open-source software and firmware, and a single device priced around $300. An optional PC-based version offers enhanced functionality, including remote access, programmable evolution cycles, programmable LED stimulation, and a data visualization tool. OpenEvo can cycle through three types of media for positive, negative, and neutral selection conditions, supporting a wide range of experimental designs. We validate the use of OpenEvo by evolving H. volcanii to grow from 15% to 12% salt over ~150 cycles, ~1,000 hours. Evolved cells grew 36% faster than wild-type at 12% salt. Whole-genome sequencing of adapted cells found SNPs and large deletions. We also demonstrate positive and negative selection using the OpenEvo LEDs to drive optogenetics via a Phytochrome B-based optogenetic tool, with light as the selection stimulus during over 4000 hours of growth. OpenEvo lowers the technical and cost barriers for continuous evolution experiments, serves as a teaching tool, and is designed to grow an open community of users who share modifications.

Identifying protein binding sites in protein–protein complexes is a central challenge in structural biology. Binding sites, consisting of groups of residues, govern how proteins recognize, and interact with protein partners. Thus, identifying them is essential for understanding biological function and guiding the design of effective biomolecules and even drug molecules. Despite major progress in computational approaches, their performance remains limited because most models underrepresent the combined influence of surface properties and residue-level information, leaving room for improvement. Recent advances in state-space models and vision-based deep learning offer an opportunity to address these limitations by efficiently modeling long-range spatial dependencies on protein surfaces. Here, we introduce BiMba (protein Binding site prediction using Vision Mamba), a state-space–driven deep learning framework that leverages the efficient long-range modeling capability of the Vision Mamba architecture to learn from three-dimensional (3D) protein surfaces represented as two-dimensional (2D) geometric or physicochemical grids. BiMba integrates complementary sources of information, capturing geometric and physicochemical determinants of molecular recognition as surface patches, encoded as 2D images, along with residue-level descriptors, yielding a unified representation that couples spatial topology with biochemical context. BiMba demonstrates competitive performance across diverse and specialized benchmark datasets, often outperforming existing state-of-the-art methods. In addition, BiMba incorporates perturbation-based and gradient-based interpretability analyses by extracting hidden attentions from Mamba layers, enabling visualization of feature relevance and biologically meaningful residue clusters. Overall, our findings establish state-space models as efficient, interpretable, and scalable architectures for molecular surface learning, advancing the application of deep learning in structural bioinformatics.

Targeted long read sequencing (LRS) of native genomic DNA (gDNA) using Oxford Nanopore Technologies (ONT) is an economically and computationally accessible method for sequencing selected genomic regions without the limitations associated with amplification-based approaches. At present, efficiency, multiplexing, and scalability remain key challenges for existing targeted LRS. We have developed Cas12a-Targeted Multiplexed Nanopore Sequencing (CTM-nSeq), which combines Cas12a-targeting, DNA fragment enrichment, and optimized adapter ligation using T7 DNA ligase. Unlike previously established protocols, CTM-nSeq is compatible with the latest ONT flow cell chemistry. Performing CTM-nSeq on a single sample with an R10.4 MinION flow cell routinely yields hundreds of on-target reads. Furthermore, CTM-nSeq enables targeting of multiple loci and is the first targeted ONT sequencing method, allowing reliable, barcode-assisted multiplexing. CTM-nSeq is an efficient and accessible method for sequencing native gDNA and analysing DNA methylation, repeat expansions, and sequence integrity. As such, CTM-nSeq has a wide range of analytical and diagnostic applications.