Uncovering cell morphology within communities is crucial to understanding how collective groups of organisms can function and adapt to their environments. Key questions remain regarding how cell morphology influences population behaviors and whether uniformity is required for coordinated actions, such as collective migration. These gaps hinder the ability to describe how individuals contribute to collective adaptability and coordinated behaviors. We developed an image capture and analysis pipeline, named Swarmetrics, for studying individual cells that are within dense bacterial communities, can participate in collective behaviors, and may have irregular cell morphologies. We used Proteus mirabilis swarms as a proof of concept. Swarmetrics achieved an unbiased analysis of most cells, even those that were unlabeled and in a densely populated environment. In P. mirabilis swarms, we found unexpected heterogeneity in cell length throughout the swarm development cycle, particularly during active collective migration. Variance in cell length was revealed to be a reliable indicator of swarm development stage, comparable to a gene expression marker. These findings questioned the traditional view of uniform or synchronized transitions in bacterial swarming for the Proteus species. This study introduces new tools and insights for studying cellular variation in complex microbial environments, with broad applications to other dense communities, and sheds light on the physiology of individuals during collective behavior.

Bacterial strain typing is key to surveillance, outbreak investigation and microbial ecology, yet current systems remain species-specific, reference-dependent and lack a universal, interpretable metric of genomic relatedness. Here, we introduce BacTaxID, a fully configurable, whole-genome k‑mer-based framework that encodes each genome as a numeric sketch and organizes strains into hierarchical clusters with user‑defined similarity thresholds. BacTaxID distances are strictly proportional to Average Nucleotide Identity (ANI), providing a direct quantitative link between vectorial typing and genome-wide divergence. Applied to 2.3 million genomes from All the Bacteria across 67 genera, BacTaxID demonstrates universal concordance species and sub-species classification systems, while capturing finer strain-level diversity than traditional reference-based approaches. In simulated surveillance and real outbreak datasets, BacTaxID reproduces SNP and cgMLST-based definitions while enabling rapid, scalable screening. Precomputed genus-level schemes and an open implementation provide a practical, genus‑agnostic alternative to classical typing systems for standardized bacterial classification.

Enrichment analysis is a cornerstone of "omics" data interpretation, enabling researchers to connect analysis results to biological processes and generate testable hypotheses. While well-established tools exist for transcriptomics and other omics layers, the development of robust enrichment resources for metabolomics remains comparatively limited. To address this gap, we developed hypeR-GEM, a methodology and associated R package that adapts gene set enrichment analysis to metabolomics. hypeR-GEM leverages genome-scale metabolic models (GEMs) to infer reaction-based links between metabolites and enzyme-coding genes, enabling the mapping of metabolite signatures to gene signatures and their subsequent annotation via gene set enrichment analysis. We validated hypeR-GEM using paired metabolomics-proteomics and metabolomics-transcriptomics datasets by assessing whether genes mapped from metabolites significantly overlapped with differentially expressed proteins or transcripts. We further evaluated whether pathways enriched via hypeR-GEM-mapped genes corresponded to those derived from paired proteomic or transcriptomic data. In most datasets analyzed, both the predicted enzyme-coding genes and the associated enriched pathways showed significant concordance with independently derived omics signatures, supporting the utility and robustness of hypeR-GEM. Finally, we applied hypeR-GEM to the analysis of age-associated metabolic signatures from the New England Centenarian Study. The results revealed consistent enrichment of lipid-related pathways, aligning with the well-established role of lipid metabolism in aging, and highlighted additional pathways not captured in the metabolites' annotation, demonstrating hypeR-GEM's practical utility in a real-world use case.

The application of microbial consortia in biotechnological areas has proven to be much more efficient than that of single microorganisms; however, the main difficulty lies in the large number of communities to be tested. The use of models to predict functional efficiency on a high-throughput scale is key to incorporating greater diversity. The BSocial tool (http://m4m.ugr.es/BSocial.html) assigns a social behavior to each strain based on its contribution to the overall growth of the consortium through a statistical analysis, defining a ‘social consortium’. To determine the effectiveness of the BSocial tool for designing a biofertilizer, the social behavior of 8 plant growth-promoting microorganisms belonging to Azospirillum, Bacillus, Bradyrhizobium, Ensifer and Pseudomonas, as well as 3 plant growth-promoting traits (siderophore production, phosphate solubilisation and indole acetic acid production) of the complete combinatorial (255 communities) were analysed. We selected 3 social consortia (X22, X93 and X149) with a diversity of 2–4 species, two of which presented high performance for more than one plant growth-promoting trait evaluated. Functional stability, following the increase in diversity, was observed in all functions except for siderophore production. Overall, the results show the effectiveness of the BSocial tool in selecting plant growth-promoting consortia to formulate efficient biofertilizers.

Global concern over food waste and plastic pollution highlights the urgent need for sustainable, high-performance materials that can replace petroleum-based plastics. Bacterial cellulose (BC), a biopolymer synthesized through microbial fermentation by Komagataeibacter and related genera, shows exceptional purity, mechanical strength, biodegradability, and structural tunability. Following PRISMA principles, this review analyzed studies from PubMed, Scopus, and Web of Science covering the period 1960–November 2025. Search terms included “bacterial cellulose”, “Komagataeibacter”, “Gluconacetobacter”, “static culture”, “agitated culture”, “in situ modification”, “ex situ modification”, “fermentation”, and “food packaging”. Inclusion and exclusion criteria ensured that only relevant and high-quality publications were considered. The review summarizes major developments in BC biosynthesis, structural organization, and modification approaches that enhance mechanical, barrier, antioxidant, and antimicrobial properties for food packaging. Recent advances in in situ and ex situ functionalization are discussed together with progress achieved through synthetic biology, green chemistry, and material engineering. Evidence shows that BC-based composites can reduce oxygen and moisture permeability, strengthen films, and prolong food shelf life while maintaining biodegradability. Remaining challenges such as high cost, lengthy fermentation, and regulatory uncertainty require coordinated strategies focused on metabolic optimization, circular bioeconomy integration, and standardized safety frameworks to unlock BC’s full industrial potential.

Engineering CRISPR-Cas systems for improved or altered function is central to both research and therapeutic applications. Unfortunately most optimization, especially directed evolution in bacterial hosts, fails to capture the functional requirements of the complex mammalian cellular milieu, where activity is usually required. Robust strategies to enable continuous directed evolution of genome-targeting agents directly in human cells remain lacking. Here, we introduce CRISPR-MACE (Mammalian cell-enabled Adenovirus-assisted Continuous Evolution) as a foundational technology to address this need. CRISPR-MACE integrates virus-based continuous evolution with anti-CRISPR-based tunable selection to generate novel Streptococcus pyogenes Cas9 variants with both increased and decreased DNA binding capacity and nearly 1000-fold–enhanced resistance to AcrIIA4, the strongest known inhibitor of SpCas9. Notably, across independent evolution campaigns the same Cas9 gatekeeper mutation reproducibly emerged first, enabling subsequent adaptive steps along two interdependent axes of Cas9 function. In addition to advancing CRISPR technologies, this work establishes key principles and synthetic circuits for continuously evolving CRISPR-Cas systems directly in human cells.

Almost all proteins are inserted or translocated across membranes by the universally conserved Sec translocon. Despite its central role, experimental access to Sec function has remained limited. Here, we present a cell-free protein synthesis platform that inserts SecYEG into synthetic vesicles, enabling direct testing of Sec in real-time and high-throughput, circumventing longstanding viability constraints. Screening 300 Sec variants in a single experiment, we consolidate three decades of Sec research, while vastly expanding mutant diversity for structure-function insights. Mapping over 30 functionally critical regions that modulate Sec activity across three orders of magnitude, we uncover dozens of super-active variants. We further leverage our system to increase membrane protein quality and nanobody export, highlighting the potential of our system for advancing applications in synthetic biology and biotechnology.

Microbiome sequencing datasets are sparse, high-dimensional, compositional, and hierarchically structured. Predictive modelling from these data typically relies on ad hoc choices of feature representation, obscuring their impact on performance and biological interpretation. A standardized, compute-efficient framework is needed to jointly optimize microbial feature representation and model algorithms with transparent model evaluation. Here, we present ritme, an open-source software package implementing Combined Algorithm Selection and Hyperparameter Optimization tailored to microbial sequencing data. ritme systematically explores feature engineering methods - taxonomic aggregation, sparsity-aware selection, compositional transforms, and metadata enrichment - alongside diverse model classes using state-of-the-art optimizers and model trackers. Applied to three real-world use cases, ritme outperforms original study pipelines and generic AutoML baselines. It further provides users with insights into how feature and model choices drive predictive performance. Together, these results establish ritme as a standardized framework for identifying optimal feature-model combinations from high-throughput sequencing data. ritme is an open-source Python package available via Anaconda.

Beyond the heterologous expression of genes to generate microbes with novel properties, evolutionary engineering offers a complementary approach by exploiting adaptive processes to refine and expand cellular functionalities in biotechnology. A promising source for the generation of novel metabolic functions is the so-called underground metabolism, i.e., the subnetwork conformed by catalytically inefficient promiscuous enzymatic activities without an apparent physiological role. In this work, the potential of this underground metabolism as a source of novel phenotypes has been assessed in the soil bacterium Pseudomonas putida KT2440. To accomplish this, the high-quality genome-scale metabolic model iJN1462 was updated and expanded by the known set of promiscuous activities in this organism. The new metabolic space was explored to detect latent metabolic traits that could emerge through adaptive laboratory evolution (ALE) in order to broaden the range of available nutrients for P. putida. Using ALE, strains capable to degrade N-acetyl-L-alanine, an overproduced metabolite in HIV patients, were obtained. A multidisciplinary characterization of these evolved strains revealed that adaptation arose through synergistic and additive effects involving modifications in enzymes and transcription factors. This work demonstrates how underground metabolism can be exploited to expand the metabolic versatility of P. putida.

In biotechnological applications, it is often necessary to introduce genes or entire pathways into a host cell, which can create a significant metabolic burden on the host, limiting productivity. In this study, we systematically investigated the physiological stress responses of Pseudomonas putida during heterologous protein production using a modular monitoring system consisting of a plasmid encoding a heterologous protein fused to eGFP and a chromosomally integrated capacity reporter. Our findings reveal that translation is the main bottleneck, with translational capacity becoming saturated under high expression loads. While increasing the strength of the RBS improved protein production for non-burdensome proteins, this effect was not observed for larger fusion proteins. Variations in fusion protein size suggested that it is not the overall mass of the produced protein, but rather the length of the mRNA transcript, that contributes to metabolic burden. We further evaluated how resource availability affects protein expression by modifying the metabolic regime or supplementing with amino acids. While the carbon source affected cellular capacity, it did not significantly alter heterologous protein production. Amino acid supplementation alleviated the growth defects of MBPeGFP-producing cells and modestly improved protein production rates. Together, these findings emphasize that metabolic burden is influenced not only by the size of the produced protein but also by transcript architecture, resource allocation, and the physiological state of the host. Therefore, successful optimization of heterologous protein production requires a holistic approach integrating construct design with host physiology and cultivation strategies.