Surface display of proteins on bacteria bears several advantages for binding studies, library screening or enzyme inhibitor testing. Here, we present a set of plasmids for autodisplay-based surface expression of recombinant proteins, named Autodisplay-ToolBox (ATB). In this set, crucial parts as required for autodisplay, including promotor, SP, linker and β-barrel can be combined in varying permutations to find the best combination for a certain protein. The plasmids can be applied individually or in library approaches, enabling optimization of surface display in a single step. By such a library approach, the activity of surface-displayed β-glucosidase (β-Gluc) is increased by a factor of 4.9, the activity of a laccase (CotA) by a factor of 4.7 and the binding capacity of the surface-displayed nucleotide binding domain of human HCN2 channel (HCN2-CNBD) by a factor of 10.3. It is shown that the ATB can be used in different strains of E. coli as well as in Pseudomonas putida. The aim is to provide a selection of plasmids, accessible by Addgene, that every scientist can use for their own protein, either individually based on personal preferences, structural features, etc., or as a library, as shown here for three different examples. A toolbox of 81 plasmids, accessible by Addgene, facilitates tailor-made autodisplay of proteins in bacteria, e.g. E. coli or P. putida. Plasmids can be applied individually or as a library for optimal promotor, SP, linker, and β-barrel combination.

Coexisting strains of the same species within metagenomic data pose a substantial challenge to inferring transmission of pathogenic and commensal microbes. Here we present TRAnsmission Clustering of Strains (TRACS), a highly accurate algorithm for estimating genetic distances between strains at the level of individual single nucleotide polymorphisms, which is robust to intra-species diversity within the host. Analysis of faecal microbiota transplantation datasets and extensive simulations demonstrates that TRACS outperforms existing methods. We use TRACS to infer transmission networks in patients colonized with multiple strains, including severe acute respiratory syndrome coronavirus 2 amplicon sequencing data, deep population sequencing data of Streptococcus pneumoniae and single-cell genome sequencing data from patients infected with Plasmodium falciparum. Applying TRACS to gut metagenomic samples from a mother–infant cohort revealed species-specific transmission rates and identified increased the persistence of Bifidobacterium breve in infants, a finding previously missed owing to the presence of multiple strains. Our study shows that TRACS can be used across microbial kingdoms to uncover strain dynamics. The TRACS algorithm tracks bacteria, viruses and parasites in metagenomic or population sequencing data, detecting microbial transmission even when multiple strains coexist within a host.

G protein-coupled receptors (GPCRs) play key roles in physiology and are central targets for drug discovery and development, but the design of protein agonists and antagonists has been challenging as GPCRs are integral membrane proteins and conformationally dynamic. Here we describe computational de novo design methods and a high-throughput “receptor diversion” microscopy-based screen for generating GPCR binding miniproteins with high affinity, potency and selectivity. We design miniprotein agonists that activate receptors involved in itch and pain, as well as antagonists that inhibit receptors implicated in cancer, metabolic disorders such as diabetes and obesity, and migraine. Cryo-electron microscopy (cryo-EM) structures of five receptor-bound designs are close to the computational design models. A designed chemokine receptor antagonist mobilizes hematopoietic stem and progenitor cells in vivo at a level comparable to a clinically used drug, with fewer adverse effects.

Orfamide A, a lipopeptide produced by Pseudomonas protegens Pf-5, is a key determinant of its biocontrol properties. In this study, we investigated the regulatory interactions among the GacS/A two-component system, small RNAs (sRNAs), repressor proteins, and two LuxR-type transcription factors in orfamide A biosynthesis. We found that GacS/A indirectly regulates orfamide A production by enhancing transcription of three sRNAs (RsmX, RsmY, and RsmZ). RsmY and RsmZ synergistically relieve repression by RsmA and RsmE, while RsmX plays a lesser role, likely counteracting only one repressor. LuxR-type transcription factors, LuxR1 and LuxR2, which positively regulate orfamide A synthesis, are directly repressed by RsmA and RsmE via binding to their 5′ untranslated regions, linking them to the Gac–Rsm signaling cascade. We further demonstrated that LuxR2 activates luxR1 expression, which in turn facilitates orfamide A production by binding to the promoter of the orfamide A biosynthetic gene cluster. Importantly, we showed that this entire regulatory cascade operates in the rhizosphere and directly influence biocontrol efficacy. These findings provide a comprehensive understanding of the Gac–Rsm–LuxR pathway in orfamide A biosynthesis and offer valuable insights for the development of biocontrol agents based on Pseudomonas strains.

The rise in antimicrobial resistance underscores the need for innovative strategies to combat gastrointestinal infections. Probiotics such as E. coli Nissle 1917 (EcN) offer promising options, but the molecular mechanisms underlying their protective effects remain unclear. We introduce a G66R point mutation in FimH, creating a high-binding EcN variant that more effectively prevents Salmonella Typhimurium attachment and induces a distinct host transcriptional profile, shifting toward adaptive rather than innate inflammatory signaling. In vivo, EcNG66R pretreatment significantly reduced intestinal colonization, fecal shedding, and systemic spread, and prevented splenic enlargement compared with EcNWT. Protection was associated with a marked expansion of CD4+ and CD8+ T cells, essential for clearing intracellular pathogens. EcNG66R further enhanced “readiness” in the spleen under non-infected conditions, without adverse effects on host physiology. EcNG66R thus functions as a dual-action probiotic—improving competitive exclusion while priming cytotoxic T-cell-mediated protection—and provides a promising platform for developing next-generation microbe-based therapies.

Genomes constantly face threats from transposable elements (TEs) and other genomic parasites. While the silencing of existing TE has been well studied, little is known about how cells recognize new invading TEs that they have not previously encountered. Here we explore this question by inserting foreign sequences into S. pombe. Our data revealed that the newly invading TE tj1 is recognized and targeted for silencing by RNAi and heterochromatin. The efficiency of recognition, as well as the degree and stability of silencing, depends on the copy number and insertion location of the TE. We demonstrated that RNA, rather than DNA, is sensed, and that the efficiency of TE recognition correlates with levels of RNA antisense to the TE, generated from upstream transcripts. We also show that various genes of non-transposable nature can initiate silencing. Our data show that silencing may not require recognition of specific elements in the transposon by the host defense systems, and suggest that disruption of host transcription patterns triggers recognition of TE. Genomes must defend against transposable elements, but how cells detect newly invading elements remains poorly understood. Here, the authors show that a newly invading transposon in S. pombe is recognized and silenced via RNAi and heterochromatin through RNA-based sensing mechanism.

Protein-ligand binding, selectivity, and affinity dictate the effects of drugs and endogenous molecules in cells. Currently, potential protein-ligand interactions are identified by qualitative interpretation of proteomic, transcriptomic, or genomic data, then binding affinities of hits are measured using purified proteins or engineered reporter systems to validate and quantify the strength of individual interactions. Few methods enable simultaneous target identification and biophysical affinity measurement, and these either apply to specific enzyme classes or proteins with ligand-dependent shifts in stability. Here we describe a general platform, termed Affinity Map, which leverages competitive binding analysis, high fidelity photocatalytic labeling, and high throughput proteomics for global quantitative binding affinity profiling. We show that this method is applicable to major classes of ligands, including small molecules, linear peptides, cyclic peptides, and proteins, and can measure affinities between unmodified ligands and proteins in cell lysates, organ extracts, and live cell surfaces. Profiling ligand-protein binding affinities across the proteome remains difficult. Here, the authors report Affinity Map, a photocatalytic labeling platform that accurately measures binding affinities for small molecules, peptides, and proteins in cell lysates, organ extracts, and live cell surfaces.

Rapid quality assessment and multiple assembly comparison are essential steps while assembling new genomes or re-sequencing known ones. Many available tools used for assembly evaluation produce global metrics, representing assembly quality or overall features, most of them working as command line tools that typically act on large data files and produce long detailed result files, where it is not always easy to identify regions of similarity or difference among different chromosome assemblies. ChromoMapperWeb is a new web tool that takes as input nucmer or QUAST output files, quickly identifies similarities and differences between the compared assemblies, and displays them using both table visualizations and pre-arranged or custom graphics. Graphical displays are interactive and allow progressive zoom levels which, in a few steps, move from full genome to very enlarged views, where even small alignment blocks are easily identified. The program, freely accessible through the web server https://chromomapperweb.ceinge.unina.it/, provides an easy-to-use graphical interface, used for experiment planning and interactive evaluation of the results, which include tables and graphical representations of whole genomes, chromosomes, or single blocks.

Microbe Decoder is a web server that predicts functional traits of microbes in microbiome sequencing datasets. Sequencing has revealed thousands of organisms in most ecosystems, but the functional traits of many organisms remain unclear. Existing tools can predict names of organisms or their genes, but they rarely predict concrete biological functions (e.g. fermentation or anaerobic growth). Microbe Decoder fills this gap using three complementary tools relying on taxonomy, metabolic networks, or machine learning. These tools accept either names or gene functions as inputs and are integrated into an easy-to-use web app. When tested against data for microbial isolates, Microbe Decoder showed good predictive performance (e.g. balanced accuracy of 0.85). When applied to datasets from the gut, sediment, and sea, it predicted shifts in functional traits over space and time. Microbe Decoder is designed for use with prokaryotes, with the goal of including eukaryotes in the future. By revealing functional traits of microbes in biological systems, Microbe Decoder will advance biology, medicine, and environmental science. Microbe Decoder is available at https://www.microbe-decoder.org/.

Fungi are a key component of the microbial community in soils, forming species-rich assemblages in soils of natural ecosystems and managed soils. Their unique physiology, absorptive nutritional mode and growth form as spore-producing filamentous eukaryotes enables them to make specific contributions to a wide range of ecological roles as potent decomposers, versatile mutualists and also destructive pathogens. Fungi have important roles in biogeochemical cycles, for example, in nutrient mineralization, plant nutrient uptake and carbon storage. Positioned at the basis of the soil food web, they are one of the pillars of the flow of carbon and energy through the soil food web. As bottom-up controlled organisms in the soil, largely controlled by their resources, they react sensitively to a range of anthropogenic factors, including climate change and other factors of global environmental change, such as chemical pollution. By influencing the food system and via their role as pathogens and in antifungal resistance, fungi are key players in One Health and planetary health. In this Review, Rillig explores the diversity of fungi in the soil ecosystem, their ecological interactions and diverse ecological roles in terrestrial ecosystems as well as anthropogenic factors that affect soil fungi.

Biomolecular condensates have emerged as versatile regulators of plant cellular processes, offering a dynamic and reversible mechanism to coordinate development, stress response, and spatial organization. Through phase separation, these condensates spatially and temporally modulate biochemical reactions, sequester or activate specific proteins and RNAs, and reshape cellular architecture. This review presents a comprehensive and multidimensional framework for understanding biomolecular condensates in plant biology, from their biophysical properties and ensemble dynamics to their roles across diverse cellular compartments, including plasma membranes, cytoskeleton, intracellular compartments, and chromatin. We highlight their functions in growth, environmental sensing, and defense and discuss current challenges in studying their composition, material properties, and context-dependent behaviors. Understanding plant condensates not only deepens our knowledge of plant cell organization and adaptability but also opens new avenues for biotechnological innovation in agriculture.