RMH

Controlling protein localization through chemical biology tools is essential for mechanistic interrogations, functional perturbations, and therapeutic development. While existing tools cover a wide range of applications, no single approach simultaneously enables inducible trapping, controlled release, and visualization of a protein of interest (POI). To address this gap, we developed Biotin-Operated Ligand Trapping (BOLT), which combines a HaloTag-fused POI with a streptavidin trap. Desthiobiotin-conjugated Janelia Fluor HaloTag ligands enable POI visualization, trapping, and biotin-operated release. We demonstrate BOLT's capacity in synchronized secretory cargo release, microtubule-directed organelle movement and hitchhiking, and ectopically localizing nuclear transcription factor. Importantly, pulse-chase BOLT further reveals bidirectional endoplasmic reticulum-Golgi trafficking of an endogenous Golgi enzyme and distinguishes recycled from newly synthesized enzyme populations using distinct HTLs. Altogether, BOLT provides an all-in-one platform to interrogate how subcellular localization regulates protein function across HaloTag-compatible systems.

Soil ecosystems represent a vast, largely uncharacterised reservoir of microbial diversity. Metagenomic assembly has unlocked access to this resource, and recent advances in long-read sequencing have improved the recovery and quality of microbial genomes from complex samples. While genomic language models have proven highly effective at capturing biological concepts from large sequence datasets, they have predominantly been trained on reference genomes or short-read assemblies. Here, we present LOAM, a family of decoder-only genomic language models ranging from 25 to 624 million parameters and trained exclusively on Oxford Nanopore long-read environmental metagenomes. LOAM model performance scales predictably with model size and training-token budget. Despite a relatively small training sequence corpus, LOAM models outperformed comparably sized models across biological benchmarks, and achieved performance competitive with substantially larger state-of-the-art models trained on much larger datasets. Context-intervention experiments further showed that LOAM models use genomic information over several kilobases, highlighting the potential value of increased contiguity provided by long-read metagenome-assembled genomes. For probe-based benchmark tasks, we systematically evaluated representations across hidden layers and revealed that task-relevant biological information was frequently more linearly accessible from intermediate than final model layers. Finally, we observed that variation in zero-shot variant-effect prediction was strongly associated with the presence of homologous target sequences in the pre-training corpus. Together, these results establish long-read environmental metagenomes as a viable foundation for training competitive genomic language models and demonstrate the importance of both model scale and training-corpus composition for biological generalisation.

L-Threonine is an essential amino acid widely used in the feed, food, and pharmaceutical industries, and microbial fermentation is now the dominant route for its production. Further increases in producer titers depend on random mutagenesis coupled with high-throughput screening (HTS), but progress has been limited by the lack of a biosensor with a broad operating range, high sensitivity, and target specificity. Here, we show that L-threonine induces the tryptophan-repressed trp promoter of Escherichia coli, a response that, to our knowledge, has not been described previously. Deletion analysis showed that both the trpO operator and the trpL attenuation element contribute to induction, and that the lowest basal expression occurs only when both elements are present. By optimizing the ribosome-binding site, the promoter -10 region, and TrpR expression, we constructed an L-threonine biosensor on a pACYC backbone. The sensor responded linearly to L-threonine from 0.1 to 30 g/L (R2 = 0.9895), with an induction ratio of up to 51.7-fold. Among the 20 proteinogenic amino acids tested, only L-threonine produced a detectable response. The biosensor distinguished strains with different L-threonine production capacities and guided the directed evolution of homoserine kinase (ThrB) at a conserved motif, yielding variants with brighter reporter fluorescence and increased L-threonine production. This work provides a high-performance biosensor based on native E. coli regulatory elements for screening L-threonine overproducers.

Adherent-invasive Escherichia coli (AIEC) is frequently isolated from the dysbiotic gut microbiome in inflammatory bowel disease and is implicated in disease progression, highlighting its potential as a therapeutic target. Here we show that iron acquisition is essential for AIEC colonization of the inflamed gut. Mechanistically, four catecholate siderophore receptors act redundantly to support AIEC growth under iron-limited conditions and during colitis. Immunization of colitis-prone Il10-/- mice against catecholate siderophores reduced intestinal AIEC colonization, particularly its association with the mucosa, while largely preserving overall gut microbiome composition. Longitudinal metagenomic profiling identified immunization-associated shifts in Akkermansiaceae and Lactobacillaceae, and experiments in gnotobiotic Il10-/- mice demonstrated that Limosilactobacillus reuteri, a member of the Lactobacillaceae, limited AIEC colonization, whereas Akkermansiaceae exacerbated disease. Our findings establish catecholate siderophore-mediated iron acquisition as a therapeutic target in AIEC and provide proof-of-principle that virulence-targeted immunization can selectively antagonize a disease-associated bacterium while preserving the gut microbiota

There are many databases of post-translational modifications, but each has different protein and PTM coverage, making the current state of post-translational modifications difficult to obtain, assess, or harmonize. ProteomeScout has been a relational database that harmonized a myriad of resources to address this challenge. In this update, we wished to create a lightweight, sustainable open source resource for the community that retained the power of harmonization, protein visualization, and Python tools for interacting with the database. ProteomeScout-v4 presents an ecosystem of open-source tools for aggregating and updating proteins and post-translational modifications and presenting the web-based and code-based access tools for interacting with the data. ProteomeScout has moved from using a complex relational database to a flat-text file format. The database file update is version controlled on FigShare and all features have processes for continuous updating, including migrating PTMs to updated protein records, when external databases are updated. The open-source nature and flexibility allow for user-based control and community-based feature integration. Fast and easy analysis is possible with the API, with example analyses provided here.

Creating highly active enzymes for arbitrary reactions is a transformative goal for the molecular sciences. Traditional protein engineering is constrained by a reliance on existing natural starting points, which are often challenging to discover. De novo design offers the potential to overcome this by creating enzymes from first principles, but has yet to achieve practically relevant catalytic properties. Here we present AlphaProtein Novo (AP Novo), a machine-learning pipeline that demonstrates, for the first time, that de novo enzyme design can outperform natural sequence mining in addressing challenging chemistry. We used AP Novo to design new-to-nature nitrene transferases to synthesise the pharmacophore piperidine with unprecedented product selectivity, and to create enzymes that degrade the environmental toxin DEHP under conditions that denature natural enzymes. We also obtained state-of-the-art catalytic efficiencies on two well-studied model reactions, and through iterative design and analysis found that key enablers of success were mechanism-inspired metrics based on AlphaFold 3 predictions and sequence-ensemble-based scoring of designed backbones. Our results show that de novo design has become a powerful complement to natural enzyme diversity for discovering catalysts for a range of applications.

Translation initiation is a critical stage of protein synthesis in prokaryotes, where ribosomes assemble on mRNA to begin decoding genetic information. The interactions between ribosomal RNA (rRNA) and mRNA significantly influence this process, with features like ribosomal binding site (RBS) affecting ribosomal dynamics. However, direct experimental validation of these types of interaction is yet to be provided. In this study, we developed an optimized small sub-unit sequencing (SSU-seq) protocol for E. coli, enabling the detailed examination of ribosomal complexes and subunits dynamics. Unlike Ribo-seq, which primarily maps fully assembled ribosomes, SSU-seq captures both ribosomal subunits and intact ribosomes, offering a comprehensive view of translation initiation and elongation events. By integrating experimental data with predictive models, our study advances the understanding of translation regulation in prokaryotes and underscores the utility of SSU-seq in decoding the complexities of ribosome-mRNA interactions. Our analysis reveals distinct patterns of small subunit distribution across the entire mRNAs with higher density near positions with stronger rRNA-mRNA hybridization. The results may suggest that rRNA-mRNA hybridization positions along the entire transcript facilitate efficient start codon recognition by maintaining proximity between the small subunit and the mRNA.

Root nodule symbiosis allows legumes to meet their nitrogen requirements. However, the causes of symbiosis failure at high temperatures remain unknown. We demonstrated that elevated temperatures affect both the host and the microbial partner in Medicago truncatula and chickpea. Rhizobial viability and nod gene expression persisted under heat stress, but plant transcriptome analysis revealed a strong downregulation of the nodule organogenesis machinery. Nodule organogenesis was restored by constitutive overexpression of NIN-LBD16-NFYA1-NFYB16, miR2111, and CLE5 knockdown but failed to restore nitrogen fixation. In contrast, the genetic manipulation of the competence pathway involving CEP peptides significantly improved both nodulation and nitrogen fixation. Constitutive loss of the autoregulation of nodulation (AON) pathway in the SUNN alleles also rescued root nodulation and nitrogen fixation. Therefore, heat stress perturbs the balance between AON and the competence pathway, and genetic manipulation of these pathways is a promising approach to engineer thermotolerant nitrogen fixation in crop legumes.

CRISPR-Cas systems form the adaptive immunity of prokaryotes, conferring sequence-specific protection against genetic parasites. Here, we functionally characterized the type I-F CRISPR-Cas system of Pseudomonas aeruginosa ATCC 10145 (PA10145), which led us to discover the existence of an isolated CRISPR array unique to this system. Its core CRISPR-Cas system is composed of a cas operon flanked by two divergently organized arrays: CRISPR2 and CRISPR1. Meanwhile, the isolated CRISPR array, CRISPR3, was found ~1.3 million bp away from cas. The cas and three CRISPR arrays together function toward adaptive immunity, eliminating plasmids engineered with protospacer targets. If plasmids possessed an intact protospacer adjacent motif (PAM), hyperactive adaptation was stimulated in all CRISPR arrays of PA10145, whereas minimal to no adaptation was observed when the PAM was mutated. Spacer acquisition via interference-driven adaptation proceeds through strand-biased priming in PA10145. Interestingly, the isolated CRISPR3 and cas-adjacent CRISPR2 have nearly identical leader sequences, with only 3 bp mismatches. Of the 1,196 P. aeruginosa genomes analyzed, all 281 isolated arrays only occur as type I-F with similarly matching leaders to CRISPR2. Integrative conjugative elements and prophages only associate with CRISPR2 and CRISPR3, suggesting that isolated arrays might have originated from recombination events involving CRISPR2 as facilitated by these highly transmissible mobile genetic elements. Tracing evolutionary trajectories of the isolated CRISPR3 relative to cas-adjacent arrays revealed that CRISPR3 is horizontally transferred across P. aeruginosa genomes. Taken together, these results implicate the role of horizontally acquired isolated arrays in CRISPR-mediated pan-immunity as gateways to mobilize genetic memories.

Integrative and conjugative elements (ICEs) are widespread in bacteria and typically confer important phenotypes to their hosts. Following excision from the host chromosome, an ICE can transfer to a new cell through the element-encoded conjugation machinery (a type IV secretion system) to create a transconjugant. Tn916, a well-studied ICE that confers tetracycline resistance, is found in several Gram-positive pathogens and has been widely studied in Bacillus subtilis. Unlike most ICEs, Tn916 causes host-cell death following its activation (excision and gene expression). We found that orf7 and orf8 (renamed sig7 and sig8), two regulatory genes in Tn916, encode a two-subunit (split) sigma factor of RNA polymerase, Sig7-8, that activates transcription from Porf7 (now Psig7) and is required for excision of the element. Importantly, production of Sig7-8, either from Psig7 or another strong promoter, caused cell death. Using a Cre recombinase reporter to detect gene expression, we detected little or no transcription from Psig7 in transconjugants. This lack of transcription from Psig7 in transconjugants was essential for fitness of both the element and its host: forcing transcription from Psig7 in transconjugants caused either loss of Tn916 or death of nascent transconjugants. Our findings indicate that, unlike other well-studied ICEs (e.g., ICEBs1) that use repressors to control gene expression and are expressed in nascent transconjugants, most Tn916 genes remain off following transfer to a new host. This regulation prevents Tn916 from killing transconjugants and allows successful horizontal transfer and maintains the potential for future activation in the new host.

Environmental change is ubiquitous in nature, yet understanding its effect on the predictability of evolution remains a challenge. Previous work in microbes has shown that growth and fitness in a particular environment can depend strongly on the environments encountered previously. If such history-dependent effects are large, then any sequence of environments must be thought of as unique, and even a modest number of environments could generate a combinatorially large number of evolutionary paths. This would make evolution in changing environments difficult to predict. Here, we use cycles of three environments, which is the smallest number of environments which allow for comparisons of different environmental orderings — X → Y → Z → X vs X → Z → Y → X — to empirically test how environmental order affects the fitness of S. cerevisiae mutants. We measured the fitness of ~900 adaptive mutants in distinct three-environment cycles, and find large history-dependent effects which cause fitness in individual environmental components to differ drastically across the two directions of the cycle. However, these effects tend to compensate for each other, resulting in similar overall fitness across the whole cycle. This is true even when we specifically conduct experimental evolution in both directions of the same three-environment cycle. Of the ~450 mutants we isolated, only a small number of strains showed higher fitness in the direction in which they evolved. By splitting the contribution of each environment on fitness into its effect on the current and next component, we show that the invariance of overall fitness to environment order emerges because the effect of an environment on the next component is largely independent of the identity of the subsequent component, and can be captured by an additive model. Despite this general pattern of compensation, we do find instances of non-additivity, some of which are partly due to history-dependent effects lasting beyond two growth components. Overall, these results suggest that despite large history-dependent effects, fitness across a sequence of environments can be surprisingly predictable.

Bacterial pathogens ensure their successful dissemination by employing specialized secretion systems to deliver a wide range of virulence proteins that interact with specific host proteins to modulate the response during infection. These interactions are often mediated by host-like elements, like short linear motifs, which can interfere with host interaction interfaces, enabling bacterial secreted proteins to compete and displace native partners of the targeted proteins. Several resources provide access to bacteria-host protein-protein interaction data. However, some are neither fully functional or up to date. Moreover, most of these databases lack virulence protein annotations, thus hampering hypothesis formulation based on protein interaction analyses. In this context, we have developed BactMentha, a unified resource that integrates bacterial-host protein-protein interactions for three organisms (human, mouse, and rat) with virulence protein annotations and bacterial strain clinical relevance information. BactMentha provides easy access to available experimentally identified binding regions of the interaction partners, complemented with putative interaction interfaces based on predicted structure complexes and on the detection of host-like elements on bacterial protein sequences interacting with human proteins. BactMentha currently contains 11 776 unique protein interactions between 2970 bacterial proteins, of which 684 have at least one virulence annotation, and 5088 host proteins. Experimentally identified binding regions are available for 532 bacteria-host interactions, most of which involve human proteins (440 interactions). Predicted interaction interfaces enabled a 3.7-fold increase in known and putative binding regions in the bacteria-human protein interaction data. By providing easy access to functionally annotated and clinically relevant protein interaction data, enriched with experimentally and predicted interaction interfaces, BactMentha can be instrumental in formulating novel hypotheses and speeding up wet lab research on the molecular mechanisms underlying pathogenic bacterial infections.

Transcriptomics is moving beyond gene-level quantification toward isoform-resolved interrogation of alternative splicing, transcript structural variation, and repeat-derived transcription. Yet short-read sequencing remains intrinsically limited in accurately reconstructing full-length transcripts and resolving complex repetitive regions, including transposable elements. Recent advances in long-read sequencing, exemplified by Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT), offer a transformative opportunity to directly observe complete RNA molecules and thereby overcome these bottlenecks. However, practical adoption is hindered by demanding library construction and the need to process noisy, fast-evolving long-read data, and the field still lacks a unified resource that guides researchers through the entire experimental and analytical workflow. This review fills that gap by providing a concise, end-to-end, and implementation-oriented roadmap for long-read transcriptomics. We distill the key decisions from platform and library strategy selection to core computational processing and downstream interpretation, and we summarize emerging frontiers and best-practice recommendations. By offering a reusable framework and practical checklists, this guide empowers a broader community to exploit long reads for standardized, reproducible, isoform-level discovery at unprecedented resolution.

A broader understanding of RNA biology requires methods for visualizing transcript dynamics in native environments. Common imaging approaches rely on fluorescent probes that require excitation light and often depend on large aptamer arrays, limiting sensitivity and compatibility with many non-coding RNAs, particularly highly structured folds. To address these challenges, we developed a miniaturized bioluminescent platform for RNA imaging. This system employs a compact RNA tag that assembles luciferase fragments upon binding of monomeric RNA-binding proteins (RNA lanterns). Systematic optimization of the RNA component yielded a structured 24-nucleotide tag, representing greater than 3-fold reduction in size relative to the original C-11-B RNA tag, placing it among the smallest RNA imaging tags reported to date. The optimized tag enabled RNA-dependent bioluminescence detection in live cells, notably as a small, single insert within highly structured non-coding RNAs, including the MALAT1 ENE domain and a small HDV-like ribozyme. Collectively, this work establishes a miniaturized bioluminescent platform for imaging a broad range of RNA targets in living systems.

Life runs on electron flow. Cells extract electrons from nutrients and transfer them through metabolism, coupling carbon use to energy conservation, biosynthesis and redox homeostasis. Yet how and even whether electron flow is organized at the system-level across metabolism remain unknown. Here we formalize the concept of the cellular electron economy: a quantitative, genome-wide, reaction-resolved representation of electron generation, transfer and disposal. This electron-centric perspective reveals a quantitative relationship governed by two distinct organizing factors: the carbon source sets the baseline and maximal rate of electron flow, whereas the capacity to dispose of electrons to terminal sinks determines where within this range the system operates. Carbon uptake can even increase as net electron flux falls. This system-level organization is shown to apply to five microorganisms with different intracellular electron routing pathways and predicts graded physiological responses to oxygen supply across carbon sources and respiratory architectures. The cellular electron economy thus links reaction-level redox chemistry to whole-cell metabolic organization and reveals how the same organizing principles govern electron flow across diverse metabolic architectures.