Sequence-based predictors of recombinant protein solubility in Escherichia coli have plateaued (NESG independent-test AUC 0.760 to 0.80 over eight years of protein language-model variants). The plateau hides a latent confound: the centrifugation regime used to separate the soluble from the insoluble fraction is a hidden variable collapsed into a single binary "soluble" label. The protein's biochemistry does not change between regimes; what changes is which fraction of the lysate is recovered as soluble. Existing predictors treat the regime as label noise rather than a feature, and sequence overlap between training and test partitions masks the resulting failure mode.  We release the Aiki-Sol Dataset, a tiered E. coli solubility corpus: a ~85K stringency-annotated benchmark, an Apache-licensed ~147K extension adding binary only-labelled proteins, and a ~229K research-tier pool incorporating non-commercially licensed sources. On the ~85K benchmark, scored on sequence-cluster-disjoint partitions, the strongest published binary comparator falls below chance on the 32,000 x g stratum (AUC 0.491 +- 0.020); a fine-tuned ESM-2 650M backbone with five protocol-matched outputs lifts pooled AUC by +0.108 (paired-bootstrap CI lower bound >= +0.090). The gain is curation, not architecture: structure-aware predictors given ESMFold structures do not outperform the sequence-only frame, and capacity scaled to 3B parameters does not exceed the conditioned 650M backbone. The released model, Aiki-Sol, jointly supervises five per stringency outputs alongside a marginal output for stringency-unknown proteins; on five external cohorts it lifts cohort-mean AUC from 0.69-0.70 to 0.825, with a +0.10-0.16 lift on the three cohorts at measurably-zero training-pool overlap.

Bacteroides thetaiotaomicron (Bt), a dominant bacterial species in the human gut, is a promising chassis for engineering in situ therapeutic delivery systems. Previously, we developed a secretion toolkit composed of endogenous lipoprotein signal peptides (SPs) and full-length secretory proteins from Bt. However, due to variations in length, structure, and amino-acid sequence, these SPs exhibited inconsistent secretion efficiencies across different cargo proteins. Because the activity of individual SP-cargo pairs is not readily predictable, screening and optimization is often required to achieve target secretion titers. To enhance the utility of our toolbox, we studied the impact of different SP sequence components on protein secretion, then applied this knowledge to develop a standardized toolkit to and enable predictable and tunable protein secretion across diverse SP-cargo pairs. To achieve this, we first identified the lipoprotein export sequence (LES) as the key determinant of efficient secretion of heterologous proteins by lipoprotein SPs. We next performed mutagenesis on the LES region of a representative lipoprotein SP to generate a pool of mutants featuring a standardized SP backbone with diversified LES regions. Screening and characterization of this mutant pool revealed a charge-dependent regulation of both secretion and surface display of heterologous cargo proteins. From these findings, we established a toolkit with improved tunability, enhanced predictability, and surface display capabilities that minimizes the need for iterative screening when developing protein secreting gene circuits for Bt and other Bacteroides species. By enhancing both the flexibility and control of therapeutic protein output, these results expand the potential of engineered living therapeutic applications, particularly those requiring tunable dosing or surface presentation of proteins.

Upstream open reading frames (uORFs) in the 5′ leader of bacterial mRNAs can modulate gene expression, yet genome-wide identification remains limited. We combined bioinformatic prediction of ribosome-binding sites (RBS), a Shine Dalgarno sequence and a start codon, with experimental validation to uncover new uORFs in Sinorhizobium meliloti 2011. From totally 1106 predicted upstream RBSs (uRBSs), we first examined 15 candidates using eGFP reporters and integrating existing RNA-seq and Ribo-seq data. Translation was detected at 13 sites, with fluorescence intensity broadly correlating with predicted initiation rates. Two uRBSs correspond to gene start sites, thereby refining gene annotations. In nine cases, uRBS mutations affected downstream gene expression in reporter fusions. Among others, the data suggests that a Type I secretion system operon, the RNA chaperone gene hfq, and metabolic genes are regulated by uORFs. Four uORFs acted through translational coupling. We also identified uRBSs that were ribosome-occupied yet (nearly) silent in eGFP assays, and closely spaced to the downstream main RBS (mRBS). These uRBSs probably mediate ribosomal occlusion downregulating lacR and SM2011_RS36230. A re-screen of the prediction set revealed 335 close uRBS/mRBS pairs. Three of them were analyzed, supporting the proposed ribosomal occlusion mechanism for SM2011_RS03630 and SM2011_RS22110, while for glnK translational coupling to an uORF was suggested. These results indicate that uORFs are more widespread in bacteria than previously recognized and suggest that direct ribosomal occlusion of the mRBS is a novel mechanism for down regulating protein synthesis.

Mechanistic understanding of gut ecology is limited by the availability of tools for precise manipulation of microbiome composition. Here, we isolate lytic phages to enable targeted removal of gut commensal Escherichia fergusonii (Ef) from complex, undefined stool-derived in vitro communities. A single phage drove resistance without fitness cost in monoculture, but resistant Ef exhibited reduced fitness in communities, enabling expansion of closely related Proteobacteria. Resistance arose via reversible promoter inversion linked to outer-membrane function. A phage cocktail overcame resistance to achieve Ef knockout across communities with minimal collateral effects. Using knockout communities, we show that Ef is necessary and sufficient for preventing Salmonella invasion. Replacement with an Ef transposon-mutant library revealed that community-specific fitness defects are enriched in genes involved in outer-membrane assembly. Disruption of these genes sensitized Ef to antagonistic community members, highlighting interspecies warfare as a key driver of microbiome ecology. These results establish phage-mediated perturbation as a framework for linking species to community-level function and for enabling precision microbiome engineering.

Bacteriophage receptor-binding proteins (RBPs) determine bacterial host recognition and are central to phage host-range evolution, yet the structural principles governing how RBPs diversify and expand host range remain not fully understood. Although phage RBPs are thought to evolve through modular exchange of receptor-binding domains, it is unclear how this modularity is organized across diverse RBP architectures, at what structural scales recombination operates, and how it shapes host-range breadth. Here we combined large-scale AlphaFold3 structural modelling, domain-level annotation, de novo pseudo-domain segmentation, sequence modularity analysis and experimentally determined host-range phenotypes across 192 Klebsiella phages and 382 high-confidence RBPs spanning up to 96 K-types. We generated the first system-wide structural atlas of Klebsiella phage RBPs, resolving 39 structurally distinct RBP-classes. Although capsule-degrading beta-helix depolymerases dominated numerically, 161 RBPs across 37 RBP-classes employed non-depolymerase architectures, including 18 novel RBP-classes. Structural and sequence analyses show that this diversity arose through modular reuse of structural domains rather than independent invention. Conserved N-terminal scaffolds linked depolymerase and non-depolymerase RBPs across morphotypes, while receptor-binding regions diversified through recombination operating at and beyond domain boundaries. We found recent modular exchange both within genera, where capsule-specific and capsule-independent RBPs can be swapped to alter host-range strategy, and across morphotypes, where depolymerase modules moved between distant lineages altering capsule specificity. Together, these architectures resolve into six receptor-recognition strategies, establishing multi-scale modularity as the primary organising principle of RBP diversification and a structure-informed framework for guiding phage isolation and engineering against Klbsiella pneumoniae. The complete atlas is freely accessible as an interactive community resource at Klebsiella-Phage-RBP-Atlas.

Fungal genomics has expanded rapidly over the past 30 years, and recently the pace and breath has further quickened for many taxa, although many taxonomic gaps persist. With three decades of rapid growth, fungal genomics now merits a re‑examination of its history, progress, and unresolved taxonomic gaps. Here, we review the development of fungal genomics from early efforts such as the Fungal Genome Initiative to current progress driven by third-generation long-read sequencing. We have compiled and summarised publicly available fungal genomes to highlight trends in assembly quality, adoption of long-read technologies, and taxonomic representation. Notably, substantial phylogenetic gaps remain, particularly outside Dikarya, and significant challenges persist for unculturable taxa. This review identifies priorities for the fungal community, including: (1) coordinated efforts to close major taxonomic gaps across the fungal tree of life; (2) improved repository metrics to facilitate identification of high-quality assemblies; and (3) improved and standardised genome annotation which is lacking for most assemblies. Together, these steps will support the development of reliable genomic resources that capture the full breadth of diversity across the fungal kingdom, generating foundational data for comparative genomics, evolutionary biology, functional studies, genetic studies and applied research.

Membrane transport is a fundamental biological process with profound implications for pharmacology, biotechnology, and microbiology. While computational approaches have largely adopted a protein-centric perspective to annotate transportomes, inferring transport function directly from the intrinsic properties of substrates remains a major challenge. Addressing transport at the compound level enables the systematic evaluation of whether molecules undergo active transport and by which mechanisms, independent of prior transporter annotation. Here, we introduce ChemProFlow, a comprehensive computational framework that redefines transport analysis from a substrate-centric perspective. By integrating geometric deep learning with orthology-based genomic mapping, ChemProFlow predicts molecular transportability, assigns transport mechanisms according to the Transporter Classification Database, and identifies the microorganisms encoding the corresponding transport systems. We show that this integrated pipeline enables scalable, end-to-end mapping of substrate-transporter-organism relationships, with broad applications in pharmacology for anticipating drug transport, in biotechnology for guiding strain engineering, and in microbiology for dissecting substrate utilization across diverse taxa. By capturing the chemical determinants of transportability, ChemProFlow generalizes to previously unseen substrates and provides a high-throughput framework for systematic exploration of molecular transport across diverse biological contexts.