The capacity to adapt to complex environments (adaptability) is a defining property of cells. Yet, its relationship across single-cell physiology, population-level responses, and evolutionary timescales remains unclear. We performed single-cell transcriptional profiling of budding yeast across 20 complex environments before and after long-term selection for increased osmotolerance. In ancestral populations, transcriptional responses organized into a reproducible hierarchy where adaptation to certain cues took precedence over others. This hierarchy mechanistically originated within individual cells through contingent regulation of translation initiation. Evolution for >3,000 generations under osmotic stress increased osmotolerance while reordering this hierarchy, selectively deprioritizing osmotic stress as an organizing axis of adaptation. The derived strain exhibited impaired integration of stress responses, defective translational coordination, and reduced fitness outside the selected condition. Together, these findings illustrate that cellular adaptability is an evolved architecture whose form is set by the history of selection.

p-Coumaric acid (p-CA) is a universal precursor of plant lignin and polyphenols. A comprehensive understanding of p-CA metabolism is essential for elucidating biological evolution, advancing polyphenol biomanufacturing, and developing lignocellulosic biorefineries. However, p-CA metabolism remains poorly characterized due to the limited number of studies and the absence of universal prediction methods. Here, we systematically elucidate p-CA metabolism in the industrial yeast Yarrowia lipolytica. Through transcriptomic analysis and a reaction-based prediction method, we identify key degradation genes in Y. lipolytica, whose collective deletion reduced p-CA degradation by 81.6% and increased production by 2.51-fold. Comparative analysis of 109 species revealed that the metabolic potential for p-CA is widespread but exhibits significant habitat-dependent divergence. This study establishes a systematic framework for understanding and engineering p-CA metabolism, providing insights for optimizing microbial cell factories and a broader exploration of metabolic pathways.

Precise monitoring of intracellular glucose dynamics is essential for understanding carbon flux, optimizing microbial bioprocesses, and enabling responsive control of engineered metabolic pathways. Here, we develop a modular whole-cell biosensor in Escherichia coli that converts the native glucose repression phenotype of a CAP-sensitive promoter into a tunable, glucose-inducible output using CRISPR interference (CRISPRi). By placing a guide RNA (gRNA) under the control of the CAP promoter and positioning dCas9 to target the -10 region of a constitutive promoter driving sfGFP, we created an inversion circuit in which glucose suppresses gRNA expression, thereby relieving dCas9-mediated repression and activating fluorescence. Systematic evaluation of gRNA strand orientation and target site selection revealed that template-strand targeting yielded strong repression (~90 %) but reduced sensing range, whereas moderately repressive non-template gRNAs (~27-35 % repression) enabled optimal signal inversion. The resulting biosensor demonstrated a robust, linear fluorescence response across 200 μM -50 mM glucose (R2 > 0.97), with high specificity against other sugars and a strong correlation between glucose consumption and fluorescence accumulation (R2 ≈ 0.996). To extend the functionality of the platform, we integrated the sensor with a secreted β-glucosidase module that hydrolyzes cellobiose to glucose. The biosensor accurately quantified glucose released during cellobiose degradation, with engineered strains producing up to 33 mM glucose from 50 mM cellobiose in a two-plasmid system. This coupling of enzymatic conversion with intracellular sensing enabled real-time, non-destructive monitoring of metabolic transitions. Together, this work establishes a programmable CRISPRi-based strategy for inverting native promoter logic and provides a sensitive, specific, and modular platform for metabolite sensing in bacteria. The approach is broadly applicable for dynamic pathway regulation, monitoring carbon fluxes, and building responsive genetic circuits in metabolic engineering and synthetic microbial ecosystems.

Polyphosphate (polyP) is a conserved inorganic polymer traditionally viewed as a stress-induced phosphate and energy reserve. In cyanobacteria, however, polyP granules are constitutively present and frequently observed in proximity to carboxysomes, the bacterial microcompartments that mediate CO2 fixation. Here we show that polyP functions as a spatially organized regulator of the photosynthetic cytoplasm in Synechococcus elongatus. PolyP granules localize to the nucleoid and are periodically arranged along the cell axis, independently of the McdAB carboxysome positioning system. Despite this independence, polyP and carboxysomes associate non-randomly, and this association is enhanced when active carboxysome positioning by the McdAB system is disrupted. Loss of polyP synthesis leads to nucleoid expansion, an increased number of smaller carboxysomes with high mobility, and severe defects in growth under ambient CO2. Perturbation of polyP turnover further reveals structural connections to both carboxysomes and thylakoid membranes. Together, these findings identify polyP as an architectural integrator that couples chromosome organization, metabolic compartmentalization, and photosynthetic fitness.

Anaerobic carbon transformation in freshwater sediments drives substantial methane emissions globally, yet the microbial taxa linking complex carbon degradation to methane production remain poorly characterized. Here, we combined metagenomics with the first metatranscriptomic dataset from the anoxic sediments of meromictic Lake Cadagno (Swiss Alps) to identify the active microbial clades, metabolic pathways, and extrachromosomal elements (ecDNA) across a depth gradient within the upper 56 cm of sediment. We recovered 802 species-level metagenome-assembled genomes (MAGs) spanning 66 phyla and identified a Bacteroidota clade (VadinHA17) as one of the most abundant and transcriptionally active populations in the sediment. This clade encodes and transcribes a broad range of diverse glycoside hydrolases (GH), indicating a central role in complex carbohydrate degradation. Transcriptional profiles suggest that this clade ferments organic substrates to acetate and hydrogen, which are key substrates for methanogenesis. In line with this, the acetoclastic methanogen Methanothrix and hydrogenotrophic Methanoregula were among the most abundant and transcriptionally active archaea in the same depth layers. Beyond microbial genomes, we detected 86,905 viral OTUs (vOTUs) and 2,136 plasmid OTUs (pOTUs), with free viruses and plasmids accounting for 5-10% and 0.2% of all sequencing reads, respectively. Notably, plasmids and viruses associated with Bacteroidota VadinHA17 encode and transcribe GHs that could augment host carbohydrate-degrading capacity. Together, these findings reveal new details on how methane production in anoxic lake sediments emerges from a network spanning primary fermentation, methanogenesis and ecDNA-mediated metabolisms.

Isoprenoid biosynthesis in E. coli is a promising source of high-value natural products. However, isoprenoid production imposes a substantial metabolic burden on cellular carbon and cofactor metabolism. Optimizing yields thus requires rebalancing gene expression throughout metabolism. To systematically identify the genes in E. coli central metabolism shaping isoprenoid yield, we established a high-throughput CRISPR interference (CRISPRi) assay to examine the impact of gene repression on production of the bright red carotenoid, lycopene. Our approach enables repression of both essential and non-essential genes and links gene expression perturbations to pathway yield and biomass. We screened a CRISPRi library targeting 180 E. coli genes and found 31 genes for which repression significantly modified lycopene yield. Genes whose repression increased lycopene yield were found across fatty acid, amino acid, central carbon, and isoprenoid metabolism as well as stress response pathways. This set included four genes in amino acid biosynthesis, one in phospholipid biosynthesis, and one in the stringent response that, to our knowledge, have not previously been implicated in lycopene production. In contrast, genes for which repression decreased yield were limited to isoprenoid biosynthesis, central carbon metabolism, and stress response pathways. Together, our work reveals genetic targets for increasing lycopene yield throughout metabolism, and defines a tractable, generalizable approach to mapping genetic factors that modulate biosynthetic pathway yield.

Intrinsically disordered protein regions are ubiquitous across all kingdoms of life. These structurally heterogeneous regions play central roles in cellular processes such as transcriptional regulation, cellular signaling, and subcellular organization, yet they have remained largely inaccessible to rational design. Structure-based generative methods are not applicable to proteins that lack a stable fold, and existing sequence-based approaches for disordered regions rely on sampling methods that do not capture the evolutionary statistics of natural disordered regions. Here, we introduce IDiom, an autoregressive protein language model trained on 37 million intrinsically disordered region sequences curated from the AlphaFold Database. Trained using a fill-in-the-middle data augmentation, IDiom generates disordered region sequences conditioned on their surrounding structured context, as well as fully disordered proteins without any context. The model generates diverse sequences that recapitulate biologically relevant sequence features of natural disordered regions, and we demonstrate that post-training via reinforcement learning with a subcellular localization reward model produces sequences with features which are consistent with known sequence determinants of compartment-specific localization. These results establish IDiom as a general platform for the generative design of intrinsically disordered proteins and regions.

Peptides occupy a unique niche as biochemical tools: they are small, modular reagents capable of perturbing protein function with a precision that is often inaccessible to small molecules or antibodies. Historically, their broader use in biochemical research has been constrained by slow discovery workflows, limited control over specificity, and poor physicochemical properties. Recent advances in artificial intelligence have begun to change this landscape by enabling the rational, data-driven design of peptides tailored for specific experimental tasks. In this review, we focus on AI-designed peptides as practical tools for biochemistry. We survey sequence-based and structure-based design paradigms, highlighting how each supports distinct classes of peptide tools, including isoform- and motif-specific binders, multi-objective assay-ready reagents, and functional peptides that enable degradation, stabilization, or biophysical interrogation of proteins. By emphasizing experimental utility, design constraints, and appropriate use cases, we aim to provide a framework for selecting and deploying AI-designed peptides as on-demand reagents in modern biochemical research.

Across the biosphere, microbiomes play essential roles in shaping the health of their host. One notable example of such a microbiome-associated phenotype is disease-suppressive soils, where susceptible plant hosts enrich and activate specific rhizosphere microbial consortia for protection against fungal root pathogens. However, identifying and reconstructing microbial consortia responsible for host protection remains challenging, given the inherent taxonomic and functional complexity of microbiomes. Here, we integrated metagenomic profiling of disease-suppressive microbiomes perturbed by dilution-to-extinction (DTE) with comprehensive culturing and synthetic ecology to identify the key bacterial taxa conferring suppressiveness to the fungal wheat pathogen Fusarium culmorum. Metagenomics of wheat rhizosphere samples along the DTE trajectory revealed bacterial taxa and functions associated with the disease-suppressive phenotype. Crosslinking these DTE metagenome data with a genome-sequenced collection of 336 rhizobacterial isolates from the suppressive soil allowed the reconstruction of synthetic communities (SynComs) of 11 de-replicated strains negatively associated with disease severity. Upon re-introduction in sterilized suppressive soils, this SynCom consistently reproduced the disease-suppressive phenotype. Paired time-series metagenomics and metatranscriptomics of the SynComs pinpointed candidate biosynthetic gene clusters, including a novel non-alpha poly-amino-acid (NAPAA) gene cluster from Arthrobacter, upregulated in presence of F. culmorum. Chemically synthesized NAPAA variants Ɛ-poly-L-lysine and δ-poly-L-ornithine significantly inhibited F. culmorum hyphal growth. Collectively, our work establishes a transformative strategy for reconstructing microbial consortia that recapitulates beneficial microbiome-associated phenotypes in plant and animal kingdoms.

Transcription initiation is regulated by proteins called transcription factors (TFs). Though TFs help determine phenotype across the tree of life, they are nonessential for minimal cellular life and are often absent in endosymbiotic and parasitic organisms. Given this and the idea that it is a certain level of organism complexity that calls for specific transcription regulation, we traced the evolutionary history of TF repertoire on a bacterio-archaeal tree of life using a dataset of ∼500,000 TFs, grouped into ∼1,700 orthologous groups (OGs) across ∼3,000 species. The most ancestral prokaryotes encoded multiple TFs. Going by known extant functions of these TFs, they possibly regulated sugar-fermentation metabolism, sensed overall metabolic state and redox, responded to DNA damage or bound metals; many of which are consistent with some reconstructions of ancestral gene pools and physiologies. The number of TFs as well as their superfamily-level diversity, through evolutionary history, matches expectations against genome size derived from extant bacteria, suggesting pre-LUCA diversification of TF sequence families. Emergence of new TFs along the phylogeny largely followed a smooth cumulative distribution curve, suggesting steady innovation, early in prokaryote evolution, in contrast to eukaryotes, in which a majority of TF families emerged in a burst manner at the ancestors of multicellular lineages. Gains of TFs late in prokaryotic evolution predominantly featured recycling of protein families discovered elsewhere in the prokaryotic tree, consistent with the dominance of horizontal gene transfer in these organisms. We speculate on the difference between the evolutionary trajectory of prokaryotic TF repertoire and compare it with the eukaryotic TF repertoire trajectory. This helps us in understanding the manner in which their TF repertoires have evolved in two different super-kingdoms. The difference between the evolutionary dynamics of TF- repertoires might be due to how complexity is envisioned in these two different kingdoms.

The disparity between the production and demand of recombinant proteins (r-proteins) has significantly hindered their commercial viability. Leveraging genomic resources offers substantial promise in enhancing our comprehension of metabolic and regulatory networks, thus facilitating the development of highly productive protein cell factories. However, the considerable gap between high-throughput strategies for monitoring r-protein secretion and genome perturbation in P. pastoris continues to obstruct the systematic linkage of genotype and phenotype, thereby limiting the optimization of production. Here, we developed a novel strategy combining dual-base editor-mediated in-situ genome engineering with nanobody-regulated biosensor-assisted droplet sorting to enhance r-protein secretion (BINDER) in P. pastoris. We successfully employed BINDER to screen recombinant human serum albumin (rHSA) hyper-producers and identified two critical SNVs conferring up to a 1.78-fold improved secretion titer from 113632 mutants, providing valuable insights into the secretion mechanism. Fed-batch cultivation of the engineered strain resulted in the highest reported rHSA titer, 23.43g/L, in P. pastoris, demonstrating its substantial potential for industrial applications. Given the high transferability of base editors and the novel biosensor's independence from the properties of the target protein, the strategy developed here might be expanded to a variety of microbial species and r-proteins.

Precise and minimally perturbative protein labelling remains a key challenge for studying biomolecular function in living systems. Here, a minimal way to specifically label proteins based on SNAP-tag and intein-mediated protein splicing reaction is introduced. Termed CLUSTER (for Chemical Label-Unfold-Splice Technology Enables Recombination), this chimeric platform supports efficient labelling across diverse targets in living cells by retaining a fluorescent, 5 kDa sized peptide on a fusion protein of interest after splicing. A bacterial screening workflow was developed to optimize the reaction efficiency and construct design. Quantitative characterization using fluorescence polarization provides mechanistic insight into labelling efficiency and dynamics, while molecular dynamics simulations elucidate its stability, grasping the intricate nature of protein behavior upon covalent labelling. This bio-orthogonal labelling technology allows for a versatile and minimally invasive approach for protein labelling, providing a powerful tool to probe protein behavior in native cellular systems.