Amino acid substitutions may substantially alter protein stability and function. However, the contribution of substitutions that arise from alternate translation (deviations from the genetic code) is unknown. Here to address this issue, we analysed deep proteomic, transcriptomic and genomic data from more than 1,000 human samples, including 6 cancer types and 26 healthy human tissues. This global analysis identified 60,803 fragmentation spectra corresponding to 8,746 unique substitutions in proteins derived from 1,767 genes, including 1,955 confidently localized sites. Some substitutions were shared across samples, whereas others exhibited strong tissue-type and cancer specificity. Notably, products of alternate translation were more abundant than their canonical counterparts for hundreds of proteins, which suggests that there is sense-codon recoding. Recoded proteins included transcription factors, proteases, signalling proteins and proteins associated with neurodegeneration. Mechanisms that contribute to substitution abundance included protein stability, codon frequency, codon–anticodon mismatches and RNA modifications. We also characterized how alternatively translated proteoform ratios vary across protein domains, tissue types and cancers. These ratios were positively associated with intrinsically disordered regions and genetic polymorphisms in the gnomAD database, although the polymorphisms could not account for the substitutions. The sequence, relative abundance and the tissue specificity of alternatively translated proteins were conserved between humans and mice. These results demonstrate the contribution of alternate translation to the diversification of mammalian proteomes and its association with protein stability, tissue-specific proteomes and disease. Alternate RNA decoding, an understudied process, leads to peptide sequence modifications that can have substantial functional effects on protein stability, tissue-specific proteomes and disease.

Natural products remain a major source of antibiotics, but discovery efforts have traditionally treated biosynthetic gene clusters as sources of individual bioactive molecules. Increasing evidence has suggested that microorganisms can instead encode coordinated multi-metabolite systems, yet the genetic architectures and biological logic of such systems remain poorly understood. Here we show that Streptomyces spp. encode a highly conserved biosynthetic megacluster that produces four structurally distinct natural product families—stravidins, acidomycin, dapamycins, and 2-methyl-7-keto-8-aminopelargonic acid (α-Me-KAPA)—alongside the biotin-binding protein streptavidin. These components converge on bacterial biotin metabolism through complementary mechanisms, including enzyme inhibition, prodrug activation, cofactor mimicry and biotin sequestration. The encoded metabolites are co-produced and act synergistically across Gram-negative and mycobacterial species, with stravidin S2 and α-Me-KAPA showing enhanced efficacy in combination in a mouse model of multidrug-resistant E. coli infection. This megacluster reveals a genetically encoded chemical arsenal that functions as a naturally evolved combination therapy against a conserved metabolic pathway. More broadly, our findings suggest that higher-order biosynthetic architectures may represent an overlooked reservoir of antibiotic mechanisms and support a shift from discovering isolated natural products to reconstructing native synergistic systems. In Streptomyces spp., a conserved biosynthetic gene megacluster produces an arsenal of distinct antimicrobials that converge on bacterial biotin biosynthesis as a naturally evolved combination therapy.

The cross-species delivery of megabase-scale synthetic DNA molecules, from microorganisms into mammalian cells, remains a major challenge for synthetic genomics. Recently, we developed nucleus isolation for chromosome extraction (NICE), a method that enables the isolation of yeast nuclei containing intact synthetic megabase-scale DNA with preserved chromatin structure. By leveraging the unique epigenomic features of Saccharomyces cerevisiae, which lacks cytosine methylation and repressive histone marks, synthetic DNA encapsulated within isolated yeast nuclei was successfully delivered into mouse early embryos, maintaining a naive state. This work established a unique platform for studying the establishment of de novo epigenetic modifications and their influence on transcriptional regulation over time. Here, we provide a detailed protocol for NICE, including the isolation of yeast nuclei and their subsequent delivery into mammalian embryos. The high-concentration and high-purity isolated nuclei can be stored at –80 °C for >6 months. Using microinjection, we achieved 100% delivery efficiency, reliably transferring isolated yeast nuclei into mouse embryos. The entire procedure, including pulsed-field gel electrophoresis verification, can be completed within ~5 d. When the isolated yeast nuclei are intended for cross-species delivery into embryos, prior familiarity with mammalian embryo microinjection techniques may be required. This protocol offers an efficient and reliable method for the delivery of large-scale genetic information, advancing the study of complex biological functions. This protocol presents a method for isolating intact yeast nuclei containing megabase-scale synthetic DNA and delivering them into mouse embryos, enabling efficient cross-species transfer and studies of de novo epigenetic regulation.

Confronting the dual crisis of escalating global protein demand and unsustainable agriculture necessitates transformative solutions. Here, we pioneer evolutionary insights from maize nitrogen optimization via asparagine synthetase (ASNS) to rewire metabolism in Pichia pastoris. Empirically, the tri-copy ASNS strain achieved superior protein titers: 62.48% crude protein, 47.86% total amino acids, and 8.05% branched-chain amino acids, nutritionally surpassing conventional protein sources. Genome-scale modeling and transcriptomic studies provided convergent evidence that ASNS overexpression drove global metabolic rewiring through predicted synergistic coupling between aspartate metabolism and the tricarboxylic acid cycle. Mechanistically, ASNS overexpression unlocked a previously uncharacterized nitrogen sensor-regulator circuit by inducing PAS_chr1-1_0158, validated in amplifying intracellular nitrogen flux and driving a self-reinforcing cycle of ammonia assimilation. This work validated evolutionary conservation of nitrogen optimization strategies across kingdoms, established the first scalable blueprint for carbon–nitrogen co-optimized microbial cell factories, decouples sustainable SCP production from agricultural constraints, and offers a scalable solution to the global protein crisis.

The term ‘gut health’ is increasingly used as a catch-all phrase by many stakeholders, including scientists, health-care professionals, industry and the general public, to describe a wide range of health-related concepts. Despite its widespread use, particularly in relation to studies on diet, fermented foods, biotics and the gut microbiome, it remains unclear what the term gut health means. Therefore, an expert panel was convened by the International Scientific Association for Probiotics and Prebiotics to address the current state of scientific and clinical knowledge on the physiology, manifestation, application and measurement of the concept of gut health. The panel evaluated the term in the context of the central role of the gastrointestinal tract in health and overall well-being and proposed a definition of gut health as “a state of normal gastrointestinal function without active gastrointestinal disease and gut-related symptoms that affect quality of life”. The definition was developed mindful of the functional, subjective and extrinsic domains that contribute to gut health. In this Consensus Statement, clinically relevant and accessible metrics to assess these domains are reviewed and a comprehensive approach to gut health is proposed that is relevant to clinical practice as well as to studies of dietary and biotic interventions. This Consensus Statement provides a definition of the term ‘gut health’, as well as a discussion of the relevant domains that contribute to gut health and a framework for appropriate use of the term in the context of therapeutic interventions.

Structure-based virtual screening (VS) via molecular docking is a pivotal approach for hit identification. Many artificial intelligence (AI)-powered protein–ligand docking and scoring methods have demonstrated impressive speed and accuracy. Retrospective benchmarking studies using enrichment rate and computational efficiency on curated datasets have corroborated their potential for discovering bioactive compounds. However, determining which method suits a specific application and implementing it efficiently remains challenging. Here we present the Comprehensive VS Platform with AI Engine (CVSP-AIE) for drug discovery from compound libraries. It integrates three AI models: KarmaDock, a fast docking model that directly updates atomic coordinates; CarsiDock, an accurate docking model that predicts protein–ligand distances and reconstructs binding poses; and RTMScore, an accurate scoring model that learns residue–atom distance distributions for affinity prediction. Their hierarchical application enables dynamical balances in screening speed and accuracy. CVSP-AIE is available as an online web server ( https://cadd.zju.edu.cn/cvsp/ ) and a local software package. Users can efficiently initiate drug screening by uploading a protein and a known binder that defines the binding pocket. The following workflow involves (1) preprocessing, including protein structure repair and molecule standardization, (2) binding pose and affinity prediction powered by KarmaDock, CarsiDock and RTMScore and (3) postprocessing, comprising protein–ligand interaction calculation and visualization. It takes 30–45 min to hierarchically screen 100,000 compounds, and the output is a ranked list of molecules with predicted binding scores, intermolecular interaction profiles and interactive chemical space analysis. Users can also install locally the hierarchical screening module through command-line package for arbitrary-scale screening. This Protocol describes an artificial intelligence-driven virtual screening platform for drug discovery that includes an online webserver and a local software package, offering a user-friendly alternative for drug screening targeting compound libraries of arbitrary-scale.

Lignocellulose is a promising renewable resource for anaerobic biochemical production, but its microbial conversion remains challenging. To elucidate metabolic networks in lignocellulose-degrading consortia, inocula of various origins were enriched on cellulose or xylan. Community composition and metabolic functions were revealed by amplicon sequencing, metagenomics, genome-scale metabolic modelling, and metabolic simulations. In cellulose-enriched communities, Fibrobacter and Lacrimispora consistently dominated as primary cellulose degraders, whereas Bacteroides likely functioned as secondary degraders. Acetic acid (up to 1.3 g l-1) and CO2 were the main fermentation products. Xylan enrichments produced C2-C6 fatty acids (up to 3.9 g l-1), lactic acid (up to 1.2 g l-1), ethanol (up to 1.2 g l-1), CO2, and H2. Clostridium dominated one xylan community and produced mainly butyric acid, while Bifidobacterium dominated another and produced mainly lactic acid. Caproic acid production was experimentally observed in one xylan enrichment. Metagenomic annotations and metabolic simulations suggest that Lacrimispora amygdalina degraded xylan and Robinsoniella peoriensis consumed xylobiose as a secondary consumer, both likely producing ethanol and lactic acid that supported caproic and butyric acid production by Caproicibacter fermentans. Integrated analysis identified functional guilds and clarified the roles of degraders and non-degraders, providing a blueprint for engineering synthetic consortia for sustainable biochemical production.

Pseudomonas putida KT2440, renowned for its diverse metabolic capabilities, is a promising platform for downstream processing and revalorisation of recalcitrant molecules. In this study, we examined and optimized P. putida KT2440's ability to utilize products of the degradation of polyethylene (PE), the most used and disposed plastic. PE degradation creates over 200 molecules that vary in oxidation level and, thus, chemical properties. Among those, long-chain alcohols represent one of the most challenging fractions to process due to their poor solubility. Using them as feedstock for microbial growth would close the plastic-derived carbon cycle, reducing environmental impact. First, we discovered that P. putida KT2440 can use the long-chain alcohols, 1-hexadecanol and 1-eicosanol, as the sole carbon and energy source. Using adaptive laboratory evolution (ALE), we generated variants with improved growth rates on such substrates. Mutations that became fixed during ALE provided insights into the mechanism, highlighting the importance of cell–substrate interaction. By heterologously expressing a hydrocarbon transporter-encoding gene, we successfully reproduced the ALE-derived phenotype, suggesting that the bottleneck in long-chain alcohol utilisation lies in uptake rather than substrate transformation. These findings lay the groundwork for the potential application of P. putida KT2440 for the valorization of PE degradation products.

Fluorescent nucleobase analogs (FBAs) are valuable tools for studying nucleic acid structure and dynamics. However, their utility is often limited by substantial fluorescence quenching upon incorporation into oligonucleotides and variable brightness influenced by neighboring bases. In this study, we present a novel turn-on nucleoside, 3b, a thiazolyl-dU analog (hereinafter referred as TzdU), engineered to overcome these limitations and enable reliable DNA fluorescence imaging. Compared to its nearly nonfluorescent free form, TzdU shows approximately a 10-fold increase in brightness in single-stranded DNA (ssDNA) and up to a 50-fold enhancement in double-stranded DNA (dsDNA). Importantly, it maintains relatively stable brightness regardless of surrounding bases by evading common quenching pathways, including solvent-induced collisional quenching and excited-state proton transfer (ESPT). The triphosphate derivative of TzdU is efficiently utilized by various DNA polymerases, including Deep Vent and KOD XL, facilitating real-time, intensity-based monitoring of critical enzymatic processes such as PCR and primer extension without external labels. Furthermore, TzdU can illuminate DNA in a gradient manner, enabling the visualization and encryption of information. As the first FBA to achieve universal turn-on characteristics, sequence insensitivity, and compatibility with enzymatic reactions, TzdU serves as a novel tool for investigating nucleic acid dynamics and advancing fluorescence-based methodologies.