Model-based design in synthetic biology is limited because bioparts are typically characterised by relative metrics that vary across genetic and physiological contexts. To address this, we introduce a host-aware framework for quantitatively characterizing parts in combinatorial libraries of plasmid-based constitutive expression constructs. The approach integrates a digital twin of E. coli, conditioned on measured growth rate, with model-in-the-loop parameter identification to separate part-associated properties from host-dependent effects. Using structured combinatorial libraries, we identify mechanistically interpretable, transferable parameters for plasmid origins, promoters and ribosome binding sites. In particular, we define an intrinsic translation initiation capacity that captures the dominant RBS-associated contribution to translation while context-dependent expression emerges from host physiology and local sequence context. The resulting parameterisation accurately predicts protein synthesis across physiological conditions, supports incremental library expansion, and reveals localised failures of modularity, providing a scalable foundation for predictive host-aware design in synthetic biology. Model-based design in synthetic biology is limited because bioparts are typically characterised by relative metrics that vary across genetic and physiological contexts. Here, using combinatorial libraries of plasmid-based expression constructs and a host-aware E. coli digital twin, the authors identify intrinsic biopart parameters that provide a basis for more predictive model-based design of synthetic gene circuits.

Terrestrial Antarctica harbors compositionally diverse and functionally distinct microbial life. Yet the eco-evolutionary processes underlying adaptation to Antarctica’s polyextreme conditions remain largely unknown. Here, we address how horizontal gene transfer (HGT) and de novo mutations influence microbial adaptation in 16 Antarctic soils using combined short- and long-read datasets. Phylogenetic reconciliation and mobile genetic element analysis of 676 metagenome-assembled genomes show frequent HGT across communities. While transferred genes span diverse functional categories, those involved in energy metabolism are exchanged at higher frequency. Genes for aerotrophy, i.e. the consumption of atmospheric trace gases to provide energy, carbon, and hydration, are among the most frequently disseminated. Approximately a quarter of carbon monoxide dehydrogenases and [NiFe]-hydrogenases are predicted to be horizontally acquired and are often associated with mobile genetic elements. Analysis of polymorphisms suggests widespread purifying selection, particularly for aerotrophy genes, providing further evidence that aerotrophy is critical for microbial survival in Antarctica. Genetic variation in hydrogenases is tightly associated with predicted protein structures, with intense selection acting on critical sites preserving stability and function. Together, these findings show that previously unrecognized eco-evolutionary dynamics shape the composition and function of Antarctic microbial communities, and confirm aerotrophy is a strongly selected and horizontally disseminated trait. Antarctica was long thought to be biologically static, but its microbial life is now known to be diverse and active. This study shows that widespread horizontal gene transfer and purifying selection drive microbial adaptation in Antarctic soils.

Microplastic and nano-plastic (MPs/NPs) pollution is a major environmental threat affecting ecosystems and human health. Soils contain higher levels of MPs/NPs than oceans, underscoring the urgent need for improved plastic waste management in agriculture. Studies show that MPs/NPs disrupt the rhizosphere by altering soil properties and microbial dynamics, impairing plant growth. This review outlines their fate in terrestrial ecosystems, their rhizosphere impacts, and emphasizing the need to understanding phytotoxicity mechanisms for safe crop production.

The gut microbiome is a dynamic ecosystem in which microorganisms constantly adjust their transcriptional programmes. Here we developed metastrand, a framework that integrates strand-aware metatranscriptomics and metagenomics to quantify mRNAs and antisense RNAs (asRNAs) in complex microbial communities at gene-level resolution. In inflammatory bowel disease (IBD), microbial asRNA programmes converged across patients during active disease, correlated with faecal metabolites and calprotectin levels and remained stable during persistent inflammation, highlighting their potential as biomarkers of inflammatory activity in the gut. These programmes involved antisense-to-sense transcriptional shifts at insertion sequence elements with functionally diverse passenger genes and preceded their detection at new genomic locations, linking asRNA dynamics to structural genome rearrangements and redistribution of adaptive functions under selective pressure. Similar dynamics were observed in a mouse model of colitis, oxidative stress in vitro and in patients with pathogen-confirmed gastroenteritis, establishing asRNAs as an important dimension of microbial adaptation in health and disease. Metastrand integrates strand-aware metatranscriptomic and metagenomic data to distinguish microbial sense from antisense transcription at gene-level resolution, and a use case study highlights the importance of antisense RNAs in understanding the microbial dynamics in inflammatory bowel disease.

Accurate detection of off-target activity in primary human cells is crucial for ensuring the safety of gene therapies, yet existing methods often lack sufficient sensitivity. To address this limitation, we develop Tracking-seq2, an advanced technology that integrates exogenous 5′ → 3′ exonuclease treatment and non-homologous end joining (NHEJ) pathway inhibitors with the original Tracking-seq. Tracking-seq2 exhibits enhanced sensitivity in profiling off-target sites of diverse genome editors—including Cas9, Cas12a, cytosine base editors (CBEs), adenine base editors (ABEs), and prime editors (PEs). Critically, Tracking-seq2 is directly applicable to clinically relevant primary human cell types, such as T cells and CD34+ hematopoietic stem and progenitor cells (HSPCs). Furthermore, our findings reveal that genomic variations drive distinct off-target heterogeneity across different individuals, highlighting the necessity for personalized safety assessment in clinical genome editing applications. Tracking-seq2 provides a robust platform for sensitive off-target detection in primary cells, with sensitivity comparable to or exceeding current state-of-the-art methods. Ensuring safe gene editing requires precise detection of unintended DNA edits. Here, authors develop Tracking-seq2, a highly sensitive method to profile off-targets for diverse genome editors, and reveal distinct off-target effects across individuals in primary human cells.

E. coli remains the premier microbial chassis in industrial biotechnology; yet, translating laboratory-scale metabolic successes into robust, hundred-ton-scale manufacturing presents persistent multidimensional bottlenecks. This review systematically elucidates a contemporary paradigm shift in E. coli fermentation regulation, transitioning from isolated, empirical process adjustments to cross-scale, deeply coupled intelligent cybernetic frameworks. We first deconstruct the precise cross-scale links between macroscopi physicochemical parameters and intracellular biological regulatory networks, detailing how fluctuations in temperature, pH, and dissolved oxygen fundamentally reshape intracellular molecular cascades alongside transmembrane proton motive forces. To mitigate spatial heterogeneity, substrate mixing delays, and carbon catabolite repression common in large-volume bioreactors, we comprehensively evaluate advanced mitigation strategies that integrate process-level model predictive control (MPC) with dynamic synthetic biology circuits, including quorum-sensing-based growth-production decoupling, metabolite-responsive feedback loops, and optogenetic switches. Critically, we highlight the convergence of multi-scale modeling and digital twins as a frontier simulation-driven tool, which dynamically couples computational fluid dynamics (CFD) with genome-scale metabolic models (GEMs) to render the industrial fermentation “black box” transparent. Finally, we outline the evolutionary trajectory of E. coli biomanufacturing toward a digitized, intelligent, and sustainable paradigm, leveraging low-carbon C1 feedstocks and paving the way for next-generation green biomanufacturing.

Transcription termination by RNA polymerase II (Pol II) defines transcriptional boundaries of protein-coding and noncoding transcription units throughout the genome. Rather than being a passive endpoint of elongation, termination is a tightly regulated and context-dependent process that shapes gene expression, RNA surveillance pathways and chromatin environments. This Review summarizes mechanistic and conceptual aspects of Pol II termination, focusing primarily on studies in metazoans and incorporating insights from yeast where relevant. We discuss the major termination pathways operating across different genomic contexts, including canonical cleavage and polyadenylation-dependent termination at 3′ ends of genes, promoter-proximal termination mediated by the Integrator–PP2A complex (INTAC), Pol II turnover via the E3 ligase CRL3ARMC5 and cap-dependent RNA surveillance. We further examine how termination restrains pervasive transcription and how its dysregulation compromises genome stability, particularly through the accumulation of R-loops. Finally, we discuss how termination interfaces with RNA processing, export and nuclear decay pathways to guide RNA fate decisions. Transcription termination by RNA polymerase II is a tightly regulated process that involves RNA surveillance and the chromatin environment. This Review discusses the interactions between transcription termination, RNA processing, nuclear RNA decay and mRNP export, which together guide RNA fate.

Gut microbial metabolites play crucial roles in regulating systemic immunity, but their mechanisms and limited drug-like properties remain unresolved. Here we report an oral nano-formulation that leverages gut microbial metabolites to modulate T cell metabolism and amplify anti-tumor immunity. Through an in vitro screening of gut microbial metabolites, we identified 3,4-dihydroxybenzoic acid that improved adoptive T cell therapy and enhanced CD8+ T cell stemness by suppressing glycolysis and regulating the Akt-mTORC1-Myc pathway. To harness the potency of 3,4-dihydroxybenzoic acid for systemic cancer immunotherapy, we engineered a 3,4-dihydroxybenzoic acid prodrug nano-emulsion, significantly increasing its oral absorption and half-life. In multiple murine tumor models, the oral nano-emulsion enhanced the expansion of antigen-specific, stem-like CD8+ T cells, sensitizing tumours to anti-PD-1 blockade and exerting robust antitumor efficacy. By integrating nanotechnology with microbial-metabolite-based immunotherapy, this study establishes a mechanistic link between the gut microbiota and T cell immunity, offering a promising approach for cancer immunotherapy. A nano-emulsion that delivers the gut microbial metabolite 3,4-dihydroxybenzoic acid in a prodrug form enhances T cell stemness and boosts the efficacy of immunotherapy approaches in different murine cancer models.

Mathematical models are increasingly used to infer traits, interactions and functional dynamics of microbial systems. One common example is a rate-based ordinary differential equation model parameterized with microbial traits. However, fitting such models with associated parameters to data requires a principled approach to extract information from time series while accounting for prior knowledge and measurement noise. These principles often remain implicit and not necessarily well defined. Here, we make the implicit, explicit: introducing Bayesian inference of ecological models for microbial time series, including three detailed case studies of algal population dynamics that follow a birth-death process. Complementing this primer, we provide an online tutorial on Bayesian inverse modelling with cross-programming language support via Python (PyMC) and Julia (Turing). By connecting theory, code, data and a series of hands-on educational modules, this primer aims to bring the utility of Bayesian learning to the broader microbial ecology research community.