Precise modeling of transcriptional regulation is essential for the rational design of genetic circuits in synthetic biology. Current computational approaches for predicting transcriptional activity (ITX) typically lack mechanistic clarity, composability, and scalability, and require extensive training data. Here, we present a modular thermodynamic modeling framework that explicitly parameterizes molecular interactions among promoters, RNA polymerase (RNAP) and transcription factors (TFs). Implemented as the computational platform, T-Pro, this approach provides robust interpretability, scalability, and predictive power. Experimental validation across three distinct bacteria—Escherichia coli, Bacillus subtilis, and Corynebacterium glutamicum—demonstrates substantial improvements (up to 20-fold) in a composite transcriptional performance metric (Fmax*FC), achieved within only three Design–Build–Test–Learn cycles and fewer than five genetic constructs in total. Furthermore, we validate the framework by engineering multispecies bacterial communication circuit, highlighting its broad utility and generalizability. The principles and tools developed here thus enable efficient, rational optimization of transcriptional regulation across diverse prokaryotic hosts.

We present a molecular strategy that enables the programmable activation of the CRISPR–Cas12a system in response to triplex DNA formation triggered by single-stranded DNA (ssDNA) or RNA inputs. Our triplex-controlled Cas12a assay leverages the high specificity of clamp-like triplex structures to control a toehold-based strand displacement reaction within a rationally designed DNA hairpin (PAM-Switch). Upon displacement and protospacer adjacent motif (PAM) complementation, the Cas12a ribonucleoprotein (RNP) is activated, initiating trans-cleavage and producing a concentration-dependent fluorescent signal. By decoupling target recognition (via triplex formation) from direct hybridization with the Cas12a–crRNA complex, the assay eliminates the need for target-specific crRNAs. This design also allows multiple detection of distinct nucleic acid (NA) targets using a single Cas12a reaction mix. Through the use of triplex-based clamps, the proposed platform achieves enhanced specificity for single-nucleotide variants and supports the detection of both ssDNA and RNA targets across a broad range of lengths (10–20 nucleotides), addressing key limitations in current Cas12a-based diagnostics and opening new avenues for NA sensing.

Engineering DNA polymerases to efficiently synthesize artificial or noncognate nucleic acids remains an essential challenge in synthetic biology. Here we describe an evolutionary campaign designed to convert a family of highly selective DNA polymerases into an unnatural homolog with strong RNA synthesis activity. Starting from a homologous recombination library, a short evolutionary path was achieved using a single-cell droplet-based microfluidic selection strategy to produce C28, a newly engineered polymerase that can synthesize RNA with a rate of ~3 nt s−1 and of >99% fidelity. C28 is capable of long-range RNA synthesis, reverse transcription and chimeric DNA–RNA amplification using the PCR. Despite strong discrimination against other genetic systems, C28 readily accepts several 2′F and base-modified RNA analogs. Together, these findings highlight the power of directed evolution as an approach for reprogramming DNA polymerases with activities that could help drive future applications in biotechnology and medicine. Engineering polymerases to synthesize alternative genetic polymers remains a challenging problem in synthetic biology. Using DNA shuffling and droplet microfluidics, the current study provides a short evolutionary path from a DNA polymerase to one with robust RNA-synthesizing activity.

Site-specific DNA recombinases are powerful tools for genome engineering. The recent convergence of DNA recombinase-mediated technology with prime editing has established a promising new frontier for large-scale genomic integration. However, this strategy has limitations, including low intrinsic catalytic efficiency of DNA recombination enzymes and inefficiency of their long DNA landing pad insertions. To overcome these challenges, we developed a novel directed evolution strategy using progressively shortening landing pads as a selective pressure. The resulting evolved variants, VK and AVK, exhibited substantially enhanced intrinsic activity that is maintained on short-length landing pads. This enables a powerful synergistic effect with increased insertion efficiency of prime editing, leading to a dramatic increase in overall integration efficiency while maintaining high genomic specificity. This work thus provides a new class of highly efficient recombinases and a robust engineering strategy with broad applicability for both the development of gene therapies and fundamental research.

The messenger RNA 3' untranslated region contains important regulatory sequences, including upstream sequence elements (USEs), which regulate gene expression. One well-characterised USE in the 3'UTR of the Drosophila polo gene affects adult fly phenotypes when disrupted. We have now identified a highly conserved sequence within this USE (DplUSE) in the 3'UTR of several vertebrate genes, including in zebrafish, mouse, and human genomes and show that DplUSE enhances gene expression in human cells and zebrafish embryos. We show that, in humans, DplUSE-containing genes are associated with congenital disease processes, and that disruption of DplUSE function impairs zebrafish development. We also found that HuR/ELAVL1, hnRNPC, and PTBP1/hnRNPI bind to DplUSE RNA and are required for its activity in a human cell line, suggesting a highly conserved mechanism across distantly related species. Our results indicate that PTBP1 has a global function in alternative polyadenylation, activating the selection of distal polyA sites and repressing intronic polyadenylation in DplUSE-containing genes while hnRNPC and HuR modulate their expression. Additionally, we found that a colon cancer-associated SNP in the POU2AF2/C11orf53 3'UTR creates an ectopic DplUSE site, increasing gene expression in zebrafish gut cells and in a human cell line. We have therefore identified a short 3'UTR motif present in diverse vertebrate genes that controls their expression through conserved RBPs interactions and is implicated in human disease.

The expansion of the genetic alphabet through the development of unnatural base pairs (UBPs) has the potential to revolutionize synthetic biology and biotechnology. However, the replication of the UBPs by eukaryotic DNA polymerases is largely unexplored and the sequencing of the UBPs remains challenging. Herein, we explored and demonstrated the activity of human DNA polymerase β (Pol β) for the efficient and specific synthesis and extension of a panel of representative UBPs, including dNaM–dTPT3, dCNMO–dTPT3, and their functionalized derivatives. Based on this, we established a method for the sequencing of DNAs containing different unnatural bases, involving stalled primer extension mediated by Pol β, selective conversion of an unnatural nucleotide into two different natural ones in parallel and further primer extension mediated by Taq DNA polymerase, and Sanger or deep sequencing of the produced natural DNAs to locate the unnatural bases. The precision, universality, and potential for high-throughput applications of this method were demonstrated by the successful sequencing of various DNAs containing one or multiple of different unnatural bases. This work suggests the possibility of integrating the UBPs into the eukaryotic DNA replication systems and provides a technical foundation for the robust sequencing of DNAs with an expanded genetic alphabet.

Nitazenes are an emergent class of synthetic opioids that often rival or exceed fentanyl in their potency. These compounds have been detected internationally in illicit drugs and are the cause of increasing numbers of hospitalizations and overdoses. New analogs are consistently released, making detection challenging — new ways of testing a wide range of nitazenes and their metabolic products are urgently needed. Here, we develop a computational protocol to redesign the plant abscisic acid receptor PYR1 to bind diverse nitazenes and maintain its dynamic transduction mechanism. The best design has a low nanomolar limit of detection in vitro against nitazene and menitazene. Deep mutational scanning yielded sensors able to recognize a range of clinically relevant nitazenes and the common metabolic byproduct in a complex biological matrix with limited cross-specificity against unrelated opioids. Application of protein design tools on privileged receptors like PYR1 may yield general sensors for a wide range of applications in vitro and in vivo. Nitazenes are potent synthetic opioids that are difficult to detect. Here, authors computationally redesign a plant receptor to create sensitive sensors capable of detecting diverse nitazenes and their metabolites in biological samples.

Although genome sequencing technologies have advanced rapidly, microbial genomes still contain numerous genes with unknown functions, posing ongoing challenges for comprehensive genome annotation. Traditional annotation methods are constrained by a lack of scalable experimental techniques and the limitations of conventional homology-based computational approaches. Recent computational innovations, particularly deep learning, have substantially improved gene function prediction, facilitating more efficient annotation of transcription factors, enzymes and other protein classes. Integrating computational and experimental approaches has enabled the development of workflows that systematize gene function discovery, paving the way for faster, more accurate and comprehensive genome annotation. Continued refinement of these integrated methods holds great promise for deepening our understanding of microorganisms. Here we review recent advances in artificial intelligence for gene function discovery and discuss future directions for achieving interpretable and high-throughput artificial intelligence-guided annotation. In this Review, the authors discuss how artificial intelligence can aid microbial gene function discovery.

Bacteriophage (phage) genome annotation is essential for understanding their functional potential and suitability for use as therapeutic agents. Here, we introduce Phold, an annotation framework utilizing protein structural information that combines the ProstT5 protein language model and structural alignment tool Foldseek. Phold assigns annotations using a database of over 1.36 million predicted phage protein structures with high-quality functional labels. Benchmarking reveals that Phold outperforms existing sequence-based homology approaches in functional annotation sensitivity whilst maintaining speed, consistency, and scalability. Applying Phold to diverse cultured and metagenomic phage genomes shows it consistently annotates over 50% of genes on an average phage and 40% on an average archaeal virus. Comparisons of phage protein structures to other protein structures across the tree of life reveal that phage proteins commonly have structural homology to proteins shared across the tree of life, particularly those that have nucleic acid metabolism and enzymatic functions. Phold is available as free and open-source software at https://github.com/gbouras13/phold.

Gene co-expression networks are commonly used to identify functionally related genes, but they often suffer from spurious associations and fail to capture genes with restricted, context-specific responses. To address these limitations, we constructed YeastCoDEGNet, a global co-differential expression network in Saccharomyces cerevisiae, by reanalyzing microarray data from 143 carefully curated experiments comprising 425 comparisons. In this network, gene connections are defined by shared context-specific responses, enabling the identification of distinct topological groups enriched in either essential or nonessential genes, often associated with metabolic or nonmetabolic biological processes, respectively. We further characterized each gene by its responsiveness, essentiality, and number of co-differentially expressed partners, uncovering positional clustering of these features across chromosomal locations. To explore higher-order functional organization, we built a cross-pathway coordination network based on context-specific responses between pathway pairs, revealing modules of functionally related pathways. This network not only recovered well-known associations but also identified novel links, with particularly strong coordination observed among pathways involved in central carbon metabolism, amino acid and antioxidant processes, protein synthesis, trafficking, degradation, and gene regulation. By capturing context-specific gene expression dynamics, YeastCoDEGNet provides a powerful framework for studying how genes and pathways adapt to genetic and environmental perturbations.