Precise control of gene expression in a cell-state-specific manner is essential for effective therapeutic interventions in complex and dynamic disease microenvironments. Traditional targeting strategies that rely on surface markers or cell type-specific promoters often assume static cellular identities, limiting effectiveness in context such as cancer and inflammation, where cell states are highly heterogeneous and dynamic. RNA sensors, such as RADAR (RNA sensing using Adenosine Deaminases Acting on RNA), provide a modular, programmable, and nonintegrating platform for classifying cell states. However, it is also characterized by low sensitivity and dynamic range, which limits its applications in detecting low-abundance transcripts. In this work, we integrate RADAR sensors with a signal amplification circuit to enhance sensitivity and dynamic range. We demonstrate that this combined RADAR-amplifier platform enables real-time monitoring of subtle changes in the abundance of endogenous transcripts under physiological conditions. Our results demonstrate the utility of this platform for fundamental biological studies and the development of precision therapeutic strategies.

T7 RNA polymerase is a foundational enzyme for biotechnology, but its utility for many potential applications is limited by low thermal stability of 43-44°C. While stabilized variants exist, the most stable commercial version has a proprietary sequence. In this work we developed a highly stable T7 RNAP using structure-based computational design. We combined mutations from previous stabilized variants (M5, M8, V7abcd) with new mutations identified by PROSS. These mutations were filtered using data-driven heuristics to preserve function. Our final design, T7T+, contains 30 point mutations from the original T7 RNAP and demonstrates a functional stability (T50) of 54.9°C in a thermal challenge assay, which is 2.4°C higher than the most stable, published open-source variant to date. Circular dichroism spectroscopy showed an apparent melting temperature of 53.8°C. T7T+ retains 59% of wild-type activity at 37°C. 16 of the 18 tested protein designs had higher stability against thermal challenge compared with the genetic background, attesting to the high success rates of existing non deep learning computational methods for the design of stable, functional proteins. A plasmid encoding T7T+ has been deposited in AddGene and is freely available for non-commercial use.

CRISPR-based epigenome editing represents a programmable strategy to precisely modulate gene expression, holding immense promise for therapeutic applications. However, the large size of the dCas proteins substantially impedes the delivery via adeno-associated virus (AAV) vectors. Here, through iterative bioinformatics analysis, structure-guided predictions, and functional assays, we identified and characterized a novel miniature subtype V-M CRISPR-Cas12m from Pelomicrobium methylotrophicum. PmCas12m exhibited flexible 5'-YTN-3' PAM-dependent recognition and robust double-stranded DNA binding properties, while lacking DNA cleavage activity, thus positioning it as an ideal tool for epigenome editing. Cryogenic electron microscopy (cryo-EM) structures of PmCas12m unveiled its unique molecular mechanism of DNA binding facilitating interference. Guided by these structural insights, we employed deep mutational scanning (DMS) and protein engineering to develop xCas12m, a hypercompact variant with highly potent and specific epigenome editing capabilities in human cells. We further constructed the xCas12m-CRISPRoff platform in a single AAV vector, which achieved durable epigenetic silencing and effective inhibition of hepatitis B virus (HBV) infection in a mouse model. Collectively, these findings establish xCas12m as a versatile epigenome editing platform with transformative potential for treating diseases, paving the way for clinical translation of epigenetic therapies.

In vivo multiple-enzyme cascades have attracted considerable interest for their ability to provide a native microenvironment that supports enzymatic activity and membrane protein function. This review outlined four pivotal strategies for their optimization, increasingly empowered by Artificial Intelligence (AI): (1) enhancing enzyme performance by enzyme discovery and engineering; (2) precisely modulating enzyme expression via rationally designed genetic regulatory elements; (3) implementing spatial and stoichiometric control using protein, nucleic acid, or synthetic scaffolds and compartments; and (4) employing multimodule systems including multiple cell modules and hybrid in vivo/in vitro cascades. Advances in AI accelerate these strategies, enabling novel approaches such as de novo protein design, directed evolution, and the computational design of genetic parts and supramolecular scaffolds. The integrated implementation of these methods substantially increased target compound titers. This lays a strong foundation for industrial implementation. However, several key challenges remain to be addressed.

Direct Coupling Analysis has been instrumental over the past decade in leveraging evolutionary information and advancing our understanding of biomolecular structure and function. Here, we introduce sparse extensions of this method that explicitly incorporate structural information. StructureDCA focuses on physically relevant interactions by selectively retaining couplings between residues in spatial contact, and StructureDCA[RSA] additionally incorporates per-residue relative solvent accessibility. These models outperform state-of-the-art approaches in describing mutational landscapes, as they more effectively integrate structural context. Moreover, their sparse formulation enables orders-of-magnitude improvements in computational efficiency while preserving interpretability, providing a powerful framework for gaining mechanistic insights into mutation effects and advancing protein design. The StructureDCA models are available as a user-friendly Python package via the PyPI repository. The source code is freely accessible at https://github.com/3BioCompBio/StructureDCA, which also includes a Colab Notebook interface.

Extracellular vesicles (EVs) are emerging as versatile therapeutic platforms, yet the mechanisms governing their biogenesis in yeast remain incompletely understood. Saccharomyces cerevisiae, a well-characterized and safe microbial chassis, naturally secretes abundant EVs and provides an attractive system for mechanistic dissection and engineering. Here, we establish S. cerevisiae as a tractable model for elucidating EV cargo loading. By combining multicopy expression of chicken interferon-λ (ChiIFN-λ) with cell wall perturbation, we achieved a tenfold increase in EV yield and efficient incorporation of ChiIFN-λ into EVs. Quantitative proteomics identified 1555 EV-associated proteins, including 501 predicted transmembrane proteins derived from multiple organelles. ChiIFN-λ overexpression and cell wall stress selectively reduced the abundance of key vesicle trafficking regulators, including SNARE, ESCRT and Rab proteins, indicating reprogramming of intracellular membrane trafficking pathways. Functional analyses further demonstrated that the SNARE proteins Sso2 and Nyv1 are enriched in the EV membrane and modulate EV size distribution and subpopulation composition. Together, these results reveal conserved protein-sorting machinery underlying yeast-derived extracellular vesicles (YDEVs) biogenesis and establish S. cerevisiae as a powerful platform for engineered EV production.