Peptides, as therapeutic molecules, offer unique advantages in targeting complex protein surfaces, yet their rational design remains limited by the vastness of the sequence space and the constraints of traditional approaches. Here, we propose High-PepBinder, a sequence-only conditional diffusion framework for target-specific peptide generation. Guided by the target protein sequence, High-PepBinder adopts a dual encoder architecture that integrates protein language models (pLMs) with the diffusion model. This approach cascades the peptide generation model with an affinity classifier and enables the generation process to capture affinity-related features of the peptides through lightweight joint optimization. Due to the scarcity of protein-peptide affinity data, we constructed PepPBA, to our knowledge the most comprehensive dataset to date, and established a structure- and physics-based screening pipeline to prioritize top candidates. Results show that High-PepBinder demonstrates competitive performance across multiple peptide generation and affinity-related tasks. For representative targets, including KEAP1, XIAP, and EGFR, the generated peptides preserve key binding geometries and interface patterns of reference peptides in predicted complexes, while maintaining sequence diversity and favorable predicted properties. Overall, High-PepBinder contributes toward a general and sequence-only strategy for peptide design, offering a computational framework for expanding peptide discovery against challenging targets.

The biotransformation of acetaminophen, a pharmaceutical contaminant widely found in crop produce, in the rice phyllosphere highlights critical xenobiotic-plant-microbiota interactions. This study investigated acetaminophen uptake, translocation, and transformation in hydroponically exposed rice shoots, revealing accumulation at 8.33 ± 0.82 μg/g and conversion into hydroxylated, glycosylated, methylated, thiomethylated, sulfonated, dimerized, and amino acid-conjugated derivatives. Specifically, these transformations may be driven by plant enzymes (cytochrome P450, glycosyltransferases, sulfotransferases, and methyltransferases) and synergistically by enriched microbial genera (Sphingomonas, Pantoea, and Pseudomonas). Furthermore, acetaminophen stress altered the rice phyllosphere metabolome (elevated linoleic acid and jasmonic acid) and reshaped microbial communities, with enhanced degradation pathways and network complexity indicating adaptive stress mitigation. Overall, this integrated transcriptome, metabolome, and microbiome profiling provides mechanistic insights into the cooperative detoxification role of plant enzymes and phyllosphere microbes, offering perspectives on leveraging plant-microbiota interactions to reduce xenobiotic impacts on crops and food safety.

Adaptive, closed-loop control of cellular behavior is essential for next-generation therapies, yet most current treatments operate in an open-loop manner and lack robustness to patient variability and disease dynamics. Here, we establish a control-theoretic platform for rational engineering of closed-loop cell-based therapies that achieve precise and robust regulation. First, we introduce multi-dimensional nullgram profiling, a high-throughput approach that enables quantitative prediction and design of advanced genetic controllers in human cells across circuit topologies and parameter regimes in a single experiment. To evaluate dynamic therapeutic behavior, we next develop Cyberpatient-in-the-loop, an optogenetic digital twin platform that interfaces engineered mammalian cells with computational disease models, enabling systematic testing of closed-loop performance under realistic perturbations. Finally, we leverage these approaches to implement integral feedback cell therapies that sense inflammatory signals and autonomously regulate cytokine levels in primary immune cell cultures. Together, these results establish a general paradigm for engineering cell-based control systems and provide a foundation for next-generation cell therapies.

The activation of cryptic biosynthetic gene clusters (BGCs) in Streptomyces has emerged as a powerful strategy to uncover novel antibiotics. Recent advances in native and synthetic promoter engineering have enabled precise transcriptional control over silent gene clusters. This review presents a comprehensive overview of constitutive and inducible promoters, both native and synthetic, used in refactoring efforts. It also highlights CRISPR-based activation systems and machine-learning-guided promoter design for BGC activation. Case studies illustrate how these strategies are translated into enhanced metabolite production, particularly in the context of antimicrobial resistance. This review aims to provide a systems-level understanding of promoter tools and their translational implications for natural product discovery.

Some microbes externalize costly biosynthetic precursors in sufficient quantities to sustain a recipient population through cross-feeding. However, it is unclear whether metabolites are externalized purely for a reciprocal benefit or if metabolite externalization also plays a physiological role for the producer. Here we focus on adenine, a metabolite externalized by some strains of the phototrophic bacterium Rhodopseudomonas palustris at sufficient levels to support E. coli growth. In 10 long-term monocultures and 22 cocultures pairing R. palustris with E. coli, extracellular adenine externalized by all 140 isolates screened was 1.7 - 3.4-fold higher than that by the ancestor, suggesting that there was selective pressure for adenine externalization. We hypothesized that adenine is toxic to R. palustris. The CGA0092 growth rate decreased by half in the presence of about 0.3 mM external adenine. This inhibitory effect increased by an order of magnitude when we overexpressed adenine phosphoribosyltransferase to overcome a bottleneck in the purine salvage pathway, suggesting that toxicity stems from a metabolite derived from adenine. To assess whether adenine tolerance is connected to adenine externalization, we surveyed 12 evolved isolates and 49 environmental strains that externalized different levels of adenine, revealing a significant positive correlation. Our data suggests a physiological role for externalization of costly-metabolites like adenine at the origin of cross-feeding. In addition to cross-feeding, resulting metabolic interactions could be negative, considering that even a biosynthetic precursor like adenine can be inhibitory.

DNA has emerged as a promising medium for the post-silicon era of information storage due to its ultrahigh density and longevity. However, current systems are bifurcated, with solid-state systems providing robust cold archival but lacking accessibility, while fluidic molecular computing systems offer dynamic processing but suffer from low density and instability. This mutual exclusivity has hindered the development of hierarchical memory, a standard in modern computing, within molecular storage systems. Here, we bridge this gap by engineering a reconfigurable DNA memory architecture driven by programmable liquid-liquid phase separation (LLPS). Our system leverages sequence-based encoding to achieve an ultrahigh storage density of 7×10^10 GB/g, approaching the theoretical limits of DNA accessibility. In its fluidic hot state, DNA droplets enable rapid data loading (~83.8% in 5 min) and function as an in-memory editing platform supporting versatile, addressable bit-level operations including selective erasure (~65.1%) and high-efficiency rewriting and replacement (>99%) via programmable strand displacement. Importantly, to resolve the stability trade-off, we engineered a programmable phase transition whereby the triggered assembly of a rigid tetrahedral DNA framework (TDF) armor transforms liquid condensates into robust armored droplets. This cold state confers exceptional resistance to enzymatic and physical degradation, projecting multi-millennial data stability. By enabling reversible transitions between an editable, high-density computing mode and a stabilized archival mode, this work establishes the architectural foundation for scalable molecular information storage capable of hierarchical data management.

There is growing interest in engineering animal and plant microbiomes to deliver double-stranded RNA (dsRNA) for RNA interference (RNAi) applications. We developed a genetically encoded biosensor that uses bimolecular fluorescence complementation to monitor dsRNA levels within bacterial cells to accelerate the symbiont-mediated RNAi design-build-test cycle. We validated performance of the sensor in Escherichia coli and demonstrated enhanced dsRNA accumulation in engineered strains of the aphid symbiont Serratia symbiotica.