ACS Synthetic Biology最新文献

Biomolecular reservoir computing, despite its potential for nontraditional information processing, encounters difficulties in realizing intricate nonlinear dynamics within biochemical systems. This research proposes a biomolecular reservoir computing framework utilizing DNA-based molecular neural networks to implement reconstructed echo state networks (RESNs) and reconstructed delay-feedback reservoir (RDFR) computing for addressing the aforementioned complex nonlinear challenge. Three key innovations underpin this work: (i) a new chemical reaction networks (CRNs)-based reservoir computing structure utilizing idealized molecular interactions with adaptive parameter optimization through gradient descent algorithms, validating short-term memory capabilities; (ii) methodical topological analysis clarifying the operational mechanisms of various biomolecular reservoir computing topologies─including RESNs and RDFR using DNA chemical reaction networks; (iii) DNA strand displacement-driven implementations of RESNs and RDFRs, allowing for the resolution of complex second-order problems and nonlinear autoregressive moving average systems, respectively. This work demonstrates feasibility and efficacy in solving intricate nonlinear systems but also establishes a programmable molecular computing paradigm, providing theoretical foundations and potential implementation architectures for biomolecular information processing in unconventional computing.

Colorectal cancer (CRC) is heavily influenced by gut microbiota and metabolites such as branched-chain amino acids (BCAAs), which provides essential growth materials for tumors and activates related cancer-promoting pathways. We engineered twoEscherichia coli Nissle 1917 strains (ECN)─ECN-Deg and ECN-Tra─to deplete BCAAs in the gut in previous work. In this work, using an AOM/DSS-induced CRC mouse model under the amino acid diet, we found that both strains significantly ameliorated CRC progression, improved survival, restored gut barrier function, and reduced systemic inflammation. Mechanistically, they lowered plasma BCAA levels, suppressed mTOR activation, and modulated retinol and drug metabolism pathways. Our results demonstrate that engineered probiotics targeting BCAAs catabolism can effectively inhibit colorectal tumorigenesis, offering a novel synthetic biology-based approach for cancer therapy.

Adipic acid (AA) is an important dicarboxylic acid that serves as a precursor in the synthesis of nylon-6,6. Given the increasing market demand for AA and the environmental concerns associated with its conventional production, the development of sustainable biosynthetic techniques for AA production has become a key research focus in both industry and academia. However, industrially viable technologies for AA biosynthesis remain constrained by several challenges, particularly incomplete raw material utilization, low strain conversion efficiency, a complex fermentation process, and high costs of downstream separation. To overcome these barriers, this review presents the current state of AA biosynthesis, critically discussing biosynthetic pathways and advanced metabolic engineering strategies and tools for constructing cell factories with high conversion efficiency. The basic principles relevant to improving the fermentation process and downstream separation technologies are also comprehensively reviewed. Key challenges and knowledge gaps are identified, providing insights to guide future research toward commercially viable biobased AA production.

Neuropeptides are endogenous signaling molecules that regulate diverse physiological and cognitive processes. However, reliable identification from primary sequence remains challenging due to high sequence diversity, weak motif conservation, and the limited experimental annotations. Solving this challenge is crucial for elucidating the molecular structure of neural communication and accelerating the development of neuropeptide-based therapies and peptide drugs. Existing computational approaches for neuropeptide identification range from traditional machine-learning models relying on handcrafted features to deep-learning architectures that learn sequence representations. However, both types of methods struggle with the high heterogeneity and weak motif conservation of neuropeptides, resulting in limited generalization and highlighting the need for more robust predictive frameworks. To address these limitations, we propose a unified multitask neuropeptide identification framework that integrates ESM-derived protein representations, a BiLSTM encoder, and multihead self-attention to capture local and long-range sequence dependencies jointly. Within this framework, the model further leverages attention-based pooling, auxiliary knowledge distillation, and contrastive representation learning to enhance generalization and ultimately improve the accuracy and robustness of neuropeptide identification. On the independent test set, our proposed multitask learning method (NeuroPred-MTCL) demonstrates strong generalization performance, achieving an accuracy of 93.6% and an AUROC of 0.977. It further maintains a balanced trade-off between precision (92.9%) and recall (94.4%), yielding an F1-score of 0.936 and an MCC of 0.872. These results highlight the method's ability to effectively capture discriminative sequence characteristics and substantially enhance the reliability of neuropeptide identification. These results establish NeuroPred-MTCL as a robust and generalizable approach that meaningfully advances the computational identification of neuropeptides.

Clostridium kluyveri is a promising biocatalyst for producing medium-chain carboxylic acids (MCCAs) from waste-derived carbon via chain elongation. MCCAs are platform chemicals with diverse applications across agriculture, food, cosmetics, and fuels and could support tandem resource recovery and sustainable chemical production. However, host defense systems have hindered efforts to engineer C. kluyveri for improved product yields, control over chain length and selectivity, and production of non-native oleochemicals. Here, we report a streamlined, biparental methylation-conjugation system developed for C. kluyveri DSM555^T to bypass the organism's restriction-modification barriers and enable stable plasmid delivery. We use this system to demonstrate heterologous expression of two anaerobic fluorescent reporters: the Fluorescence-Activating and absorption-Shifting Tag (FAST) and an evolved Flavin-binding Fluorescent Protein (FbFP) from Pseudomonas putida. This system supports advances in the metabolic engineering of C. kluyveri and the broader adoption of genetic tools in chain elongating bacteria to expand the applications of anaerobic chain elongation in industrial biomanufacturing.

Compartmentalization of reactions is essential for life and allows nonequilibrium conditions to be maintained within cells. For cell growth, the membranes need to expand through lipid synthesis and a continuous supply of ATP and building blocks. Here, we build a minimal system in vesicles that integrates ATP supply, CTP and CMP recycling, and glycerol-3-phosphate synthesis with the conversion of phosphatidic acid to phosphatidylglycerol. We use four transmembrane proteins and three soluble enzymes to enable autonomous phospholipid synthesis in both the outer and inner leaflets of the membrane. The system displays biphasic lipid synthesis kinetics: a rapid phase with phosphatidylglycerol production in the cis leaflet of the membrane and a slower phase dependent on lipid scrambling. We present previously unreported scramblase activity of two integral membrane proteins: phosphatidylglycerophosphatase A and the mitochondrial ATP/ADP carrier. This work lays the foundation for autonomous lipid biosynthesis in synthetic cells and enables the exploration of emergent properties in compartmentalized systems.

Engineered programmable RNA sensors have been applied in low-cost diagnostics, endogenous RNA detection, and multi-input genetic circuits. However, designing, producing, and screening high-performance RNA sensors remains time-consuming and labor intensive. Here, we present an automated plasmid assembly pipeline using liquid handling robotics to enable high-throughput construction of plasmids with arbitrary sequences. We compare automated and manual assembly methods using the NGS Hamilton Microlab STAR across two plasmid backbones to evaluate efficiency and reliability. As a proof of concept, we use this modular platform to construct a diverse set of programmable RNA regulators, including toehold switch riboregulators targeting viral RNAs, single-nucleotide-specific programmable riboregulators for discrimination of SARS-CoV-2 spike gene mutations, and metal-responsive riboswitches. In total, we construct 174 plasmids and test the designed methods by comparing both manual and automated assembly. We further demonstrate that the assembled toehold switch plasmids are functional in both bacterial and cell-free expression systems.

The oxaloacetate (OAA) pathway represents a promising biosynthetic route to produce 3-hydroxypropionic acid (3-HP), comprising two steps: the decarboxylation of OAA to malonic semialdehyde, followed by its reduction to 3-HP. A thiamine diphosphate (ThDP)-dependent α-keto acid decarboxylase was identified as a potential bottleneck in this pathway due to its low catalytic efficiency toward the non-natural substrate OAA. In this study, rational protein engineering is employed to enhance the catalytic efficiency of KdcA. By rearranging the interaction network within the enzyme's binding pocket, variants S286R and S286K are developed, exhibiting 4.6-fold and 6.2-fold increases in activity, respectively, compared to wild-type KdcA (WT). Further reduction of the binding pocket volume leads to the creation of enhanced variants S286K/V461I/M538Y and S286K/F381W/V461I/M538Y, which display significantly lower K_m values (6.6 and 6.0 mM, respectively) relative to those of WT (K_m > 20 mM), along with up to about 120-fold increases in catalytic efficiency (k_cat/K_m). When the variant S286K/V461I/M538Y is integrated into Escherichia coli (E. coli), 3-HP production reaches 1.6 ± 0.2 mM in shake flask cultures. This study demonstrates the effectiveness of protein engineering in overcoming enzymatic bottlenecks to improve biochemical production.

Virus-like particles (VLPs) are promising platforms for next-generation vaccines due to their ability to present antigens in highly ordered, repetitive geometries emulating pathogen-associated patterns to elicit potent immune responses. The ADDomer is a synthetic dodecahedral VLP scaffold derived from the penton base protein (PBP) of human adenovirus serotype 3 (Ad3). PBP tolerates insertion of multiple antigenic epitopes in flexible surface-exposed loops, and spontaneously self-assembles in vitro into ADDomer nanoparticles, but faces limitations including incomplete assembly and susceptibility to preexisting antihuman adenovirus immunity. Here, we report two complementary engineering strategies to enhance ADDomer robustness. First, we developed a Chimpanzee adenovirus Y25-based ADDomer (CHIMPSELS) to circumvent preexisting antihuman adenovirus immunity, and introduced a point mutation to restore a motif critical for dodecahedron integrity. Second, we introduced targeted intersubunit disulfide bonds to reinforce particle assembly. High-resolution electron cryo-microscopy confirmed the formation of intact dodecahedral particles, revealing that disulfide bonds stabilize distinct conformations of the PBP N-termini. Differential scanning fluorimetry and dynamic light scattering demonstrated thermal stability and elevated aggregation onset temperatures in the disulfide-stabilized ADDomers, providing a scalable assay for screening ADDomer-based VLP constructs for vaccine development. Incorporation of validated immunogenic epitopes, including a SARS-CoV-2 receptor-binding motif segment and the Chikungunya E2EP3 peptide, demonstrated structural integrity and epitope display by the modified scaffolds. Our results establish a versatile, thermostable VLP platform with reduced susceptibility to preexisting immunity, improved particle integrity, and capacity for modular epitope presentation. This work advances the ADDomer toward practical applications in vaccine development and highlights engineering strategies that can be broadly applied to enhance the performance of protein-based VLP vaccines.

Rhodobacter sphaeroides is a purple nonsulfur alphaproteobacterium with a highly versatile metabolism. This microorganism holds promise as a chassis for sustainable biomanufacturing of numerous chemicals. Yet, its potential is constrained by a lack of standardized, well-characterized genetic elements to tune gene expression such as transcriptional promoters and ribosome binding sites (RBSs). In this study, we present Rhodo-Box, a comprehensive toolkit for R. sphaeroides created by adapting and extending the Zymo-Parts modular cloning framework. Using Rhodo-Box we built and characterized: (a) three broad-host origins of replication (pBBR1, RK2 and RSF1010), (b) a set of 13 promoters, (c) four inducible expression systems (NahR-P_salTTC, LacI-P_{lacT7A1_O3O4}, VanR-P_vanCC, and XylS-P_m), (d) 11 RBSs, and (e) four transcriptional terminators. Furthermore, we present a semiautomated, user-friendly cloning approach which enables rapid construction of R. sphaeroides strains. The Rhodo-Box toolkit equips R. sphaeroides with a standardized, automation-compatible collection of parts and workflows essential for efficient design-build-test-learn cycles and advanced metabolic engineering.