Plant RNA virus-based expression vectors are emerging as modular biological chassis for rapid, scalable, and sustainable production of recombinant proteins in plants. From a chemical and biological engineering perspective, these systems integrate upstream vector design, host selection, inoculation strategy, intracellular amplification, product accumulation, and downstream recovery into a flexible biomanufacturing workflow. Compared with stable transformation, RNA viral platforms shorten development timelines, enable high-copy amplification, and support deployment in whole plants, plant tissues, cell cultures, and specialized secretion systems. The studies reviewed here show that Potyviridae, Virgaviridae, Alphaflexiviridae, Tombusviridae, Bromoviridae, Secoviridae/Comoviridae, and emerging Kitaviridae-derived systems have been engineered to produce reporters, vaccine antigens, virus-like particles, antibodies, nanobodies, enzymes, therapeutic proteins, metabolic enzymes, genome-editing components, and agronomic regulators. Reported outputs span qualitative accumulation to process-relevant yields, including approximately 5 g/kg fresh biomass, 3-5 mg/g fresh weight, 30 -80% of total soluble protein, and hundreds of mg/kg fresh tissue, depending on vector architecture, host chassis, insert properties, and delivery mode. We discuss these platforms as bioengineering systems rather than only virological tools, emphasizing manufacturing speed, process scalability, product classes, yield determinants, purification and quality-control challenges, biosafety, and opportunities for synthetic biology, computer-aided vector optimization, and sustainable plant-based production.

Microalgae have long been considered promising platforms for producing biochemicals and supporting environmental applications. Realizing this potential will require turning microalgal species into predictable and reusable engineering platforms. However, it remains challenging to engineer such evolutionarily diverse, compartmentalized photosynthetic eukaryotes in a predictable way, because their behavior depends strongly on organelle organization, gene-expression context, and cultivation conditions. Here, we focus on 3 well-developed but functionally distinct chassis. Chlamydomonas reinhardtii provides the strongest genetic and cell-biological foundation among green microalgae; Phaeodactylum tricornutum serves as the main diatom system for genome engineering and systems-level pathway analysis; and Nannochloropsis spp., especially N. oceanica and N. gaditana, are more production-oriented and are particularly important for lipid engineering. We assess these chassis from the perspective of practical engineerability, including transformation and genome access, expression design, precise editing and regulatory control, and iterative design–build–test–learn workflows. We then discuss 4 representative trait areas: photosynthetic productivity and stress tolerance, storage lipids and omega-3 fatty acids, terpenoids and carotenoids, and pollutant removal. Despite substantial progress, the field is still limited by unstable transgene expression, weak portability of genetic parts, incomplete understanding of intracellular pathway organization, and poor agreement between laboratory phenotypes and process performance. Progress will require genetic design to be evaluated together with chassis physiology, cultivation performance, product recovery, and deployment-specific biosafety.

Precise, multiplexed gene regulation in mammalian cells remains constrained by a heavy reliance on heterologous protein regulators, whose associated immunogenicity and strict cargo-size limits impede clinical translation. Here, we introduce SpliceRACER (Splice-based Riboswitch Array for Combinatorial Expression Regulation), a multiplexed RNA switch platform that achieves scalable, orthogonal, and combinatorial control of gene expression in mammalian cells through alternative splicing, eliminating the need for exogenous protein co-expression. SpliceRACER independently orchestrates three orthogonal channels to regulate fluorescent reporter outputs, yielding 27 discrete combinatorial expression states, vastly exceeding the capacity of conventional binary gene switches. Furthermore, we engineered three generalizable strategies to effectively suppress the basal leakage inherent to synthetic riboswitches, thereby enabling tight OFF-state control of the potent pyroptotic effector GSDMD-NT for precise cell-death induction. This work offers broad utility in synthetic biology and therapeutic applications.

Studying protein binding sites helps explain ligand recognition and selectivity, informing drug development and repurposing. Here, we combine protein language model embeddings with network analysis to investigate how local recognition environments organize across scales. We represented 121,673 non-redundant, experimentally identified binding sites using ProtT5 embeddings of ligand-contacting residues and constructed a mutual nearest-neighbor network. Embedding proximity was associated with local structural similarity, supporting the structural relevance of network relationships. Globally, the network exhibited ligand-related organization. At the mesoscopic scale, communities revealed heterogeneous landscapes within established protein classifications, with InterPro categories ranging from compact neighborhoods to dispersed recognition environments. Focusing on kinases, a major class of drug targets, we found that communities within individual proteins distinguished separate binding regions and resolved recurrent residue-defined microenvironments within broad structural classes. Local similarities also connected sites in proteins with low sequence identity and non-overlapping InterPro annotations, suggesting shared recognition features across distant proteins. Together, these results show how network organization captures both diversification within protein categories and local similarities across their boundaries. This multiscale framework complements conventional protein classifications and provides a basis for prioritizing structural and biochemical investigations of ligand cross-reactivity, selectivity, and repurposing opportunities.

Genome foundation models hold great potential for advancing biological research, but modeling diverse complex genomes and generating controllable genome-scale sequences remain challenging. We present DeepD, a phylogeny-informed sparse mixture-of-experts model operating at single-nucleotide resolution over a 1-million-token context while reducing active computation. Pretrained on unselected whole-genome sequences across all domains of life, DeepD learns genic and intergenic sequence features and supports broad sequence-to-function prediction across species. DeepD performs well in human protein fitness and disease variants effect prediction, such as BRCA1 and BRCA2, with particular strength in regulatory regions, such as promoter and transcription-factor binding sites. Expert routing captures exon-intron boundaries, transposable elements, and distal cis-regulatory elements. DeepD also generates experimentally validated core promoters with specified transcriptional strengths. Finally, the Context-Anchor-Body strategy enables generation of complete yeast chromosome sequences and megabase-scale Arabidopsis segments, preserving gene structure and order while introducing variations within natural ranges. Together, DeepD is an efficient genome foundation model that unifies accurate sequence-to-function prediction with controllable genome-scale generation, advancing predictive and generative design of complex genomes.

Artificial photosynthetic cells are nature-inspired systems that reproduce photosynthetic functions within cell/cell-mimetic compartments. Unlike discrete photocatalytic platforms, they integrate light harvesting, charge separation, multistep catalysis, selective transport, and compartmentalization to convert isolated photochemical reactions into coordinated cell-like functions. Driven by needs in carbon-neutral manufacturing, sustainable solar conversion, and precision medicine, the field is advancing from simple photocatalytic vesicles toward integrated and increasingly synthetic-cell platforms. In this review, we systematically elaborate on the rational design principles and emergent functional landscapes of artificial photosynthetic cells from a bioinspired perspective. We first decode the fundamental operational logic of natural photosynthesis, serving as a design blueprint. We then summarize the core construction modules of artificial photosynthetic cells, including cell-mimetic chassis, high-performance light-harvesting engines, multi-level catalytic networks, and precise interfacial engineering strategies. Furthermore, we categorize assembly paradigms into three mainstream branches: semi-artificial biohybrid systems, bottom-up reconstituted synthetic systems, and fully abiotic photosynthetic mimics. We further highlight a series of current and prospective functionalities, ranging from solar-driven CO2 fixation and high-value chemical synthesis to in situ biomedical drug generation, as well as primitive life-like characteristics including intercellular communication, motility, and compartment division. Finally, we pinpoint the key technical bottlenecks, common pitfalls and outline opportunities enabled by the deep integration of synthetic biology, functional materials chemistry, and systematic bioengineering.

Bacterial defenses against DNA phages are well documented, yet responses to RNA phages remain largely unexplored. Here, we characterize the population dynamics and host responses of E. coli infected by the lytic RNA phage MS2 across multiple timescales, using bulk and single-cell transcriptomics, fluorescence microscopy, whole-genome sequencing, and genetic perturbations. Despite rapid viral takeover and extensive cell death, the bacterial population subsequently rebounds, revealing an unusual state in which host proliferation persists under intense phage pressure. Early in infection, heterogeneous subpopulations are evident, including one with low or undetectable viral transcript levels, low expression of the F-plasmid genes encoding the F-pilus (the MS2 receptor), and a distinct metabolic state. Strikingly, the rebounding population carries MS2 RNA in the great majority of cells, with evidence for active viral replication and continued cell division despite high viral loads. This rebound coincides with strong induction of biofilm-associated genes, including the pga operon, which synthesizes the biofilm-matrix exopolysaccharide PNAG. Deletion of pgaA causes earlier population decline and reduced population survival, indicating that pga-dependent matrix production delays viral lysis. Over longer timescales, phage-exposed populations show heterogeneous F-plasmid prevalence and plasmid-wide expression, consistent with dynamic cycling between plasmid-bearing and plasmid-free states: MS2 selects against F-plasmid-bearing cells, whereas conjugation may restore plasmid carriage. We propose that the biofilm-associated matrix delays viral lysis relative to continued cell division, long enough for rare F-plasmid-free daughter cells to arise and expand. Our results suggest that bacterial survival is determined by a dynamic race between viral lysis and host-cell division.

Microbial communities drive essential ecosystem processes, yet community-level behavior often deviates from predictions based on single-species studies. This mismatch reflects the context-dependent nature of nutrient dependencies, metabolic interactions, and resource availability in complex environments. Here, we present a mechanistic, species-resolved framework to dissect metabolism and nutrient exchange in a defined 21-member soil synthetic community spanning five bacterial phyla. For each isolate, we reconstructed and manually curated thermodynamically constrained genome-scale metabolic models, reconciling predictions with growth phenotypes across rich and minimal media. We mapped minimal nutrient requirements, cataloged environment-specific uptake and secretion profiles, and compared metabolic pathways to identify potential competitive and cooperative interactions. Using community-scale metabolomics, we assigned putative producers and consumers for detected metabolites and revealed medium-dependent shifts in niche overlap. This approach exposes metabolic strategies that govern community function and resilience, offering a predictive basis for understanding and manipulating microbiomes. Our findings highlight how species-level metabolic maps can clarify why microbial interventions succeed or fail and guide targeted manipulations in soil ecosystems.

Designing ribonucleic acid (RNA) molecules with prescribed structures and functions is central to RNA engineering. Backbone-first approaches generate three-dimensional geometry before assigning a nucleotide sequence, yet leave open how that geometry should be represented for downstream sequence design. Here we introduce RiboBridge, which infers a probabilistic residue-pair state from backbone geometry, recycles binary pair probabilities through a second pass and transfers binary and typed signals as soft attention biases for sequence decoding. On 550 generated backbones, pair-type decoding achieved joint refolding validity of 0.451 versus 0.335 for an architecture-matched topology-free decoder. On a matched 110-backbone intervention cohort, replacing aligned pair states with exact-length donors matched on pair-state statistics reduced joint validity from 0.464 to 0.214, showing backbone alignment matters. Pair-confidence ranking enriched the joint-validity rate from 0.451 overall to 0.596 among the top 248 before external refolding, with the same ranking signal transferring to independently generated RFDpoly and RNA-FrameFlow backbones. On 53 experimentally determined backbones, pair-coupled decoding improved secondary-structure agreement relative to residue-wise argmax. These results support a learned relational interface between sequence-free backbone generation and sequence design.

Reticulate evolutionary processes (events in which lineages merge, such as hybridization, recombination, and horizontal gene transfer) are widespread across nature but cannot be represented by phylogenetic trees alone. Phylogenetic networks have therefore become an important modelling tool, yet existing software is typically tied to specific inference paradigms and provides limited support for working with multiple network representations in a unified and programmable environment. PhyloZoo is an open-source Python framework that lowers the barrier to developing practical, easy-to-use software for phylogenetic network analysis. It provides data structures and algorithms covering the main representations used in the field, together with dedicated visualization tools and robust I/O for all major phylogenetic file formats. A particular emphasis lies on semi-directed phylogenetic networks, which explicitly represent root uncertainty and have so far received limited support in existing software. By offering a shared, scalable foundation for developing interoperable tools and a combinatorial layer that supports computational proofs and theoretical exploration, PhyloZoo enables reproducible workflows for applied, methodological, and theoretical studies of reticulate evolution.