Defined microbial communities (DMCs; also known as SynComs) offer a promising strategy to enhance plant growth and stress tolerance by harnessing beneficial plant-associated microbes. However, the rational design and efficient exploration of complex DMC configurations remain challenging. Here, we present an interpretable model-guided framework that integrates plant phenotyping, microbial genomics, and machine learning to optimize DMC outcomes and identify microbial interactions relevant to plant performance. Using tomato as a model, we evaluated diverse DMC, temperature, and metabolite combinations in growth experiment and used a quality-controlled dataset comprising 301 plants representing 102 DMC compositions for predictive modeling. An Elastic Net regression model trained on plant biomass data and DMC composition features enabled prediction of unseen DMC outcomes, and incorporating genomic features substantially improved predictive performance, supporting the importance of functional potential in modeling community effects. We applied the model to prioritize and design improved DMCs, which were validated in laboratory assays and field trials. One model-guided DMC significantly enhanced plant growth in the field and improved heat stress tolerance under controlled conditions. Model interpretation and multi-omics analyses highlighted specific microbial interactions, including metabolite-associated relationships involving Sphingobium sp. and tomatine, that were linked to host stress-responsive gene expression. Together, our results demonstrate a scalable framework for predicting and prioritizing DMCs and identify candidate metabolite-associated microbial interactions that may contribute to plant growth promotion and abiotic stress tolerance.

Malnutrition poses a substantial global health burden with long-lasting physical, social and economic effects. Now recognized as an spectrum of interrelated conditions, malnutrition manifests differently based on an individual’s geography, health-care access and lifestyle. There is growing evidence that malnutrition is also driven by defects in the composition and function of the gut microbiota, a metabolically active ‘virtual organ’ known to play a central role in intestinal barrier function, energy harvest, immune regulation and metabolic signalling. Across the malnutrition spectrum, perturbed microbial composition and function have been consistently observed in both children and adults. This ‘malnourished microbiota’ is associated with impaired nutrient utilization, chronic inflammation and altered energy balance and is known to persist despite nutritional interventions. This Review synthesizes evidence from human cohorts and animal models showing that the gut microbiota functions as a central player in malnutrition. We discuss implications for diagnostic and therapeutic strategies, arguing that microbiome restoration could provide a partial solution for malnutrition. This reframing is particularly relevant given current challenges such as armed conflict, climate change and declining international support, all of which are associated with an increased global burden of malnutrition. Thus, integrating microbiota-based strategies could protect vulnerable populations and accelerate progress towards overcoming malnutrition. In this Review, Littlejohn et al. discuss the gut microbiota as a central contributor to malnutrition, including undernutrition, overnutrition, the double burden, and disease-related malnutrition. They synthesize evidence linking alterations in the gut microbiota to impaired nutrient utilization, inflammation and altered energy balance and discuss microbiota-based diagnostic and therapeutic strategies for improving outcomes across diverse populations and settings.

Plants have evolved sophisticated immune systems to defend against a wide array of pathogens. These defence mechanisms are primarily mediated by cell surface pattern recognition receptors (PRRs) and intracellular nucleotide-binding leucine-rich repeat receptors (NLRs). Recent studies indicate that immune signalling involving PRRs and NLRs converges on cytosolic calcium (Ca2+) and other signals, which act synergistically to mount robust plant defences. However, uncontrolled activation of these defence mechanisms often negatively affects plant growth. In this Review, we discuss recent findings on the signalling mechanisms of PRRs and NLRs, including microbial recognition, receptor activation and signal transduction, as well as mechanisms that mitigate the negative impact on plant fitness associated with growth–defence trade-offs. We also discuss the interplay between the immune system and environmental stresses and explore strategies for engineering disease-resistant plants with optimized fitness. This Review discusses recent insights into the mechanisms of immune signalling in plants and their regulation to achieve effective defence against pathogens while preserving plant fitness. It also explores the interplay between the immune system and environmental stresses, and strategies for engineering optimal disease-resistant plants.

Spatial organization is a defining feature of multispecies biofilms and critically influences microbial interactions and emergent community properties. However, understanding and manipulating how microbes assemble into spatially structured biofilms remains challenging because most experimental frameworks emphasize species composition and pairwise interactions, while often overlooking the spatial constraints on biofilms imposed by the environment. In this study, we focus on how carbon substrate type, distinguishing between diffusible sugars and polymeric substrates, affects biofilm self-organization in a four-member synthetic bacterial community (SynCom). Across all tested conditions, the SynCom consistently formed more biofilm biomass than any of its subsets, indicating a robust synergistic phenotype. Using chemically defined, 3D-printed hydrogel substrates with consistent physical properties, we varied carbon source composition to identify its impact on biofilm assembly. Microscopic imaging showed that carbon substrate type strongly influenced biofilm self-organization with diffusible simple carbon substrates yielding relatively intermixed communities, whereas polymer-rich carbon substrates promoted a highly structured biofilm organization characterized by the dominance and peripheral localization of polymer-degrading species. Bioinformatic analyses of carbohydrate-active enzyme (CAZyme) repertoires and genome-scale metabolic modeling suggested bidirectional metabolite exchange among the SynCom members, which was also supported by analysis of biofilm formation in conditioned community supernatants. Together, our findings suggest carbon substrate type as an important ecological determinant of biofilm self-organization, highlighting the need to integrate environmental factors alongside species composition and metabolic potential to fully understand and manipulate natural and engineered multispecies biofilms.

DNA storage is a promising solution to data explosion due to its high density and longevity. However, existing coding methods struggle to efficiently avoid error-prone sequences while retaining usable ones, leading to low density and high copy numbers. Here we show Siyuan Code, a DNA storage codec that achieves theoretically optimal density. Our method maps binary data to nearly all suitable low-error sequences in a one-to-one manner, using our data structure to catalog and avoid error-prone patterns. This allows operation under very strict biochemical constraints with minimal redundancy. Experiments demonstrate a storage density of 1.66 bits per nucleotide and a capacity of 41 exabytes per gram of DNA, with perfect data retrieval using only six copies per strand. Encoding and decoding speeds reach approximately 1 megabyte per second on commodity hardware, comparable to early USB flash drives. These results bring DNA storage closer to practical deployment. Existing DNA storage methods struggle to avoid error-prone sequences while retaining usable ones. Here the authors develop Siyuan Code, a bijective codec that avoids high-error patterns, utilises all suitable strands, and reaches 1.66 bits/nucleotide, 41 EB/gram, and only uses 6 copies/strand.

Transcriptional repression is a fundamental regulatory mechanism that enables precise control of gene expression in response to developmental signals and environmental stimuli. Synthetic biology can leverage this process within plants to engineer programmable transgene repression systems. This review examines strategies for harnessing prokaryotic repressors in eukaryotic systems to develop synthetic repression systems in plants. These systems utilize modular promoter and repressor architectures that can be tuned through operator placement and repression-domain fusion, respectively, to adjust transcriptional regulation. Chemically dependent inducibility can also be introduced either through use of native derepression mechanisms of the prokaryotic repressors or the incorporation of ligand-binding domains. Finally, this review explores key challenges in designing synthetic repression systems, including kinetics constraints, balancing ON and OFF states, and differences between transient and transgenic expression contexts. Overall, this review highlights modular design frameworks for tunable transgene expression in plants. synbio