The accumulation of persistent plastics in the ocean is causing wide-ranging environmental problems. Some commercially available compostable plastics, developed to address these issues, degrade efficiently under industrial composting conditions; however, their degradation in marine environments, particularly in the deep sea, remains limited. Here we demonstrate that thermally embedding enzymes into compostable polyesters (poly(butylene succinate) (PBS), poly(butylene succinate-co-adipate) (PBSA), poly(ε-caprolactone) (PCL) and poly(butylene adipate-co-terephthalate) (PBAT)) significantly accelerates their degradation in both coastal and deep-sea environments from water depths of 2–6 m to 5,503 m. Enzyme-embedded polyesters achieved near-complete degradation at a deep-sea depth of 757 m within 5 months. Furthermore, moderate biodegradation of enzyme-embedded polyesters was observed under extreme conditions on deep-sea floors of 5,503 m. In coastal seawater, films embedded with trace amounts (0.1 wt%) of enzyme underwent rapid degradation. The mechanical properties of enzyme-embedded polyesters were similar to those of enzyme-free polyesters. Chemical analysis confirmed the formation of low-molecular-mass degradation products and biochemical oxygen demand measurements supported the mineralization of the enzyme-embedded polyesters. The enzyme-embedding strategy enables enhanced marine degradation of compostable polyesters and represents a potential solution to marine plastic pollution. Near-complete deep-sea degradation of enzyme-embedded compostable polyesters shows that thermal enzyme embedding greatly accelerates breakdown across marine environments while preserving material performance, offering a promising strategy to mitigate persistent ocean plastic pollution.

Plant-based meat alternatives (PBMAs), designed to mimic the sensory properties of meat, may help reduce animal-sourced protein consumption and support a shift towards plant-based diets. Here we integrated PBMAs into a land-use model to assess their effects on nutrition, greenhouse gas emissions and food system sustainability. PBMA adoption not only substantially reduced agricultural land expansion and greenhouse gas emissions from the agriculture, forestry and other land use sector but also increased risks of micronutrient inadequacy. Across regions, PBMA diets exhibited diminishing marginal returns. Replacing ruminant meat alone achieved nearly half of the potential emission reductions while incurring relatively small nutritional costs. By contrast, broader substitution of livestock products delivered additional environmental benefits but markedly increased micronutrient deficiencies. Targeted policy support, sustainable land management and improved PBMA formulations will be essential to ensure equitable dietary transitions and progress towards global climate and sustainability goals. Substitution of ruminant meat with plant-based meat alternatives can support lower food system greenhouse gas emissions and limit agricultural land expansion, but micronutrient inadequacy may arise from this dietary shift.

G-quadruplexes (G4s) are non-canonical nucleic acid structures with critical roles in gene regulation, genomic stability, and disease, making them prime targets for therapeutic and biotechnological applications. However, the absence of a curated, centralized, experimentally validated repository of short (mostly synthetic) G4-forming sequences, paired with appropriate single-stranded or hairpin controls, has limited reproducibility and hindered progress in the field. Here, we introduce G4All, a comprehensive, curated database of G4-forming short DNA and RNA sequences, complemented by rigorously selected non-G4 controls, all studied under roughly similar experimental conditions (generally around 100 mM potassium and near neutral pH). G4All consolidates sequences validated by diverse experimental methods, including circular dichroism, NMR, UV spectroscopy, with annotations for topology, thermal stability, and experimental conditions. By providing both positive and negative datasets, G4All enables rigorous comparative analyses, assay benchmarking, and the development of predictive models. The database supports a broad range of applications, from fundamental studies of G4 biophysics to the rational design of aptamers, as well as benchmarking prediction algorithms. Future developments will expand G4All to include user-submitted datasets and more RNA sequences. Freely accessible at https://g4-all.streamlit.app/, G4All offers a searchable interface and downloadable datasets, establishing a community-driven hub to accelerate discovery and standardization in G4 research.

Characterizing noncoding variant function remains an important challenge in human genetics. Genomic deep learning models have emerged as a promising approach to enable in silico prediction of variant effects. These models include supervised sequence-to-activity models, which predict molecular phenotypes such as genome-wide chromatin states or gene expression levels directly from DNA sequence, and self-supervised genomic language models. Here we review progress in leveraging these models for noncoding variant effect prediction. We describe practical considerations for making such predictions and categorize the types of ground-truth data used to evaluate variant effect predictions, providing insight into the settings in which current models are most useful. Our Review highlights key considerations for practitioners and opportunities for improvement in model development and evaluation. This Review outlines progress in leveraging genomic deep learning models for the prediction of noncoding variant effects in humans, highlighting key considerations for practitioners, current performance trends and opportunities for improvement.

DNA represents a compelling medium for data storage due to its density, durability, and compatibility with biological systems. Current in vivo DNA data storage strategies are largely restricted to single-population systems, limiting scalability and access control. Here, we present DNA CODEC, a multi-population in vivo DNA data storage platform that combines high-density encoding with molecular obfuscation across engineered human cell populations. CODEC uses 10-ary Huffman coding with degenerate bases to optimize compressibility while maintaining decoding accuracy despite biological and technical noise. Data payloads are stably integrated into the human genome using CRISPR-based targeted insertion. Obfuscation is introduced through heterogeneous populations containing decoy clones with structurally similar, non-informative masking sequences to conceal the true payload. CODEC stably maintains genomic payloads across extended cell culture, demonstrating robustness to replication errors and population drift. Together, these features establish a scalable framework for high-fidelity and secure DNA data storage in biological systems. DNA is a dense and durable storage medium for digital information but its use in living cells remains difficult to scale and protect. Here, authors present DNA CODEC, a CRISPR-based system that distributes compressed data across engineered cell populations for stable storage and controlled access.

Deep soils store over half of terrestrial organic carbon, yet the viral mechanisms governing carbon persistence in subsurface contexts remain unclear. Here, by conducting a soil profile survey (0–700 cm) across four agroecosystems and a global synthesis of subsurface metagenomes, we established the Deep Soil Viral Resources Database, comprising 50,949 viral operational taxonomic units and 3,902 auxiliary metabolic genes (AMGs). Viral richness increased significantly with depth, accompanied by a shift towards lysogeny driven by declining carbon availability. The deep-soil virome carried AMGs and lysozymes involved in polymer degradation and cell endolysis. Viral endolysin significantly promoted necromass carbon accumulation, which contributed to increases in mineral-associated organic carbon. A synthesis of 138 global soil profiles further revealed a positive effect of viral abundance on soil organic carbon. Our findings establish a framework where viruses strengthen the microbial carbon pump in deep soils by facilitating host carbon scavenging via AMGs and promoting necromass formation via endolysin activity. Combining a metagenomic analysis of agroecosystem soils along a 7-metre depth profile with microcosm incubations and a global synthesis, the authors find increases in viral diversity, lysogeny and necromass carbon accumulation in deeper soils.

Programming biological function—designing bespoke proteins to perform specified molecular tasks—is a foundational goal of molecular engineering. However, current design models remain fundamentally limited: they typically require either natural proteins as starting points for optimization or manual reformulation of functional goals as geometric and sequence-level constraints to guide candidate generation. Here we introduce Pinal, a 16-billion-parameter foundation model that designs candidate proteins from natural-language descriptions of desired function. Trained on 1.7 billion synthetically annotated protein–text pairs, Pinal links functional intent to protein sequence and structure. In computational evaluations, generated candidates combined high predicted foldability with functional-description alignment and sequence diversity, providing a basis for prioritizing experimentally testable designs. We applied Pinal to four distinct design tasks—a fluorescent protein, a polyethylene terephthalate hydrolase, an alcohol dehydrogenase and a metabolic H-protein—and experimentally observed the intended function in each case, including catalytic activity for both designed enzymes. Without iterative experimental optimization, a Pinal-designed H-protein increased product titre by 1.7-fold relative to the corresponding native Escherichia coli H-protein in a multi-enzyme CO2 fixation pathway. These findings support natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints. Pinal, a 16-billion-parameter artificial intelligence model, designs functional proteins from natural-language descriptions, demonstrating experimentally validated activities and improved CO2-fixation product yields without iterative optimization.

Mitochondria are essential organelles containing their own genomes, encoding a few proteins essential for energy production. Most of the mitochondrial proteins are nucleus-encoded, translated as precursors in the cytosol, with a large fraction of these precursors properly addressed by an N-terminal mitochondrial targeting sequence (MTS). These MTS share common features but no consensus sequence can explain their functionality nor the precursors-specific determinants of mitochondrial import. To help understand this mechanism, we created a simple computational model to generate functional synthetic MTS while maintaining a tight control on the design parameters. Using the budding yeast, we demonstrated the presence of precursors-specific import capabilities in addressing artificially nucleus-relocated OXPHOS proteins. Our research confirms the existence of cargo-dependent functional compatibilities and takes us one step closer towards improving gene therapy-based treatment of mitochondrial diseases.