Fatty acid esters are building blocks in next-generation biofuels, eco-friendly detergents, crop-enhancing adjuvants, and high-value cosmetic emollients. However, their industrial production relies on two unsustainable approaches: petrochemical-derived chemical catalysis that poses sustainability challenges and botanical extraction processes that exacerbates land-use conflicts. Here we develop a de novo microbial biosynthesis platform using the non-conventional oleaginous yeast Rhodotorula toruloides as a chassis, and achieve high-level biosynthesis of structurally diversified esters without the addition of alcohol or lipid precursors. By screening specific pathway enzymes and conducting modular pathway engineering, we successfully reprogram the native lipogenic metabolism of R. toruloides for de novo synthesis of fatty acid ethyl esters (FAEEs) at 579 mg/L, fatty acid short-branched chain esters (FASBEs) at 169 mg/L, and wax esters (WEs) at 1.30 g/L in shake-flask fermentation. As a case study, we optimize the synthesis of WEs in a 5 L fermenter, and achieve a production of 13.04 g/L. These engineered strains potentially offer an efficient, economical and environmentally friendly platform for the industrial production of fatty acid esters and oleochemicals. Fatty acid esters are biofuels, eco-friendly detergents, and cosmetic emollients, however, their industrial production is unsustainable. Here the authors develop a de novo biosynthesis platform using the yeast Rhodotorula toruloides and achieve high-level production of structurally diverse esters.

Antibacterial peptides (ABPs) represent a promising alternative strategy for combating antimicrobial resistance. Graph neural networks and their variants have shown substantial potential in ABP identification. However, most existing graph-based methods focus on node features while neglecting edge features. Edge features play an important role in characterizing peptide conformation and spatial stability, thereby directly influencing interactions with bacterial membranes and antibacterial activity. Moreover, node and edge features extracted by heterogeneous encoders reside in disparate feature spaces, making their effective integration nontrivial. These challenges highlight the need for an explicit alignment mechanism to bridge heterogeneous representations. In this study, we propose CLABP, a contrastive learning-based framework for ABP identification that integrates edge features. Specifically, backbone dihedral angles, ProtT5 embeddings, and Define Secondary Structure of Proteins-derived secondary structures were employed as node features, while inter-residue distance, motion vectors, and rotation quaternions were used to construct edge features. Node and edge representations were independently extracted using dedicated encoders and subsequently aligned into a shared latent space via contrastive learning, which minimizes the distance between paired representations derived from the same peptide. The aligned features were then fused through a dual cross-attention mechanism for downstream prediction. CLABP outperforms other state-of-the-art methods, achieving an accuracy of 92.6% and a Matthews correlation coefficient of 0.853. Ablation studies further confirm that both edge features and the alignment mechanism are critical to model performance. 

The skin microbiome maintains cutaneous homeostasis through colonization resistance and immune modulation, targeted prebiotic interventions however remain largely unexplored. The present study addresses this lacuna effectively demonstrating the ability of plant-derived prebiotics to selectively modulate key skin commensals and pathogenic bacteria thereby unraveling a new dimension to use of prebiotics in skin care. Linum usitatissimum (flaxseed) and Allium sativum (garlic) extracts used herein exhibited complete growth inhibition of Staphylococcus aureus within 6 h, while Curcuma amada (mango ginger) rapidly halted the growth of Cutibacterium acnes within 15 min. Gas chromatography-mass spectrometry was carried out to unveil the bioactive constituents in plant-prebiotics as well as the exposed organisms. Field emission gun-scanning electron microscopy validated bacterial cell membrane disruption correlating with antimicrobial efficacy. Remarkably, Allium cepa (onion) and Tinospora cordifolia (guduchi) selectively enhanced the proliferation of Staphylococcus epidermidis while simultaneously inhibiting pathogenic species. Metabolomic profiling revealed that prebiotic-stimulated S.epidermidis produced elevated levels of butyric and succinic acids that are documented to have anti-bacterial activity. Plant-based prebiotics can thus be strategically reinforced to benefit skin microbiota with simultaneous inhibition of skin pathogens, providing a scientific foundation for microbiome-targeted cosmeceuticals.

Despite the groundbreaking advances in deep learning-enabled methods for biomolecular modeling, predicting accurate three-dimensional (3D) structures of RNA remains challenging owing to the highly flexible nature of RNA molecules combined with the limited availability of evolutionary sequences or structural homology. Here we introduce RNAbpFlow, a sequence- and base pair-conditioned SE(3)-equivariant flow-matching model for generating RNA 3D structural ensembles. Leveraging a nucleobase center representation, RNAbpFlow enables end-to-end generation of all-atom RNA structures without the explicit or implicit use of evolutionary information or homologous structural templates. Experimental results show that base-pairing conditioning leads to broadly generalizable performance improvements over current approaches for RNA topology sampling and predictive modeling in large-scale benchmarking. RNAbpFlow generates all-atom RNA conformational ensembles for single-chain RNA monomers without the explicit or implicit use of evolutionary information or homologous structural templates.

Serpentinization produces hyperalkaline, H₂- and CH₄-rich fluids that support microbial life and serve as analogs for ocean worlds such as Enceladus. While methane production in these systems has been well studied, methane consumption—especially under high pH—remains poorly understood. Here, we present isotopic, geochemical, and genomic evidence for hyperalkaliphilic (pH > 11) methanotrophy in the Samail ophiolite of Oman. Using models that account for fluid mixing and gas exsolution, we identify δ¹³CH₄ enrichment that cannot be explained by abiotic processes alone. The enrichment of 13CH4 co-occurs with methanotroph 16S rRNA gene sequences, particularly in fluids formed by mixing CH₄-rich, reduced fluids with oxidant-rich waters. Shotgun metagenome sequencing reveals a metagenome-assembled genome affiliated with Methylovulum, encoding a complete methane oxidation pathway, multiple carbon assimilation routes, and Na⁺/H⁺ antiporters—adaptations likely enabling growth above pH 11. Our findings highlight the viability of methanotrophy under extreme high pH conditions and provide a framework for interpreting δ¹³CH₄ signals in serpentinizing environments on Earth and beyond. Serpentinization makes H₂- and CH₂-rich fluids analogous to ocean worlds like Enceladus. Isotopes, chemistry, and genomes show methane consumption at pH >11 in Oman—revealing extreme methanotrophy on Earth and insights into CH4 as a biosignature.

Nano-sized outer membrane vesicles (OMV) are lipid-bilayered structures that primarily encapsulate periplasmic components, with minor inclusion of cytoplasmic materials. Rather than passive byproducts of cellular damage, OMVs are now understood as active mediators of bacterial physiology, environmental adaptation, and host interaction. Recent evidence identifies envelope instability as a key mechanistic driver of OMV biogenesis. Disruptions in outer membrane–peptidoglycan connectivity, imbalances in periplasmic homeostasis, and alterations in lipid asymmetry collectively promote vesicle formation as a regulated adaptive response. Under stress conditions, OMVs acquire specialized functional roles, selectively enriching specific cargo and exhibiting surface properties that enable them to sequester host-derived antimicrobial factors. OMV-associated biomolecules further influence vesicle uptake into host cells through distinct endocytic pathways, shaping intracellular trafficking and downstream functional outcomes. Following internalization, pathogen-derived OMVs disrupt host signaling pathways and are exploited to promote immune evasion, whereas commensal-derived OMVs contribute to microbiota homeostasis and immune modulation. In parallel, growing efforts to harness OMVs as vaccine platforms highlight their potential as innovative tools for both therapeutic and prophylactic applications. Collectively, these insights position OMVs as critical mediators of bacterial pathogenicity and as promising targets for anti-infective strategies. A review summarizes recent insights into outer membrane vesicles (OMVs) in Gram negative bacteria, including their biogenesis mechanisms, stress-associated cargo remodeling, host-cell interactions, immunomodulatory roles, and therapeutic applications.