Small RNAs (sRNAs) mediate gene silencing through RNA interference (RNAi). These sRNAs include small interfering RNAs (siRNAs) and microRNAs (miRNAs) that regulate gene expression and immunity in plant–microbe interactions. Cross-kingdom RNA interference (ckRNAi) refers to RNAi between organisms belonging to different kingdoms of life. A well-studied example of ckRNAi is the bidirectional exchange of sRNAs between plants and their pathogens where sRNAs silence host defence genes or pathogen virulence factors. Thus, ckRNAi is part of the molecular dialogue between plants and associated microbes. Pioneering studies indicated that fungal pathogens such as Botrytis cinerea deliver sRNAs to hijack the host silencing machinery and to suppress immunity, while plants, in turn, export sRNAs to silence the virulence genes of pathogens. ckRNAi has lately been found in symbiotic interactions, that is in arbuscular mycorrhizas and ectomycorrhizas. In arbuscular mycorrhizas, fungal sRNAs from Rhizophagus irregularis target host transcription factors that restrict symbiosis, thereby facilitating fungal accommodation. Studies have also demonstrated that bidirectional RNA exchange enhances symbiotic efficiency by modulating gene expression in both partners. The ectomycorrhizal fungus Pisolithus microcarpus deploys a symbiosis-induced miRNA in colonisation of Eucalyptus grandis. Therefore, sRNAs regulate host transcriptional networks in both mutualistic and pathogenic plant–fungal relations. The study (2026; doi: 10.1111/nph.71187), represents a further expansion of this paradigm. An essential finding of this work is that the characterisation of sRNA-based communication within the tripartite interaction between Arabidopsis thaliana, the fungus Trichoderma atroviride, and the fungal pathogen B. cinerea. Trichoderma atroviride has plant-beneficial effects by protecting plants from pathogens. The molecular mechanisms underlying this protective effect have, however, remained largely elusive.

Programmable molecular biology increasingly requires strategies for converting engineered recognition or proximity modules into measurable outputs, particularly within transcriptional regulation, RNA imaging, and CRISPR-associated systems. Synthetic chemically induced dimerization (CID) systems provide a class of programmable recognition modules for such applications, yet generalized strategies for coupling structurally diverse CIDs to functional readouts remain limited. Here, we introduce a CID-to-output conversion strategy based on engineering of the linker-mediated coupling interface. Using single-fluorescent-protein sensors as an experimentally tractable optical model readout, we systematically varied paired N- and C-terminal linkers flanking circularly permuted green fluorescent protein (cpGFP) to map coupling landscapes across synthetic CID systems derived from combinatorial selection and computational protein design. The results revealed strong non-additive interactions across paired linkers and suggest that linker length is a first-order determinant of CID-to-output coupling. Across nanobody-, monobody-, and de novo-designed CID architectures, this framework yielded functional sensors with dynamic ranges up to 1270% and robust responses in mammalian cells. Together, this work demonstrates that effective CID-to-output conversion can be achieved by empirically mapping the linker-mediated coupling interface, providing a practical route for adapting synthetic CID to diverse programmable molecular readouts and nucleic-acid-associated synthetic biology systems

Currently, fossil fuels are the main and dominant sources for producing fuels and commodity chemicals. The rising demand for fossil fuels continues to entrench global dependency on non-renewable sources creating the climate risk, the economic instability and sustainability challenges. Hence, there is a need to shift towards globally available, sustainable and renewable resources such as lignocellulosic biomass (LCB) that offers a sustainable pathway for producing biofuels and chemicals. Though various technologies are available for LCB conversion to biofuels and chemicals, scaling up of biorefineries remains stifled by its recalcitrance nature and volatile supply chain economics. LCB processing often requires pretreatment to disrupt the rigid lignin–hemicellulose barrier and decrystallize cellulose. This structural opening is essential to maximize the enzymatic hydrolysis and sugar yields for biofuel production. The pretreatment process is energy-intensive and expensive accounting for 40% of the overall biofuel production cost followed by hydrolysis using expensive enzyme cocktails. These economic barriers currently limit the adoption of LCB as a cost-competitive fuel resource. The promising strategy for low cost LCB derived ethanol production is to adopt integrated biorefinery approach utilizing physical, chemical and biological processes. The integrated biorefinery approach tackles the high costs of second generation ethanol production by mimicking traditional petroleum refineries. It valorizes all three LCB components to marketable fuels and high-value chemicals maximizing the overall process profitability. This review discusses on the latest developments in biofuels production processes especially in relation to ethanol and butanol production.

"Aerobic glycolysis” is a widely used term whose current meaning has drifted from its original usage in a way that has created confusion and inaccuracy. This drift has weakened “aerobic glycolysis” as a hypothesis-testing framework, despite the critical importance of glycolysis in understanding cellular bioenergetic behavior. Here, we examine the historical and contemporary uses of “aerobic glycolysis” and the related “Warburg effect”. We argue that “aerobic glycolysis” as originally investigated was essentially a bioenergetic phenomenon. We review the bioenergetic model of glycolysis and mitochondrial respiration as ATP supply pathways operating together to meet cellular ATP demand. A bioenergetic view of aerobic glycolysis clarifies that it is not a less desirable contingency or indicator of pathology, but rather a part of a kinetically regulated system of cellular energy supply. On this basis, the operation of glycolysis under many different physiological and pathological conditions can be better interrogated and understood. This Perspective examines how “aerobic glycolysis” originally described a bioenergetic system behavior. Reintroducing a bioenergetic conceptual framework to this term could strengthen its meaning and resolve confusion in the field.

Millions of phage genomes have been mined from metagenomic data recently but the genome completeness remains poor because of the limitations of existing phage detection methods, which rely on metagenomic contigs that fragment phage genomes. Here, we present PALACE, a conjugate-graph-based framework for assembling high-quality phage genomes from metagenomes. PALACE incorporates homology-based and deep-learning-based methods to detect phage signals and constructs a conjugate graph from the metagenomic sample. On simulated data, PALACE generates accurate and complete phage genomes, achieving an F1 score of 0.92–1.00 across simulation settings, outperforming the second-best method by 0.21–0.48. Applying PALACE to 914 gut metagenomic samples from healthy controls and participants with colorectal cancer (CRC) yielded 5,306 high-quality phage genomes, outperforming the second-best benchmark method by 55.98% in median genome completeness. We observed a high degree of functional organization for genes within phage genomes. Phages from participants with CRC exhibited a notable enrichment of metabolic factors, suggesting their adaptation to nutrient availability in the CRC gut environment. PALACE combines homology features and deep learning to improve phage assembly from metagenomics.

Adenine base editors (ABEs) enable efficient A•T to G•C conversion, but their broad activity windows frequently cause unintended bystander edits. We hypothesized that insertion of a bulky, inert protein domain into the base editor would limit the effective reach of the deaminase, thereby preferentially directing editing to the intended target adenine. Here, we systematically map domain insertion sites within the high-activity TadA8e adenine base editor using structure-guided design and computational inference. We find that TadA8e accepts domain insertions at multiple surface sites, with overall activity and editing window width dependent on insert position rather than domain identity. Excitingly, inserting domains at residue L68 preserved robust editing across multiple genomic loci while tightly focusing the editing window around position 5. Insertion of superfolder GFP at this site produced a base editor variant with a narrower editing window, the ability to track edited cells by fluorescence, and markedly reduced Cas-independent off-target editing. Our work highlights domain insertion engineering as a powerful strategy to create more focused and precise CRISPR base editors.

Recent advances in large-scale sequence mining have expanded our knowledge of RNA virus diversity. Most genome mining approaches for detecting RNA viruses rely on identifying the conserved RNA-dependent RNA polymerase (RdRp) by scanning sequencing datasets with specialized profile Hidden Markov Models (pHMMs). Recently, several new pHMM databases for RdRp detection have been released, each following distinct design principles. However, their relative performance remains unclear, and their accessibility to users without advanced computational expertise is limited. Here, we introduce the RdRp Collaborative Analysis Tool with Collections of pHMMs (RdRpCATCH: https://github.com/dimitris-karapliafis/RdRpCATCH), a platform that consolidates publicly available RdRp pHMM resources into a single, user-friendly framework. RdRpCATCH enables the scanning of (meta)transcriptomic assemblies to discover RNA viruses and provides subsequent taxonomic annotation of detected contigs. A comparative analysis of RdRp pHMM databases reveals that most are highly effective at detecting the known diversity of RNA viruses while minimizing false positives, supporting their joint use within RdRpCATCH. RdRpCATCH is distributed as both a conda package and a web server application (https://rdrpcatch.bioinformatics.nl), facilitating access for researchers with diverse levels of computational expertise. By integrating multiple pHMM resources, this unified framework addresses fragmentation in the field and reduces technical barriers, enabling comprehensive viral discovery.

Plant viruses, once viewed as harmful agricultural pathogens, are now powerful tools in biotechnology. Their nanoscale structure, self-assembly, and biocompatibility enable applications in agriculture, medicine, and environmental sustainability. They serve in gene delivery, genome editing, diagnostics, and nanomaterials for vaccines and drug delivery. Integration with AI, ML, and bioinformatics enhances virus discovery and prediction. Despite challenges, plant viruses are emerging as versatile, sustainable resources for global biotechnological innovations.