RMH

Citric acid is the most widely produced organic acid in industrial biotechnology, with a global annual output exceeding 2 million tons and broad applications across the food, pharmaceutical, chemical, and environmental sectors. Despite more than a century of industrial development since the discovery of citric acid fermentation by Aspergillus niger in 1917, current production systems are approaching critical bottlenecks in strain performance, process efficiency, and environmental sustainability. Increasing evidence indicates that citrate overproduction is not governed by a single metabolic pathway but emerges as a multi-constraint-driven systems phenomenon involving coordinated regulation of metabolism, cellular physiology, and environmental conditions. Recent advances in systems biology and synthetic biology have enabled a paradigm shift from empirical optimization to rational and programmable biomanufacturing. The integration of multi-omics data with genome-scale metabolic models has substantially deepened the understanding of citrate overflow metabolism. In parallel, the development of CRISPR/Cas9-based genome editing, dynamic gene regulation systems, and promoter engineering has enabled precise and multiplexed rewiring of metabolic networks. Targeted engineering strategies focusing on carbon uptake, mitochondrial metabolism, citrate transport, and morphology regulation have demonstrated that high-level production requires coordinated multi-layer optimization rather than single-gene modifications. At the process level, emerging strategies, including immobilized fermentation, in situ product recovery, and the utilization of alternative non-food feedstocks, are being increasingly integrated with engineered strains to enhance productivity and sustainability. These advances collectively point toward a transition from conventional fermentation to a multi-scale, integrated biomanufacturing framework. This review provides a comprehensive overview of recent progress in citric acid biomanufacturing, covering the evolution of industrial A. niger strains, systems-level mechanisms of citrate accumulation, synthetic biology-enabled tool development, rational chassis engineering strategies, and process intensification approaches. Finally, we discuss future directions, including dynamic metabolic control, artificial intelligence-assisted design, and circular biorefinery systems, aiming to establish a fully integrated, intelligent, and sustainable paradigm for next-generation citric acid production.

Beneficial root-colonizing fungi such as Trichoderma promote plant growth and immunity, yet the contribution of fungal small RNAs (sRNAs) to these interactions remains poorly understood. Here, we identified Ta_sRNA1, a highly abundant Trichoderma atroviride sRNA that accumulates in Arabidopsis root cells and associates with ARGONAUTE 1 and 2 (AGO1/2) complexes to modulate host gene expression.  Using stem-loop reverse transcription quantitative polymerase chain reaction, AGO immunoprecipitation, transgenic lines and a fungal overexpression strain, we examined the function of Ta_sRNA1 and identified PRIM2, encoding the large subunit of DNA primase, as a host target associated with Ta_sRNA1 activity. Ta_sRNA1-mediated repression of PRIM2 restricts fungal overcolonization and enhances resistance to Botrytis cinerea. This regulation is associated with systemic immune priming, increased reactive oxygen species (ROS) accumulation and maintenance of plant growth. By contrast, PRIM2 overexpression suppresses ROS and increases pathogen susceptibility. These findings indicate that a Trichoderma-derived cross-kingdom sRNA modulates plant immunity by targeting the susceptibility-associated gene PRIM2. This mechanism fine-tunes the balance between beneficial colonization and defense responses, highlighting fungal sRNAs as regulators of plant immune homeostasis during beneficial plant–microbe interactions.

Enzymatic assays are fundamental tools in clinical diagnostics and biomedical research owing to their high specificity and sensitivity for biomarker quantification. However, their translation from centralized laboratories to point-of-care (POC) and resource-limited settings remains limited by lengthy assay procedures, high reagent consumption, and reliance on conventional analytical instrumentation. Microfluidic technologies address many of these limitations by enabling precise fluid handling, reducing sample and reagent volumes, and integrating multiple analytical processes within miniaturized platforms. In this review, we critically examine recent advances in enzymatic microfluidic systems for biomarker analysis, with particular emphasis on their applicability to POC diagnostics. Rather than providing a descriptive overview, we systematically compare pressure-driven, centrifugal, droplet-based, and digital microfluidic platforms with respect to device architecture, reaction kinetics, detection strategies, analytical performance, system integration, and clinical applicability. We further discuss how microscale factors, including thermal regulation, mass transfer, surface interactions, and enzyme immobilization, influence the performance of enzymatic assays across different platforms. In addition, we critically assess the current level of experimental and clinical validation, highlighting persistent challenges related to reproducibility, standardization, scalability, and clinical translation. By linking microfluidic design principles with enzymatic assay performance, this review provides a conceptual framework for the rational development and future clinical implementation of next-generation microfluidic enzymatic assays.

The human microbiome, the collection of microorganisms that inhabit the human body, has been extensively studied over the past two decades and has been shown to play a critical part in health and disease. These research efforts renewed interest in developing microbial therapeutics to promote human health and treat disease. However, the vast majority of human gut microbiome research has been focused on fecal microbial strains and communities, leaving most of the gastrointestinal tract understudied. The dynamic environment of the small intestine provides distinct opportunities for microorganisms to interact with the host and diet. In this Review, we discuss the current state of research on the microbiome of the small intestine, how interactions between the small intestine and its microbiome influence host physiology, and we posit that small intestinal microorganisms are a unique source of novel therapeutics for treating human disease. Next-generation microbial therapeutics that emerge from the small intestinal microbiome are anticipated to be relevant to improving small intestinal diseases as well as systemic human health. In this Review, Britton and colleagues examine the composition of the small intestinal microbiome and its interactions with the host, highlighting emerging insights into its roles in physiology and health. They also discuss the challenges of studying this understudied microbial community and its potential as a source of future microbial therapeutics.

Methane emissions account for nearly a third of the Earth’s effective radiative forcing, with methanotrophs playing a critical role in mitigating emissions by oxidising methane in diverse environments. Despite their ecological importance, methanotrophic diversity and environmental distribution remain incompletely characterised due to cultivation challenges, incomplete or low-quality metagenome-assembled genomes, and limited taxonomic resolution in marker gene surveys. Here, we present a national study of the biogeography of novel and known methanotrophs across Denmark’s major natural, urban and agricultural habitats, using genome-resolved classification of 10,683 metagenomes and 102 new methanotrophic species. By linking metabolic potential to habitat-specific distributions, we reveal uncharacterised methanotrophs as dominant in natural ecosystems. These findings provide a comprehensive baseline of methanotroph diversity, reveal clear contrasts between natural and disturbed habitats, and highlight candidate species and habitats for future methane-mitigation strategies. In this study, the authors use metagenomics to map the diverse and previously unknown bacteria responsible for taking up methane across Denmark’s varied environments and highlight the natural habitats and methanotroph species with the potential for methane mitigation.

Accurate reference-free assessment of predicted RNA 3D structures is essential for large-scale RNA modeling, yet the impact of training data on deep learning–based quality predictors remains poorly understood. Here, we present a systematic cross-training analysis of three deep learning predictors for RNA 3D structure quality assessment, each originally developed on distinct training sets. By retraining each predictor on alternative (nonnative) datasets—including RNA3DQA, a new set created specifically for this project—and evaluating performance on independent RNA-Puzzles benchmarks, we systematically assess the relative contributions of model architecture and training data composition. Our results reveal pronounced differences in cross-dataset generalization, with predictors often performing better when trained on nonnative datasets than on their native ones. Notably, the impact of the training datasets varied across predictors, with RNA3DQA yielding the overall best accuracy and ranking stability results specifically for the ARES model. Native training sets yielded better performance for RNA3DCNN and lociPARSE. These findings demonstrate that the characteristics of the training data play a dominant, model-dependent role in shaping the performance and robustness of reference-free RNA 3D structure quality assessment methods. We conclude with practical recommendations for designing transferable training datasets and for benchmarking deep learning–based quality predictors in RNA structural bioinformatics.