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Polymicrobial co- and superinfections involving bacterial and fungal pathogens pose serious challenges for diagnosis and therapy, and are associated with elevated morbidity and mortality. However, the metabolic dynamics of bacterial-fungal interactions (BFI) and the resulting impact on disease outcome remain largely unknown. The fungus Aspergillus fumigatus and the bacterium Klebsiella pneumoniae are clinically important pathogens sharing common niches in the human body, especially in the lower respiratory tract. We have exploited an integrated multi-omics approach to unravel the complex and multifaceted processes implicated in the interspecies communication involving these pathogens in mixed biofilms. In this setting, A. fumigatus responds to the bacterial challenge by rewiring its metabolism, attenuating the translational machineries, and by connecting secondary with primary metabolism, while K. pneumoniae maintains its central metabolism and translation activity. The flexibility in the metabolism of A. fumigatus and the ability to quickly adapt to the changing microenvironment mediated by the bacteria highlight new possibilities for studying the impact of cross-communication between competing interaction partners. The data underscore the complexity governing the dynamics underlying BFI, such as pronounced metabolic changes mounted in A. fumigatus interacting with K. pneumoniae. Our findings identify candidate biomarkers potentially exploitable for improved clinical management of BFI.
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http://dx.doi.org/10.1038/s42003-024-07145-x | DOI Listing |
Stem Cell Rev Rep
September 2025
Department of Medical Genetics and Prenatal Diagnostics, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
The emergence of organoid models has significantly bridged the gap between traditional cell cultures/animal models and authentic human disease states, particularly for genetic disorders, where their inherent genetic fidelity enables more biologically relevant research directions and enhances translational validity. This review systematically analyzes established organoid models of genetic diseases across organs (e.g.
View Article and Find Full Text PDFTheor Appl Genet
September 2025
Institute for Breeding Research on Agricultural Crops, Julius Kühn Institute (JKI) - Federal Research Centre for Cultivated Plants, Sanitz, 18190, Germany.
Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations.
View Article and Find Full Text PDFTrends Plant Sci
September 2025
Crop and Soils Sciences, University of Georgia, Athens, GA 30602, USA; Institute of Plant Breeding and Genetics and Genomics, University of Georgia, Athens, GA 30602, USA.
Synthetic biology holds great potential to transform agriculture, yet its progress is constrained by the complexity of multigenomic, multitrait, and multi-environment data. Desirable traits often arise from complex gene networks acting across diverse conditions, making them difficult to predict and optimize manually. In the past decade, artificial intelligence (AI) has supported this process, but its large data needs and poor integration limit its role to pattern recognition rather than explanatory trait design.
View Article and Find Full Text PDFJ Genet Genomics
September 2025
State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, Guangd
The genetic basis of early-stage salt tolerance in alfalfa (Medicago sativa L.), a key factor limiting its productivity, remains poorly understand. To dissect this complex trait, we integrate genome-wide association study (GWAS) and transcriptomics (RNA-seq) from 176 accessions within a machine learning based genomic prediction framework.
View Article and Find Full Text PDFBiotechnol Adv
September 2025
Key Laboratory of Microbiological Metrology, Measurement & Bio-product Quality Security, State Administration for Market Regulation, China Jiliang University, Hangzhou 310018, China. Electronic address:
Nanopore direct RNA sequencing (DRS) is a transformative technology that enables full-length, single-molecule sequencing of native RNA, capturing transcript isoforms and preserving epitranscriptomic modifications without cDNA conversion. This review outlines key advances in DRS, including optimized protocols for mRNA, rRNA, tRNA, circRNA, and viral RNA, as well as analytical tools for isoform quantification, poly(A) tail measurement, fusion transcript identification, and base modification profiling. We highlight how DRS has redefined transcriptomic studies across diverse systems-from uncovering novel transcripts and alternative splicing events in cancer, plants, and parasites to enabling the direct detection of m6A, m5C, pseudouridine, and RNA editing events.
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