ABSTRACT Barley grain quality is influenced by nitrogen content and storage conditions; however, their impact on the composition and function of the grain microbiome is not well understood. This study combined metataxonomic (16S rRNA and ITS) profiling, metagenome sequencing, and metaproteome analyses to characterize the structure and function of the barley grain microbiome. Grains with high (> 1.5%) and low (< 1.5%) nitrogen content from a single barley cultivar (Kadie) were sampled at harvest and after 3, 6, and 9 months of storage. Amplicon sequencing revealed a community dominated by Proteobacteria, Firmicutes, and Ascomycota, while metagenomics confirmed the abundance of genera such as Erwinia, Pantoea, and Pseudomonas, aligning with previous reports of barley endophytes. While a consistent set of core microbial genera was identified, their relative abundances varied throughout storage. Metagenomic analysis revealed the high‐nitrogen grain microbiome had potential for rapid metabolic activity that declined post‐harvest, whereas the low nitrogen grain community sustained prolonged metabolic potential. Metaproteomics confirmed that these functional shifts revealed a temporal transition from active growth to stress tolerance. Findings from this work contribute to a better understanding of the barley grain microbiome during prolonged storage, offering insights that could help optimize storage for malting and brewing.
K. A. Tshisekedi, T. Van Den Bossche, Lennart Martens et al.· Food Science & Nutrition· 0 citations
Multi-omics approaches have revolutionized our understanding of microbial communities by enabling simultaneous interrogation of genomic, transcriptomic, proteomic, and metabolomic data. The systematic integration and analysis of these deep datasets help decipher the functional roles of microbiomes, providing critical insights into microbial activities, interactions, and dynamics across diverse environments. Biological complexity makes multi-omics analysis of a single, isolated organism demanding but highly informative, yet this complexity increases further when samples comprise hundreds to thousands of individual species. As microbiome research continues to expand into clinical, environmental, and engineered systems, standardized workflows, benchmarked datasets, and community-driven initiatives are essential to ensure reproducibility, standardization and interpretability. Establishing and disseminating best practices for experimental design, data processing, and integrative analyses will be critical for maximizing comparability and scientific rigor across studies. This perspective highlights recent advances in multi-omics microbiome research, outlines key obstacles in data integration and metadata harmonization, and proposes a collaborative roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence (AI) advances comparable to those of AlphaFold in the field of microbiome science. In this Perspective, the authors discuss recent advances in multi-omics microbiome research, outlining key obstacles in data integration and metadata harmonization, and proposing a roadmap for scalable, FAIR-compliant multi-omics investigations and potentially disruptive Artificial Intelligence advances.
T. Van Den Bossche, Eunice Lazau, Velma T. E. Aho et al.· Nature Communications· 0 citations
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