Aug 2026· Journal of Proteomics· pp.
105721
· 0 citations
Medicine
TL;DR
This study addresses current limitations in the application of metaproteomics to rumen microbiome research by developing a streamlined and scalable sample preparation workflow that enables more efficient processing of larger sample sets.
Abstract
Metaproteomics can provide direct functional insights into complex microbial communities, yet its application in rumen research remains limited due to labor-intensive and low-throughput sample preparation workflows before the MS analysis. This work aimed to develop and characterize a streamlined, high throughput metaproteomic workflow optimized for rumen samples. Key steps, including microbial cell extraction, cell lysis, protein digestion, and LC-MS/MS acquisition, were systematically assessed and optimized to reduce hands-on time while maintaining deep proteome coverage. The optimized workflow integrates a minimized cell extraction protocol using 0.5 g starting material and in-solution tryptic digestion. Application of the final workflow to 72 samples from in vitro fermentation revealed that biological variability between inocula dominated technical variability, which remained moderate (median CV of 21-24% across batches). Overall, the optimized workflow supports robust taxonomic and functional characterization of the rumen microbiome with improved scalability. These advances provide a foundation for applying metaproteomics to larger experimental designs, including nutritional trials and cohort studies, thereby enabling broader functional interrogation of rumen microbial ecosystems. SIGNIFICANCE: This study addresses current limitations in the application of metaproteomics to rumen microbiome research by developing a streamlined and scalable sample preparation workflow. By optimizing key steps and reducing sample input while maintaining reproducibility and proteome coverage, this work enables more efficient processing of larger sample sets. These advances support the broader use of metaproteomics in rumen studies and facilitate functional investigations relevant to animal nutrition and sustainable livestock production.
The quality, flavor, and stability of fermented foods depend on the microbial community. However, microbial dynamics are difficult to observe directly, leading to limited control over fermentation. High-throughput sequencing is a revolutionary tool for microbial characterization, among which DNA-based amplicon and metagenomic sequencing are core techniques. Nevertheless, the related data processing workflows in the context of fermented foods have not yet been systematically summarized, hindering the translation of research findings into fermentation practices. This review clarifies the applications of amplicon and metagenomic sequencing in fermented foods. For amplicon sequencing, the impacts of target regions, data preprocessing, and reference databases are addressed. For metagenomic sequencing, sequencing strategies, read-based and binning-based analytical methods, functional annotation, and species-specific databases are discussed. In addition, major strategies for downstream analysis of community data are summarized, including microbial diversity, co-occurrence networks, niche and community assembly, key environmental drivers, and machine learning-based prediction. Amplicon sequencing efficiently reveals microbial succession during fermentation but has limitations in functional annotation. Metagenomic sequencing is notable for functional annotation, enabling the linkage between microbial communities and metabolic potential alongside community characterization. Standardized data preprocessing and specific databases are critical for improving characterization. For community data, integrated analysis allows uncovering the driving factors of microbial succession, thereby helping to regulate fermentation. Notably, the compositional nature of the data must be considered and validated to avoid spurious associations. In summary, the exponential growth of sequencing data will propel the era of precision fermentation.
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