Dataset Information

MetaDIA: A Novel Database Reduction Strategy for DIA Human Gut Metaproteomics

ABSTRACT: Microbiomes, especially within the gut, are complex and may comprise hundreds of species. The identification of peptides in metaproteomics presents a significant challenge, as it involves matching peptides to mass spectra within an enormous search space for complex and unknown samples. This poses difficulties for both the accuracy and the speed of identification. Specifically, analysis of data-independent acquisition (DIA) datasets has relied on libraries constructed from prior data-dependent acquisition (DDA) results. However, this method is resource-intensive, consumes samples, and limits identification to peptides previously identified. These limitations restrict the application of DIA in metaproteomics research. We introduced a novel strategy to reduce the search space by utilizing species abundance and functional abundance information from the microbiome to score each peptide and prioritize those most likely to be detected. Using this strategy, we have developed and optimized a workflow called MetaDIA for the analysis of microbiome data generated by DIA, which operates independently of DDA assistance. Our approach successfully created a smaller, yet sufficient database for DIA data search in metaproteomics. The results demonstrated strong consistency with the traditional DDA-based library approach at both protein and functional levels. MetaDIA is readily accessible as an open-source project hosted on GitHub (https://github.com/northomics/MetaDIA).

INSTRUMENT(S):

ORGANISM(S): Bacteria Human Gut Metagenome

TISSUE(S): Feces

SUBMITTER: Haonan Duan

LAB HEAD: Daniel Figeys

PROVIDER: PXD063632 | Pride | 2026-03-06

REPOSITORIES: Pride

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Source:

			Action	DRS
	GCF_000157975.1_ASM15797v1_protein_DSM10507.fasta	Fasta
	Haonan_20220809_DIA_MIX_17_1.raw	Raw
	Haonan_20220809_DIA_MIX_17_2.raw	Raw
	Haonan_20220809_DIA_MIX_17_3.raw	Raw
	Haonan_20220809_DIA_MIX_1_1.raw	Raw

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Project description:Background Metaproteomics provides comprehensive insights into the taxonomic composition and functional dynamics of microbiome communities across ecologically diverse niches. However, the inherent complexity and highly dynamic nature of microbial communities pose significant obstacles to achieving accurate and comprehensive metaproteomic profiling. Data-independent acquisition (DIA) mass spectrometry has emerged as a powerful quantitative approach, offering high proteome coverage, excellent reproducibility and robust quantitative accuracy. However, the immense scale of candidate sequences in metaproteomics currently precludes the direct application of DIA strategy, unless supplemented by metagenomic sequencing or data-dependent acquisition (DDA)‐based spectral library construction, thereby limiting the widespread applicability of DIA in metaproteomic research. Results Herein, we introduce MetaSpaR-DIA, a search space reduction strategy for DIA-based metaproteomic quantification, which obviates the need for sample-specific metagenomic sequencing and DDA-based spectral libraries. By randomly partitioning publicly available reference catalogs into appropriately equal-sized subsets and implementing an iterative false discovery rate (FDR) control strategy, MetaSpaR-DIA enables the construction of a reduced, sample-specific database with markedly decreased search space, thereby facilitating accurate and high-coverage metaproteomic quantification. MetaSpaR-DIA enables peptide identification with < 2% actual FDR in simulated microbial communities and demonstrates high peptide coverage and quantitative accuracy comparable to metagenomic-based DIA method, and provides more comprehensive functional and taxonomic annotation in spike-in fecal samples. The MetaSpaR-DIA pipeline was further applied to investigate gut microbiota functional dynamics during Alzheimer's disease (AD) progression. Energy metabolism dysregulation along with the subsequent oxidative stress, may accelerate disease progression. Observed alterations in short-chain fatty acid synthesis further indicate potential avenues for disease modulation and therapeutic intervention. Conclusion Overall1, we developed an efficient workflow for large-scale DIA-based metaproteomic analysis without the need for additional experimental procedures and has strong potential for widespread applicability, particularly for studies involving rare or limited microbiome samples. This method demonstrated excellent quantitative performance in profiling microbial communities and was further applied to explore functional dynamics in the mice gut microbiome during the progression of AD. MetaSpaR-DIA is expected to provide novel insights into gut microbiota-mediated pathogenesis and advance the application of DIA-based metaproteomics to a wide range of research fields.

Dataset Information

MetaDIA: A Novel Database Reduction Strategy for DIA Human Gut Metaproteomics

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