김민지 (울산대학교 )

김경곤 (울산대학교)

김민지 (울산대학교 )

유지영 (서울아산병원)

김보경 (울산대학교)

이예린 (울산대학교)

이광선 (울산대학교)

김민중 (울산대학교)

김가빈 (울산대학교)

장귀주 (울산대학교 산학협력단)

김경곤 (울산대학교)

 Fecal metaproteomics profiles host and microbial proteins, providing insight into host–microbiome interactions and microbial function. However, large microbiome databases complicate protein inference, taxonomic assignment, and false-positive microbial identification. We evaluated an integrated fecal metaproteomics workflow using germ-free (GF) mice as a biological negative control.

Fecal samples from GF and specific pathogen-free (SPF) mice were processed using an S-Trap–based workflow and analyzed on an Orbitrap Eclipse Tribrid Mass Spectrometer using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Proteins were identified using the mouse Swiss-Prot database and the Mouse Gut Gene Catalog (CNGB).

DIA-NN (v2.5) analysis identified 19,421 microbial proteins in SPF fecal samples, including 14,329 proteins assigned to 99 genera. Proteome Discoverer (v2.4) analysis identified 19,369 microbial proteins, including 14,221 proteins assigned to 95 genera. Microbial proteins were nearly absent in GF samples, supporting the specificity of the identification strategy. DDA and DIA provided comparable microbial coverage, whereas DDA achieved greater host proteome depth. GF mice provide an effective biological negative control for validating microbial protein identification. The established workflow reliably distinguishes host and microbial proteins and supports taxonomic and functional characterization of the fecal metaproteome.