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Wang et al. Microbiome Res Rep 2024;3:39 https://dx.doi.org/10.20517/mrr.2024.21 Page 5 of 17

LC-MSMS analysis
Dried tryptic peptides were resuspended in 0.1% (v/v) FA to a final concentration of 1µg/µL, and 3 µg
peptides were loaded for MS analysis using a timsTOF Pro 2 mass spectrometer (Bruker Daltonik, Bremen,
Germany) coupled to a nanoElute 2 UPLC system (Bruker Daltonik). The instrument was calibrated prior
to analysis with Chip Cube High Mass Reference Standard (Agilent, Cat#G1982-85001). A two-column
system of HPLC was used consisting of a C8 trap column before separating on a PepSep Twenty-five
analytical column (25 cm × 75 μm column packed with 1.9 μm C18 particles) (Bruker Daltonik,
Cat#1893477). Chromatographic separation was achieved at a flow rate of 0.5 µL/min over 48 min in linear
steps as follows (solvent A was 0.1% FA in water, solvent B was 0.1% FA in ACN): initial, 2%B; 40 min,
35%B; 40.5 min, 95%B; 45 min, 95%B; 48 min, 95%B. The eluting peptides were analyzed in either data-
dependent acquisition coupled with parallel accumulation-serial fragmentation (DDA-PASEF) mode or
data-independent acquisition coupled with PASEF (DIA-PASEF) mode in the timsTOF Pro 2 mass
spectrometer.

2
For DDA-PASEF mode, a MS survey scan of 100-1,700 m/z and ion mobility range of 0.85-1.30 Vs/cm was
performed in the timsTOF MS. During the MS/MS scan, 4 PASEF ramps were run with an intensity
threshold of 2,500, target intensity of 20,000, and a maximum precursor charge of 5. The TIMS analyzer was
operated in a 100% duty cycle with equal accumulation and ramp times of 100 ms each and a total cycle
time of 0.53 s. The collision energy was ramped linearly as a function of mobility from 59 eV at 1/k0 =
2
2
1.6 Vs/cm to 20 eV at 1/k0 = 0.6 Vs/cm .
For DIA-PASEF mode, a MS survey scan of 100-1,700 m/z with an ion mobility range of 0.6-1.60 Vs/cm 2
was performed. The TIMS analyzer was operated in a 100% duty cycle with equal accumulation and ramp
times of 100 ms each and a total cycle time estimated at 1.8 s. During DIA-PASEF MS/MS scan, precursors
with m/z between 400 and 1,200 were defined in 16 scans containing 32 ion mobility steps with an isolation
window of 26 Da in each step with 1 Da overlap with neighboring windows. The collision energy for DIA-
PASEF scan was ramped linearly from 59 eV at 1/k0 = 1.3 V·s/cm to 20 eV at 1/k0 = 0.85 V·s/cm .
2
2
Bioinformatic data processing
Spectral library and reduced database generation
The mouse gut microbial gene catalog database, containing ~2.6 million nonredundant protein sequences,
was downloaded from GigaScience Database (http://gigadb.org/dataset/100114) . The reviewed mouse
[33]
UniProtKB database was downloaded from UniProt (downloaded on 2024/01/01, 17,179 protein entries).
These two databases were combined as the starting original database for generating a spectral library or a
reduced FASTA database using the DDA-PASEF dataset, employing either pFind or MSFragger . For
[35]
[34]
pFind search, MS raw files were first converted from .d to .mgf files using MSconvert (v3.0.23240). The
resulting MGF files were then searched using open search mode against the combined database with the
following parameters: enzyme: Trypsin KR_C, number of missed cleavages: 3, precursor and fragment
tolerance: +/- 20 ppm, and an false discovery rate (FDR) of < 1% at both peptide and protein levels. The
protein sequences of all identified proteins in the pFind search, including indistinguishable proteins, were
extracted from the original database to generate a pFind-generated reduced database using an in-house Perl
script. For MSfragger search, FragPipe (v20.0) was used with either the full combined database or the pFind-
generated reduced database for generating spectral libraries that will be used for analysis for the DIA-PASEF
dataset. For MSfragger search using the full combined database, a database split factor of 10 was used. Both
searches followed the default workflow and used ciRT for spectral library generation with a peptide and
protein level FDR threshold of 1%. In addition to spectral libraries generated, the protein sequences of all
identified proteins, including indistinguishable proteins, of MSFragger search against the original database
were extracted using an in-house Perl script to generate a MSFragger-derived reduced FASTA database.