Lung tissues from uninfected and infected mice (n = 5 per group) were homogenized in 0.5 mL of guanidine buffer. After heating and sonication, the lysates were centrifuged at 20,000 g for 15 min at 4° C. The resulting supernatants were collected, and the proteins (20 µg each) were purified using the SP3 method and digested with 200 ng trypsin/Lys-C mixture at 37° C overnight. The digests were acidified, centrifuged, and desalted using a GL-Tip SDB. The eluates were evaporated and dissolved in 0.1% TFA and 3% ACN. LC-MS/MS analysis of the resulting peptides was performed using a timsTOF HT system (equipped with a CaptiveSpray 2 ion source) coupled to a nanoElute 2 (Bruker, Billerica, MA). A 150-mm C18 reversed-phase column with an inner diameter of 75 µm was used. Mobile phase A consisted of ultrapure water containing 0.1% formic acid, and mobile phase B consisted of ACN containing 0.1% formic acid. The gradient was initiated at 5% solvent B and increased to 20% at 40 min. Then, it increased to 35% at 60 min, followed by a rapid increase to 95% at 61 min. Finally, it was held at 95% until 75 min. Data were acquired in DIA-PASEF mode with an MS1 m/z range of 100–1700 and an ion mobility (1/K0) range of 0.6–1.6. The MS2 polygon was manually defined based on the region where peptides were predominantly detected. Specifically, it was defined using the following four vertices: Point 1 (m/z = 300, 1/K0 = 0.64), Point 2 (m/z = 300, 1/K0 = 0.8), Point 3 (m/z = 770, 1/K0 = 0.97), and Point 4 (m/z = 770, 1/K0 = 1.15). An 8 Da isolation window with an overlap of 0.5 Da was employed within the defined polygon, resulting in 62 windows per cycle and a cycle time of 3.36 s. DIA-MS data were searched using DIA-NN (version 1.9) against a mouse in silico spectral library. The following parameters were applied to construct the library from the UniProt mouse protein sequence database: trypsin as the digestion enzyme; one missed cleavage; peptide lengths ranging from seven to 45 amino acids; precursor charges of two to four; and a fragment ion m/z range of 200–1,800. The following features were enabled: library-free search, deep learning–based retention time and ion mobility predictions, N-terminal methionine cleavage, and cysteine carbamidomethylation. MS1 and MS2 mass accuracies were automatically optimized; both neural network classifiers were run in single-pass mode; and quantification was performed using the high-precision QuantUMS strategy.