2.1. Animal Model

Wild-type C57BL/6J mice (male, 6 weeks old) were purchased from the Experimental Animal Center of Nantong University. All mice were housed in a specific-pathogen free room with a 12 h light-dark cycle and adequate chow and water. The animals were randomly divided into two groups: T1DM group and control group. The mice of the T1DM group were fasted overnight and then intraperitoneally injected with STZ (Sigma-Aldrich) at a dose of 45 mg/kg body weight for 5 consecutive days, whereas the mice of control group received the same volume of citrate acid-sodium citrate buffer (pH 4.54). Mice with random blood glucose levels greater than 16.7 mM were considered diabetic. This experiment was approved by the Animal Ethics Committee of the Affiliated Maternity and Child Health Care Hospital of Nantong University in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals.

2.2. Peptide Extraction

Each collected pancreatic tissue was washed with precooled PBS, then ground into powder in liquid nitrogen, and finally mixed with a four times volume of lysate (PBS containing 1 mM PMSF). The mixture was placed on ice for 5 minutes and then centrifuged at 12,000 g for 10 min at 4°C. The equal volume of 100% ACN (acetonitrile) was added to the supernatant and then centrifuged at 10,000 g for 10 min. Transfer the freeze-dried supernatant to a prewetted 10 kD ultrafiltration tube (Merck Millipore, UFC501096, Germany) and centrifuged at 10,000 g for 20 min at 4°C. The filtrates were collected, and the peptides were desalted using C18 columns. The desalted peptide solution was vacuum dried and immediately frozen at −80°C until MS analysis.

2.3. Isobaric Tags for Relative and Absolute Quantitation (iTRAQ)

The peptides were dissolved with 0.5 M TEAB (100 μg), labeled according to manufacturer’s instructions of iTRAQ-8 kit (SCIEX), and then, the mixed peptide samples were separated by grade using Ultimate 3000 HPLC system (Thermo DINOEX, USA). The chromatographic column was Durashell C18 column (5 m, 100A, 4.6 × 250 mm). The concentration of ACN gradually increased under alkaline conditions to separate the peptide segment, and the flow rate was 1 ml/min, and one tube was collected every minute. A total of 42 secondary fractions were collected and incorporated into 12 components, which were desalted and vacuum dried on the strata-x column.

2.4. LC-MS/MS Analysis

The peptide samples were redissolved in a 2% acetonitrile/0.1% formic acid (FA) solution and analyzed using a TripleTOF 5,600 plus mass spectrometer coupled to an Eksigent NanoLC System (SCIEX, USA). Peptides were loaded onto a C18 trap column (5 μm, 100 μm × 20 mm) and eluted at 300 nl/min onto a C18 analytical column (3 μm, 75 μm × 150 mm) in the gradient as long as 90 min. These two mobile phases included buffer A (2% acetonitrile/0.1% formic acid/98% H₂O) and buffer B (98% acetonitrile/0.1% formic acid/2% H₂O). For information-dependent acquisition (IDA), the first-order mass spectrogram was scanned at 250 ms accumulation time, and the second-order mass spectrogram of 30 precursor ions was collected at 50 ms accumulation time. The collections of MS1 spectra were in the range 350-1500 m/z, and MS2 spectra were in the range of 100-1500 m/z. The dynamic exclusion time of the precursor ions was set to 15 s.

2.5. MS/MS File Data Analysis

ProteinpilotTM v4.5 was adopted to analyze the original MS/MS file data. For peptides identification, the Paragon algorithm was employed against the Mus-musculus SwissProt sequence database. We further filtered these data according to the result of proteinpilot. We consider unused score ≥ 1.3 (confidence > 95%) and proteins containing at least one unique peptide per protein as trusted proteins. For the identified peptide and protein quantification, we consider that the credible peptide is used for protein quantification with the credibility of more than 95%.

2.6. Bioinformatics Analyses

We analyzed the molecular weight (MW) and isoelectric point (pI) of the peptides by using the online tool ProtParam (http://web.expasy.org/protparam/). The UniProt database (https://www.uniprot.org/peptidesearch/), Pfam (http://pfam.xfam.org/), and STRING (http://string-db.org/) were used to determine the potential function and interaction of peptides and their protein precursors. Gene Ontology (GO) analysis (http://www.geneontology.org), containing cellular component, molecular functions, and biological processes, was used predict the latent functions of the identified peptides. Each peptide and their protein precursors were imported into the Ingenuity Pathway Analysis software v7.1 for pathway analysis and predicting latent biologic, biochemical, and molecular functions of the given proteins, which were incorporated into an associated network map. The online search tool (https://blast.ncbi.nlm.nih.gov/Blast.cgi) was employed for homology comparison. The PyMOL Molecular Graphics System (CA) was established to model the structures of indicated peptides.

2.7. Statistical Analysis

The results of the bioinformatics analysis were analyzed using GraphPad Prism 7, and the data were shown as the mean ± standard deviation (SD). Peptides with a fold change larger than 1.2 or <0.83 with a Student’s t-test p value <0.05 were selected as differently expressed peptides.

3.1. Identification of Differentially Expressed Peptides in the Pancreatic Tissue from T1DM Mice

Endogenous peptides from the pancreatic tissue of T1DM mice and control groups were directly analyzed using LC-MS/MS after iTRAQ labeling. A total of 1063 nonredundant peptides were identified, among which 106 peptides were differentially expressed in the T1DM mice and control groups (fold change > 1.2, p < 0.05), including 43 upregulated peptides and 63 downregulated peptides (Figure 1(a)). All of the differentially expressed peptides are listed in Table S1. The twenty upregulated and downregulated peptides detected in all samples had the highest range of change, which was graphically represented by hierarchical clustering analysis (Figures 1(b) and 1(c)).

3.2. Characteristics of Differentially Expressed Peptides

To characterize the general feature of differentially peptides, we further analyzed the relative molecular weight (MW), isoelectric point (pI), amino numbers, and distribution of pI vs. MW. The results shown that the majority of these peptides were 8-16 amino acids in length (Figure 2(a)); the MW varied from 769.9 Da to 2324.5 Da with 75% between 1000 and 1800 Da (Figure 2(b)), and pI varied from 3.0 to 9.3, with 77% between 3 and 5 (Figure 2(c)). Furthermore, the distribution of pI vs. MW could divide these peptides into two groups around pI3 and pI7 (Figure 2(d)). There were several peptides derived from the same precursor protein. The top ten precursor proteins are listed in Figure 2(e), and the largest number of peptides is derived from INS2. Then, UniProt and Pfam databases were used to analyze the functional domains of these peptides. A total of 36 downregulated and 12 upregulated peptides located in the domains of their precursor proteins are listed in Table 1.

3.3. Cleavage Site Patterns of Differentially Expressed Peptides

Peptides are mainly cleaved by proteolytic enzyme, and the regulation of their level depends on the type and activity of protease. In other words, by analyzing the characteristic pattern of peptide cleavage sites, the functional differences that may be caused by the enzyme cleavage process can be found. The diagram of the cleavage site distribution is showed in Figure 2(f). Four cleavage sites (C-terminal amino acid of the preceding peptide, C-terminal amino acid of the identified peptide, N-terminal amino acid of the identified peptide, N-terminal amino acid of the identified peptide) were researched for each up- and downregulated peptide (Figures 2(g) and 2(h)). The four dominant cleavage sites in the upregulated peptides were alanine (A), lysine (K), threonine (T), and glycine (G), while in the downregulated peptides were alanine (A), lysine (K), lysine (K), and proline (P).

3.4. Gene Ontology (GO) and Pathway Analysis

Gene Ontology (GO) and pathway analysis were used to further understand the potential functions of these differential peptides and their corresponding precursor proteins. Cell projection membrane, H4/H2A histone acetyltransferase complex, small nucleolar ribonucleoprotein complex, histone acetyltransferase complex, pigment granule, cell projection part, etc., were the most highly enriched cellular components (Figure 3(a)), whereas hormone activity, glycoprotein binding, nitric-oxide synthase regulator activity, TPR domain binding, RNA cap binding, etc., were the highest enriched molecular functions (Figure 3(b)). Finally, response to endoplasmic reticulum stress, cellular protein metabolic process, cellular macromolecule metabolic process, protein secretion, Wnt receptor signaling pathway, ER overload response, etc., were the most highly enriched biological processes (Figure 3(c)). The pathway analysis revealed that the differentially expressed peptides and their corresponding precursor proteins were involved in protein processing in endoplasmic reticulum, insulin signaling pathway, endocytosis, oxidative phosphorylation, and PPAR signaling pathway, among others (Figure 3(d)).

3.5. Functional Clustering by IPA

The IPA software was employed to produce the interaction network of the precursor proteins of 106 differentially expressed peptides and high-scoring networks that may be linked to the endocrine system. Both “cancer, endocrine system disorders, organismal injury and abnormalities” and “carbohydrate metabolism, cell death and survival, skeletal and muscular disorders” were identified (Figures 4(a) and 4(b)).

The differentially expressed peptides were also entered into the IPA software to conduct a comprehensive analysis of the precursor proteins and their functional analysis of related diseases. The data analysis suggested that some of the precursor proteins were related to endocrine system development and function. These precursor proteins were predicted to have roles in necrosis (p = 7.82E − 09), morphology of pancreatic cells (p = 3.69E − 06), morphology of endocrine cells (p = 6.93E − 06), and morphology of beta islet cells (p = 2.02E − 05). The top twenty precursor proteins related disease function are listed in Figure 4(c).