Methods
Cell culture. APOA1-overexpression (OE) and control lentivirus-transfected (negative control, NC) SiHa and Caski cells were cultured in DMEM containing 10% fetal bovine serum at 37℃ in a 5% CO2 incubator. The medium was changed every 2–3 days, and cells in logarithmic growth phase were used in the experiments.
Clone formation assay. SiHa and Caski OE and NC cells were collected, trypsinized, and counted; then, the cell suspensions were diluted and inoculated into 6-well plates at a concentration of 500 cells per well. The cells were cultured in a cell culture incubator for 14 days, until visible cell clones appeared. Clones were counted with the naked eye, and the number of clones with > 50 cells were counted under a microscope. The clone formation rate was calculated using the following formula: Clone formation rate = (number of clones/number of inoculated cells) × 100%.
MTT assay. SiHa and Caski OE and NC cells were collected, trypsinized, and counted. The density of the cell suspensions was adjusted to 2 × 103 cells/ml, and the cells were inoculated into 96-well plates and incubated for 1, 2, 3, 4, and 5 days. After incubation, the medium was discarded, and 20 µL of MTT (5 mg/mL) was added to each well. Then, 100 µL of DMSO was added to dissolve the formazan crystals, and the OD at 490 nm was measured with a microplate reader. Finally, the relative cell viability was calculated based on the OD, and a growth curve was generated.
TUNEL assay. SiHa and Caski OE and NC cells were collected, trypsinized, and counted. The cells were then washed, placed in complete medium containing carboplatin (CBP), and incubated for 48 h. Cells were fixed with 4% paraformaldehyde in PBS solution (pH 7.4) at 15–25°C for 1 h and then washed with PBS. Cells were permeabilized using a sodium citrate solution containing 0.1% Triton X-100 and incubated in an ice bath (2–8°C) for 2 min. Then, 50 µl of prepared DNase I was added, and the cells were incubated at 20°C for 10 min. TUNEL reaction mix was prepared by mixing 50 µl of TdT with 450 µl of fluorescein-labeled dUTP solution. The prepared TUNEL reaction mix (50 µl) was added to each specimen, which was incubated at 37°C for 1 h. The negative control contained fluorescein-labeled dUTP solution only. Nuclei were stained with 50 µl of 5 µg/ml DAPI for 5 min at 20℃ and rinsed thrice in PBS. Finally, TUNEL and DAPI-labeled cells were counted separately under a fluorescence microscope.
Tandem Mass Tag (TMT) screening of ApoA1 downstream related proteins. The samples were labeled with isotopes. Biological analyses included GO term analysis, KEGG pathway annotation and enrichment analysis, subcellular localization analysis, domain annotation and enrichment analysis, and clustering and interaction network analysis. Proteins that met the screening criteria, i.e., expression > 1.2 times higher or lower and a p value less than 0.05 by t-test, were regarded as differentially expressed proteins. The IPA bioinformatics analysis was performed using the following keywords: JAK stat, Notch signaling, p38 MAPK signaling, PI3K signaling, tumor progression, chemotherapy, recurrence, drug resistance, and metastasis.
Protein sampling and electrophoresis. Cell lysate samples were mixed with SDT buffer (4% SDS, 100 mM DTT, 150 mM Tris-HCl, pH 8.0), sonicated, and then boiled for 15 min. After centrifugation at 14000 × g for 40 min, the protein concentration in the supernatant was quantified using the BCA Protein Assay Kit (P0012; Beyotime,Shanghai༌China). The sample was stored at -20°C until use.
Filter-aided sample preparation (FASP). Samples containing 200 µg of protein were mixed with 30 µl of SDT buffer. Then, the detergent, DTT, and other low-molecular-weight components were removed by repeated ultrafiltration (30 kDa; Sartorius) into UA buffer (8 M Urea, 150 mM Tris-HCl, pH 8.5). Then, 100 µl of iodoacetamide (100 mM in UA buffer) was added to block reduced cysteine residues, and the samples were incubated for 30 min in the dark. The filters were washed thrice with 100 µl of UA buffer and then twice with 100 µl of 0.1 M TEAB buffer. Finally, the proteins were digested with 4 µg of trypsin (Promega,Madison༌USA) in 40 µl of 0.1 M TEAB buffer overnight at 37°C, and the resulting peptides were collected as a filtrate. The peptide content was estimated by measuring UV light spectral density at 280 nm using an extinction coefficient of 1.1 in a 0.1% (g/l) solution, which was calculated based on the frequency of tryptophan and tyrosine residues in vertebrate proteins.
TMT Labeling. An aliquot of each sample (containing 100 µg of peptides) was labeled with TMT reagent according to the manufacturer’s instructions (Thermo Fisher Scientific, Waltham, USA).
Peptide fractionation using reversed phase (RP) chromatography. TMT-labeled peptides were fractionated by RP chromatography using an Agilent 1260 Infinity II HPLC system. The peptide mixture was diluted with buffer A (10 mM HCOONH4, 5% ACN, pH 10.0) and loaded onto a XBridge Peptide BEH C18 Column (130 Å, 5 µm, 4.6 mm × 100 mm). The peptides were eluted at a flow rate of 1 ml/min with a gradient of 0–7% buffer B (10 mM HCOONH4, 85% ACN, pH 10.0) for 5 min, 7–40% buffer B from 5 to 40 min, 40–100% buffer B from 45 to 50 min, and 100% buffer B for 50 to 65 min. Elution was monitored at 214 nm based on the UV light trace, and fractions were collected every 1 min from 5 to 50 min. The collected fractions were combined into 10 fractions and dried via vacuum centrifugation at 45°C.
Easy nLC mass spectrometry analysis. Each fraction was subjected to nanoLC-MS/MS analysis. Each peptide mixture was loaded onto a C18-reversed phase analytical column (Acclaim PepMap RSLC 50 µm × 15 cm, nano viper, P/N164943; Thermo Fisher Scientific) in buffer A (0.1% formic acid) and separated with a linear gradient of buffer B (80% acetonitrile and 0.1% formic acid) at a flow rate of 300 nl/min: 6% buffer B for 3 min, 6–28% buffer B for 42 min, 28–38% buffer B for 5 min, 38–100% buffer B for 5 min, and 100% buffer B for 5 min.
Data analysis. MS/MS raw files were processed using the MASCOT engine (version 2.6; Matrix Science, London, UK) in Proteome Discoverer 2.2 and searched against the database Uniprot_HomoSapiens_20367_20200226. The search parameters included trypsin as the enzyme used to generate peptides and a maximum of two missed cleavages. A precursor mass tolerance of 10 ppm was specified as well as a 0.05 Da tolerance for MS2 fragments. Except for TMT labels, carbamidomethyl (C) was set as a fixed modification. Variable modifications were Oxidation (M) and Acetyl (Protein N-term). A peptide and protein false discovery rate of 1% was enforced using a reverse database search strategy. Proteins with a fold change greater than 1.2 and a p value less than 0.05 (Student’s t-test) were considered differentially expressed.
Bioinformatics analyses
Gene Ontology (GO) annotation. First, all protein sequences were aligned to a database downloaded from NCBI (ncbi-blast-2.2.28+-win32.exe), and only the top 10 sequences with an E-value ≤ 1×10− 3 were included in the analysis. Second, the GO term (database version: go_201504.obo) for the sequence with the top Bit-Score as determined by Blast2GO was selected. Then, the annotation from the GO terms for the proteins was completed using Blast2GO Command Line. After simple annotation, InterProScan was used to search the EBI database by motif, and the functional information for the motifs was added to the proteins to improve the annotation. Next, the annotation and connections between GO terms were further improved using ANNEX. Fisher’s exact test was used to enrich GO terms by comparing the number of differentially expressed proteins and total proteins correlated to the GO terms.
KEGG annotation. Pathway analysis was performed using the KEGG database. Fisher’s exact test was used to identify the significantly enriched pathways by comparing the number of differentially expressed proteins and total proteins in each pathway.
Subcellular localization analysis. We used Wolf PSORT to predict the location of different proteins. Wolf PSORT is a tool that is commonly used to predict the subcellular localization of proteins (https://wolfpsort.hgc.jp/). The program transforms protein sequences into digital location features based on sorting signals, the amino acid composition, and functional motifs. Then, the k-nearest neighbor classifier is used to predict their subcellular localization.
Domain annotation and enrichment analysis. The InterPro database integrates the functions of protein sequence family classification with domain and special site prediction. We used this database to annotate the functional domains of proteins of interest. Fisher’s exact test was used to compare the distribution of different proteins in the total protein set to evaluate the significance of the enrichment of a specific functional domain.
Clustering. For the first step of cluster analysis, the quantitative information for the target protein set was normalized. Then, Matplotlib was used to classify the two dimensions of sample and protein expression (distance algorithm: Euclidean, connection method: average link). Finally, a hierarchical clustering heat map was generated.
Protein-protein interaction network. To analyze the PPI networks, the gene symbols of the target protein were obtained from the database containing the target protein sequences, and these gene symbols were used in intAct (http://www.ebi.ac.uk/intact/main.xhtml). The direct and indirect interactions between the target proteins were found in the database. The interaction network was generated and analyzed using Cytoscape software (version: 3.2.1)
Analysis of IPA-related words. The IPA analysis was performed with the following keywords as input: JAK stat, Notch signaling, p38 MAPK signaling, PI3K signaling, tumor progression, chemotherapy, recurrence, drug resistance, and metastasis.
Identification of AOPA1 downstream proteins by PRM.
Ion screening of PRM peptides. Using Proteome Discoverer 2.1 (Thermo Fisher Scientific) software, the original spectrum file (.Raw file) generated by Q Exactive was transformed into an MGF file, which was submitted to the Mascot 2.6 server for database retrieval using the built-in tool of the software. The database used was UniProt_HomoSapiens_20367_20200226 (http://www.uniprot.org). Based on the results of the analysis, the unique peptides of the target proteins were screened, and information, such as the mass charge ratio, number of charges, and retention time, were obtained and imported into the inclusion list.
Quantitative identification of PRM. Mass spectrometry was performed using an Easy nLC according to the manufacturer’s instructions. The mass spectrum parameters were as follows: for Full-MS. scan range (m/z) 350–1800, resolution = 70,000, AGC target = 3e6, maximum injection time = 50 ms; for PRM, resolution = 17,500, AGC target = 2e5, maximum injection time = 45 ms, Loop count = 10, Isolation window = 2 m/z, and NCE = 27%.
Statistical methods. SPSS 20.0 was used for all statistical analyses and for generating graphics. Measurement data were expressed as mean ± standard deviation (x ± s). Statistical t-test was used to compare the means between two groups, ANOVA was used to compare the means among multiple groups, and the chi square test was used to compare rates between groups, A p value less than 0.05 was considered statistically significant.