RA patients and healthy controls
Four patients (three females and one male, 45-66 years of age) with RA were recruited from the Department of Rheumatology and Immunology of First Affiliated hospital of Anhui University of Traditional Chinese Medicine from June 2019 to December 2019. Seven healthy control (five females and two males, 45-66 years of age) without previous history selected from the Physical Examination Center in First Affiliated hospital of Anhui University of Traditional Chinese Medicine. These RA patients and healthy controls samples (frst cohort) were used for the protein microarray and the associated ELISA verifcation.
We further recruited more RA patients and healthy controls samples per each group (second cohort) for the independent ELISA verifcation experiments (30 RA patients and 30 healthy controls). All RA patients fulfilled the 2010 ACR/EULAR (American College of Rheumatology/European League Against Rheumatism) criteria for the classification of RA24. In addition, 30 age- and sex-matched healthy controls with no clinical history of tumors, trauma, infectious diseases or autoimmune diseases, who underwent routine Physical Examination Center in the same hospital during the same period. All samples from RA patients and healthy controls were obtained with written informed consent. This study was approved by the ethics committee of the First Affiliated hospital of Anhui University of Traditional Chinese Medicine.
We collected demographic and clinical data from the subjects, including age, gender, disease duration, blood chemistry. All clinical measurements were performed by the clinical laboratory staff of our hospital. The clinical laboratory data such as erythrocyte sedimentation rate (ESR), high-sensitivity C-reactive protein (CRP), rheumatoid factor (RF), anti-cyclic citrullinated peptide antibody (CCP) and clinical characteristics are determined.
All of the participants enrolled were asked to fill in the DAS28, VAS, SDS, SAS, SF-36 under the guidance of clinical doctors, SF-36 consists of 8 dimensions, namely physical functioning (PF), role-physical (RP), body pain (BP), general health (GH), vitality (VT), social functioning (SF), role-remotional (RE), mental health (MH).
PBMCs preparation and total RNA extraction
An amount of 5 mL of whole blood was obtained from RA patients and healthy controls and PBMCs were isolated through Ficoll–Paque density gradient centrifugation (GE Healthcare, Uppsala, Sweden). The concentration of cells was adjusted to 5-7×106 cells per ml and reserved at − 80 °C until use.
Total RNA was extracted from PBMCs of all samples using Trizol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) according to the manufacturers instructions. The RNA was then reverse transcribed to single stranded cDNA, which was used as a template to synthesize the second cDNA strand. Aliquots of total RNA samples were used to determine the RNA concentration and purity using the Nano Drop ND-1000 spectral photometer (peqlab). The RNAs were selected on the basis of a combination of p-value, fold change, raw intensity and type. In addition, RNAs with miRNA response elements (MREs) related to RA reported in the literatures were selected preferentially. All qPCR assays were performed on the Viia7 Real-Time PCR System, each sample was replicated three times. Library quality was assessed on the Agilent Bioanalyzer 2100 system. The relative expression levels of RNAs were calculated using the 2-ΔΔCt method.
The apoptosis-related protein microarray analysis
Capture antibodies were spotted onto a glass slide at a volume of 350 pL per spot at a pitch of 500 mm using a microarray printer+ . Biotinylated goat anti-bovine IgG antibody was also spotted as a detection control. Each capture antibody was printed in quadruplicate spots within a subarray. Each glass slide contained 16 identical subarrays separated by a 16-well gasketed hybridization chamber to prevent sample cross-contamination. Antibody arrays were stored at -20 ℃ until use. The raw signal data was extracted using MapPix 6.0 software and quantitative data were extracted and analyzed with software for the multiplexed antibody array.
The raw data obtained by the chip scan was subjected to chip background removal and inter-chip normalization processing by Raybiotech software.2.3.2. After the raw data were normalized by the software, the resulting data were selected for analysis. DEPs with P<0.05 were first retained and then further screened by Foldchange (expression difference multiple). The selection conditions were as follows: ①Foldchange≤0.83 or Foldchange ≥1.2; and ② Fluorescent signal > 150. For cluster analysis, the heatmap.2 function and gplots package from R/bioconductor were used. The distance between two samples was calculated as the Euclidean distance; the distance between the two clusters was calculated with the furthest neighbor method (complete), and the distance between classes was defined as the maximum distance. Fisher's exact test and the clusterProfiler package from R/bioconductor were used. For selection, the number of genes that differed on a certain GO term and KEGG pathway were ≥ 2, and P < 0.05. The normalized data were exported to SIMCA-p 11.5 for principal component analysis (PCA), partial least-squares discriminant analysis (PLSDA), and orthogonal partial least-squares discriminant analysis (OPLSDA).
Validation with ELISA
According to the manufacturers’instructions, the differentially expressed proteins levels in serum were quantified using ELISA kit (eBioscience, Inc., San Diego, CA, USA). Briefly, whole-blood samples were centrifuged at 4,000 g for 10 min to collect serum. After that, conditioned media or supernatants of lysed samples were collected and added to 96-well plates pre-coated with appropriate capture antibody, followed by incubation with the appropriate biotinylated detection antibody. Lastly, Streptavidin horseradish-peroxidase (HRP) was added to each well and incubated for 1 h at room temperature. The results were detected using an ELISA microplate reader (BioTek) at 450 nm.
SPSS 23.0 software for Windows was used for statistical analysis. Continuous variables are expressed as means ± standard deviation and categorical variables are expressed as numerals. When normally distributed, independent samples will be compared for numerical variables between the two groups using Student’s t-test. Mann-Whitney U-test was used for the data not conforming to a normal distribution. Categorical data were compared between groups using the Chi-square test. Correlations between the variables were performed using Spearman Correlation Analysis and Logistic-regression. A p value < 0.05 was statistically significant (*p < 0.05, **p < 0.01, ***p < 0.001).