Rat studies
Animal experiments were approved by the Animal Care and Ethics Committee of Shandong Provincial Hospital affiliated to Shandong First Medical University (No. HSRF 2022-0027). Six-week-old male Sprague-Dawley rats were purchased from Vital River Laboratory Animal Technology Co., Ltd (Beijing, China). Rats were housed three per cage in a controlled environment with a 12 h light/dark cycle in the Institute of Laboratory Animal Science.
Rat CKD was simulated with a 5/6 nephrectomy (5/6Nx) model. Briefly, rats were anesthetized with tribromoethanol, shaved, and disinfected with 70% ethanol. An incision near the left waist was performed to expose the left kidney completely. One-third of each of the upper and lower poles of the left kidney were removed with sharp scissors, and the remaining tissue was quickly compressed with a pair of hemostatic sponges for 1 min. Hemostasis was confirmed, and the remaining 1/3 of the kidney was returned to the abdominal cavity. Antibiotic-containing saline was applied before the abdominal cavity was closed carefully. After recovery from anesthesia, the rats were placed in a cage with adequate water and food. One week later, the entire right kidney was removed following a similar procedure on the other side of the abdomen. Sham surgery involved all procedures except nephrectomy.
Patients
The patient study was conducted in compliance with the Declaration of Helsinki and was approved by the local ethics committee of Shandong Provincial Hospital affiliated to Shandong First Medical University (SZRJJ: No.2022-188). Lithium-heparin blood samples were obtained from patients with long-term hemodialysis (HD) therapy (n=40) at the Shandong Provincial Hospital Dialysis Unit. Subjects with concurrent malignancy, history of autoimmune diseases, acute infective/inflammatory response were excluded. A total of 20 healthy participants were recruited to match HD participants according to age and sex. All human samples were collected after informed consent. Clinical characteristics of the patients are stated in Table 1.
Table 1. General characteristics of the study HD participants
Variable
|
HD (n=40)
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Age (mean ± SD) (years)
|
53.7 ± 12.0
|
Sex (male/female)
|
23/17
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HD vintage (mean ± SD) (months)
|
53.2 ± 41.8
|
Residual urine volume (median, range) (mL)
|
300 (0, 1200)
|
Hemoglobin (mean ± SD) (g/L)
|
111.4 ± 12.4
|
Reticulocyte ratio (mean ± SD) (%)
|
1.88 ± 0.69
|
WBC (mean ± SD) (109/L)
|
5.97 ± 1.94
|
PLT (mean ± SD) (109/L)
|
184.8 ± 50.3
|
SCR (mean ± SD) (μmol/L)
|
911.7 ± 253.0
|
BUN (mean ± SD) (μmol/L)
|
27.0 ± 7.4
|
β2-MG (mean ± SD) (mg/L)
|
28.2 ± 3.4
|
ALB (mean ± SD) (g/L)
|
41.6 ± 4.4
|
Ca (mean ± SD) (mmol/L)
|
2.30 ± 0.22
|
P (mean ± SD) (mmol/L)
|
1.98 ± 0.53
|
Ferritin (mean ± SD) (μg/L)
|
329.8 ± 179.1
|
Transferrin saturation (mean ± SD) (%)
|
31.5 ± 11.0
|
PTH (mean ± SD) (pg/mL)
|
338.7 ± 211.9
|
CRP (mean ± SD) (mg/L)
|
4.24 ± 5.5
|
Abbreviations: SD, standard deviation; HD, hemodialysis; WBC, white blood cells; PLT, platelets; SCR, serum creatinine; BUN, blood urea nitrogen; β2-MG, β2 microglobulin; ALB, albumin; Ca, calcium; P, phosphate; PTH, parathyroid hormone; CRP, C-reactive protein.
RBC osmotic fragility (resistance of erythrocytes to hypotonic shock)
The tubes containing 1 mL of 0–300 mOsm/L NaCl were preincubated for 20 min at 37℃ after addition of 10 μL of whole blood with mixing. Vials were then centrifuged (1300 g, 10 min) at the assay temperature. Absorbance (A) at 540 nm for each supernatant was measured and converted to percentage haemolysis using the following equation: 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)
RBC deformability measurement
The blood sample was separated, washed and left with washed and compacted RBCs. The RBC suspension samples were prepared according to the standard hematocrit of 5%. The deformability of erythrocytes is characterized by the erythrocyte filtration index (EFI): EFI = (ts − tb)/tb × 1/H, where ts is the filtration time of the RBC suspension, tb is the filtration time of the buffer (without RBCs) and H is the volume percentage of red blood cells. The EFI refers to the relative resistance that RBCs experience when passing through a nuclear pore filter compared with the same volume of buffer. Therefore, if the EFI of erythrocytes is larger, it means that the deformability of erythrocytes is worse; in contrast, the smaller the EFI, the better the deformability of erythrocytes.
Detection of phosphatidylserine exposure
Erythrocytes (isolated from 0.5 mL of blood) were washed and resuspended in 500 μL of binding buffer containing 5 μL FITC-annexin V (Keygen, Nanjing, China), then incubated for 15 min under protection from light. A total of 1× 105 cells were analyzed by flow cytometer (BD Biosciences, CA, USA) using the FL1 channel (488/530 nm).
In vivo phagocytosis assay
In vivo phagocytosis assay was performed according to a previously described method with mild modification [18]. In brief, erythrocytes from rats (n = 3 per group) were collected via tail vein bleeding and labeled with CFDA-SE (CFSE; 5 μM; MedChemExpress, Monmouth Junction, NJ, USA) at 37 ◦C for 15 min. Rats were then injected with the autologous erythrocytes via tail vein. After housing for 2 h, rats were euthanized followed by collection of spleens. Spleens were lysed and the fluorescence intensity of CSFE in lysates was measured by using a microplate reader SpectraMax M5 (Molecular Devices, San Jose, USA).
In vitro phagocytosis assay
To determine erythrophagocytosis in vitro, the peritoneal macrophages of rats were harvested as follows: 10 mL of RPMI medium 1640 (Gibco, Grand Island, NY) were injected into the peritoneal cavity of the rats, which were gently shaken and rotated. After 5 min, 8 mL of peritoneal fluid was withdrawn. The cells from each rat were sedimented by centrifugation of the fluid for 5 min at 800 rpm, resuspended in RPMI 1640 supplemented with 10% fetal bovine serum (FBS) [20]. Then 4× 105 macrophages per well were seeded into a 12-well plate and incubated for 4-5 h at 37 °C in a humidified 5% CO2 incubator. For the erythrophagocytosis assay, PKH26-labeled rat RBCs (1 × 108 cells/well) were added and cocultured with the macrophages at 37 °C for 2 h. Non-phagocytosed RBCs were lysed by adding 1 mL of ice-cold 0.2% hypotonic saline for 2 min followed by the addition of 1 mL of ice-cold 1.6% hypertonic saline to restore isotonicity [21]. Then the macrophages were detached from the surface with accutase and analyzed by flow cytometry.
RNA extraction and real-time quantitative PCR (RT-qPCR)
Total RNA was isolated from macrophages by using the Ultrapure RNA Kit (cwbio, Beijing, China) and a total of 500 ng isolated RNA for each sample were reversely transcribed to cDNA using PrimeScript™ RT reagent Kit (Perfect Real Time) (Takara, Beijing, China). The qPCR was performed by utilizing SYBR® Green Realtime PCR Master Mix (Toyobo Biotech Co., Ltd., Shanghai, China) and EasyPGX qPCR instrument 96 (Diatech Pharmacogenetics srl, Jesi, Ancona, Italy) under the following parameters: 95 ◦C for 2 min, 50 cycles at 95 ◦C for 10 s, 60 ◦C for 30 s, and 72 ◦C for 30 s. The mRNA levels of target genes were normalized to the levels of GAPDH and calculated by the 2−ΔΔCT method and three biological replicates were analyzed for each group. The qPCR primers were synthesized from Sangon Biotech Co., Ltd. (Shanghai, China).
Immunohistochemistry
For immunohistochemical procedures, spleen sections (n = 3 per group) were air-dried, fixed, heated with citrate buffer (10 mM, pH 6.0) for antigen retrieval, and blocked with 5% normal goat serum (Yeasen, Shanghai, China). Sections were then incubated with anti-p-NFκB (AF2006; 1:100; Affinity, Changzhou, China), anti-IL6 (DF6087; 1:100; Affinity, Changzhou, China), anti-iNOS (AF0199; 1:100; Affinity, Changzhou, China), or anti-CXCL10 (DF6417; 1:100; Affinity, Changzhou, China), overnight at 4 ◦C followed by incubation with HRP conjugated secondary antibody (S0001; 1:200; Affinity, Changzhou, China) for 1 h at room temperature. Then, the target proteins in spleen sections were visualized using a DAB kit (ZSGB-Bio, Beijing, China) and the nuclei were counterstained with hematoxylin (Leagene, Beijing, China). After sealing, the slides were scanned by using a Pannoramic MIDI (3DHISTECH, Hungary).
Immunoassays for IL-6 and CXCL-10
Plasma levels of IL-6 and CXCL-10 plasma levels of patients with long-term HD were determined by ELISAs according to the manufacturer’s suggestions (Elabscience).
Statistical analysis
Statistical analysis was performed using SPSS software (version 25.0; IBM, NY, USA) and Graphpad prism (version 9, San Diego, California, USA) was used for graphing. Data with a normal distribution were expressed as mean ± standard deviation (SD) and data with a non-normal distribution were presented as the median. The data from the two groups were evaluated by a two-tailed unpaired Student’s t-test. P < 0.05 was considered statistically significant.