MicroRNA-146a (miR-146a) acts as a pivotal regulatory molecule in immune response and various diseases, such as carcinoma and autoimmune diseases. Growing evidences have demonstrated the association of miR-146a gene single-nucleotide polymorphisms (SNPs) with risk of several diseases, but no genetic relevance studies of miR-146a gene polymorphisms to sepsis have been reported by now. Our study has analyzed the association of sepsis with two functional miR-146a gene SNPs rs2910164 G/C and rs57095329 A/G in a Chinese Han population (226 sepsis cases; 206 healthy controls). Our results indicated a higher prevalence of the miR-146a gene SNP rs2910164 C allele and CC genotype in patients with severe sepsis (rs2910164G versus rs2910164C:
Sepsis is a systemic disease characterized by microbial infection and systemic inflammatory response syndrome (SIRS), which has high morbidity and mortality rates in Intensive Care Units (ICUs) [
MicroRNAs (miRNAs) are a type of small, noncoding, single-stranded RNAs that can posttranscriptionally downregulate gene expression by binding to the 3′ untranslated region (3′UTR) of target mRNAs [
Currently, many studies have shown that two functional single nucleotide polymorphisms (SNPs), rs2910164 G/C and rs57095329 A/G, in the miR-146a gene may influence the expression level of mature miR-146a and can result in genetic predisposition to diseases [
In the present study, a case-control study was carried out to determine whether there is an association between the two polymorphisms within the miR-146a gene and sepsis in a Chinese population. Moreover, the expression levels of IRAK1, TRAF6, and miR-146a were also determined in the severe sepsis and healthy subjects to analyze whether these polymorphisms are associated with the expression levels of these cytokines.
A total of 226 septic patients of a Chinese population were recruited from the ICU department of the Affiliated Hospital of Guangdong Medical College between April 2011 and June 2013. Their blood samples were collected upon the diagnosis of sepsis, severe sepsis, or septic shock, which was established according to the International Sepsis Definitions Conference [
Genomic DNA was extracted from whole blood samples from all of the patients and controls using the TIANamp Blood DNA Kit (Tiangen Biotech, Beijing, China) according to the manufacturer’s instructions and was stored at −80°C before genotyping. A total of 432 individuals were genotyped for the two SNPs (rs2910164 G/C and rs57095329 A/G) using the SNaPshot technique (Applied Biosystems, Foster City, CA, USA). The PCR primers used for the polymorphic site rs2910164 were 5-GAACTGAATTCCATGGGTTG-3 and 5-CACGATGACAGAGATATCCC-3, and the primers used for rs57095329 were 5-TCATTGGGCAGCCGATAAAG-3 and 5-AGGAAGTTCTGGTCAGGCG-3. Genotyping was conducted by polymerase chain reaction (PCR). The 10-
In total, 37 cases from 127 patients with severe sepsis and 40 healthy controls form 206 healthy subjects that were chosen at random for the isolation of mononuclear cells. The peripheral blood mononuclear cells (PBMCs) using density gradient centrifugation method with LymphoprepTM (Axis-Shield PoCAS, Oslo, Norway) were isolated as soon as possible when the blood samples were collected. In brief, blood samples were mixed with equal volume of 0.9% NaCl, and then the diluted blood was slowly added to tubes containing a Ficoll premium solution to make the blood layered upon the Ficoll. Samples were centrifuged at 800 ×g for 30 min at room temperature. After centrifugation, the mononuclear cells form a distinct band at the medium interface. The cells were then shifted to other tubes using Pasteur pipette without removing the upper layer and washed with 0.9% NaCl. Then, samples were centrifuged again at 250 ×g for 10 min. The mononuclear cells were harvested and stored at −80°C.
RNA was extracted from PBMCs of the 37 cases and 40 controls immediately after the isolation of mononuclear cells using the UNIQ-10 Column TRizol Total RNA Extraction Kit (Sangon Biotech, Shanghai, China) as per the manufacturer’s instructions. The integrity of the RNA was checked using 1% agarose gel electrophoresis. The RNA was reverse transcribed using the First Strand cDNA Synthesis Kit (Thermo) as per the manufacturer’s instructions. The expression levels of IRAK-1 and TRAF-6 were analyzed by quantitative real-time PCR with the SYBR green method. The IRAK-1 and TRAF-6 expression levels were analyzed in triplicate, and expression was normalized to the level of glyceraldehyde 3-phosphate dehydrogenase (GAPDH), which was used as an internal control. IRAK-1, TRAF-6, and GAPDH specific primers were diluted to a final concentration of 1
miRNA was also extracted from PBMCs of the same 37 cases and 40 controls using the miRcute miRNA Isolation Kit (Tiangen Biotech, Beijing, China) according to the manufacturer’s instructions. miR-146a and U6 were reverse transcribed using the following protocol immediately. The 20
Statistical analyses were conducted using SPSS version 19.0 (IBM, NY, USA) and GraphPad Prism 4.0 (GraphPad Software Inc., San Diego, CA, USA). Genotype and allele frequencies between the cases and controls were calculated using Chi-squared test or Fisher’s exact test. Deviation of the genotype or allele frequency was assessed using Hardy-Weinberg equilibrium (HWE). Power analysis was performed using the software QUANTO 1.2 (
The clinical parameters of 226 patients with sepsis and 206 healthy controls were shown in Table
Clinical characteristics of sepsis cases and healthy controls.
Characteristics | Cases |
Controls ( |
|
---|---|---|---|
|
|
||
Age (years) |
|
|
0.174 |
Male/female, |
153/73 | 140/66 | 0.954 |
Organ dysfunction | |||
One, |
37 (16.4) | N.A | |
Two, |
72 (31.9) | N.A | |
Three or above, |
83 (36.7) | N.A | |
Sepsis status | |||
Sepsis, |
34 (15.0) | N.A | |
Septic shock, |
65 (28.8) | N.A | |
Severe sepsis, |
127 (56.2) | N.A | |
Source of infection, |
|||
Respiratory tract infection | 181 (80.1) | N.A | |
Primary bloodstream infection | 90 (39.8) | N.A | |
Wound infection | 23 (10.2) | N.A | |
Abdominal infection | 84 (37.2) | N.A | |
Urinary tract infection | 7 (3.1) | N.A | |
Catheter-associated infection | 31 (13.7) | N.A | |
Others | 22 (9.7) | N.A | |
Pathogens, |
|||
Gram-negative | 82 (36.3) | N.A | |
Gram-positive | 40 (17.7) | N.A | |
Mixed Gram-negative and -positive | 78 (34.5) | N.A | |
Fungus | 47 (20.8) | N.A | |
Negative blood cultures | 12 (5.3) | N.A | |
APACHE II score |
|
N.A | |
28-day mortality, |
97 (42.9) | N.A |
N.A: not applicable; APACHE II: Acute Physiology and Chronic Health Evaluation II.
In total, 226 sepsis patients and 206 healthy individuals were successfully analyzed for the rs2910164 SNP and 222 patients and 205 controls were genotyped for the rs57095329 SNP. The genotype and allele frequency distributions of the two SNPs in the cases and controls are listed in Table
Genotype and allele frequencies distribution in patients with sepsis and controls.
Genotype | All sepsis cases, |
Controls, |
|
|
OR (95% CI) |
---|---|---|---|---|---|
rs2910164 | |||||
Total |
|
|
|
|
|
CC | 88 (38.94) | 101 (49.03) | |||
GC | 114 (50.44) | 93 (45.15) | |||
GG | 24 (10.62) | 12 (5.82) | |||
GG/GC | 138 (61.06) | 105 (50.97) | 0.035 | 0.063 | 1.508 (1.029–2.211) |
GC/CC | 202 (89.38) | 194 (94.18) | 0.072 | 0.072 | 0.521 (0.253–1.070) |
Allele | |||||
C allele | 290 (64.16) | 295 (71.60) | 1.000 (reference) | ||
G allele | 162 (35.84) | 117 (28.40) | 0.019 | 0.063 | 1.408 (1.056–1.878) |
|
|||||
rs57095329 | |||||
Total |
|
|
|
|
|
AA | 144 (64.86) | 135 (65.85) | |||
GA | 69 (31.08) | 64 (31.22) | |||
GG | 9 (4.05) | 6 (2.93) | |||
GG/GA | 78 (35.14) | 70 (34.15) | 0.830 | 0.830 | 1.045 (0.701–1.557) |
GA/AA | 213 (95.95) | 199 (97.07) | 0.527 | 0.830 | 0.714 (0.249–2.042) |
Allele | |||||
A allele | 357 (80.41) | 334 (81.46) | 1.000 (reference) | ||
G allele | 87 (19.59) | 76 (18.54) | 0.694 | 0.830 | 1.071 (0.761–1.508) |
OR: odds ratio; 95% CI: 95% confidence interval; *false discovery rate-adjusted
All sepsis cases were divided into three subtypes (sepsis, septic shock, and severe sepsis) to investigate the association of the two SNPs with the sepsis subtypes. As presented in Table
Genotype and allele frequencies distribution in different sepsis status and healthy controls.
Genotype | Healthy control |
Sepsis (subtype) |
Septic shock |
Severe sepsis |
|
|
||||
---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|
|
| |||||
rs2910164 | ||||||||||
Total |
|
|
|
|
|
|
|
|
|
|
CC | 101 (49.03) | 15 (44.12) | 31 (47.69) | 42 (33.07) | ||||||
GG/GC | 105 (50.97) | 19 (55.88) | 34 (52.31) | 85 (66.93) | ||||||
Allele | ||||||||||
C | 295 (71.60) | 46 (67.65) | 91 (70.00) | 153 (60.24) | ||||||
G | 117 (28.40) | 22 (32.35) | 39 (30.00) | 101 (39.76) | 0.564 | 0.740 | 0.0029 | 0.712 | 0.888 | 0.0045 |
|
||||||||||
rs57095329 | ||||||||||
Total |
|
|
|
|
|
|
|
|
|
|
AA | 135 (65.85) | 20 (60.61) | 46 (74.19) | 78 (61.42) | ||||||
GG/GA | 70 (34.15) | 13 (39.39) | 16 (25.81) | 49 (38.58) | ||||||
Allele | ||||||||||
A | 334 (81.46) | 52 (78.79) | 105 (84.68) | 200 (78.74) | ||||||
G | 76 (18.54) | 14 (21.21) | 19 (15.32) | 54 (21.26) | 0.613 | 0.503 | 0.421 | 0.613 | 0.503 | 0.421 |
OR: odds ratio; 95% CI: 95% confidence interval. *False discovery rate-adjusted
Genotype and allele frequencies distribution in surviving and nonsurviving patients.
Genotype | Survivors |
Nonsurvivors |
|
|
OR (95% CI) |
---|---|---|---|---|---|
rs2910164 | |||||
Total |
|
|
|
|
|
CC | 48 (37.21) | 40 (41.24) | |||
GC | 71 (55.04) | 43 (44.33) | |||
GG | 10 (7.75) | 14 (14.43) | |||
Allele | |||||
C | 167 (64.73) | 123 (63.40) | 1.000 (reference) | ||
G | 91 (35.27) | 71 (36.60) | 0.771 | 0.771 | 0.944 (0.640–1.392) |
|
|||||
rs57095329 | |||||
Total |
|
|
|
|
|
AA | 90 (72.00) | 54 (55.67) | |||
GA | 29 (23.20) | 40 (41.23) | |||
GG | 6 (4.80) | 3 (3.10) | |||
Allele | |||||
A | 209 (83.60) | 148 (76.29) | 1.000 (reference) | ||
G | 41 (16.40) | 46 (23.71) | 0.054 | 0.054 | 0.631 (0.394–1.011) |
OR: odds ratio; 95% CI: 95% confidence interval; *False discovery rate-adjusted
In total, 37 patients with severe sepsis and 40 healthy individuals were stochastically chosen to investigate the expression levels of miR-146a, TRAF-6, and IRAK-1 in PBMCs. Our results revealed that the relative expression levels of miR-146a in patients with severe sepsis were significantly lower than those in the healthy controls (
Expression levels of miR-146a (a), TRAF-6 (b), and IRAK-1 (c) in severe sepsis patients (
We further explored the influence of the two polymorphisms on the expression levels of miR-146a, TRAF-6, and IRAK-1. As shown in Figure
The distribution of miR-146a (a), TRAF-6 (b), and IRAK-1 (c) expression levels in groups of severe sepsis patients with different rs2910164 genotypes. The distribution of miR-146a (d), TRAF-6 (e), and IRAK-1 (f) expression levels between groups of severe sepsis with different rs57095329 genotypes. The horizontal line stands for the median expression level with each group.
To the best of our knowledge, the present study was the first to investigate the relevance of two miRNA-146a gene polymorphisms rs2910164 and rs57095329 to the risk of sepsis in a Chinese population. Our data demonstrated that patients carrying the rs2910164 C allele of miRNA-146a had suffered from a lower risk of severe sepsis. Severe sepsis patients with the CC genotype of rs2910164 expressed a lower level of miRNA-146a compared with the GG/GC genotypes. No significant association was observed between the rs57095329 SNP and the risk of sepsis. Moreover, we identified a statistically significant difference concerning the genotype frequency of rs57095329 polymorphism between the survivors and nonsurvivors of all sepsis patients, but not the allele frequency. It seemed that AA genotype acted as a protective factor in the survivors of sepsis. However, as most data related to the association between SNP rs57095329 and sepsis were negative and this association was only observed in genotype frequency, this association still needs to be confirmed in larger and multiple populations.
Our study demonstrated that severe sepsis patients expressed lower levels of miR-146a and higher levels of TRAF-6 and IRAK-1 than the healthy controls. This observation is consistent with the previous findings that the level of miR-146a in PBMCs was significantly lower in sepsis patients than in SIRS patients and normal controls [
Recently, two SNPs in miRNA-146a, rs2910164, and rs57095329 were reported that they could influence the expression level of mature miR-146a and thus affect an individual’s susceptibility to various diseases [
Considering the functional importance of the rs2910164 and rs57095329 SNPs, we further investigated the association of the two SNPs with the expression levels of miR-146a and key adapter molecules downstream of TRAF-6 and IRAK-1. Our data revealed that the GG/GC genotypes of rs2910164 showed a higher expression level of mature miR-146a, which was in accordance with the previous investigations reported in healthy individuals in the Chinese population [
Many studies have reported that miR-146a can negatively regulate the expression levels of TRAF-6 and IRAK-1 in different cell lines and diseases [
Our present study demonstrated a significant association of the rs2910164 SNP in the pre-miR-146a gene with the susceptibility of a patient to severe sepsis in a Chinese population. Moreover, the rs2910164 and rs57095329 SNPs could functionally affect the miR-146a expression levels and the miR-146a could negatively regulate the expression of IRAK1 and TRAF6 in severe sepsis patients. We proposed that the rs2910164 polymorphism of miR-146a could influence the susceptibility of an individual to severe sepsis and might be used as a potential biomarker for diagnosing severe sepsis in clinical practice.
The authors declare that they have no conflict of interests regarding the publication of this paper.
Yiming Shao and Jia Li contributed equally to this work.
Support for this work includes funding from the National Nature Science Foundation of China (31171219, 81271213, 81070878, 81271214, 81300929, and 81261120404), the Science and Technology Planning Project of Guangdong Province ([2011]106-5), the special competitive assignment fiscal funds of Zhanjiang City (2012C0302-46), and the PhD Startup Fund of Guangdong Medical College (B2012020).