Clinical characteristics and biochemical parameters of patients and controls.
A total of 36 patients and 52 controls participated in the study. Clinical characteristics and biochemical parameters determined at the time of sampling are presented in Table 2. The values between patients and normouricemic controls without gout were compared, in general the patients presented a higher percentage of obesity (35.3%), hyperuricemia (7.88 ± 0.34 mg/dl), high TG (244.45 ± 28.92 mg/dl), very low HDL (38.15 ± 1.69 mg/dl) and high creatinine above the reference values (1.21 ± 0.18). These values were significantly higher than in the controls as shown in Table 3.
An important part of this work was to determine the profile of the metabolic status of the volunteers at the time of the study and to record their clinical history. All the patients were Mexican and lived in Mexico City. Mexico has approximately 70% of its adult population between 30 and 60 years old who are overweight (32, 33). The clinical data analysis of the patients showed that the subjects did not have adequate treatment or follow-up of their comorbidities associated with gout, since regardless of the age or stage of the disease, acute gout was (15.55%) or asymptomatic (84.44 %) we found metabolic alterations in most of them (1, 12, 34). 67.56% of the patients presented hyperuricemia, 75% hypertriglyceridemia, 67.64% low HDLs, 19.44% high creatinine, a high percentage of obesity (35.3%) and overweight (41.2%) and 10.8% of them had kidney disease previously diagnosed. It is important to mention that only 18.91% had hyperglycemia and that none of them had a diagnosis of diabetes. Therefore, this cohort of patients with asymptomatic or acute gout had poor monitoring of their comorbidities, which puts them at risk of new episodes of gout and of developing cardiovascular and kidney diseases. The percentage of people with a family history of gout in our study was 45.7%.
Analysis of gene expression in PBMC and PMNL of patients
We first analized gene expression in PBMCs and in PMNLs of patients to know how is gene expression between cells groups, This is important to know if a group enriched in neutrophils could express more or less each gene compared to the other rich in monocytes. We compared their relative expression as shown in the figure 1. According results, IL-1β increased its expression significantly in PBMCs compared to PMNLs in patients, therefore the other genes were expressed in a similar way between different cell groups. Neutrophils are approximately 50% of the total leukocytes in peripheral blood and 10 times more abundant than monocytes, so that under inflammation conditions their number increases this proportion (35, 36). Our results showed that there were significant differences in IL-1β (p<0.01) expression between PBMCs and PMNLs from patients . These results could be explained if we consider that monocytes and macrophages are producers of IL-1β. They express IL-1β as a regulator and amplifier of the immune response to activate other cells such as neutrophils, B and T lymphocytes and natural killers (37, 38).
To date, it is not clear if there is a gene expression profile in the blood in the patient with gout that can help us to know if the organism responds or is altered by frequent episodes of gout, by the frequency of hyperuricemia or if this profile Gene expression can change and leave a mark as the disease progresses. We compare the expression of the 4 genes between both cell groups in the patient samples.This is to know which genes are expressed more than others in the same group of patient's leukocytes. In PBMCs ABCG2 were expressed more than the other genes, but were only significantly higher with respect to URAT1 gene (p <0.05), while IL-1β was significantly higher than SLC22A12 (p <0.05) as shown in figure 2. In PMNLs, it was observed that ABCG2 are expressed more than the other genes, but it was only significant with respect to ALPK1 (p <0.05) and IL-1β (p <0.01). These results show that ABCG2 is the gene that is most expressed in patients, with a recent acute attack and regardless of the time with the disease, however a larger study population would help us to have these groups better represented to analyze differences with respect to to the progression or control of the disease.
Finally gene expression was analyzed in order to know if transporter genes increase their expression in the blood due to frequent exposure to high concentrations of uric acid and due to the presence of dyslipidemia and metabolic syndrome. We analized gene expression in PMNLs and PBMCs of ABCG2, ALPK1, SLC22A12 and IL-1β to compare results between patients and controls as showed in figure 1. ABCG2 expression in PMNLs had a significantly higher expression (p <0.05) in patients (n=18) than in controls (n=18) with a P= 0.8875. In PBMCs ABCG2 had a significantly higher expression (p <0.05) in patients (n=15) than in controls (n=13) see figure 3. IL-1β expression in PMNLs had a significantly higher expression (p <0.05) in patients (n=13) than in controls (n=14).
The higher expression of IL-1β in PMNLs from patients, compared to controls (p <0.05), could indicate that neutrophils are activated and, therefore, are capable of inducing cytokine expression due to the effect of circulating sUA in patients with hyperuricemia or by MSU in an affected joint during an acute attack.
SLC22A12 is expressed mainly in the luminal membrane of the proximal tubule of the kidney, although it can also be expressed in other tissues such as the liver, brain or in adipocytes (21, 24). URAT1 the product of SLC22A12 is important for the reabsorption of uric acid and mutations and polymorphisms of gain of function have been associated with hyperuricemia and gout (39, 40). We did not find differences in the expression between PBMCs and PMNLs, as seen in Figure 2; This is probably due to the fact that it was the gene with the lowest expression in both PBMCs and PMNLs.The main function of URAT1 is more important in the kidney to reabsorb urate and not only eliminate it as ABCG2 does, there could also be more restrictions for its expression in blood as the interaction with regulatory proteins such as PDZK (PSD-95, Drosophila discs- large protein, Zonula occludens protein 1) 1 or hepatocyte nuclear factor (HNF1α / β) as mentioned in some studies (41-43).
Correlation Analysis of gene expression of patients
A correlation analysis of gene expression was performed with the biochemical parameters and the clinical history of the patients and of the total population (patients more controls). The results are shown in Table 4. In PBMCs from patients we found a positive correlation of the expression of ABCG2 with the creatinine levels of patients, this correlation was maintained when analyzed with the total study population. The mean levels of serum creatinine were found to be high in the patients as seen in Table 3; from the demographic data of the patients, we know that the history of kidney disease ranged from 10 to 16% as shown in Table 2. These data in PBMC suggest that the increase in the expression of ABCG2 in blood could be related to the presence of renal dysregulation.
Monocytes induce the expression of IL-1β releasing it to amplify the response and allow the recruitment of neutrophils, which also express the cytokine when activated (44). The expression of IL-1β significantly correlated in PMNLs with the history of hypertension of the patients as shown in Table 4. Hypertension has been associated with chronic inflammatory diseases, aging, diabetes and gout (45, 46). IL-1β is important in the inflammatory process that leads to endothelial dysfunction of blood vessels to develop hypertension. Comorbidities associated with gout such as obesity and hyperuricemia also favor endothelial dysfunction and dysregulation of the renin angiotensin system. Therefore, the increase in the expression of this cytokine could be a biomarker of damage and inflammation in patients with gout and its increase together with ABCG2 in the blood could be associated with a pre-symptomatic state.
In PBMCs from patients, we found significant positive correlations between SLC22A12 expression with metabolic syndrome, sUA and triglyceride levels (these correlations were not found in controls, data not shown). These correlations may suggest that despite the low expression of the gene, the gene could be activated by metabolic alterations in the patient. An obesity / metabolic syndrome model in mice on a high-fat diet found increased SLC22A12, GLUT9, and ABCG2 expression in the kidney after 8 weeks but not in other transporters (47), While in another study the expression of SLC22A12 and GLUT9 was measured in mice after a diet rich in fructose, inducing metabolic syndrome and hyperuricemia in the animals, they reported an increase in the expression of these two genes (36). There are also different reports that link hyperuricemia with increased expression of URAT1 in the kidney, blood and salivary gland (48, 49). In PMNLs, as seen in Table 4, the expression of SLC22A12 correlated positively with hypertension only in patients. A possible relationship in humans of URAT1 with metabolic syndrome and hypertriglyceridemia has been little studied. In humans, associations of metabolic syndrome and obesity with variants of SLC22A12 (rs11602903 and rs11231825) that predispose to gout have been reported (50, 51). Finally, in PMNLs we also found a correlation of the expression of SLC22A12 with the presence of kidney disease in patients and this was maintained in the total population, as seen in Table 4.
In PBMCs from patients we found correlations of ALPK1 expression with creatinine and uric acid levels. In human monocytes, induction of ALPK1 in response to MSU has been reported in monocytes and macrophages (52), its could indicate that leukocytes express ALPK1 in response to high uric acid concentrations or subsequent local activation of monocytes and macrophages during an acute attack. ALPK1 is known to be associated with kidney disease through association studies and its expression in animal models can regulate the release of the chemokines CCL2 and CCL5 in kidney cells (53). However, we did not find a correlation with a history of kidney disease, so the expression of ALPK1 in PBMCs may be increased by inflammatory signals in the blood, kidney or intestine due to frequent hyperuricemia in patients even in an asymptomatic state.
In PBMCs of the total population we found the correlation of the expression of ALPK1 with the BMI. In our patients the expression of the gene was not different from the controls that have a lower percentage of obesity and overweight, however the controls do have a positive correlation of BMI with the expression of ALPK1 (p <0.05 n = 22). This could be due to the larger differences in BMI between controls than between patients, since 56.6% were in normal weight as opposed to 23.5% of patients. There are few studies on the relationship of ALPK1 with obesity. A study in transgenic murines reported that testosterone could negatively regulate ALPK1 by decreasing the production of IL1-β and TNF-α. Kuo et al., Proposed ALPK1 as a negative regulator of SLC22A12 in kidney, they measured in ALPK1-deficient transgenic mice the expression of SLC22A12 in kidney, which was higher than in wild types, while testosterone levels decreased in blood and testicles (16). In our work we did not find a negative or positive correlation between ALPK1 and SLC22A12 in the blood, however it does not mean that it is not found in the kidney or intestine.
In PMNLs of patients we found a correlation of ALPK1 with CRP and although there are no significant differences in CRP with controls, the average CRP in patients was high (5.98 ± 4.6 mg / L), however, this increase could indicate a pro-inflammatory state in some patients which together with the presence of hyperuricemia favors the recruitment of neutrophils and a new attack of gout. Correlation of ALPK1 gene expression with sUA could support this idea, while the correlation with serum creatinine levels could support the presence of pro-inflammatory factors (54). The increased expression of ALPK1 and SLC22A12 in the blood of patients with gout could be associated with their altered metabolic status, which favors a long-term chronic condition with recurrent acute episodes if hyperuricemia and comorbidities are maintained (55). A genomic study analyzed the epistasis of ALPK1 with the loci of GLUT9, ALPK1, SLC22A12 and ABCG2, finding a positive prediction value (PPV≥81%) to measure the risk of gout (OR≥12.30) using variants of these genes in different populations. with gout, so this gene seems to continue to be linked to gout, although its importance in its progression is not evident (56).