Changes in DNA Methylation and Gene Expression of Insulin and Obesity-Related Gene PIK3R1 after Roux-en-Y Gastric Bypass

Weight regulation and the magnitude of weight loss after a Roux-en-Y gastric bypass (RYGB) can be genetically determined. DNA methylation patterns and the expression of some genes can be altered after weight loss interventions, including RYGB. The present study aimed to evaluate how the gene expression and DNA methylation of PIK3R1, an obesity and insulin-related gene, change after RYGB. Blood samples were obtained from 13 women (35.9 ± 9.2 years) with severe obesity before and six months after surgical procedure. Whole blood transcriptome and epigenomic patterns were assessed by microarray-based, genome-wide technologies. A total of 1966 differentially expressed genes were identified in the pre- and postoperative periods of RYGB. From these, we observed that genes involved in obesity and insulin pathways were upregulated after surgery. Then, the PIK3R1 gene was selected for further RT-qPCR analysis and cytosine-guanine nucleotide (CpG) sites methylation evaluation. We observed that the PI3KR1 gene was upregulated, and six DNA methylation CpG sites were differently methylated after bariatric surgery. In conclusion, we found that RYGB upregulates genes involved in obesity and insulin pathways.


Introduction
The worldwide increase in the obesity epidemic is a consequence of an obesogenic environment characterized by an imbalance between energy intake and expenditure [1]. Such environmental factors have been linked to a rapid weight gain; however, genetic alterations may also contribute substantially to obesity [2,3]. Consequently, obesity can result from genetic and epigenetic factors that influence body weight maintenance [4], in addition to an unbalanced glucose and insulin homeostasis. Different metabolic pathways affect the development of this complex disease, namely energy expenditure regulation, adipocyte differentiation, adipose tissue accumulation, and pathways related to eating behavior [5]. Table 1. Anthropometric data and biochemical profile of obese patients in preoperative and postoperative (six months after Roux-en-Y gastric bypass (RYGB)).

Gene Expression
The comparison of gene expression profiles in obese patients before and after RYGB revealed that 1188 genes were upregulated (Supplementary File 1). Gene Ontology (GO) enrichment analysis showed that 13 genes were significantly involved with the onset of obesity and insulin-related pathways (p-value < 0.05). Then we selected genes with p-values ≤ 0.05 and fold change cutoff of ≥2 are presented in Table 2. The description of the pipeline for GO calculations can be accessed at http://geneontology.org/docs/go-enrichment-analysis/. Phosphoinositide-3-kinase, regulatory subunit 1 alpha 6.55 × 10 6 * p-value < 0.05.
PIK3R1 gene expression was significantly enriched in both lists (p < 0.05) from Table 2, because it was chosen to be validated and analyzed more deeply by DNA methylation. PIK3R1 gene expression was verified by quantitative RT-qPCR ( Figure 1).

95
We compared results for PIK3R1 gene expression, including and excluding the data from two 96 patients that had blood glucose values above the recommended. Since we did not find any 97 difference in such comparison, the patients were kept for further analyses.  We compared results for PIK3R1 gene expression, including and excluding the data from two patients that had blood glucose values above the recommended. Since we did not find any difference in such comparison, the patients were kept for further analyses.

CpG Methylation of PIK3R1
After quality control checks, we obtained 410.586 valid probes. Using iMETHYL [15], we found that 31 probes were mapped to the PIK3R1 gene. M values for all probes are shown in Figure 2. When comparing the data before and after RYGB, we found six differentially methylated CpG sites (cg19358016, cg23036683, cg00067720, cg05914150, cg01239651, and cg09101894). These probes were primarily hypomethylated in patients after surgery, and the majority of them were located in CpGs of the promoter region (TSS 200 and 1500) and 5' UTR of PIK3R1 gene.

Relationship between CpG Methylation and PI3KR1 Gene Expression
After stepwise linear regressions, three CpG sites were associated with RT-qPCR values (p < 0.005): cg22592475, cg26878542, and cg20474370. However, only cg20474370 strongly correlated with gene expression in the preoperative group (Pearson correlation, r = −0.709, p = 0.001) ( Figure 3). To build a multivariable linear regression model, we included the cycle threshold (CT) gene expression data values and the M values for DNA methylation. Moreover, the model was adjusted by the endogenous control expression data. The β values, multiple r 2 , and p-values are shown in Table 3. The β values represent the direction of the association and r 2 the percentage of explanation of the model.  diabetes development and maintenance [17]. Here, we show that PIK3R1 expression levels have 135 increased significantly after RYGB.

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PIK3 signaling is one of the main pathways by which leptin and insulin alter neuronal 137 excitability in the hypothalamus [18]. The deletion of PIK3 in POMC neurons suppresses food 138 intake [19]. Moreover, PIK3 signaling partially modulates the action of leptin and insulin on food 139 intake [20]. On the other hand, peripheral inflammation due to obesity is a key component for 140 developing type 2 diabetes in these patients. The excess of adipose tissue increases secretion of 141 inflammatory cytokines such as TNF-alpha and IL-6 that inhibit phosphorylation of insulin 142 receptors (IRS-1), promoting insulin resistance [21]. Therefore, JAK2 autophosphorylation leads to 143 the recruitment and phosphorylation of IRS-1, which then recruits PIK3 and activates downstream 144 signals [22]. For this reason, we hypothesized that reduced adipose tissue after RYGB improves 145 insulin resistance and activates PIK3. This might decrease weight and increase hyperglycemia 146 through activation of FOXO1 and GLUT4 ( Figure 4).

Discussion
We have longitudinally determined whether surgical weight loss alters the transcriptome of leucocytes in obese women. In this report, we show that genes involved in obesity and insulin pathways are upregulated after RYGB. Our results are in keeping with the findings from a pilot study showing that the expression levels of 200 transcripts linked to obesity and type 2 diabetes change significantly after bariatric surgery [16]. Genetic and environmental factors alter signaling and enzymatic networks that control glucose and lipid metabolism, resulting in obesity and diabetes development and maintenance [17]. Here, we show that PIK3R1 expression levels have increased significantly after RYGB.
PIK3 signaling is one of the main pathways by which leptin and insulin alter neuronal excitability in the hypothalamus [18]. The deletion of PIK3 in POMC neurons suppresses food intake [19]. Moreover, PIK3 signaling partially modulates the action of leptin and insulin on food intake [20]. On the other hand, peripheral inflammation due to obesity is a key component for developing type 2 diabetes in these patients. The excess of adipose tissue increases secretion of inflammatory cytokines such as TNF-alpha and IL-6 that inhibit phosphorylation of insulin receptors (IRS-1), promoting insulin resistance [21]. Therefore, JAK2 autophosphorylation leads to the recruitment and phosphorylation of IRS-1, which then recruits PIK3 and activates downstream signals [22]. For this reason, we hypothesized that reduced adipose tissue after RYGB improves insulin resistance and activates PIK3. This might decrease weight and increase hyperglycemia through activation of FOXO1 and GLUT4 (Figure 4).
We selected the PIK3R1 gene for an in-depth analysis since it is critical for obesity, insulin resistance, and may also be affected by DNA methylation [23]. Some authors suggest that activation of PIK3 can affect the response of cells to energy balance modulation; however, this mechanism is still not fully understood [24]. The downregulation of several genes has already been reported to initiate an inflammatory response in the adipose tissue, decreasing insulin sensitivity [12] (p. 2). We hypothesized that DNA methylation would be a critical factor in explaining the increased gene expression in the postoperative period.
After bariatric surgery, several DNA methylation sites were differentially methylated, and the majority were hypomethylated. M-values close to 0 indicate that the site is half methylated, positive M-values indicate that the site is hypermethylated, and negative values indicate the opposite [25]. A strong negative correlation was observed between cg20474370 methylation and PIK3R1 expression data in the preoperative period. Some other weak and medium (<0.6) correlations were found, but those correlations changed after surgery. This result suggests that epigenetic triggers are different in obese patients after bariatric surgery.
The presence of significant metabolic pathway changes in the postoperative period may per se be an epigenetic stimulus to change the methylome. Zuo et al. (2012) suggested that the activation of PIK3 might influence downstream epigenetic control in cultured cancer cells [26]. This statement suggests that PIK3 may both be affected and promote epigenetic alterations. Using a multivariate linear regression model, we found that the methylation levels of cg20474370 could explain around 70% of PIK3R1 gene expression when corrected using endogenous controls in the preoperative period. Nevertheless, in the postoperative period, correlations were weaker, suggesting that epigenetic mechanisms that control PIK3R1 gene expression are altered after surgery. The punctual effects of DNA methylation are still unclear and should be further examined.

150
The phosphorylation of IRS-1 triggered by PIK3 allows glucose to enter the cell improving blood 151 glucose levels.

152
We selected the PIK3R1 gene for an in-depth analysis since it is critical for obesity, insulin 153 resistance, and may also be affected by DNA methylation [23]. Some authors suggest that activation 154 of PIK3 can affect the response of cells to energy balance modulation; however, this mechanism is 155 still not fully understood [24]. The downregulation of several genes has already been reported to 156 initiate an inflammatory response in the adipose tissue, decreasing insulin sensitivity [12] (p. 2). We

175
We found a weak correlation between PIK3R1 gene expression and methylation. However, one 176 cannot disregard that DNA methylation has effects on PIK3R1 expression since we observed 177 differentially methylated probes located at the promoter and 5' UTR regions. These regions control We found a weak correlation between PIK3R1 gene expression and methylation. However, one cannot disregard that DNA methylation has effects on PIK3R1 expression since we observed differentially methylated probes located at the promoter and 5 UTR regions. These regions control gene expression and are part of the traditional DNA methylation paradox [27]. Indeed, studies have verified that PIK3 expression is influenced by DNA methylation [28]. It is known that methylation changes in CpGs present in the gene body can promote biological effects [27] (p. 6). Thus, experiments with higher resolution of CpG sites may increase the accuracy of our findings. Similarly, next-generation sequencing experiments may provide a better overview of the transcriptomic changes after RYGB.

Materials and Methods
We investigated 13 women with severe obesity (Body Mass Index, BMI > 40 kg/m 2 ) before and six months after RYGB. Patients who were not submitted to the standard surgical technique, patients who lost follow-up with the multidisciplinary team, pregnant women, and patients with thyroid disease, cancer, and psychiatric disorders were excluded. Anthropometric measurements (weight, height, and BMI) and blood samples were obtained before and after surgery. All subjects signed an informed consent term before participating in the study. The study was conducted following the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Clinical Hospital of Ribeirão Preto Medical School (Certificate of Presentation for Ethical Consideration-CAAE: 18973913.0.0000.5440, approval date: 14 June 2016).

RNA Isolation
Total RNA was extracted and purified from blood cells using the phenol-chloroform extraction method. The quality and concentration of RNA were determined with the Agilent 2100 Bioanalyzer and the Agilent RNA 6000 Nano Kit (Agilent, Amstelveen, Netherlands). To evaluate gene expression, samples with RNA integrity number (RIN) higher than 7.0 were used in the analysis.

Gene Expression Analysis
RNA amplification and labeling were performed with an Epicenter Target Amp Nano-g Biotin a-RNA kit (Labeling kit for Illumina ® ) that produced amplified biotinylated cRNAs with an input of 500 ng of total RNA. The hybridization protocol used 750 ng cRNA on HumanHT-12_v4 slides in specific buffer at 58 • C for 16 h, which was followed by washings and streptavidin-Cy3 staining. The slides were scanned with the Illumina Bead Array Reader (iSCAN ® ) on green channel only. The global gene expression analysis of these patients was previously reported by our group [14].
The first-line check and background correction were performed with the GenomeStudio Software (Illumina ® ). Samples with less than 6000 significantly detected probes (detection p-value < 0.01) were excluded from further analyses. Differentially expressed genes obtained between pre-and post-RYGB were identified after a cutoff for fold change >2 in gene expression and of a t-test p-value of less than 0.05. These p-values were corrected for multiple comparisons by using a false discovery rate (FDR) of 0.05 [11] (p. 2). The microarray data were deposited in the Gene Expression Omnibus database under access number GSE83223 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83223).

Validation Analysis
Microarray gene expression was confirmed through RT-qPCR using TaqMan ® Gene Expression Assays (Thermo Fisher, Foster City, CA) for three genes (PIK3R1, GAPDH and ACTINB). First, total RNA was reverse transcribed into cDNA using the High Capacity cDNA Reverse Transcription Kit (ThermoFisher). The TaqMan technology suitably quantifies relative gene expression [13] (p. 2). GAPDH and ACTINB were used as reference genes since their expression does not vary, as corroborated by the data obtained from the microarray analysis. Triplicates and positive and negative controls were included in all reactions. Table 4 presents the details of expression assays used. The final reaction was 20 uL, and each reaction was used as an input of 50 ng of total RNA.

DNA Extraction
The blood for DNA methylation analysis was collected after 12 hours of fasting. DNA was extracted from peripheral blood leukocytes and used to hybridize 450k BeadChip according to the manufacturer's instructions.

DNA Methylation Analysis
The methylation data were analyzed using the Infinium Human Methylation 450k BeadChip array following Illumina Methylation HD BeadChip protocol, as previously described by our group in the same set of patients [29]. The Beadchips were scanned with the Illumina IScanSQ system, and the image intensities were extracted with the Genome Studio 2.0. The methylation level was expressed as a beta (β) value-calculated as the intensity of the methylated channel divided by the total intensity β values range from 0 (unmethylated) to 1 (fully methylated). β values can be broadly interpreted as the percentage of CpG methylation.
Quality control and normalization of β values (BMIQ method) were performed using the Champ package for R. We filtered out probes with p-values above 0.01 and beadcount <3 in at least 5%, confounding starts (NoCG, SNPS, Multhit, and XY). For statistical analyses, β values were transformed in M values using the equation M value = log2 (β/1 − β), as described elsewhere [30]. Comparison of β values and M values methods for quantifying methylation levels by microarray analysis were performed. Further, we mapped the CpGs of interest located in genomic regions of the three targets validated by RT-qPCR using the integrative multi-omics database iMETHYL [12] (p. 2).

Statistical Analysis
Statistical analyses were performed using Statistical Package for the Social Sciences-SPSS v.20. The Shapiro-Wilk test was used to verify if clinical data were normally distributed, and values were compared using parametric or equivalent non-parametric tests. p-values <0.05 were considered as statistically significant. For RT-qPCR data, 2 −∆∆Ct was calculated based on Livak recommendations, geometric average for GAPDH, and ACTINB were used as endogenous control [14]. Eutrophic women were used as reference groups for the analyses. To investigate the relationship between gene expression and DNA methylation data, we used M values. Individual linear regressions were conducted to check associations before building multivariable linear regression models [31]. All CpG sites significantly associated with gene expression data (p-value < 0.05) were included in the final model. Glycemia was tested but did not change r 2 considerably; thus, endogenous control genes were used to adjust the final model. The linear regression models were conducted using the lm( ) function in R v.3.5.2 (RStudio, Inc., Boston, MA, USA).

Conclusions
The weight loss induced by bariatric surgery can alter the expression of genes involved in insulin metabolism and obesity pathways. Overexpression of genes related to these pathways may be associated with the improvement of glucose metabolism, and DNA methylation impacts it partially.

Conflicts of Interest:
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.