| Background and objective: Obesity is a major health challenge and one of the major public health problems in the world.It has greatly increased the risk of type 2diabetes mellitus(T2DM),cardiovascular disease,non-alcoholic fatty liver disease(NAFLD),stroke,polycystic ovarian syndrome(PCOS),obstructive sleep apnea syndrome(OSAS)and even various types of cancer.This situation seriously affects the quality of life and life expectancy of patients,increasing the social and economic burden of the country,is an urgent problem to be solved.DNA methylation is one of the earliest gene epigenetic modifications,which plays an important role in maintaining the normal function of cells,the development of embryos,genetic imprinting and the occurrence and development of tumors.In obese patients,DNA methylation is also strongly correlated with the occurrence and development of obesity and related metabolic diseases,but the specific mechanism is still not well understood.Therefore,the purpose of this study was to screen out genes related to DNA methylation in obese patients by Me DIP-Seq and RNA-Seq sequencing techniques.It provides a theoretical basis for further exploration of obesity-related candidate genes and further mechanism research.Methods: Obese patients who were scheduled to undergo bariatric surgery in the department of general surgery,the Second Affiliated Hospital of Anhui Medical University from August 2020 to December 2022 were prospectively enrolled.Healthy subjects with body mass index(BMI)between 18.5-23.9 kg/m2,who did not receive bariatric surgery or weight-loss drugs,gender and age matched with obese patients,were selected as controls.The basic clinical data such as gender,age,height,weight,BMI and peripheral blood of the two groups were collected.Firstly,whole-genome DNA methylation sequencing and transcriptome sequencing were performed on peripheral blood using Me DIP-Seq and RNA-seq technologies,respectively.Then,quality control was conducted on the original data obtained from sequencing using Fast QC、Multi QC v1.9 and Trim＿Galore software.For Me DIP-Seq sequencing data,Bowtie 2 version 2.3.4.1 software was used to align the quality control data to the genome,and then SAMtools v1.7 software was used to compare and sort and deduplicate the data.We then used the R language MEDIPS package to perform methylation quantification every 300 bp sliding window on the deduplicated data,and finally obtained the Medip Seq methylation profile.While for RNA-seq data,the STAR software was used to align the quality control data to the genome and annotation files in the GENCODE(release 35)database,and then the RSEM software was used to assemble,merge and quantify the transcripts,and finally obtain the transcriptome expression profile.The parameter |log2FC|≥1 and P≤0.01 was used to screen the differentially methylated genes and differentially expressed genes in the methylation profile and transcriptome.The Cluster Profiler package was used to perform GO functional annotation and KEGG enrichment analysis of the screened differential genes,and the functional genes(including differentially methylated genes and differentially expressed genes)and regulatory pathways related to obesity were screened out.The molecular mechanism of DNA methylation regulating the pathogenesis of obesity was initially elaborated.At the same time,the results of Me DIP-Seq and RNA-Seq were combined for DNA methylation and m RNA transcriptome analysis,and the genes with differences both in DNA methylation and m RNA transcription levels were screened out for preliminary exploration of candidate genes related to obesity.Results: A total of 15 obese patients and 15 healthy controls were included in this study.The average height,weight,BMI and age of the obese group were 167.87±7.71 cm,112.99±21.48 kg,39.83±5.13 kg/m2,32.07±5.28 years,7 males and 8 females,respectively.While the average height,weight,BMI and age of the healthy control group was 167.40±8.39 cm,60.87±8.60 kg,21.63±1.54 kg/m2,32.33±5.05 years,7males and 8 females,respectively.There were statistical differences in body weight and BMI between the two groups(all P<0.05),while there were no statistical differences in height,gender and age(all P>0.05).Next,DNA methylation and transcriptome sequencing were performed by Me DIP-Seq and RNA-Seq,respectively.The results of PCA analysis showed that the samples between the groups were scattered,while the samples within the group were relatively clustered,indicating that the sequencing data basically met the requirements.Volcano diagram results showed that a total of 493 differentially expressed genes(DEGs)were screened in this study,including 171up-regulated genes and 322 down-regulated genes,and 2252 methylation differential intervals,including 1468 methylation levels up-regulated intervals and 784 methylation levels down-regulated intervals,and then the differential methylation intervals were annotated to genes.A total of 2041 genes were obtained,including 1348 up-regulated genes and 693 down-regulated genes.Finally,only genes with significant differences in promoter regions were retained,and 198 differentially methylated genes were obtained,including 144 up-regulated genes and 54 down-regulated genes.Further GO functional annotation and KEGG enrichment analysis showed that 512 DEGs were enriched in 367 biological processes,525 DEGs were enriched in 41 cellular components,512 DEGs were enriched in 63 molecular functions,and 249 DEGs were enriched in 30 KEGG signaling pathways.Similarly,1468 differentially methylated genes were enriched for715 biological processes,1522 genes for 67 cellular components,1465 genes for 10 molecular functions and 651 genes for 5 KEGG signaling pathways,respectively.For genes with differential methylation interval in the promoter region,156 genes were enriched in 15 cellular components,158 genes were enriched in 1 molecular function,and 65 genes were enriched in 3 KEGG signaling pathways.PPI protein interaction analysis showed that all DEGs clustered into 11 subnetworks.The combined analysis of Me DIP-Seq and RNA-Seq results showed that a total of two differentially expressed genes were screened.One was DEFA1 with hypomethylation and high expression,which was related to immune response and involved in a variety of immune responses.Such as neutrophil mediated immunity,defense response to fungus,etc.It is also involved in NOD-like receptor signaling pathway.Another high methylated low expression NR4A1,it is mainly involved in PI3K-Akt signaling pathway,cell chemotaxis,and positive regulation of epithelial cell proliferation and other biological processes.The two differential genes are located in MCODE4 and MCODE5 modules respectively,which can be used as candidate genes related to obesity for subsequent mechanism research.Conclusion: In this study,the genome-wide DNA methylation and transcriptome profiles of obese patients were obtained by Me DIP-Seq and RNA-Seq sequencing analysis,and the key candidate genes that may cause obesity were screened out through the combined analysis of the two,which laid the laboratory and theoretical basis for further research on the regulatory mechanism of these candidate genes in the pathogenesis of obesity. |
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