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Back to Journal »International Journal of General Medicine» Volume 14
A Whole Exome Sequencing Study of a Type 2 Diabetes Family
Authors: Zhou Xu, Guo Wei, Yin Hua, Chen Jie, Ma Ling, Yang Qi, Zhao Ya, Li Shi, Liu Wei, Li Hua
Published on November 16, 2021, the 2021 volume: 14 pages 8217-8229
DOI https://doi.org/10.2147/IJGM.S335090
Single anonymous peer review
Editor who approved for publication: Dr. Scott Fraser
Xiaowei Zhou,1,* Weichang Guo,2,* Hejia Yin,1 Jie Chen,1 Liju Ma,3 Qiuping Yang,4 Yan Zhao,1 Shaoyou Li,5 Weijun Liu,1 Huifang Li1 1 Department of Diabetes, First Affiliated Hospital Kunming Medical University, Kunming, People’s Republic of China; 2 Department of Physical Education, Kunming Medical University, Kunming; 3 Department of Laboratory Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, People’s Republic of China; 4 Department of Geriatrics, First Affiliated Hospital of Kunming Medical University, Kunming, People’s Republic of China Republic; 5Department of NHC Key Laboratory of Drug Addiction Medicine, First Affiliated Hospital of Kunming Medical University, Kunming, People's Republic of China No. 295, Xichang Road, Wuhua District, 650032 Email [email protected] Background: Type 2 diabetes (T2DM) is characterized by decreased beta cells in the pancreas and insulin resistance. The purpose of this study is to use whole exome sequencing to study the possible pathogenic gene mutation sites in patients with T2DM. Materials and methods: We recruited a Chinese family with a history of 3 generations of diabetes. The whole blood genomic DNA of seven members of the family was extracted and sent for whole exome sequencing. The biological information is analyzed using computer prediction methods, including the significance analysis of single nucleotide polymorphisms (SNP)/Indel sites, and the analysis of specific SNP/Indel proteins and their underlying mechanisms. Results: Six out of seven family members were diagnosed with diabetes. All DNA samples (23 kb) meet the quality requirements for library construction. The clean reads of each sample show high Q20 and Q30 (> 80%), indicating that the sequencing quality of the sequencing data is good. A total of 130,693 SNPs and 15,928 indels were found in the DNA samples. A total of 22 significant SNP and Indel mutation sites on 19 genes were obtained, including ZCCHC3, SYN2, RPL14, SRRD, AMD1, CAMKK2, ZNF787, RNF157, NPIPB15, ALG3, KIAA0040, MAST2, ESRRA, C8DACIPHRP18, MACC1 CAPN9 and DMKN. PNLIPRP1 gene rs2305205mutation and CAMKK2 gene rs778701848 mutation may be related to the pathogenesis of T2DM in this family. Conclusion: The exons of these diabetic patients showed mutations in PNLIPRP1 gene rs2305205and CAMKK2 gene rs778701848. These two mutations may promote the occurrence of T2DM by reducing the sensitivity of peripheral tissues to insulin and reducing insulin secretion. Keywords: Type 2 diabetes, gene mutation, whole exome sequencing, PNLIPRP1, CAMKK2
Type 2 diabetes mellitus (T2DM) is a polygenic genetic disease, mainly manifested as pancreatic β-cell reduction and insulin resistance, usually caused by a combination of genetic and environmental factors. The main clinical manifestations of T2DM are diseases related to glucose and lipid metabolism. In recent years, with the aging of the population and the improvement of living standards, the global incidence of diabetes has increased year by year. According to 2017 data from the International Diabetes Federation, there are approximately 451 million diabetic patients in the world, with the potential to reach 693 million by 2045. 1 In these diabetic patients, T2DM accounts for more than 90%. A previous study 2 reported that the occurrence of T2DM is related to genetic susceptibility and has a certain familial aggregation. 2 In Arabia, Tunisia, France, Sweden, Greece and China, more than 50% of diabetic patients show a positive family history 2-7 Family history, genetic factors and similar environmental factors are associated with an increased risk of T2DM. 8 In a similar living environment, the prevalence of siblings is 4.2 times that of their spouses, indicating that genetic factors play a key role in the development of family T2DM. 9 Due to the common incidence of familial diabetes in the clinic, screening the disease-related susceptibility genes of T2DM family members is of great significance for the prevention and treatment of T2DM.
Some previous studies that focused on genetic associations10,11 showed that single nucleotide polymorphisms (SNPs) are clearly related to T2DM susceptibility. There is a lot of information about the genetic structure of T2DM, such as the high level of polygenicity and small effect size of most genetic risk variants. 12 So far, at least 75 independent genetic loci related to T2DM have been discovered. 9 Some of these variants are associated with T2DM risk protection, and some variants are considered risk markers and are associated with T2DM susceptibility. 10 Therefore, it is important to clarify the risk and pathogenesis of T2DM through the discovery of genetic variation.
Whole-exome sequencing provides a useful strategy for identifying genes related to human diseases such as diabetes. 13 Whole exome sequencing In some studies, 14,15 indicate that the occurrence of T2DM in family members is related to 14,15 In recent years, whole exome sequencing has been widely used to identify susceptible gene variants in diabetic patients 12 or Insulin mutation 16, and other diseases caused by underlying genetic mutations in China. 17 The clinical morbidity of T2DM is very high, with obvious genetic predisposition. At present, there is no research on familial diabetes based on whole exome sequencing method in China. Therefore, whole-exome sequencing of T2DM family members and discovery of disease-related susceptibility genes are of great significance for the prevention and treatment of diabetes.
As a genetic heterogeneous disease, T2DM has a common feature that a few specific genes involve families of different races or the same race. Currently, there is no research on the application of whole-exome sequencing technology in identifying diabetic families in Yunnan Province, China. Therefore, the purpose of this study is to use the whole exome sequencing method of the Han nationality in Kunming, China, to discover possible pathogenic genes and mutation sites of pathogenic genes in T2DM patients. This research will provide promising clues for the prevention and treatment of familial diabetes.
We recruited Chinese patients (6 diabetic members and 1 non-diabetic member) with a family history of diabetes for three generations (Figure 1). Kindai is a male patient, the second generation with a 10-year history of diabetes. The oral glucose tolerance test (OGTT) confirmed the diagnosis of diabetes. According to the characteristics of medical history and laboratory examinations, all patients except for the ex-wife were considered for T2DM. There is no information that this family has close relatives married. Figure 1 Family pedigree with type 2 diabetes. Subjects 1, 2, 3, 4, 5, 6, and 7 represent diabetic patients 1, 2, 3, 4, 5, non-diabetic patients 6 and diabetic patients 7, respectively. I G1, II G2 and III G3 represent the first, second and third generations, respectively.
Figure 1 Family pedigree with type 2 diabetes. Subjects 1, 2, 3, 4, 5, 6, and 7 represent diabetic patients 1, 2, 3, 4, 5, non-diabetic patients 6 and diabetic patients 7, respectively. I G1, II G2 and III G3 represent the first, second and third generations, respectively.
Diabetes according to the diabetes diagnostic criteria published by WHO in 1999, including: fasting blood glucose (FPG) ≥ 7 mmol/L, 2-hour postprandial blood glucose (2hPG) and OGTT 2hPG> 11.1 mmol/L. The diagnostic criteria of T2DM are based on the following items: ① Polyuria, polydipsia, and no typical symptoms of weight loss. ②Oral hypoglycemic drugs are effective in controlling blood sugar, and there is no history of spontaneous diabetic ketoacidosis. ③Islet cell antibody (ICA), insulin antibody (IAA) and glutamate decarboxylase (GAD) antibody were all negative. ④The fasting serum C-peptide level is within the lower limit of the normal reference range. ⑤ Clinical manifestations of insulin resistance (acanthosis nigricans, hypertension, dyslipidemia). Coupled with the age of the patients (all over 40 years old), all support the diagnosis of T2DM. In addition, type 1 diabetes (T1DM) is excluded, with autoantibodies positive, severe deficiency, and no other symptoms suggestive of diabetes.
This study has been approved by the First Affiliated Hospital of Kunming Medical University (approval number: 2020-L-18). This research was conducted in accordance with the Declaration of Helsinki. All family members provided written consent and approved the study.
A total of 2 mL of peripheral blood was collected from each of the seven family members. Following the manufacturer’s instructions, DNA samples were extracted from the peripheral blood of these seven family members using the Whole Blood Genomic DNA Rapid Extraction Kit (Biomed Corporation, China). Then, DNA samples of seven members of the family were sent to BGI-Shenzhen (Shenzhen, China) for whole-exome sequencing.
Agilent SureSelect Kit V6 is used for exome capture. The sequencing data was generated on the BGISEQ-500 platform using a paired-end 100bp sequencing strategy.
The probability of exon DNA mutation at any point in a family member can be calculated according to the following binomial distribution probability formula: Pv = pm(1-p)nm. Among them, "p" is the minor allele mutation frequency (MAF) in the normal population (0<p<1), "n" is the total sample, "m" is the number of samples where mutations are observed, and "Pv" is The probability of observing "m" mutations in "n" samples. In this study, according to calculations, n=7 and m=6. Use the corresponding MAF in the Thousand Genome Database to analyze the mutation frequency of each locus in the normal population. 18 Some single nucleotide polymorphisms (SNP) and Indel have no mutation frequency information in the Thousand Genome Database. At this time, use SNP and Indel's rs to query the dbSNP database, and get the maximum MAF value of the site 19. The 1000 human genome database and the dbSNP database have no new SNP and Indel site corresponding MAF values. A relatively small MAF value was set for the Manhattan map and subsequent analysis. In this study, the minimum MAF value is set to 1e-4.
The Manhattan diagram is a universal method for displaying and analyzing the importance of each mutation in the genome. In order to obtain the significant SNP and Indel sites of the diabetic members gathered in this family, the Manhattan map was used to display the information of the SNP and Indel sites. The specific process is described as follows: ①Using the binomial distribution probability formula to calculate the mutation probability Pv of each SNP and Indel site. ②Manhattan function 22 in qqman is a software package used to analyze SNP in R language, which is used to display the information of diabetic exon SNP and Indel in this family. ③The cutoff value of rs in the Manhattan map is set to Pv=1e-9. ④There are many significant SNP and Indel sites. In order to obtain a significant and relatively clear biological significance of SNP and Indel sites, the Manhattan map is divided into two categories, including all SNP/Indel sites and SNP/Indel sites that only display protein function changes.
In order to determine whether a specific SNP/Indel is directly related to the occurrence of diabetes, first map the SNP/Indel to the structure of the corresponding protein. Use PDB sum to predict the effect of SNP/Indel on protein function. 23 Then locate the protein with SNP/Indel mutation in the KEGG pathway to analyze whether the pathway is related to the pathogenesis of diabetes. 20
All statistical analysis of bioinformatics data is performed using R software (version: 2.15). Analyze the bioinformatics data according to the SNP/Indel site and Manhattan plot. Count and analyze the average number of original bases, clean bases and average GC content. SNP and Indel are also calculated. A Q20 or Q30 value exceeding 80% is designated as good sequencing quality.
The quality of 7 DNA samples was evaluated. Use Qubit to detect the concentration of the DNA sample, and then calculate the total amount of DNA. If the total amount of DNA exceeds 1 μg, the standard is met. Then the DNA integrity was measured by 1% agarose gel electrophoresis. It can be seen from the electrophoresis that the main bands of DNA samples are all above 23kb (Figure 2). Although some samples are slightly degraded, they meet the quality requirements of library construction. Figure 2 Agarose gel electrophoresis of 7 DNA samples from family members. M1 is the λ-Hind III digestion DNA ladder. M2 is D2000 DNA Marker, and 1-7 are DNA samples.
Figure 2 Agarose gel electrophoresis of 7 DNA samples from family members. M1 is the λ-Hind III digestion DNA ladder. M2 is D2000 DNA Marker, and 1-7 are DNA samples.
All exons were sequenced on 7 DNA samples, and the average number of original bases was 16065.63Mb, and the number of clean bases was 16024.34Mb. The clean readings of each sample showed high Q20 and Q30 (>80%), indicating that the sequencing data has good sequencing quality. The average GC content is 50.68%. The average sequencing depth of the target area is about 137.37X (Table 1). Table 1 Statistics of exome sequencing data of 7 family members
Table 1 Statistics of exome sequencing data of 7 family members
A total of 130,693 SNPs were found in all samples. In the coding region, there are 10,367 synonymous mutations, 10,368 missense mutations, 115 stop gain, 38 stop loss, 30 start loss, and 147 splicing.
A total of 15,928 Indels were found in all samples. In the coding area, there are 278 shifts, 91 non-shift insertion, 144 non-shift deletion, 0 stop loss, 3 start loss, and 58 splicing.
The whole exome sequencing results showed that there were 8383 exon DNA mutations in 6 diabetic family members, and no mutation was found in 1 non-diabetic control member. Among them, there are 7603 SNP mutation sites and 780 indel mutation sites.
After calculating the significance of each SNP/Indel locus, a Manhattan plot was used to show the mutation sites. By setting a specific cut-off value, important SNP/Indel sites can be found. In Figure 3A, there are 59 highly significant SNP sites (P=1e-9), of which 24 are SNP sites with moderate and high biological functions. After removing the SNP sites with no coding function in the intergenic region, 10 SNP sites with significant and high biological functions were finally found (Figure 3B). Using the same method, 23 highly significant (P=1e-9) Indel mutation sites were found (Figure 4A), among which 12 Indel mutation sites with significant and moderately high biological functions were found (Figure 4B). Figure 3 Manhattan plot of all SNP sites (A) and Manhattan plot of significant SNP mutation sites, marking the SNP sites with high significance (P<1e-9) (B). Note: The X axis is the position of the chromosome where the SNP is located. The Y-axis is the -log10(P) value corresponding to each SNP site. -log10(P) reflects the degree of association between the mutation site and the occurrence of the disease. The larger the value of -log10(P), the stronger the correlation. The cut-off value of the blue horizontal line is P=1e-5, and the cut-off value of the red horizontal line is P=1e-8. Figure 4 Manhattan map of all Indel mutation sites (A) and Manhattan map of Indel mutation sites that can change protein function, marking the highly significant Indel sites (P<1e-9) (B). Note: The X axis is the position of the chromosome where Indel is located. The Y-axis is the -log10(P) value corresponding to each Indel mutation site; -log10(P) reflects the degree of association between the mutation site and the occurrence of the disease. The larger the value of -log10(P), the stronger the correlation. The cut-off value of the blue horizontal line is P=1e-5, and the cut-off value of the red horizontal line is P=1e-8.
Figure 3 Manhattan plots of all SNP sites (A) and Manhattan plots of significant SNP mutation sites, marking the highly significant SNP sites (P<1e-9) (B).
Note: The X axis is the position of the chromosome where the SNP is located. The Y-axis is the -log10(P) value corresponding to each SNP site. -log10(P) reflects the degree of association between the mutation site and the occurrence of the disease. The larger the value of -log10(P), the stronger the correlation. The cut-off value of the blue horizontal line is P=1e-5, and the cut-off value of the red horizontal line is P=1e-8.
Figure 4 Manhattan map of all Indel mutation sites (A) and Manhattan map of Indel mutation sites that can change protein function, marking the highly significant Indel sites (P<1e-9) (B).
Note: The X axis is the position of the chromosome where Indel is located. The Y-axis is the -log10(P) value corresponding to each Indel mutation site; -log10(P) reflects the degree of association between the mutation site and the occurrence of the disease. The larger the value of -log10(P), the stronger the correlation. The cut-off value of the blue horizontal line is P=1e-5, and the cut-off value of the red horizontal line is P=1e-8.
In summary, 22 significant SNP mutation sites and Indel mutation sites in this family were obtained (Table 2). These gene mutation sites are located on 19 genes, including zinc finger CCHC domain-containing protein 3 (ZCCHC3), synaptic protein 2 (SYN2), ribosomal protein L14 (RPL14), selenite [Se(IV)] Reductase D (SRRD), adenosylmethionine decarboxylase 1 (AMD1), calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2), zinc finger protein 787 (ZNF787), ring finger protein 157 (RNF157), nuclear pore Complex interacting protein family B15 (NPIPB15), asparagine-associated glycosylation 3 (ALG3), KIAA0040, microtubule-associated serine/threonine kinase 2 (MAST2), estrogen-associated receptor alpha (ESRRA), chromosome 8 Open reading frame 58 (C8orf58), pancrelipase-related protein 1 (PNLIPRP1), dachshund homolog 1 (DACH1), metastasis-associated colon cancer 1 (MACC1), calpain 9 (CAPN9) and skin factor (DMKN) . Table 2 List of important mutation sites in the pedigree
Table 2 List of important mutation sites in the pedigree
In order to analyze how the aforementioned significant SNP and Indel mutation sites affect the occurrence of diabetes, we mapped the genes corresponding to 22 mutation sites in the KEGG pathway. Finally, the PNLIPRP1 gene and CAMKK2 gene were mapped to the KEGG diabetes-related metabolic pathway.
The PNLIPRP1 gene is located on chromosome 10 and contains 13 exons. The rs2305205 mutation site in the 8th exon caused 271 alanines (Ala) of the PNLIPRP1 protein to be replaced by valine (Val) (Figure 5). The SNP site is located in the N-terminal domain of PNLIPRP1 and is involved in triglyceride metabolism. After the mutation, the surface accessibility of the entire protein increased by 27.4 Å2, and the relative surface accessibility increased from 55% to 58.2%. This indicates that the mutation of rs2305205 may change the function of the protein. Figure 5 The effect of rs2305205mutation on protein function. (A) shows the interaction mode of Ala271 with other amino acid residues in the unmutated local structure. The hydrogen bond is indicated by a green dashed line, the bond length is indicated by a number, and the hydrophobic interaction is indicated by a semicircle. (B) How Val271 interacts with other amino acid residues in the local structure after mutation. The effect of the mutation on the protein structure before and after the mutation was obtained from the PDB sum, and the PDB ID number of PNLIPRP1 was 2 ppl.
Figure 5 The effect of rs2305205mutation on protein function. (A) shows the interaction mode of Ala271 with other amino acid residues in the unmutated local structure. The hydrogen bond is indicated by a green dashed line, the bond length is indicated by a number, and the hydrophobic interaction is indicated by a semicircle. (B) How Val271 interacts with other amino acid residues in the local structure after mutation. The effect of the mutation on the protein structure before and after the mutation was obtained from the PDB sum, and the PDB ID number of PNLIPRP1 was 2 ppl.
According to the KEGG pathway diagram of PNLIPRP1 involved in triglyceride metabolism (Figure 6), the pink 3.1.1.3 is pancreatic lipase, including pancreatic triglyceride lipase (PLT), PLRP1 and PLRP2. At the same time, the pink 3.1.1.3 is mainly directly involved in the metabolism of fatty acids, monoacylglycerol and 1.2-diacyl-sn-glycerol (Figure 6). Figure 6 PNLIPRP1 is involved in the KEGG pathway of triglyceride metabolism. Note: The rectangle in the figure represents the enzyme that catalyzes the reaction. Each enzyme is marked with an EC (Enzyme Committee) number. The circles in the figure indicate reaction substrates and products. 3.1.1.3 Pink is pancrelipase.
Figure 6 PNLIPRP1 is involved in the KEGG pathway of triglyceride metabolism.
Note: The rectangle in the figure represents the enzyme that catalyzes the reaction. Each enzyme is marked with an EC (Enzyme Committee) number. The circles in the figure indicate reaction substrates and products. 3.1.1.3 Pink is pancrelipase.
The location in the KEGG signaling pathway indicates that CAMKK2 is a key node connecting endogenous and metabolically related adiponectin (ADIPOQ), leptin, and adenylate-activated protein kinase (AMPK) (Figure 7). Adiponectin and leptin activate AMPK through CAMKK2 phosphorylation to regulate glucose metabolism. As shown in Figure 7, AMPK phosphorylation is involved in the gene expression of a large number of genes, including receptor γ coactivator 1α (PGC-1α), alcohol regulatory element binding protein 1c (SREBP1c), CREB transcription coactivator 2 ( TORC2), glucose transporter 4 (GLUT4), acetyl-coenzyme A carboxylase (ACC), target of rapamycin (mTOR), S6 kinase 1 (S6K1) and insulin receptor substrate (IRS). Therefore, the CAMKK2 gene mutation may participate in the occurrence and development of diabetes by inhibiting the downstream AMPK pathway. Figure 7 KEGG pathway diagram of CAMKK2 involved in AMPK signaling pathway. Note: The reactions involved in CAMKK2 are marked in light red.
Figure 7 KEGG pathway diagram of CAMKK2 involved in AMPK signaling pathway.
Note: The reactions involved in CAMKK2 are marked in light red.
The PNLIPRP1 gene was cloned from cDNA in 1992. 21 consists of 467 amino acids and is located in the 10q24-q26 region of human chromosome. 24 PTL can hydrolyze triglycerides into diglycerides, which are then converted into monoglycerides and free fatty acids, which are then absorbed by intestinal epithelial cells. Both PLRP1 and PTL are secreted by pancreatic acinar cells and have the same affinity as colipase. 25,26 In order to study the biological functions of PLRP1, a previous study27 showed that food intake can promote the secretion of PLRP1 in the pancreas. Therefore, PLRP1 may play a role in food digestion. In vitro studies26, 28 show that PLRP1 can regulate PTL activity by competing for coenzymes and regulating body fat, obesity, insulin resistance and blood sugar levels.
In this study, 22 significant SNP mutation sites and Indel mutation sites were found, and they were located in the KEGG pathway. We found that the PNLIPRP1 gene and the CAMKK2 gene are mapped to the KEGG diabetes-related metabolic pathway, therefore, these two variants were discussed. According to the computer prediction results, 6 diabetic patients in this family showed mutations at the rs2305205 site of the PNLIPRP1 gene, while normal controls did not. The rs2305205mutation changes the amino acid coded by the PNLIPRP1 gene from alanine to valine at position 271. The rs2305205mutation reduces the function of PLRP1, weakens the function of the coenzyme and PTL competition, increases the PTL activity, and promotes the digestion and absorption of fatty acids. At the same time, the mutation of rs2305205 will also increase the body fat content, which in turn causes obesity, insulin resistance and increased blood sugar. Therefore, we concluded that mutations in the PNLIPRP1 gene may promote the development of T2DM through the aforementioned pathways.
As the KEGG pathway map of PNLIPRP1 involved in triglyceride metabolism, the pink 3.1.1.3 is considered to be a pancreatic lipase, including pancreatic triglyceride lipase (PLT), PLRP1 and PLRP2. Because lipid metabolism disorders can cause triglycerides to be deposited in the target tissues of insulin action, reduce insulin sensitivity, and increase the risk of type 2 diabetes 29, it is further speculated that the rs2305205 mutation of PNLIPR1 is related to the occurrence of diabetes. family. At present, there is no research on the correlation between PNLIPRP1 gene mutation and the occurrence of diabetes at home and abroad, and there is no research report on the mutation of this gene in lineage diabetes.
The calcium/calmodulin-dependent protein kinase kinase 2 (CAMKK2) gene is located on chromosome 12q24.2 and contains 16 exons. 30 The mutation at position rs778701848 in the 16th exon resulted in the insertion of amino acid 538. The protein encoded by the CAMKK2 gene participates in energy metabolism and glucose homeostasis by regulating adiponectin, leptin and insulin. 31 The current KEGG pathway diagram shows that adiponectin and leptin can activate AMPK through CAMKK2 phosphorylation to regulate glucose metabolism. At the same time, AMPK phosphorylation is involved in the gene expression of a large number of genes in the metabolic process. Therefore, the loss of CAMKK2 function not only reduces the functions of adiponectin and leptin, but also reduces the levels of adiponectin and leptin in the blood. In addition, the current discovery of the KEGG pathway map for the CAMKK2 gene also illustrates a large number of targeted genes and related signaling pathways, including PGC-1α, SREBP1c, TORC2, GLUT4, ACC, mTOR, S6K1 and IRS, which are different from the previous ones. The research is consistent. 32-46 Therefore, this study further proves that the CAMKK2 gene is closely related to glucose homeostasis, gluconeogenesis and hepatic glucose production.
In this study, we found that 6 diabetic patients in one family showed mutations at rs778701848 of the CAMKK2 gene and rs2305205 of the PNLIPRP1 gene, while normal family members (controls) did not. Both rs778701848 and rs2305205 can affect the biological functions of the CAMKK2 gene and PNLIPRP1 gene through the aforementioned signaling pathways, and increase the risk of diabetes in these family members, which has not been discussed in the previous literature. The rs778701848 mutation and the rs2305205 mutation were identified as the main cause of familial diabetes in this study, indicating that the CAMKK2 gene and PNLIPRP1 gene may be promising new targets for the treatment of diabetes. Therefore, the mutations of PNLIPRP1 gene rs2305205and CAMKK2 gene rs7787018848 may be related to the diabetes of 6 type 2 diabetes family members.
There are still some shortcomings in this study. First, the peripheral blood of non-diabetic relatives was not collected, which is more suitable for clarifying the genomic exons found. In the next study, we will compare the data in this study with diabetic patients or healthy individuals who have no family history. Secondly, this study only included one family, so relevant research should be carried out to explore family diabetes. Third, this study did not study the correlation between PNLIPRP1 gene and CAMKK2 gene in diabetic patients. It is very important to provide a theoretical basis for the early prevention and treatment of diabetes. Fourth, the sample size of this study is small (only 3 generations and 7 family members are involved). Fifth, there is no analysis of the clinical problems of diabetic patients, including the course of diabetes, gender, diabetes medication, and overweight/obesity, which are important for designing treatment strategies. Finally, for the genes identified in this study, we have not determined which genes belong to the family cluster and which genes belong to the diabetes disease itself. The analysis of the aforementioned genes may be crucial to the treatment of diabetes.
In summary, by performing whole-exome sequencing and bioinformatics data analysis, we found that all exons of diabetic patients in this family showed the rs2305205 mutation of the PNLIPRP1 gene and the rs778701848 mutation of the CAMKK2 gene. The rs2305205mutation and rs778701848 mutation may promote the occurrence of type 2 diabetes by reducing the sensitivity of peripheral tissues to insulin and reducing insulin secretion.
The research was supported by the National Natural Science Foundation of China (81160104, 30760087, and 82160165), the Joint Fundamental Research Project of Yunnan Provincial Department of Science and Technology-Kunming Medical University [2018FE001(-043)], and the Yunnan Provincial Health and Family Planning Commission Medical Discipline Leader Training Program (approved) No.: D-2017039) and the Yunnan Province Young and Middle-aged Academic and Technical Leader Reserve Talent Project (Approval No.: 202105AC160093).
The authors report no conflicts of interest in this work.
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