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The Latest in Sequencing Technologies

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DNA Sequencing Uncovers New Variant of Diabetes

DNA Sequencing and Medical Discovery

Diagnostic Applications

The ability to rapidly know the exact cause of an ailment or disease is the first and often a difficult challenge in the path to recovery.  A symptom or set of symptoms can point to a number of different conditions. Diagnostic approaches are employed to decipher the historical and symptom information. However, there are many diagnostic approaches that still do not unequivocally reveal the exact diagnosis in a given person. In addition to this, there is the anxiety of the unknown when these approaches are not straightforward or do not yield information quickly.

There are conditions that, although diagnosed fairly easily, are accompanied by additional conditions and syndromes that are difficult to explain much less treat. Many of these conditions have a genetic basis. DNA sequencing has proven to be a valuable tool to unravel these diagnostic puzzles and to pinpoint specific genetic anomalies associated with a syndrome or medical condition. For many years, however, the cost and time to achieve this was not practical and often prohibitive. The advent of next-generation sequencing (NGS) has dramatically changed this and has provided a means to discover genetic associations with many disease states.

Search for Disease Mechanisms

High-throughput sequencing technologies such as NGS have opened the door to the discovery of the genetic basis of a number of diseases from cancer to neurological disorders. Given the cost advantages over traditional sequencing methods, this technology has much utility and reach for the clinical medicine community. The search for the molecular mechanisms underlying many diseases has always been an intense pursuit. The newest sequencing technologies are providing new and readily usable knowledge regarding molecular mechanisms underlying many diseases and medical conditions. It has revolutionized the approach to ascertain the basis of multi-symptom syndromes and the connection of wide-ranging symptoms within a disease state.

These advances have proven particularly important where a multigenic cause for a disease is present. This is possible with whole genome sequencing techniques using NGS technology. The NGS approach in the search for disease mechanisms has provided a means to reveal the obscure underlying causes for many syndromes with complex etiologies and effects. New and previously hidden information is now coming available and can be interpreted so that it can be used to predict susceptibility to specific diseases, allow early diagnosis, and provide data so that the best treatment approaches can be designed for a given individual.

 

Search for the Genetic Basis of Diabetes and Related Syndromes  

Questions and Challenges

Type 2 diabetes (T2D) continues to be poorly understood. It is well known that T2D is characterized by an insufficient β-cell response to insulin resistance, however, the cause of the associated syndromes such as obesity have not been fully elucidated. The incidence of diabetes is rising globally, which is particularly notable in the US.  The individual variation in the propensity and severity of diabetes remains elusive for the most part.

Various mechanisms have been hypothesized for different complications of T2D. One example is the study of oxidative stress–related mechanisms in the development of diabetic nephropathy (1). Data derived from massive parallel sequencing technologies are revealing information regarding possible mechanisms of multifactorial T2D complications. Given the gaps in information regarding T2D, successful therapy and relief of complications are also challenging. Additional mechanistic knowledge can be used to determine therapeutic targets and the means to prevent the development of T2D, something that remains to be an enigma.

Genetic Approaches

One means to study the genetic mechanisms associated with the heritability of T2D has been the use of positional cloning based on linkage analysis. With this approach, the position of a disease-related gene on a chromosome is located. Other approaches include further analysis of genes that have been found to be associated with T2D. Conclusive outcomes have often proven difficult with this approach due to reporting bias and inability to replicate studies (2).

The use of genome-wide association studies led to the identification of over 65 genetic variants associated with an increased risk of T2D (3). However, much of this data is difficult to interpret in regards to clinical significance. Using genetic information to predict susceptibility continues to be a goal. The use of NGS is making significant headway as an efficient diagnostic tool for T2D despite its genetically heterogeneous nature.

Search for the Basis of Diabetes Complications (Focus on Obesity)

Scientists continue to battle with the understanding of how T2D complications develop. One of the most studied complications is diabetes-related obesity. Obesity is a major risk factor in the development of T2D. Genome-wide searching and positional cloning in mouse models for obesity have been conducted in the search for the genetic basis of obesity-associated T2D (4). The results of these endeavors have led to the identification of genes that may play a role in the regulation of glucose and/or lipid metabolism in humans.

Although it has been observed that increased fat levels are associated with insulin resistance, the nature of this connection at the molecular level has been difficult to ascertain. It has been hypothesized that obesity causes inflammation and this inflammation leads to T2D. The missing information is the link between inflammation and the development of T2D.  Published studies in mice showed that an inflammatory molecule, LTB4, causes insulin resistance and may represent a therapeutic marker for diabetes-related obesity (5). Inhibition of the LTB4 receptor leads to protection from insulin resistance.

 

Discovery of Diabetes Genetic Variants

Type 2 Diabetes and Obesity

Results of sequencing studies performed by Alsters et al. on DNA from an obese diabetic woman were just published within the last couple of months (6). Whole-exome sequencing of DNA from a family with a Mendelian pattern of a complex obesity syndrome led to the discovery of a mutation in the carboxypeptidase E (CPE) gene in an obese woman with T2D, intellectual disability, and hypogonadotropic hypogonadism. This corresponds to the phenotype that exists in the in the fat/fat and Cpe knockout mouse models. With the application of sequencing technology, a homozygous deleterious mutation in CPE (resulting in loss of its expression) was described in a human for the first time.  

 

Biomarkers for Type 2 Diabetes

Metabolomics, proteomics, and genomics technologies have helped to identify a number of biomarkers of disease including T2D. Dr. Michael Snyder, chair of the Stanford University School of Medicine’s Genetics Department, is a geneticist who, using genome sequencing data, caught his own diabetes very early. Dr. Snyder and his team developed the Personal Omics Profile (iPOP). This involves determining the whole genome sequence of an individual then combining with transcriptomic, proteomic, metabolomic, and autoantibody profiles to generate the iPOP (7).

The iPOP is analyzed during healthy and disease states. This data can provide valuable insight regarding changes to expect during disease states. Dr. Snyder included himself as a subject in the development of the iPOP process. It is through this that he discovered his pre-diabetic state, despite his phenotypic profile and apparent state of health. The diabetic state later appeared to be triggered by a viral infection. With the genomic and other information regarding his health risks, he applies diet and other interventions to protect his health status.

A number of physiological and biochemical biomarkers for T2D are known and used more routinely in diagnostic medicine (8). Candidates for genetic biomarkers of TD2 are being searched for and some variants have been identified (GCKR, KCNJ11, TCF7)(7). Genomic sequence data has the potential to improve the ability to identify individuals with an elevated risk for developing T2D. This is particularly important for individuals who do not fit phenotypic profiles or who do not yet present with signs or symptoms of pre-diabetes.

 

Variants Linked to Risk of Type 2 Diabetes Development

New variants associated with T2D were discovered by using whole-genome sequencing technology (9).  DNA samples from Danish and Iranian individuals were analyzed leading to the discovery of 4 variants that affect the risk of developing T2D. One low-frequency variant, the CCND2 gene (leading to an increase in its expression) reduces the risk of T2D by half. Two variants in the PAM and PDX1 genes lead to a higher risk of T2D.  

Bonnefond et al (10) performed large-scale exon resequencing in thousands of individuals. This lead to the identification of 36 rare variants associated with T2D. They found that the rare MTNR1B variant impairs a melatonin receptor function which in turn contributes to type 2 diabetes. With this information, a link between MTNR1B and T2D risk was established.

Using exome sequencing, Francis S. Collins and other investigators (11) identified a novel nonsynonymous variant in the Wolfram syndrome 1 (WFS1) gene that is associated with an autosomal dominant diabetes phenotype. They sequenced the exomes of diabetic individuals that represent three generations. Thousands of variants were found; however, after filters applied, confirmation with Sanger sequencing, and testing in addition family members, one variant (WFS1) segregated completely with diabetes status.

 

Gene Discovery in Patients with Type 1 Diabetes

Unlike T2D, type 1 diabetes (T1D) is an autoimmune disease where the immune system attacks the insulin-producing β-cells of the pancreas. The use of sequencing technologies has lead to the discovery of variants associated with this form of diabetes. Using PCR products as templates, Guo et al. (12) sequenced the untranslated regions, coding regions, and the exon-intron junctions of MAP3K7IP2 and SUMO4. A functional variant of SUMO4 was found to be strongly associated with T1D.

Linkage studies have previously determined that the HLA region is the major genetic determinant of T1D risk. Via HLA genotyping using NGS, an association between T1D and DRB3 haplotype was identified. Additional studies determined that this association indicates an increase in susceptibility to T1D rather than only serve as a biomarker for T1D (13).  

 

Conclusion

The discovery of genetic variants associated with diabetes has important implications for diagnostic medicine. This can drastically change the paradigm in the approach to diabetes diagnostic efforts. More importantly, this information can allow the identification of those individuals at risk who can then implement measures to prevent the development of diabetes. The impact of genetic variant identification of diabetes and another disease on preventive medicine is very high.

Another area of impact of the utility of genetic variant information is the identification of possible therapeutic targets. In addition to understanding the best therapeutic approach using known means and designing personalized treatment measures, new therapies can be developed with the new genomic sequencing data. Given the power of NGS technology, DNA sequencing approaches can become a more routine aspect of the diagnostic armament used in medical science. This adds new possibilities for very early diagnosis of diabetes and other diseases or even for preventing them before they can begin to develop.

 

References

  1. Pergola PE, Raskin P, Toto RD, Meyer CJ, Huff JW, Grossman EB, et al. Bardoxolone methyl and kidney function in CKD with type 2 diabetes. The New England Journal of Medicine. 2011;365(4):327–336.
  2. McCarthy MI, Froguel P. Genetic approaches to the molecular understanding of type 2 diabetes. American journal of physiology Endocrinology and metabolism. 2002;283(2):E217-25.
  1. McCarthy MI. Genomics, type 2 diabetes, and obesity. The New England journal of medicine. 2010;363(24):2339-50.
  1. Joost HG, Schurmann A. The genetic basis of obesity-associated type 2 diabetes (diabesity) in polygenic mouse models. Mammalian genome : official journal of the International Mammalian Genome Society. 2014;25(9-10):401-12.
  1. Li P, Oh da Y, Bandyopadhyay G, Lagakos WS, Talukdar S, Osborn O, et al. LTB4 promotes insulin resistance in obese mice by acting on macrophages, hepatocytes and myocytes. 2015;21(3):239-47.
  1. Alsters SI, Goldstone AP, Buxton JL, Zekavati A, Sosinsky A, Yiorkas AM, et al. Truncating Homozygous Mutation of Carboxypeptidase E (CPE) in a Morbidly Obese Female with Type 2 Diabetes Mellitus, Intellectual Disability and Hypogonadotrophic Hypogonadism. PloS one. 2015;10(6):e0131417.
  2. Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Chen R, Miriami E, Karczewski KJ, Hariharan M, Dewey FE, Cheng Y, Clark MJ, Im H, Habegger L, Balasubramanian S, O’Huallachain M, Dudley JT, Hillenmeyer S, Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K, Boyle AP, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-Carrillo M, Gallardo M, Blasco MA, Greenberg PL, Snyder P, Klein TE, Altman RB, Butte AJ, Ashley EA, Gerstein M, Nadeau KC, Tang H, Snyder M. Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell. 2012;148(6):1293-307.
  3. Echouffo-Tcheugui JB, Dieffenbach SD, Kengne AP. Added value of novel circulating and genetic biomarkers in type 2 diabetes prediction: a systematic review. Diabetes Res Clin Pract. 2013;101(3):255-69.
  4. Steinthorsdottir V, Thorleifsson G, Sulem P. Identification of low-frequency and rare sequence variants associated with elevated or reduced risk of type 2 diabetes. 2014;46(3):294-8.
  1. Bonnefond A, Clement N, Fawcett K, Yengo L, Vaillant E, Guillaume JL, et al. Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes. Nature genetics. 2012;44(3):297-301.
  1. Bonnycastle LL, Chines PS, Hara T, Huyghe JR, Swift AJ, Heikinheimo P, et al. Autosomal dominant diabetes arising from a Wolfram syndrome 1 mutation. Diabetes. 2013;62(11):3943-50.
  2. Guo D, Li M, Zhang Y, Yang P, Eckenrode S, Hopkins D, et al. A functional variant of SUMO4, a new I kappa B alpha modifier, is associated with type 1 diabetes. Nature genetics. 2004;36(8):837-41.
  3. Erlich HA, Valdes AM, McDevitt SL, Simen BB, Blake LA, McGowan KR, et al. Next generation sequencing reveals the association of DRB3*02:02 with type 1 diabetes. Diabetes. 2013;62(7):2618-22.

Written by Macrogen Corp.

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