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Intro to current collaboration project Why type 2 diabetes? Overview of glucose metabolism and OXPHOS Discussion of Mootha et. al Nature Genetics, 2003.

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Presentation on theme: "Intro to current collaboration project Why type 2 diabetes? Overview of glucose metabolism and OXPHOS Discussion of Mootha et. al Nature Genetics, 2003."— Presentation transcript:

1 Intro to current collaboration project Why type 2 diabetes? Overview of glucose metabolism and OXPHOS Discussion of Mootha et. al Nature Genetics, 2003 Recommended reading: motifADE Mootha et al. PNAS, 2004

2 B6 ob/ob mice are resistant to type 2 diabetes while BTBR ob/ob mice develop severe diabetes.

3 t2dm3, 348 ob/ob mice Stoehr et al. 2000 Affymetrix mouse 430 2.0 Array 60 ob/ob mice Lan et al. in progress

4 Affymetrix mouse 430 2.0 Array 60 ob/ob mice webQTL Some top OXPHOS/glycolysis genes localized to D2Mit241 by Christina, mmcl method In addition, Brian and others are working on multiple QTL interval mapping of mRNA as meta-traits

5 www.photosof.net There are 18.2 million people in the United States, or 6.3% of the population, who have diabetes. Heart disease and stroke strike people with diabetes more than twice as often as they do others. Diabetes is the leading cause of: -new cases of blindness in people ages 20-74 -end-stage renal disease -non-traumatic lower-limb amputation If current trends continue, more than one-third of those born in the United States in the year 2000 will be diagnosed with diabetes. Over 20% of the United States population has pre-diabetes

6 Type 2 diabetes can be prevented or delayed if symptoms are controlled in the pre-diabetic stage. In the Diabetes Prevention Program, diet changes and moderate- intensity physical activity reduced the development of diabetes by 58% and the drug metformin reduced the risk of developing diabetes by 31% over three (3) years. In the STOP-NIDDM Trial, treatment of pre-diabetic patients with acarbose reduced the risk of developing diabetes by 25% over three (3) years. Identification of additional symptoms of pre-diabetes will allow for earlier identification and intervention. New pharmacologic treatments may be more effective at preventing type 2 diabetes.

7 Figure 16.3. Stages of Glycolysis. The glycolytic pathway can be divided into three stages: (1) glucose is trapped and destabilized; (2) two interconvertible three-carbon molecules are generated by cleavage of six-carbon fructose; and (3) ATP is generated. Biochemistry 5 th ed. Berg, Tymoczka, Stryer Glucose metabolism begins with glycolysis

8 Biochemistry 5 th ed. Berg, Tymoczka, Stryer TCA Cycle Different fates of pyruvate

9 Mootha et al. Nature Genetics, 2002 Gene Set Enrichment Analysis Step 1. Define gene sets Step 2. Order genes on basis of expression difference Step 3. Compute a running sum, increasing when a gene of a particular set is encountered, decreasing otherwise Step 4. Record the maximal enrichment scores Step 5. Permute class labels 1000 times within the gene sets to determine if they are enriched in one diagnostic class Step 6. Compare the MES obtained to the MES using actual data

10 OXPHOS genes indicated in red have reduced expression in diabetic muscle. Changes are also observed in individuals with impaired glucose tolerance and in healthy 1 st degree relatives of type 2 diabetics. An expression profile with reduced expression of OXPHOS genes could be used as an early indicator to identify susceptible individuals.

11 As subset of OXPHOS genes are highly expressed in tissues with greater OXPHOS and are induced by PGC1a expression liveradipose tissue

12 Attie AD, Kendziorski CM. Nat Genet. 2003 Why PGC1a?

13 Diabetes status and OXPHOS-CR gene expression correlate with total body aerobic capacity. Value of ~70 for trained male athlete with 100 lbs lean body mass. For 60+ <20.5 very poor, 32-36 good, >44 superior

14 25 0.95 2.3 5.6, 7 7.8-11 PGC1a overexpression in muscle Lin, J. et al. Nature. 2002 What’s the best indicator of disease status? oxidative glycolytic

15 Copyright ©2004 by the National Academy of Sciences Mootha, Vamsi K. et al. (2004) Proc. Natl. Acad. Sci. USA 101, 6570-6575 Fig. 1. Schematic overview of motifADE and application to the PGC-1{alpha} time course


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