Presentation Title

Using RNA Sequencing to Observe Biased Agonism in PPAR&gamma

Authors' Names

Mariah Rayl

Presentation Type

Oral Presentation

Abstract/Artist Statement

Diabetes currently affects 9.4% of the United States’ population, costing approximately $245 billion dollars both direct and indirect costs. Additionally, 33.9% of the population has prediabetes which can progress to diabetes without adequate preventative measures. Between 90% and 95% of the diabetes cases are specifically type two diabetes (T2D). A major hallmark of T2D is that it reduces the cellular response to insulin. Due to the increasing prevalence of T2D in the United States, medications such as rosiglitazone and pioglitazone have become increasingly important in order to manage the condition, but like many other medications, they come with side effects. Ideally, the downstream effects of medication can be controlled by creating medications that have less side effects.

Rosiglitazone and pioglitazone are FDA approved drugs used for insulin sensitization in T2D and act through binding of peroxisome proliferator-activated receptor γ (PPARγ). PPARγ changes cellular transcription based on which type of drug binds to it. When agonists bind, PPARγ activity increases and when antagonists bind, PPARγ activity decreases. Unfortunately, PPARγ affects a wide range of biological processes so drug treatment causes a wide range of side effects. These side effects include organ failure, weight gain, fluid retention, bone fractures, and more.

The classic model of PPARγ implies that when PPARγ is treated with an agonist, it recruits a coactivator protein thereby activating transcription, but when treated with an antagonist, PPARγ recruits a corepressor protein thereby repressing transcription. More recent data indicates that slight structural changes in PPARγ due to drug treatment affect the affinity for various coactivators in more ways than two, making the classic model inadequate. We propose a model of biased agonism in which differential recruitment of coactivators lead to differential sets of genes transcribed. If the gene sets can be determined and drugs can be designed to better select one gene set over another, side effects may be reduced or eliminated by exploiting a drug’s bias toward a certain gene set.

Because PPARγ directly affects transcription, we developed an mRNA sequencing experiment in order to determine exactly which genes PPARγ binding drugs affect in isolated human adipose cells. We observed that the drug GW1929 affects these cells differently than the drug rosiglitazone even though they are both agonists. GW1929 could be displaying biased agonism in that it preferentially recruits one coactivator over others and leads to a distinct transcriptional pattern that is not observed in the other drugs.

Mentor Name

Travis Hughes

This document is currently not available here.

Share

COinS
 
Feb 22nd, 1:30 PM Feb 22nd, 1:45 PM

Using RNA Sequencing to Observe Biased Agonism in PPAR&gamma

UC 333

Diabetes currently affects 9.4% of the United States’ population, costing approximately $245 billion dollars both direct and indirect costs. Additionally, 33.9% of the population has prediabetes which can progress to diabetes without adequate preventative measures. Between 90% and 95% of the diabetes cases are specifically type two diabetes (T2D). A major hallmark of T2D is that it reduces the cellular response to insulin. Due to the increasing prevalence of T2D in the United States, medications such as rosiglitazone and pioglitazone have become increasingly important in order to manage the condition, but like many other medications, they come with side effects. Ideally, the downstream effects of medication can be controlled by creating medications that have less side effects.

Rosiglitazone and pioglitazone are FDA approved drugs used for insulin sensitization in T2D and act through binding of peroxisome proliferator-activated receptor γ (PPARγ). PPARγ changes cellular transcription based on which type of drug binds to it. When agonists bind, PPARγ activity increases and when antagonists bind, PPARγ activity decreases. Unfortunately, PPARγ affects a wide range of biological processes so drug treatment causes a wide range of side effects. These side effects include organ failure, weight gain, fluid retention, bone fractures, and more.

The classic model of PPARγ implies that when PPARγ is treated with an agonist, it recruits a coactivator protein thereby activating transcription, but when treated with an antagonist, PPARγ recruits a corepressor protein thereby repressing transcription. More recent data indicates that slight structural changes in PPARγ due to drug treatment affect the affinity for various coactivators in more ways than two, making the classic model inadequate. We propose a model of biased agonism in which differential recruitment of coactivators lead to differential sets of genes transcribed. If the gene sets can be determined and drugs can be designed to better select one gene set over another, side effects may be reduced or eliminated by exploiting a drug’s bias toward a certain gene set.

Because PPARγ directly affects transcription, we developed an mRNA sequencing experiment in order to determine exactly which genes PPARγ binding drugs affect in isolated human adipose cells. We observed that the drug GW1929 affects these cells differently than the drug rosiglitazone even though they are both agonists. GW1929 could be displaying biased agonism in that it preferentially recruits one coactivator over others and leads to a distinct transcriptional pattern that is not observed in the other drugs.