genomics and transcriptomics : finding genes for obesity-related cvd risk factors

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Genomics and Transcriptomics: Finding genes for obesity-related CVD risk factors RAUL A. BASTARRACHEA, M.D. Staff Scientist Department of Genetics TEXAS BIOMEDICAL RESEARCH INSTITUTE San Antonio, Texas, USA E-mail: [email protected]

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Genomics and Transcriptomics : Finding genes for obesity-related CVD risk factors . RAUL A. BASTARRACHEA, M.D. Staff Scientist Department of Genetics TEXAS BIOMEDICAL RESEARCH INSTITUTE San Antonio, Texas, USA E-mail: [email protected]. ANUNCIO PRELIMINAR - PowerPoint PPT Presentation

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Page 1: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors

Genomics and Transcriptomics: Finding genes for obesity-related CVD risk factors

RAUL A. BASTARRACHEA, M.D.Staff Scientist

Department of GeneticsTEXAS BIOMEDICAL RESEARCH INSTITUTE

San Antonio, Texas, USA

E-mail: [email protected]

Page 2: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors

ANUNCIO PRELIMINARV Curso Internacional sobre Obesidad en Español

SAN ANTONIO, TEXAS 2012

OBESIDAD Y METABOLISMO DEL TEJIDO ADIPOSO: CONCEPTOS ACTUALES

(De sus bases biológicas a su tratamiento clínico-farmacológico integral)

Tema Central:LA CIENCIA DE LA SACIEDAD Y EL APETITO II

Fecha: Jueves 15, Viernes 16 y Sábado 17 de Noviembre de 2012

Informes: [email protected]

Page 3: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors

Current treatment programs for obese individuals are not very effective over the long term, leading to the common wisdom that persons who successfully lose weight will regain it all within 5 years.

Brownell KD, Jeffrey RW. Improving long-term weight loss: pushing the limits of treatment. Behav Ther 1987;18:353–74.

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Ghrelin-PYY-OXM-GLP-1-Incretins-Amylin

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Zheng, H. et al. Physiology 23: 75-83 2008

Filtros Biológicos• FILTRO CORTICAL: Se integran las

señales en la corteza cerebral provenientes de estímulos asociados a los alimentos y al hambre (olor, visión, sabor, etc).

• FILTRO EMOCIONAL Y DE PLACER: Incluye los centros dopaminérgicos y canabinoides localizados en la amígdala, el sistema mesocorticolímbico y el núcleo acumbente cerebral donde se integran las señales de ingesta por recompensa y adicción.

• FILTRO METABÓLICO: Incluye el centro del hambre del hipotálamo y las vías catabólicas y anabólicas y sus conecciones periféricas con los factores de saciedad gastrointestinales y las señales de adiposidad representadas por la leptina y la insulina en la grasa y la amilina en el páncreas.

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NON-HUMAN PRIMATE STUDIESMetabolic profiling in baboons

Pharmacogenomics of hunger and satiety

Euglycemic-Hyperinsulinemic clamp in baboons

Omentectomy project – Fatty acid kinetics

Type 2 diabetes through surgery – hemipancreatectomy

Development of an obese baboon colony

Page 9: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors
Page 10: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors

Reversal of STZ-induced diabetes using ultrasound destruction of microbubbles for the delivery of genes to the baboon pancreas.1251PC Protocol/2011/Grayburn-BastarracheaPHENOTYPE MEASUREMENTS

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Identificación de la secuencia de DNA

Búsqueda de polimorfismos asociados a fenotipos

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Genome-Wide Scan

~9.5 cM

Disease risk

Phenotypes:

Circulating Proteins

Physiological Parameters

Quantitative Linkage Analysis

~ 363 markers 9.5 cM = 9.500 Kb

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QTL in Chr 16

Page 18: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors

Searching for Genes for Complex Genetic Disease: The Standard Model

1. pedigrees

2. Linkage(allele sharing)

3. AssociationLD mapping

4. positional candidate gene

variants

SNPs

mutation

MicroarraysExpressionHomologyTg/Ko/RNAi

5. Function/activity

Page 19: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors

Mérida*

Monterrey*

San Luis Potosi

Guadalajara

Veracruz

Oaxaca

Ciudad Victoria

Chihuahua

San Antonio

GEMM Family StudyGenética de las

Enfermedades Metabólicasen México

Morelia

Cuernavaca

DF

PROGRAMA PARA LA INVESTIGACIÓN GENÉTICA DE LAS ENFERMEDADES METABÓLICAS RELACIONADAS CON LA NUTRICIÓN A NIVEL POBLACIONAL

Bases Genómicas del Metabolismo Posprandial

CONSORCIO

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GENOMICSDNA (30,000) Transcription – mRNA (100,000)

PROTEOMICSPolypeptide chain – Modification – Functional Protein (1,000,000)

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Illumina Beadscan GX

• Confocal laser scanning system• Resolution, 0.8 micron• Two lasers 532, 635 nm

– Supports Cy3 & Cy5 – imaging

• Fully automated operation• Supports genotyping, expression and

proteomics applications

High throughput (whole genome) gene expression

Fibre optic bead (Illumina) technology facilitates rapid measurement of genome-wide gene expression profiles

Software to visualize and summarize large data sets in graphical format Increased sensitivity, high throughput

Illumina Beadscan GX technology detects expression of 48,000 transcripts simultaneously

Whole genome 48,000 probe array

Page 22: Genomics and  Transcriptomics : Finding genes for obesity-related CVD risk factors

Gene Expression: Preliminary Results

• Transcriptional profiles on 1013 Mexican Americans

from the San Antonio Family Heart Study• RNA extracted from stored lymphocytes• 22,120 detected quantitative transcripts• Of these, 16,028 are heritable• Developed new statistical methods for high dimensional biomarker/endophenotype search

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Gen Transcripción Traducción ModificacionesPost-traduccionales Función

Interacción entre Genes y Nutrimentos

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La utilización lenta de los metabolitos es la huella digital del proceso patológico (disregulación molecular de nutrientes)

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Date Patient ID Age Height Weight BMI Fat Mass Kg Fat (%) Lean Mass (Kg)

10/25/2010 1 49 156 71 29.2 27.8 38.8 43.510/25/2010 2 27 153 57.3 24.5 17.5 30.5 39.810/25/2010 3 22 156 40.4 16.6 4.8 11.9 35.610/25/2010 4 47 160 83.3 32.5 31.6 38 51.7

0 15 30 45 60 90 12060

70

80

90

100

110

120

130

140

150

160

Mixed Meal GEMM Study

Patient 1Patient 2Patient 3

Time (minutes)

Glu

cose

-mg/

dl

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GEMM Study: Monterrey

Even in this very small sample we observed substantial correlation ofexpression across tissues.

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IPA functional class assignments for correlated transcripts: IPA is a powerful curated database and analysis system for understanding how proteins work together to effect cellular changes. We use this system to classify the correlated sets by gene function. This figure shows the top ten functional classes for each focal transcript. There is considerable consistency in functional assignment across tissues.

Ingenuity Pathway Analysis (IPA)

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FUTURE APPROACH:Genetically-based

programs COMPLEMENTING “Environmentally”-based

programsof prevention and therapy

for Common Complex Diseases:

Obesity and T2DM