0.75 0.50 0.25 0.00 -0.25 -0.50 S factor  body weight during WM  body weight during ER Supplementary figure 1: S factor distribution in each diet. S.

Slides:



Advertisements
Similar presentations
Original Figures for "Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring"
Advertisements

Introduction to Physiology
Human growth is associated with distinct patterns of gene expression in evolutionarily conserved networks Adam Stevens, Daniel Hanson, Andrew Whatmore,
PROLIFERAZIONE CELLULARE E RESISTENZA AI FARMACI.
Multidimensional Analysis If you are comparing more than two conditions (for example 10 types of cancer) or if you are looking at a time series (cell cycle.
Figure S1. ID#1 Lipid Metabolism, Small Molecule Biochemistry, Drug Metabolism.
Analysis of GO annotation at cluster level by H. Bjørn Nielsen Slides from Agnieszka S. Juncker.
Unit 1 Organization of the Human Body. OBJECTIVES Define Anatomy and Physiology Describe the Structural Organization of the Human Body Explain how the.
Analysis of microarray data
Comprehensive Gene Expression Analysis of Prostate Cancer Reveals Distinct Transcriptional Programs Associated With Metastatic Disease Kevin Paiz-Ramirez.
The New AP Bio Test Format
Unit 1: The Language of Science  communicate and apply scientific information extracted from various sources (3.B)  evaluate models according to their.
Apostolos Zaravinos, Myrtani Pieri, Nikos Mourmouras, Natassa Anastasiadou, Ioanna Zouvani, Dimitris Delakas, Constantinos Deltas Department of Biological.
Human Anatomy and Body Systems
Human Body Systems Review
Control of the Cell Cycle Cancer. Objectives Why do some types of cells divide rapidly, while others divide slowly? What tells a cell when it is time.
Human Anatomy and Physiology
Lecture # 1: Fundamentals 1
Biology-Driven Clustering of Microarray Data Applications to the NCI60 Data Set K.R. Coombes, K.A. Baggerly, D.N. Stivers, J. Wang, D. Gold, H.G. Sung,
Apostolos Zaravinos and Constantinos C Deltas Molecular Medicine Research Center and Laboratory of Molecular and Medical Genetics, Department of Biological.
Intro to Anatomy & Physiology First…. Brief History – Andreas Vesalius.
Objective 8: TSWBAT explain how cancerous cell division is different from normal cell division.
Endocrine (Overview) Ⅰ Introduction 1. Endocrine System 2. Paracrine and endocrine actions 3. Chemical Composition Ⅱ Mechanisms of Hormone Actions 1. Hormone.
Body Systems. Objective  Explain the functioning of the major human organ systems and their interactions.
DIFFERENTIALLY EXPRESSED GENES AND THEIR ASSOCIATED NETWORKS IN CLEAR-CELL RENAL CELL CARCINOMA (RCC) Apostolos Zaravinos and Constantinos C. Deltas Molecular.
Glutamyl-tRNA 5-Aminolevulinic acid Protoporphyrinogen IX Mg-Proto IX Heme Mg-Proto IX ME HEMA GSA PPO FC PORB CHLD, CHLH, CHLI Glutamate 1-semialdehyde.
E14.5E16.5E18.5 Normalized mRNA level Get1 Nfix Smarcd3 A Supplementary Figure 1 (A) The microarray expression levels of bladder terminal differentiation.
Supplemental Figure 2. IPA Gene Network Analysis. The top gene networks for DEGS more highly expressed (Panels A- B) and less highly expressed (Panels.
Life Science. Explain that cells are the basic unit of structures and function of living organisms. Cells are the basic unit of structures of living organisms.
Large black circles: query genes grey circles: interacting or related proteins Light grey line: consolidated pathways Yellow: predicted pathways Green:
Supplementary Figure 1 List of top 50 most abundantly expressed lncRNAs and mRNAs in HP and PFC from RNAseq. Expression abundance of lncRNAs and mRNAs.
Comparative Profiling of Triple-Negative Breast Carcinomas Tissue Glycoproteome by Sequential Purification of Glycoproteins and Stable Isotope Labeling.
Volume 12, Issue 5, Pages (November 2007)
E. Bapteste, C. Bicep, P. Lopez  Clinical Microbiology and Infection 
Copyright © 2009 American Medical Association. All rights reserved.
Cancer, reproductive system diseases, lipid metabolism 48 22
Fig. 3. Mean body weight of women randomized to low-carbohydrate and low-fat diets over the course of the 4-month trial. The first time point (wk 1) represents.
Volume 141, Issue 3, Pages (March 2012)
M. Fu, G. Huang, Z. Zhang, J. Liu, Z. Zhang, Z. Huang, B. Yu, F. Meng 
Lecture #3 The Cell Cycle & Cancer
Figure S1 Mean and 95% confidence interval for the mean of tumor protein 53 (p53; Fig.S1a), mouse double minute 2 (MDM2; Fig.S1b), glucose transporter.
Volume 2, Issue 2, Pages (February 2016)
Volume 12, Issue 5, Pages (November 2007)
Cell Signaling.
Altered microRNA expression in stenoses of native arteriovenous fistulas in hemodialysis patients  Lei Lv, MD, Weibin Huang, MD, Jiwei Zhang, MD, Yaxue.
A gene expression study of normal and damaged cartilage in anteromedial gonarthrosis, a phenotype of osteoarthritis  S. Snelling, R. Rout, R. Davidson,
Volume 23, Issue 4, Pages (April 2018)
Volume 85, Issue 2, Pages (January 2014)
INTRODUCTION Nutrigenomics Dr. Muhamad Firdaus
E. Bapteste, C. Bicep, P. Lopez  Clinical Microbiology and Infection 
10.3 Regulating the Cell Cycle
Supplementary Figure S1: Purity for naïve and memory subsets
Novel functional roles of uncharacterized genes as functional regulators of cellular cholesterol levels. Novel functional roles of uncharacterized genes.
The BRD4 bromodomain is critical for expression of SASP genes.
Differential gene expression in whole blood from SJIA patients and healthy controls. A. Data were normalized in Beadstudio using the "average" method and.
STAAR Notebook 3.
Design and optimization of the computational model.
Kathryn E. Wellen, Craig B. Thompson  Molecular Cell 
Volume 15, Issue 12, Pages (June 2016)
Differential protein, mRNA, lncRNA and miRNA regulation by p53.
ACF1 loss perturbs gene expression in early embryos.
Kathryn E. Wellen, Craig B. Thompson  Molecular Cell 
Volume 139, Issue 1, Pages (October 2009)
Loyola Marymount University
Volume 2, Issue 3, Pages (March 2016)
HPV–human protein network map.
Gene expression profiles of T cells.
Organization of the Human Body
Volume 25, Issue 5, Pages e4 (May 2017)
Bioinformatic analyses suggest that PI3K/AKT signaling may be a key downstream pathway of tazarotene signaling. Bioinformatic analyses suggest that PI3K/AKT.
Presentation transcript:

S factor  body weight during WM  body weight during ER Supplementary figure 1: S factor distribution in each diet. S factor corresponds to weight variations during WM normalized by the weight loss during ER. Two populations were defined according to S factor, those subjects stabilizing or continuing to lose weight were classified as successful subjects (green circles), and those subjects regaining weight were classified as unsuccessful subjects (red circles). LGI: low glycemic index-; HGI: high glycemic index-; LP: low protein-; HP: high protein-diet.

FactorMean F ratio 3-way ANOVA 3-way ANCOVA Class (successful, unsuccessful) Diet Total Energy Intake 1.34 Individu Supplementary figure 2: Exploratory analyses Data was validated by both multivariate principal component analysis (PCA) and univariate 3-way ANOVA. A: multivariate principal component analysis first two compontents [t1] and [t2] explained the 12.5% and the the 10.7%. of the total variance, respectively. triangle. LGI/LP diet; box. HGI/LP diet; dot. LGI/HP diet; diamond. HGI/HP diet; in black. successful group; in grey. unsuccessful group B: univariate 3-way ANOVA with (ANCOVA) energy intake as cofactor. Both analysis show that the main source of variation in gene expression dataset was the difference between successful and unsuccessful groups (class). A B

Supplementary figure 3: Hierarchical clustering of genes differentiating between successful and unsuccessful groups independently of diet composition. These transcripts were significant to both multivariate analysis and ANCOVA analysis (VIP > 1, qValue < 0.05, respectively). Su: successful group; Un: unsuccessful group; LGI: low glycemic index-; HGI: high glycemic index-; LP: low protein-; HP: high protein-diet.

IDScoreFocus MoleculesTop Functions Nervous System Development and Function, Carbohydrate Metabolism, Cell Death 231 RNA Post-Transcriptional Modification, Gene Expression, Cell-To-Cell Signaling and Interaction Cellular Growth and Proliferation, Skeletal and Muscular System Development and Function, Gene Expression Cell-To-Cell Signaling and Interaction, Hematological System Development and Function, Infection Mechanism 51118Cellular Growth and Proliferation, Gene Expression, Cancer 61017Cell Death, Cancer, Cellular Growth and Proliferation 7915Cell Death, Respiratory Disease, Cancer 8916Protein Synthesis, Gene Expression, Infectious Disease 9916 Gene Expression, Reproductive System Development and Function, Infection Mechanism Cellular Assembly and Organization, Cellular Compromise, Gene Expression 11814Cell Cycle, Cancer, Cellular Development 12714Cancer, Tumor Morphology, Cell Cycle Tissue Development, Cell Signaling, Post-Translational Modification 147 Cellular Movement, Hematological System Development and Function, Immune Cell Trafficking 15714Cell Cycle, Cellular Growth and Proliferation, Cancer 16714Cancer, Organ Development, Tumor Morphology 17714Cell Signaling, Gene Expression, Cell Death Cell Cycle, Cellular Growth and Proliferation, Connective Tissue Development and Function 19613Cell Cycle, Cancer, Cellular Growth and Proliferation Supplementary figure 4: Networks associated with genes differentiating successful and unsuccessful groups as annotated by Ingenuity Pathways Analysis.