Volume 150, Issue 1, Pages (July 2016)

Slides:



Advertisements
Similar presentations
Volume 3, Issue 4, Pages (April 2013)
Advertisements

MicroRNA signature in patients with eosinophilic esophagitis, reversibility with glucocorticoids, and assessment as disease biomarkers  Thomas X. Lu,
Stephen Lory, PhD, Jeffrey K. Ichikawa, PhD  CHEST 
Figure 3. Active enhancers located in intergenic DMRs
Volume 141, Issue 3, Pages (March 2012)
Soyoun Kim, Jaewon Hwang, Daeyeol Lee  Neuron 
M. Fu, G. Huang, Z. Zhang, J. Liu, Z. Zhang, Z. Huang, B. Yu, F. Meng 
Volume 44, Issue 1, Pages (January 2016)
COUNTERPOINT: Is the Apnea-Hypopnea Index the Best Way to Quantify the Severity of Sleep-Disordered Breathing? No  Naresh M. Punjabi, MD, PhD, FCCP  CHEST 
A Strategy to Find Suitable Reference Genes for miRNA Quantitative PCR Analysis and Its Application to Cervical Specimens  Iris Babion, Barbara C. Snoek,
Bead Array–Based microRNA Expression Profiling of Peripheral Blood and the Impact of Different RNA Isolation Approaches  Andrea Gaarz, Svenja Debey-Pascher,
MicroRNAs in spent blastocyst culture medium are derived from trophectoderm cells and can be explored for human embryo reproductive competence assessment 
Volume 3, Issue 4, Pages (April 2013)
MicroRNA signature in patients with eosinophilic esophagitis, reversibility with glucocorticoids, and assessment as disease biomarkers  Thomas X. Lu,
Potential role of circulating microRNAs as a biomarker for unexplained recurrent spontaneous abortion  Weibing Qin, M.D., Ph.D., Yunge Tang, M.D., Ning.
Volume 67, Issue 6, Pages (June 2005)
Volume 18, Issue 9, Pages (February 2017)
Volume 129, Issue 3, Pages (September 2005)
C. -H. Chou, M. T. M. Lee, I. -W. Song, L. -S. Lu, H. -C. Shen, C. -H
Volume 9, Issue 3, Pages (September 2017)
Chun Feng, M. D. , Shen Tian, Ph. D. , Yu Zhang, M. D. , Jing He, M. D
Jessica Cooksey, MD, Babak Mokhlesi, MD, FCCP  CHEST 
Revealing Global Regulatory Perturbations across Human Cancers
Volume 35, Issue 2, Pages (July 2009)
Impulse Control: Temporal Dynamics in Gene Transcription
Small RNA profiling reveals deregulated phosphatase and tensin homolog (PTEN)/phosphoinositide 3-kinase (PI3K)/Akt pathway in bronchial smooth muscle.
Volume 23, Issue 4, Pages (April 2018)
Ying-Ying Yu, Ph. D. , Cui-Xiang Sun, Ph. D. , Yin-Kun Liu, Ph. D
Identification and Validation of Genetic Variants that Influence Transcription Factor and Cell Signaling Protein Levels  Ronald J. Hause, Amy L. Stark,
Volume 44, Issue 1, Pages (October 2011)
Evolutionary Rewiring of Human Regulatory Networks by Waves of Genome Expansion  Davide Marnetto, Federica Mantica, Ivan Molineris, Elena Grassi, Igor.
Understanding Tissue-Specific Gene Regulation
Volume 85, Issue 2, Pages (January 2014)
DNA methylation and childhood asthma in the inner city
Dynamic Gene Regulatory Networks of Human Myeloid Differentiation
Mapping Global Histone Acetylation Patterns to Gene Expression
Volume 22, Issue 3, Pages (January 2018)
Volume 11, Pages (January 2019)
Transcriptional Profiling of Quiescent Muscle Stem Cells In Vivo
Volume 47, Issue 2, Pages e5 (August 2017)
Li Xia, David S. Schrump, Jeffrey C. Gildersleeve 
Volume 23, Issue 1, Pages 9-22 (January 2013)
Daniel F. Tardiff, Scott A. Lacadie, Michael Rosbash  Molecular Cell 
Optimal gene expression analysis by microarrays
Volume 23, Issue 7, Pages (May 2018)
Volume 7, Issue 9, Pages (September 2014)
Histopathological Image QTL Discovery of Immune Infiltration Variants
Revealing Global Regulatory Perturbations across Human Cancers
Microarray Gene Expression Analysis of Fixed Archival Tissue Permits Molecular Classification and Identification of Potential Therapeutic Targets in Diffuse.
Volume 22, Issue 3, Pages (January 2018)
Early gene expression profiles during intraoperative myocardial ischemia-reperfusion in cardiac surgery  Sara Arab, PhD, Igor E. Konstantinov, MD, PhD,
Volume 67, Issue 6, Pages e6 (September 2017)
Functional enrichment of differentially expressed genes.
Wei Jiang, Yuting Liu, Rui Liu, Kun Zhang, Yi Zhang  Cell Reports 
Molecular Therapy - Nucleic Acids
Alterations in mRNA 3′ UTR Isoform Abundance Accompany Gene Expression Changes in Human Huntington’s Disease Brains  Lindsay Romo, Ami Ashar-Patel, Edith.
Volume 122, Issue 6, Pages (September 2005)
R.H. Brophy, B. Zhang, L. Cai, R.W. Wright, L.J. Sandell, M.F. Rai 
P53 Pulses Diversify Target Gene Expression Dynamics in an mRNA Half-Life- Dependent Manner and Delineate Co-regulated Target Gene Subnetworks  Joshua R.
DNA Looping Facilitates Targeting of a Chromatin Remodeling Enzyme
Volume 7, Issue 2, Pages (August 2010)
Different IgE recognition of mite allergen components in asthmatic and nonasthmatic children  Yvonne Resch, MSc, Sven Michel, MSc, Michael Kabesch, MD,
IL-9 is a key component of memory TH cell peanut-specific responses from children with peanut allergy  Helen A. Brough, MSc, FRCPCH, David J. Cousins,
Brandon Ho, Anastasia Baryshnikova, Grant W. Brown  Cell Systems 
Maria S. Robles, Sean J. Humphrey, Matthias Mann  Cell Metabolism 
Volume 21, Issue 2, Pages (February 2013)
Pleiotropic Effects of Trait-Associated Genetic Variation on DNA Methylation: Utility for Refining GWAS Loci  Eilis Hannon, Mike Weedon, Nicholas Bray,
Volume 2, Issue 3, Pages (March 2016)
Genome-wide Functional Analysis Reveals Factors Needed at the Transition Steps of Induced Reprogramming  Chao-Shun Yang, Kung-Yen Chang, Tariq M. Rana 
Mapping of Small RNAs in the Human ENCODE Regions
Presentation transcript:

Volume 150, Issue 1, Pages 91-101 (July 2016) DNA Methylation Profiling of Blood Monocytes in Patients With Obesity Hypoventilation Syndrome  Rene Cortese, PhD, Chunling Zhang, MSc, Riyue Bao, PhD, Jorge Andrade, PhD, Abdelnaby Khalyfa, PhD, Babak Mokhlesi, MD, David Gozal, MD, FCCP  CHEST  Volume 150, Issue 1, Pages 91-101 (July 2016) DOI: 10.1016/j.chest.2016.02.648 Copyright © 2016 American College of Chest Physicians Terms and Conditions

Figure 1 Differential DNA methylation profiles in PRE and POST groups. A, Principal component analysis was performed using the full microarray data from PRE samples (red points) and POST samples (blue points). Five samples from the same group clustered together, with the exception of one patient (Patient 5), whose PRE and POST samples clustered separately from the rest. Three principal components determine sample clustering: PC1 (10.5%, x axis), PC2 (9.92%, y axis), and PC3 (9.49%, z axis). B, Pairwise sample correlation was performed using significant DMRs (P < .001 and MAT score > 4). Bidimensional unsupervised clustering was performed in samples from the PRE and POST groups. Correlation coefficients are shown as a color gradient ranging from light blue (0.5) over white (0.75) to light pink (1.0). DMR = differentially methylated region; MAT = model-based analysis of tiling arrays; PRE group = blood samples collected from patients before starting positive airway pressure treatment; POST group = blood samples collected from patients 6 weeks after positive airway pressure treatment. CHEST 2016 150, 91-101DOI: (10.1016/j.chest.2016.02.648) Copyright © 2016 American College of Chest Physicians Terms and Conditions

Figure 2 Characterization of differential DNA methylation between PRE and POST groups. A, Volcano plot of microarray data at probe level. The x axis represents the magnitude of the difference in signal intensity between the PRE and POST groups for each probe in the microarray, expressed as fold changes in log2 scale. Probes with increased microarray signals in POST and PRE groups had positive and negative values on the x axis, respectively. The y axis represents the significance of the difference in signal intensity between the PRE and POST groups for each probe in the microarray, expressed as the –log10-transformed P values. The vertical dashed red lines depict the cutoff values for the fold changes [log2(4) = 2]. Probes showing significant differences (P < .001 and fold change > 4) are shown in red. B, DNA methylation differences in the top 100 DMRs distinguished between the PRE and POST groups. Samples belonging to the same group clustered together by unsupervised clustering based solely on the DNA methylation differences of those 100 DMRs. Samples are accommodated in columns and DMRs in rows in the matrix. DNA methylation differences (expressed as z score) between the groups for each DMR in each sample are represented by a color gradient ranging from blue (negative z scores, meaning higher DNA methylation in the PRE group than in the POST group) through white (no differences) to red (positive z scores, meaning higher DNA methylation in the POST group than in the PRE group). C and D, DMRs with higher DNA methylation in the POST group were longer (C) and contained more probes (D) than those with higher DNA methylation in the PRE group (P < .0001, paired t test). E, Distance to TSS did not differ significantly between DMRs more highly methylated in the POST and PRE groups (P = .949; paired t test). The distance from the beginning of each region to the closest TSS is shown on the x axis. Red and blue lines represent the PRE and POST groups, respectively. F, Association with RefSeq (yellow column portions) and ncRNAs (green column portions) did not significantly differ between DMRs with higher DNA methylation in the POST or PRE groups (P = .889; Fisher’s exact test). ncRNAs = noncoding RNAs; TSS = transcription start site. See Figure 1 legend for expansion of other abbreviations. CHEST 2016 150, 91-101DOI: (10.1016/j.chest.2016.02.648) Copyright © 2016 American College of Chest Physicians Terms and Conditions

Figure 3 Pathway and biological processes associated with DMRs. Biologically relevant gene interaction networks were identified by statistically significant overrepresentation in genes associated with the DMRs. Pathways and biological processes associated with DMRs show higher DNA methylation in the PRE group (red bars, panel A) and POST group (blue bars, panel B). Vertical orange dashed bars depicts the significance cutoff value for the overrepresentation test [–log10(P = .05) = 1.3; hypergeometric test). C, DMR-associated gene networks. Genes associated with DMRs with higher DNA methylation in POST and PRE samples are shown in red and green, respectively. Molecules with a reported function in immune response are circled in purple. Molecules with a role in communication between immune cells are indicated with blue lines. D, Gene network corresponding to mechanisms of gene regulation by peroxisome proliferation-activated receptors (PPARs) overrepresented in DMRs with high DNA methylation in the POST group (shown in red). Molecules with a reported role in cardiometabolic diseases are circled in purple. Molecules associated with PPAR pathways are indicated with blue lines. PPARA = peroxisome proliferation-activated receptor α; RAR = retinoic acid receptor; RefSeq = Reference Sequence; VDR/RXR = vitamin D receptor/retinoid X receptor. See Figure 1 legend for expansion of other abbreviations. CHEST 2016 150, 91-101DOI: (10.1016/j.chest.2016.02.648) Copyright © 2016 American College of Chest Physicians Terms and Conditions

Figure 4 Single-locus DNA methylation analysis in peroxisome proliferation-activated receptor-responsive elements (PPAREs) of candidate genes. Single-locus MeDIP-quantitative PCR results for the samples in the microarray study (red columns), the second sample set (blue columns), and the full sample set (gray columns). Quantitative PCR assays were defined in PPAREs of eight peroxisome proliferation-activated receptor γ target genes. Fold changes (POST/PRE) were calculated as the ratio of the percentage of INPUT recovery in each group and are shown on the y axis as means. Error bars correspond to the SEM. *P < .05, t test. MeDIP = methylated DNA immunoprecipitation. See Figure 1 legend for expansion of other abbreviations. CHEST 2016 150, 91-101DOI: (10.1016/j.chest.2016.02.648) Copyright © 2016 American College of Chest Physicians Terms and Conditions

Figure 5 Association with demographic and clinical variables. Matrix shows the correlation coefficients with continuous variables. Spearman’s correlation test was performed for each variable and the fold changes (POST/PRE) for DNA methylation in each target gene. ρ correlation coefficients are shown as a color gradient from red (negative correlation) through white (no correlation) to blue (positive correlation). AHI = apnea-hypopnea index; ODI = oxygen desaturation index; PAP = positive airway pressure; Spo2 = oxygen saturation as determined by pulse oximetry; T90 = time spent at less than 90% oxygen saturation. CHEST 2016 150, 91-101DOI: (10.1016/j.chest.2016.02.648) Copyright © 2016 American College of Chest Physicians Terms and Conditions