scholarly journals Enhancing resolution of natural methylome reprogramming behavior in plants

2018 ◽  
Author(s):  
Robersy Sanchez ◽  
Xiaodong Yang ◽  
Jose R Barreras ◽  
Hardik Kundariya ◽  
Sally A. Mackenzie
Keyword(s):  
Signal Detection ◽  
Thermal Fluctuation ◽  
Methylation Analysis ◽  
Dynamic Background ◽  
Discrimination Power ◽  
Stress Effects ◽  
Enhanced Resolution ◽  
Differentially Methylated Genes ◽  
Methylome Analysis ◽  
Hellinger Divergence

AbstractBackgroundNatural methylome reprogramming within chromatin involves changes in local energy landscapes that are subject to thermodynamic principles. Signal detection permits the discrimination of methylation signal from dynamic background noise that is induced by thermal fluctuation. Current genome-wide methylation analysis methods do not incorporate biophysical properties of DNA, and focus largely on DNA methylation density changes, which limits resolution of natural, more subtle methylome behavior in relation to gene activity.ResultsWe present here a novel methylome analysis procedure, Methyl-IT, based on information thermodynamics and signal detection. Methylation analysis involves a signal detection step, and the method was designed to discriminate methylation regulatory signal from background variation. Comparisons with commonly used programs and two publicly available methylome datasets, involving stages of seed development and drought stress effects, were implemented. Information divergence between methylation levels from different groups, measured in terms of Hellinger divergence, provides discrimination power between control and treatment samples. Differentially informative methylation positions (DIMPs) achieved higher sensitivity and accuracy than standard differentially methylated positions (DMPs) identified by other methods. Differentially methylated genes (DMG) that are based on DIMPs were significantly enriched in biologically meaningful networks.ConclusionsMethyl-IT analysis enhanced resolution of natural methylome reprogramming behavior to reveal network-associated responses, offering resolution of gene pathway influences not attainable with previous methods.

scholarly journals Example of Methylome Analysis with MethylIT using Cancer Datasets

2018 ◽  
Author(s):  
Robersy Sanchez ◽  
Sally Mackenzie
Keyword(s):  
Signal Detection ◽  
Background Noise ◽  
Thermal Fluctuations ◽  
Analysis Procedure ◽  
Methylation Analysis ◽  
Detection Problem ◽  
Gene Pathway ◽  
Genome Methylation ◽  
Regulatory Signal ◽  
Methylome Analysis

AbstractMethyl-IT, a novel methylome analysis procedure based on information thermodynamics and signal detection was recently released. Methylation analysis involves a signal detection problem, and the method was designed to discriminate methylation regulatory signal from background noise induced by thermal fluctuations. Methyl-IT enhances the resolution of genome methylation behavior to reveal network-associated responses, offering resolution of gene pathway influences not attainable with previous methods. Herein, an example of MethylIT application to the analysis of breast cancer methylomes is presented.

Differential methylation in visceral adipose tissue of obese men discordant for metabolic disturbances

2014 ◽  
Vol 46 (6) ◽  
pp. 216-222 ◽  
Author(s):  
Frédéric Guénard ◽  
André Tchernof ◽  
Yves Deshaies ◽  
Louis Pérusse ◽  
Simon Biron ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Pathway Analysis ◽  
Differential Methylation ◽  
Methylation Analysis ◽  
Metabolic Disturbances ◽  
Cvd Risk ◽  
Differentially Methylated Genes ◽  
Increased Risk ◽  
Severely Obese ◽  
Analysis System

Obesity is associated with an increased risk of Type 2 diabetes and cardiovascular diseases (CVD). The severely obese population is heterogeneous regarding CVD risk profile. Our objective was to identify metabolic pathways potentially associated with development of metabolic syndrome (MetS) through an analysis of overrepresented pathways from differentially methylated genes between severely obese men with (MetS+) and without (MetS−) the MetS. Genome-wide quantitative DNA methylation analysis in VAT of severely obese men was carried out using the Infinium HumanMethylation450 BeadChip. Differences in methylation levels between MetS+ ( n = 7) and MetS− ( n = 7) groups were tested. Overrepresented pathways from the list of differentially methylated genes were identified and visualized with the Ingenuity Pathway Analysis system. Differential methylation analysis between MetS+ and MetS− groups identified 8,578 methylation probes (3,258 annotated genes) with significant differences in methylation levels (false discovery rate-corrected DiffScore ≥ |13| ∼ P ≤ 0.05). Pathway analysis from differentially methylated genes identified 41 overrepresented ( P ≤ 0.05) pathways. The most overrepresented pathways were related to structural components of the cell membrane, inflammation and immunity and cell cycle regulation. This study provides potential targets associated with adipose tissue dysfunction and development of the MetS.

scholarly journals Whole-methylome analysis of circulating monocytes in acute diabetic Charcot foot reveals differentially methylated genes involved in the formation of osteoclasts

Epigenomics ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 281-296 ◽  
Author(s):  
Jennifer Pasquier ◽  
Mark Spurgeon ◽  
Martina Bradic ◽  
Binitha Thomas ◽  
Amal Robay ◽  
...  
Keyword(s):  
Charcot Foot ◽  
Differentially Methylated Genes ◽  
Methylome Analysis

scholarly journals Genome-wide DNA methylation analysis in Chinese Chenghua and Yorkshire pigs

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kai Wang ◽  
Pingxian Wu ◽  
Shujie Wang ◽  
Xiang Ji ◽  
Dong Chen ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Meat Quality ◽  
Muscle Development ◽  
Production Performance ◽  
High Rate ◽  
Methylation Analysis ◽  
Phenotypic Differences ◽  
Genome Wide ◽  
Differentially Methylated Genes ◽  
Dna Methylation Analysis

Abstract Background The Chinese Chenghua pig (CHP) is a typical Chinese domestic fatty pig breed with superior meat quality characteristics, while the Yorkshire pig (YP) has the characteristics of fast growth and a high rate of lean meat. Long term natural selection and artificial selection resulted in great phenotypic differences between the two breeds, including growth, development, production performance, meat quality, and coat color. However, genome-wide DNA methylation differences between CHP and YP remain unclear. Results DNA methylation data were generated for muscle tissues of CHP and YP using reduced representation bisulfite sequencing (RRBS). In this study, a total of 2,416,211 CpG sites were identified. Besides, the genome-wide DNA methylation analysis revealed 722 differentially methylated regions (DMRs) and 466 differentially methylated genes (DMGs) in pairwise CHP vs. YP comparison. Six key genomic regions (Sus scrofa chromosome (SSC)1:253.47–274.23 Mb, SSC6:148.71–169.49 Mb, SSC7:0.25–9.86 Mb, SSC12:43.06–61.49 Mb, SSC14:126.43–140.95 Mb, and SSC18:49.17–54.54 Mb) containing multiple DMRs were identified, and differences of methylation patterns in these regions may be related to phenotypic differences between CHP and YP. Based on the functional analysis of DMGs, 8 DMGs (ADCY1, AGBL4, EXOC2, FUBP3, PAPPA2, PIK3R1, MGMT and MYH8) were considered as important candidate genes associated with muscle development and meat quality traits in pigs. Conclusions This study explored the difference in meat quality between CHP and YP from the epigenetic point of view, which has important reference significance for the local pork industry and pork food processing.

scholarly journals Comparative Transcriptome and Methylome Analysis in Human Skeletal Muscle Anabolism, Hypertrophy and Epigenetic Memory

2018 ◽  
Author(s):  
Daniel C. Turner ◽  
Robert A. Seaborne ◽  
Adam P. Sharples
Keyword(s):  
Skeletal Muscle ◽  
Dna Methylation ◽  
Large Scale ◽  
Human Skeletal Muscle ◽  
Large Data ◽  
Bioinformatic Analysis ◽  
Cancer Genes ◽  
Epigenetic Memory ◽  
Differentially Methylated Genes ◽  
Methylome Analysis

AbstractTranscriptome wide changes in human skeletal muscle after acute (anabolic) and chronic resistance exercise (RE) induced hypertrophy have been extensively determined in the literature. We have also recently undertaken DNA methylome analysis (850,000 + CpG sites) in human skeletal muscle after acute and chronic RE, detaining and retraining, where we identified an association between DNA methylation in an epigenetic memory of exercise induced skeletal muscle hypertrophy. However, it is currently unknown as to whether all the genes identified in the transcriptome studies to date are also epigenetically regulated at the DNA level after acute, chronic or repeated RE exposure. We therefore aimed to undertake large scale bioinformatical analysis by pooling the publicly available transcriptome data after acute (110 samples) and chronic RE (181 samples) and comparing these large data sets with our genome-wide DNA methylation analysis in human skeletal muscle after acute and chronic RE, detraining and retraining. Indeed, after acute RE we identified 866 up- and 936 down-regulated genes at the expression level, with 270 (out of the 866 up- regulated) identified as being hypomethylated, and 216 (out of 936 downregulated) as hypermethylated. After chronic RE we identified 2,018 up- and 430 down-regulated genes with 592 (out of 2,018 upregulated) identified as being hypomethylated and 98 (out of 430 genes downregulated) as hypermethylated. After KEGG pathway analysis, genes associated with ‘cancer’ pathways were significantly enriched in both bioinformatic analysis of the pooled transcriptome and methylome data after both acute and chronic RE. This resulted in 23 (out of 69) and 28 (out of 49) upregulated and hypomethylated and 12 (out of 37) and 2 (out of 4) downregulated and hypermethylated ‘cancer’ genes following acute and chronic RE respectively. Within skeletal muscle tissue, these ‘cancer’ genes predominant functions were associated with matrix/ actin structure and remodelling, mechano-transduction (including PTK2/Focal Adhesion Kinase and Phospholipase D-following chronic RE only), TGF-beta signalling and protein synthesis (GSK3B after acute RE only). Interestingly, 51 genes were also identified to be up/downregulated in both the acute and chronic RE pooled transcriptome analysis as well as significantly hypo/hypermethylated after acute RE, chronic RE, detraining and retraining. Five genes; FLNB, MYH9, SRGAP1, SRGN, ZMIZ1 demonstrated increased gene expression in the acute and chronic RE transcriptome and also demonstrated hypomethylation in these conditions. Importantly, these 5 genes demonstrated retained hypomethylation even during detraining (following training induced hypertrophy) when exercise was ceased and lean mass returned to baseline (pre-training) levels, identifying them as potential epigenetic memory genes. Importantly, for the first time across the transcriptome and epigenome combined, this study identifies novel differentially methylated genes associated with human skeletal muscle anabolism, hypertrophy and epigenetic memory.

A Many-Beam Technique for Enhanced Resolution of Partial Dislocations

1972 ◽  
Vol 30 ◽  
pp. 646-647
Author(s):  
J. M. Oblak ◽  
B. H. Kear
Keyword(s):  
Phase Shift ◽  
Field Method ◽  
Dark Field ◽  
Bright Field ◽  
Field Technique ◽  
Weak Beam ◽  
Partial Dislocations ◽  
Enhanced Resolution ◽  
Nickel Base ◽  
Beam Technique

The “weak-beam” and systematic many-beam techniques are the currently available methods for resolution of closely spaced dislocations or other inhomogeneities imaged through strain contrast. The former is a dark field technique and image intensities are usually very weak. The latter is a bright field technique, but generally use of a high voltage instrument is required. In what follows a bright field method for obtaining enhanced resolution of partial dislocations at 100 KV accelerating potential will be described.A brief discussion of an application will first be given. A study of intermediate temperature creep processes in commercial nickel-base alloys strengthened by the Ll2 Ni3 Al γ precipitate has suggested that partial dislocations such as those labelled 1 and 2 in Fig. 1(a) are in reality composed of two closely spaced a/6 <112> Shockley partials. Stacking fault contrast, when present, tends to obscure resolution of the partials; thus, conditions for resolution must be chosen such that the phase shift at the fault is 0 or a multiple of 2π.

Sample Size and the Detection of Correlation--A Signal Detection Account: Comment on Kareev (2000) and Juslin and Olsson (2005).

2005 ◽  
Vol 112 (1) ◽  
pp. 268-279 ◽  
Author(s):  
Richard B. Anderson ◽  
Michael E. Doherty ◽  
Neil D. Berg ◽  
Jeff C. Friedrich
Keyword(s):  
Signal Detection ◽  
Sample Size

So, Whatever Happened to Signal Detection Theory?

1995 ◽  
Vol 40 (10) ◽  
pp. 972-972
Author(s):  
Jerome R. Busemeyer
Keyword(s):  
Signal Detection ◽  
Signal Detection Theory ◽  
Detection Theory

Do logistic regression and signal detection identify different subgroups at risk? Implications for the design of tailored interventions.

2001 ◽  
Vol 6 (1) ◽  
pp. 35-48 ◽  
Author(s):  
Michaela Kiernan ◽  
Helena C. Kraemer ◽  
Marilyn A. Winkleby ◽  
Abby C. King ◽  
C. Barr Taylor
Keyword(s):  
At Risk ◽  
Logistic Regression ◽  
Signal Detection ◽  
Tailored Interventions
Sign in / Sign up

Export Citation Format

Share Document