The Contribution of PGPR in Salt Stress Tolerance in Crops: Unravelling the Molecular Mechanisms of Cross-Talk between Plant and Bacteria by Gianluigi Giannelli, Silvia Potestio, Giovanna Visioli

“…In addition, very recent -OMICs approaches were reported, dissecting the role of PGPR in modulating plant genomes and epigenomes, opening up the possibility of combining the high genetic variations of plants with the action of PGPR for the selection of useful plant traits to cope with salt stress conditions.…”
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Hepatic Dysfunction Caused by Consumption of a High-Fat Diet by Anthony R. Soltis, Norman J. Kennedy, Xiaofeng Xin, Feng Zhou, Scott B. Ficarro, Yoon Sing Yap, Bryan J. Matthews, Douglas A. Lauffenburger, Forest M. White, Jarrod A. Marto, Roger J. Davis, Ernest Fraenkel

“…We interrogated diet-induced epigenomic, transcriptomic, proteomic, and metabolomic alterations using high-throughput omic methods and used a network modeling approach to integrate these diverse molecular signals. …”
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Inhibition of EZH2 Promotes Human Embryonic Stem Cell Differentiation into Mesoderm by Reducing H3K27me3 by Yongxin Yu, Peng Deng, Bo Yu, John M. Szymanski, Tara Aghaloo, Christine Hong, Cun-Yu Wang

“…Here, we conducted an epigenome-wide analysis of hESCs and MSCs and uncovered that EZH2 was enriched in hESCs and was downregulated significantly in MSCs. …”
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Integrative Proteome Analysis Revels 3-Hydroxybutyrate Exerts Neuroprotective Effect by Influencing Chromatin Bivalency by Xin-Liang Zhu, Huan Du, Lei-Lei Wang, Er-Ling Hu, Ning Li, Hai-Xia Lu, Guo-Qiang Chen, Xiao-Yun Lu

“…Integrative analysis of transcriptomic and epigenomic datasets highlighted the involvement of bivalent transcription factors in 3OHB-mediated disease protection and its alteration of neuronal development processes. …”
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Large-Scale Identification of Coregulated Enhancer Networks in the Adult Human Brain by Marit W. Vermunt, Peter Reinink, Jeroen Korving, Ewart de Bruijn, Paul M. Creyghton, Onur Basak, Geert Geeven, Pim W. Toonen, Nico Lansu, Charles Meunier, Sebastiaan van Heesch, Hans Clevers, Wouter de Laat, Edwin Cuppen, Menno P. Creyghton

“…Here, we survey the epigenomic landscape of promoters and cis-regulatory elements in 136 regions of the adult human brain. …”
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Protein Arginine Methyltransferase (PRMT) Inhibitors—AMI-1 and SAH Are Effective in Attenuating Rhabdomyosarcoma Growth and Proliferation in Cell Cultures by Joanna Janisiak, Patrycja Kopytko, Marta Tkacz, Dorota Rogińska, Magdalena Perużyńska, Bogusław Machaliński, Andrzej Pawlik, Maciej Tarnowski

“…Currently, increased amounts of evidence indicate that not only gene mutations, but also epigenetic modifications may be involved in the development of RMS. Epigenomic changes regulate the chromatin architecture and affect the interaction between DNA strands, histones and chromatin binding proteins, thus, are able to control gene expression. …”
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HaploReg: a resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants by Ward, Lucas D., Manolis, Kellis

“…Predicting functional non-coding sequence has been recently facilitated by the availability of conservation and epigenomic information. We present HaploReg, a tool for exploring annotations of the non-coding genome among the results of published GWAS or novel sets of variants. …”
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Methylation-Sensitive Expression of a DNA Demethylase Gene Serves As an Epigenetic Rheostat by Williams, Ben P., Pignatta, Daniela, Henikoff, Steven, Gehring, Mary

“…We propose that the ROS1 locus functions as an epigenetic rheostat, tuning the level of demethylase activity in response to methylation alterations, thus ensuring epigenomic stability.…”
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Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells by Chen, L, Ge, B, Casale, FP, Vasquez, L, Kwan, T, Garrido-Martín, D, Watt, S, Yan, Y, Kundu, K, Ecker, S, Datta, A, Richardson, D, Burden, F, Mead, D, Mann, AL, Fernandez, JM, Rowlston, S, Wilder, SP, Farrow, S, Shao, X, Lambourne, JJ, Redensek, A, Albers, CA, Amstislavskiy, V, Ashford, S, Berentsen, K, Bomba, L, Bourque, G, Bujold, D, Busche, S, Caron, M, Chen, S-H, Cheung, W, Delaneau, O, Dermitzakis, ET, Elding, H, Colgiu, I, Bagger, FO, Flicek, P, Habibi, E, Iotchkova, V, Janssen-Megens, E, Kim, B, Lehrach, H, Lowy, E, Mandoli, A, Matarese, F, Maurano, MT, Morris, JA, Pancaldi, V, Pourfarzad, F, Rehnstrom, K, Rendon, A, Risch, T, Sharifi, N, Simon, M-M, Sultan, M, Valencia, A, Walter, K, Wang, S-Y, Frontini, M, Antonarakis, SE, Clarke, L, Yaspo, M-L, Beck, S, Guigo, R, Rico, D, Martens, JHA, Ouwehand, WH, Kuijpers, TW, Paul, DS, Stunnenberg, HG, Stegle, O, Downes, K, Pastinen, T, Soranzo, N

“…We assess, quantitatively, the relative contribution of cis-genetic and epigenetic factors to transcription and evaluate their impact as potential sources of confounding in epigenome-wide association studies. Further, we characterize highly coordinated genetic effects on gene expression, methylation, and histone variation through quantitative trait locus (QTL) mapping and allele-specific (AS) analyses. …”

From pioneer to repressor: bimodal foxd3 activity dynamically remodels neural crest regulatory landscape in vivo by Lukoseviciute, M, Gavriouchkina, D, Williams, R, Hochgreb-Hagele, T, Senanayake, U, Chong-Morrison, V, Thongjuea, S, Repapi, E, Mead, A, Sauka-Spengler, T

“…Here, we perform NC-specific transcriptional and epigenomic profiling of foxd3-mutant versus wild-type cells in vivo to define the gene regulatory circuits controlling NC specification. …”

Distinct epicardial gene regulatory programs drive development and regeneration of the zebrafish heart by Weinberger, M, Simões, FC, Gungoosingh, T, Sauka-Spengler, T, Riley, PR

“…Here, we compared the transcriptome and epigenome of the developing and regenerating zebrafish epicardia. …”

How nanotechnology and biomedical engineering are supporting the identification of predictive biomarkers in neuro-oncology by Ganau, M, Paris, M, Syrmos, N, Ganau, L, Ligarotti, G, Moghaddamjou, A, Prisco, L, Ambu, R, Chibbaro, S

“…Based on the results of this systematic review we can conclude that: (1) the advances in nanotechnology and bioengineering are supporting tremendous efforts in optimizing the methods for genomic, epigenomic and proteomic profiling; (2) a successful translational approach is attempting to identify a growing number of biomarkers, some of which appear to be promising candidates in many areas of neuro-oncology; (3) the designing of Randomized Controlled Trials will be warranted to better define the prognostic value of those biomarkers and biosignatures.…”

Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated variants by Pasquali, L, Gaulton, K, Rodríguez-Seguí, SA, Mularoni, L, Miguel-Escalada, I, Akerman, I, Tena, J, Morán, I, Gómez-Marín, C, Van De Bunt, M, Ponsa-Cobas, J, Castro, N, Nammo, T, Cebola, I, García-Hurtado, J, Maestro, M, Pattou, F, Piemonti, L, Berney, T, Gloyn, A, Ravassard, P, Skarmeta, J, Müller, F, Mccarthy, M, Ferrer, J

“…Our studies illustrate how islet transcription factors interact functionally with the epigenome and provide systematic evidence that the dysregulation of islet enhancers is relevant to the mechanisms underlying type 2 diabetes. © 2014 Nature America, Inc.…”

A chemical biology toolbox to study protein methyltransferases and epigenetic signaling by Scheer, S, Ackloo, S, Medina, T, Schapira, M, Li, F, Ward, J, Lewis, A, Northrop, J, Richardson, P, Kaniskan, H, Shen, Y, Liu, J, Smil, D, McLeod, D, Zepeda-Velazquez, C, Luo, M, Jin, J, Barsyte-Lovejoy, D, Huber, K, De Carvalho, D, Vedadi, M, Zaph, C, Brown, P, Arrowsmith, C

“…Our collection provides inhibitors and antagonists that together modulate most of the key regulatory methylation marks on histones H3 and H4, providing an important resource for modulating cellular epigenomes. We describe a comprehensive and comparative characterization of the probe collection with respect to their potency, selectivity, and mode of inhibition. …”

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis by Liu, J, Carnero-Montoro, E, Van Dongen, J, Lent, S, Nedeljkovic, I, Ligthart, S, Tsai, P, Martin, T, Mandaviya, P, Jansen, R, Peters, M, Duijts, L, Jaddoe, V, Tiemeier, H, Felix, J, Willemsen, G, De Geus, E, Chu, A, Levy, D, Hwang, S, Bressler, J, Gondalia, R, Salfati, E, Herder, C, Hidalgo, B, Tanaka, T, Moore, A, Lemaitre, R, Jhun, M, Smith, J, Sotoodehnia, N, Bandinelli, S, Ferrucci, L, Arnett, D, Grallert, H, Assimes, T, Hou, L, Baccarelli, A, Whitsel, E, Van Dijk, K, Amin, N, Uitterlinden, A, Sijbrands, E, Franco, O, Dehghan, A, Spector, T, Dupuis, J, Hivert, M, Rotter, J, Meigs, J, Pankow, J, Van Meurs, J, Isaacs, A, Boomsma, D, Bell, J, Demirkan, A, Van Duijn, C

“…Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. …”

GATA3 induces the pathogenicity of Th17 cells via regulating GM-CSF expression by Matthew J. Butcher, Rama Krishna Gurram, Xiaoliang Zhu, Xi Chen, Gangqing Hu, Gangqing Hu, Vanja Lazarevic, Keji Zhao, Jinfang Zhu

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Processing and analysis of DNA methylation and statistical integration with genetic and clinical data by Pan, Hong

“…This thesis provides a comprehensive comparison of array and sequencing data, across key functional genomic regions in terms of their coverage and concordance of methylation calls and the use in epigenomic wide analysis study. The second part presents a suite of statistical models developed to study the complicated relationships between Gene, Environment and Methylation. …”
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DNA methylation age mediates effect of metabolic profile on cardiovascular and general aging by Si, J, Ma, Y, Yu, C, Sun, D, Pang, Y, Pei, P, Yang, L, Millwood, IY, Walters, RG, Chen, Y, Du, H, Zheng, X, Avery, D, Chen, J, Chen, Z, Liang, L, Li, L, Lv, J

“…Connecting metabolomic, epigenomic, and aging outcomes help unravel the complex mechanisms underlying aging. …”

Tissue-specific RNA expression marks distant-acting developmental enhancers. by Han Wu, Alex S Nord, Jennifer A Akiyama, Malak Shoukry, Veena Afzal, Edward M Rubin, Len A Pennacchio, Axel Visel

“…Together, our results demonstrate that tissue-specific eRNA expression is a common feature of in vivo enhancers, as well as a major source of extragenic transcription, and that eRNA expression signatures can be used to predict tissue-specific enhancers independent of known epigenomic enhancer marks.…”
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Machine learning for pan-cancer classification based on RNA sequencing data by Paula Štancl, Rosa Karlić

“…Data produced by large-scale cancer genomics initiatives, which aim to determine the genomic, epigenomic, and transcriptomic characteristics of a large number of individual patients of multiple cancer types, have led to the introduction of various methods that use machine learning to predict the TOO of cancer patients. …”
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