[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Issue
Volume 7, June
Previous Issue
Volume 6, December
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.8
2.5
Journals
Epigenomes
Volume 7
Issue 1
share announcement

Epigenomes, Volume 7, Issue 1 (March 2023) – 8 articles

Cover Story (view full-size image): Abnormal epigenetic regulation is strongly linked to the initiation and progression of breast cancer. The epigenetic changes depend on specific enzymes, including DNA methyltransferases and histone deacetylases. In addition, epigenetic mechanisms are regulated via various signaling pathways, such as 17-beta estradiol (E2) and Wnt signaling, which are promising targets for epigenetic-based therapy in breast cancer patients. However, there are several challenges related to the main strategies of epidrugs with respect to cytotoxicity, resistance, and efficacy. Therefore, in recent epigenetic-targeted therapies, the use of epidrugs in combination with other anti-cancer drugs for tumor remission, reduced resistance to chemotherapy, and fewer side effects has emerged as a promising cancer treatment. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
28 pages, 9189 KiB  
Article
Epigenome-Wide Changes in the Cell Layers of the Vein Wall When Exposing the Venous Endothelium to Oscillatory Shear Stress
by Mariya A. Smetanina, Valeria A. Korolenya, Alexander E. Kel, Ksenia S. Sevostyanova, Konstantin A. Gavrilov, Andrey I. Shevela and Maxim L. Filipenko
Epigenomes 2023, 7(1), 8; https://doi.org/10.3390/epigenomes7010008 - 20 Mar 2023
Cited by 1 | Viewed by 3158
Abstract
Epigenomic changes in the venous cells exerted by oscillatory shear stress towards the endothelium may result in consolidation of gene expression alterations upon vein wall remodeling during varicose transformation. We aimed to reveal such epigenome-wide methylation changes. Primary culture cells were obtained from [...] Read more.
Epigenomic changes in the venous cells exerted by oscillatory shear stress towards the endothelium may result in consolidation of gene expression alterations upon vein wall remodeling during varicose transformation. We aimed to reveal such epigenome-wide methylation changes. Primary culture cells were obtained from non-varicose vein segments left after surgery of 3 patients by growing the cells in selective media after magnetic immunosorting. Endothelial cells were either exposed to oscillatory shear stress or left at the static condition. Then, other cell types were treated with preconditioned media from the adjacent layer’s cells. DNA isolated from the harvested cells was subjected to epigenome-wide study using Illumina microarrays followed by data analysis with GenomeStudio (Illumina), Excel (Microsoft), and Genome Enhancer (geneXplain) software packages. Differential (hypo-/hyper-) methylation was revealed for each cell layer’s DNA. The most targetable master regulators controlling the activity of certain transcription factors regulating the genes near the differentially methylated sites appeared to be the following: (1) HGS, PDGFB, and AR for endothelial cells; (2) HGS, CDH2, SPRY2, SMAD2, ZFYVE9, and P2RY1 for smooth muscle cells; and (3) WWOX, F8, IGF2R, NFKB1, RELA, SOCS1, and FXN for fibroblasts. Some of the identified master regulators may serve as promising druggable targets for treating varicose veins in the future. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Cluster heatmap depicting methylation levels of the top 20 genes among those significantly hypo- and hypermethylated in 3 cell types upon exposure. (<b>A</b>–<b>C</b>) represent a heatmap for −/+ treated ECs, SMCs, and FBs, respectively. Heatmap displays differentially methylated genes ranging from hypomethylated (blue) to hypermethylated (red). avg_Beta represents an average methylation beta value and corresponds to a certain color within the range. Cluster pattern is shown on the left side of each diagram.</p> Full article ">Figure 2
<p>Enriched GO (biological process) tree map of the list of genes provided as input for ECs.</p> Full article ">Figure 3
<p>Enriched GO (biological process) tree map of the list of genes provided as input for SMCs.</p> Full article ">Figure 4
<p>Enriched GO (biological process) tree map of the list of genes provided as input for FBs.</p> Full article ">Figure 5
<p>The most significant functional GO categories overrepresented among the observed genes near differentially methylated sites.</p> Full article ">Figure 6
<p>Association of genes (and their functions) differentially methylated in different cell types with the pathological processes during varicose transformation of the vein wall.</p> Full article ">Figure 7
<p>Enhancer model potentially involved in the regulation of target genes. (<b>A</b>–<b>C</b>)—the most specific composite modules obtained as the results of CMA analysis for ECs, SMCs, and FBs, correspondingly. “Module width” is the preferable distance between sites; “V<span>$</span>” stands for “vertebrates”; the right part of the PWM name represents the TF family name. Score of the best match is shown as the optimized cut-off of the PWM score; number of individual matches (N) for each PWM gives the maximal number of TF sites with the highest scores, which are computed in the module. (<b>D</b>–<b>F</b>)—for ECs, SMCs, and FBs, correspondingly, contain two histograms of the distributions of model scores (reflecting the number of TF site pairs found in the sequence) in the CpG regulatory regions (red) versus CpG sites of unchanged genes (blue). AUC = 0.76 for ECs and SMCs, and 0.81 for FBs.</p> Full article ">Figure 8
<p>Diagram of intracellular regulatory signal transduction pathways of genes near differentially methylated sites in ECs. Master regulators are indicated by red rectangles, transcription factors are indicated by purple rectangles, and green rectangles represent intermediate molecules, which were added to the network during the search for master regulators from the selected TFs. Orange and blue frames highlight molecules that are encoded by genes mapped to differentially methylated sites. Positive feedbacks are represented by dotted lines.</p> Full article ">Figure 9
<p>Diagram of intracellular regulatory signal transduction pathways of genes near differentially methylated sites in SMCs. Master regulators are indicated by red rectangles; transcription factors are indicated by purple rectangles; and green rectangles represent intermediate molecules, which were added to the network during the search for master regulators from the selected TFs. Orange and blue frames highlight molecules that are encoded by genes mapped to differentially methylated sites. Positive feedbacks are represented by dotted lines.</p> Full article ">Figure 10
<p>Diagram of intracellular regulatory signal transduction pathways of genes near differentially methylated sites in FBs. Master regulators are indicated by red rectangles, transcription factors are indicated by purple rectangles, and green rectangles represent intermediate molecules, which were added to the network during the search for master regulators from the selected TFs. Orange and blue frames highlight molecules that are encoded by genes mapped to differentially methylated sites. Positive feedbacks are represented by shown by dotted lines.</p> Full article ">Figure 11
<p>Venn diagram showing the intersection of 3 tables with top master regulators for ECs (yellow), SMCs (pink), and FBs (blue).</p> Full article ">Figure 12
<p>Potential targetable master regulators for each cell type representing the layers of the vein wall.</p> Full article ">
17 pages, 1071 KiB  
Review
Chemical Inhibitors Targeting the Histone Lysine Demethylase Families with Potential for Drug Discovery
by Nando Dulal Das, Hideaki Niwa and Takashi Umehara
Epigenomes 2023, 7(1), 7; https://doi.org/10.3390/epigenomes7010007 - 11 Mar 2023
Cited by 9 | Viewed by 3931
Abstract
The dynamic regulation of histone methylation and demethylation plays an important role in the regulation of gene expression. Aberrant expression of histone lysine demethylases has been implicated in various diseases including intractable cancers, and thus lysine demethylases serve as promising therapeutic targets. Recent [...] Read more.
The dynamic regulation of histone methylation and demethylation plays an important role in the regulation of gene expression. Aberrant expression of histone lysine demethylases has been implicated in various diseases including intractable cancers, and thus lysine demethylases serve as promising therapeutic targets. Recent studies in epigenomics and chemical biology have led to the development of a series of small-molecule demethylase inhibitors that are potent, specific, and have in vivo efficacy. In this review, we highlight emerging small-molecule inhibitors targeting the histone lysine demethylases and their progress toward drug discovery. Full article
(This article belongs to the Special Issue Epidrugs: Toward Understanding and Treating Diverse Diseases)
►▼ Show Figures

Figure 1

Figure 1
<p>Inhibitors of the FAD–containing lysine demethylases. (<b>A</b>) Mechanism of the demethylation by KDM1A using FAD as a cofactor. (<b>B</b>) Representative inhibitors. (<b>C</b>) Structure of <span class="html-italic">trans</span>-(1R,2S)-2-phenylcyclopropylamine (PDB ID: 2XAJ) bound to KDM1A [<a href="#B37-epigenomes-07-00007" class="html-bibr">37</a>]. The adduct structure formed by this compound and FAD was assumed to be the same as the one formed by ORY-1001. The inhibitor–FAD adduct and KDM1A are colored in cyan and blue, respectively. The residues close to the adduct are drawn in sticks. (<b>D</b>) Structure of CC-90011 bound to KDM1A (PDB ID: 6W4K) [<a href="#B28-epigenomes-07-00007" class="html-bibr">28</a>]. The inhibitor and KDM1A are colored orange and blue, respectively, and FAD is colored cyan. The residues close to the adduct are drawn in sticks. Hydrogen bonds are shown by black dashed lines.</p> Full article ">Figure 2
<p>Inhibitors of the JmjC domain–containing lysine demethylases. (<b>A</b>) Mechanism of demethylation using 2-OG and Fe(II) as cofactors. (<b>B</b>) Representative inhibitors. (<b>C</b>) Structure of CPI-455 bound to KDM5A (PDB ID: 5CEH) [<a href="#B64-epigenomes-07-00007" class="html-bibr">64</a>]. The inhibitor and protein are colored orange and blue, respectively. The metal ion (Ni<sup>2+</sup>) and a coordinating water molecule are drawn in spheres, colored purple and red, respectively. Hydrogen bonds and metal coordination are shown by black dashed lines. (<b>D</b>) Structure of GSK-J1 bound to KDM6B (PDB ID: 4ASK) [<a href="#B65-epigenomes-07-00007" class="html-bibr">65</a>]. The inhibitor and protein are colored as in (<b>C</b>). The metal ion (Co<sup>2+</sup>) and coordinating water molecules are drawn in spheres, colored purple and red, respectively. Hydrogen bonds and metal coordination are shown as in (<b>C</b>).</p> Full article ">
16 pages, 995 KiB  
Review
Epigenetic Regulation in Breast Cancer: Insights on Epidrugs
by Ayoung Kim, Kyumin Mo, Hyeonseok Kwon, Soohyun Choe, Misung Park, Woori Kwak and Hyunho Yoon
Epigenomes 2023, 7(1), 6; https://doi.org/10.3390/epigenomes7010006 - 18 Feb 2023
Cited by 23 | Viewed by 6176
Abstract
Breast cancer remains a common cause of cancer-related death in women. Therefore, further studies are necessary for the comprehension of breast cancer and the revolution of breast cancer treatment. Cancer is a heterogeneous disease that results from epigenetic alterations in normal cells. Aberrant [...] Read more.
Breast cancer remains a common cause of cancer-related death in women. Therefore, further studies are necessary for the comprehension of breast cancer and the revolution of breast cancer treatment. Cancer is a heterogeneous disease that results from epigenetic alterations in normal cells. Aberrant epigenetic regulation is strongly associated with the development of breast cancer. Current therapeutic approaches target epigenetic alterations rather than genetic mutations due to their reversibility. The formation and maintenance of epigenetic changes depend on specific enzymes, including DNA methyltransferases and histone deacetylases, which are promising targets for epigenetic-based therapy. Epidrugs target different epigenetic alterations, including DNA methylation, histone acetylation, and histone methylation, which can restore normal cellular memory in cancerous diseases. Epigenetic-targeted therapy using epidrugs has anti-tumor effects on malignancies, including breast cancer. This review focuses on the importance of epigenetic regulation and the clinical implications of epidrugs in breast cancer. Full article
(This article belongs to the Special Issue Epidrugs: Toward Understanding and Treating Diverse Diseases)
►▼ Show Figures

Figure 1

Figure 1
<p>Epigenetic mechanisms regulated via 17-beta estradiol (E2) and Wnt signaling in breast cancer. E2 is a type of the hormone estrogen, which can be a risk factor for breast cancer. Wnt signaling is one of the critical signaling pathways in tumor progression. EZH2 is expressed by E2-estrogen receptor (ER) binding and connects estrogen signaling and Wnt signaling. Wnt signaling facilitates oncogenesis when Wnt antagonist gene, DKK3, is silenced via promoter hypermethylation.</p> Full article ">Figure 2
<p>Epigenetic regulation by enzymes and Epidrugs. Two epigenetic targets or mechanisms of epidrugs are known. HDAC inhibitors act at histone deacetylases (HDACs), which inhibit histone acetyltransferases (HATs). DNMT inhibitors act at DNA methyltransferases (DNMTs).</p> Full article ">
13 pages, 2596 KiB  
Article
SNCA Gene Methylation in Parkinson’s Disease and Multiple System Atrophy
by Ekaterina Yu. Fedotova, Elena V. Iakovenko, Natalia Yu. Abramycheva and Sergey N. Illarioshkin
Epigenomes 2023, 7(1), 5; https://doi.org/10.3390/epigenomes7010005 - 6 Feb 2023
Cited by 5 | Viewed by 2926
Abstract
In recent years, epigenetic mechanisms have been implicated in the development of multifactorial diseases including neurodegenerative disorders. In Parkinson’s disease (PD), as a synucleinopathy, most studies focused on DNA methylation of SNCA gene coding alpha-synuclein but obtained results were rather contradictory. In another [...] Read more.
In recent years, epigenetic mechanisms have been implicated in the development of multifactorial diseases including neurodegenerative disorders. In Parkinson’s disease (PD), as a synucleinopathy, most studies focused on DNA methylation of SNCA gene coding alpha-synuclein but obtained results were rather contradictory. In another neurodegenerative synucleinopathy, multiple system atrophy (MSA), very few studies investigated the epigenetic regulation. This study included patients with PD (n = 82), patients with MSA (n = 24), and a control group (n = 50). In three groups, methylation levels of CpG and non-CpG sites in regulatory regions of the SNCA gene were analyzed. We revealed hypomethylation of CpG sites in the SNCA intron 1 in PD and hypermethylation of predominantly non-CpG sites in the SNCA promoter region in MSA. In PD patients, hypomethylation in the intron 1 was associated with earlier age at the disease onset. In MSA patients, hypermethylation in the promotor was associated with shorter disease duration (before examination). These results showed different patterns of the epigenetic regulation in two synucleinopathies—PD and MSA. Full article
(This article belongs to the Special Issue Non-CpG Methylation)
►▼ Show Figures

Figure 1

Figure 1
<p>Methylation of CpG sites in intron 1 of the <span class="html-italic">SNCA</span> gene. @, statistically significant differences between MSA and control groups; *, statistically significant differences between PD and control groups; #, statistically significant differences between MSA and PD groups. Blue, MSA group; yellow, PD group; green, control group.</p> Full article ">Figure 2
<p>Methylation of CpG sites in intron 2 of the <span class="html-italic">SNCA</span> gene. *, statistically significant differences between MSA and control groups; @, statistically significant differences between PD and control groups. Blue, MSA group; yellow, PD group; green—control group.</p> Full article ">Figure 3
<p>Methylation of CpG sites in the promoter region of the <span class="html-italic">SNCA</span> gene. *, statistically significant differences between MSA and control groups; #, statistically significant differences between MSA and PD groups. Blue, MSA group; yellow, PD group; green, control group.</p> Full article ">Figure 4
<p>Methylation of non-CpG sites in the promoter region of the <span class="html-italic">SNCA</span> gene. *, statistically significant differences between MSA and control groups; #, statistically significant differences between MSA and PD groups. Blue, MSA group; yellow, PD group; green, control group.</p> Full article ">Figure 5
<p>Correlations between age of PD onset and methylation levels of CpG-22 (<b>a</b>), CpG-26 (<b>b</b>), CpG-39 (<b>c</b>), and CpG-40 (<b>d</b>) in <span class="html-italic">SNCA</span> intron 1.</p> Full article ">Figure 6
<p>Correlations between the disease duration in MSA patients and methylation levels of non-CpG-4I (<b>a</b>), non-CpG-4J (<b>b</b>), non-CpG-4K (<b>c</b>) in the <span class="html-italic">SNCA</span> promoter.</p> Full article ">
17 pages, 551 KiB  
Article
DNA Methylation Is a Potential Biomarker for Cardiometabolic Health in Mexican Children and Adolescents
by Abeer A. Aljahdali, Jaclyn M. Goodrich, Dana C. Dolinoy, Hyungjin M. Kim, Edward A. Ruiz-Narváez, Ana Baylin, Alejandra Cantoral, Libni A. Torres-Olascoaga, Martha M. Téllez-Rojo and Karen E. Peterson
Epigenomes 2023, 7(1), 4; https://doi.org/10.3390/epigenomes7010004 - 3 Feb 2023
Cited by 1 | Viewed by 2850
Abstract
DNA methylation (DNAm) is a plausible mechanism underlying cardiometabolic abnormalities, but evidence is limited among youth. This analysis included 410 offspring of the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) birth cohort followed up to two time points in late childhood/adolescence. [...] Read more.
DNA methylation (DNAm) is a plausible mechanism underlying cardiometabolic abnormalities, but evidence is limited among youth. This analysis included 410 offspring of the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) birth cohort followed up to two time points in late childhood/adolescence. At Time 1, DNAm was quantified in blood leukocytes at long interspersed nuclear elements (LINE-1), H19, and 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD-2), and at Time 2 in peroxisome proliferator-activated receptor alpha (PPAR-α). At each time point, cardiometabolic risk factors were assessed including lipid profiles, glucose, blood pressure, and anthropometry. Linear mixed effects models were used for LINE-1, H19, and 11β-HSD-2 to account for the repeated-measure outcomes. Linear regression models were conducted for the cross-sectional association between PPAR-α with the outcomes. DNAm at LINE-1 was associated with log glucose at site 1 [β = −0.029, p = 0.0006] and with log high-density lipoprotein cholesterol at site 3 [β = 0.063, p = 0.0072]. 11β-HSD-2 DNAm at site 4 was associated with log glucose (β = −0.018, p = 0.0018). DNAm at LINE-1 and 11β-HSD-2 was associated with few cardiometabolic risk factors among youth in a locus-specific manner. These findings underscore the potential for epigenetic biomarkers to increase our understanding of cardiometabolic risk earlier in life. Full article
(This article belongs to the Special Issue Environmental Epigenomes)
►▼ Show Figures

Figure 1

Figure 1
<p>Summary of the Main Predictors and Outcomes for this Study and Number of Participants with the Data from the Early Life Exposures in Mexico to ENvironmental Toxicants (ELEMENT) Cohort. Abbreviations: DNAm = DNA methylation; Long interspersed nuclear elements (LINE-1); 11β-hydroxysteroid dehydrogenase type 2 (<span class="html-italic">11β-HSD-2</span>); Peroxisome proliferator-activated receptor alpha (<span class="html-italic">PPAR-α</span>).</p> Full article ">
2 pages, 177 KiB  
Editorial
Acknowledgment to the Reviewers of Epigenomes in 2022
by Epigenomes Editorial Office
Epigenomes 2023, 7(1), 3; https://doi.org/10.3390/epigenomes7010003 - 13 Jan 2023
Viewed by 1540
Abstract
High-quality academic publishing is built on rigorous peer review [...] Full article
11 pages, 1037 KiB  
Brief Report
Sex-Specific miRNA Differences in Liquid Biopsies from Subjects with Solid Tumors and Healthy Controls
by Elena Tomeva, Ulrike D. B. Krammer, Olivier J. Switzeny, Alexander G. Haslberger and Berit Hippe
Epigenomes 2023, 7(1), 2; https://doi.org/10.3390/epigenomes7010002 - 10 Jan 2023
Cited by 8 | Viewed by 4313
Abstract
Dysregulation of epigenetic mechanisms has been recognized to play a crucial role in cancer development, but these mechanisms vary between sexes. Therefore, we focused on sex-specific differences in the context of cancer-based data from a recent study. A total of 12 cell-free DNA [...] Read more.
Dysregulation of epigenetic mechanisms has been recognized to play a crucial role in cancer development, but these mechanisms vary between sexes. Therefore, we focused on sex-specific differences in the context of cancer-based data from a recent study. A total of 12 cell-free DNA methylation targets in CpG-rich promoter regions and 48 miRNAs were analyzed by qPCR in plasma samples from 8 female and 7 male healthy controls as well as 48 female and 80 male subjects with solid tumors of the bladder, brain, colorectal region (CRC), lung, stomach, pancreas, and liver. Due to the small sample size in some groups and/or the non-balanced distribution of men and women, sex-specific differences were evaluated statistically only in healthy subjects, CRC, stomach or pancreas cancer patients, and all cancer subjects combined (n female/male—8/7, 14/14, 8/15, 6/6, 48/80, respectively). Several miRNAs with opposing expressions between the sexes were observed for healthy subjects (miR-17-5p, miR-26b-5p); CRC patients (miR-186-5p, miR-22-3p, miR-22-5p, miR-25-3p, miR-92a-3p, miR-16-5p); stomach cancer patients (miR-133a-3p, miR-22-5p); and all cancer patients combined (miR-126-3p, miR-21-5p, miR-92a-3p, miR-183-5p). Moreover, sex-specific correlations that were dependent on cancer stage were observed in women (miR-27a-3p) and men (miR-17-5p, miR-20a-5p). Our results indicate the complex and distinct role of epigenetic regulation, particularly miRNAs, depending not only on the health status but also on the sex of the patient. The same miRNAs could have diverse effects in different tissues and opposing effects between the biological sexes, which should be considered in biomarker research. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Different miRNA expression levels between female and male participants when tested across different groups after adjustments for covariates. Bars represent the mean value with standard deviation (SD). The asterisks indicate a statistically significant difference between the groups * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01. A healthy control group, <span class="html-italic">n</span> female = 8, <span class="html-italic">n</span> male = 7 B all cancer samples, <span class="html-italic">n</span> female = 48, <span class="html-italic">n</span> male = 80 C colorectal cancer group, <span class="html-italic">n</span> female = 14, <span class="html-italic">n</span> male = 14 D stomach cancer, <span class="html-italic">n</span> female = 15, <span class="html-italic">n</span> male = 8.</p> Full article ">Figure 2
<p>Relative expression of miRNAs in the healthy control group and all cancer samples across cancer stages in female (<b>A</b>–<b>C</b>) and male subjects (<b>D</b>–<b>F</b>). The lines in the scatter dot plots represent the mean expression values and the error bars represent the standard deviation (SD). Each dot represents a sample in a specific group as follows: green healthy subjects, yellow subjects with cancer Stage I, orange subjects with cancer Stage II, and red subjects with cancers Stage III; ns not significant.</p> Full article ">
29 pages, 21798 KiB  
Review
Environmental Adaptation of Genetically Uniform Organisms with the Help of Epigenetic Mechanisms—An Insightful Perspective on Ecoepigenetics
by Günter Vogt
Epigenomes 2023, 7(1), 1; https://doi.org/10.3390/epigenomes7010001 - 26 Dec 2022
Cited by 10 | Viewed by 5606
Abstract
Organisms adapt to different environments by selection of the most suitable phenotypes from the standing genetic variation or by phenotypic plasticity, the ability of single genotypes to produce different phenotypes in different environments. Because of near genetic identity, asexually reproducing populations are particularly [...] Read more.
Organisms adapt to different environments by selection of the most suitable phenotypes from the standing genetic variation or by phenotypic plasticity, the ability of single genotypes to produce different phenotypes in different environments. Because of near genetic identity, asexually reproducing populations are particularly suitable for the investigation of the potential and molecular underpinning of the latter alternative in depth. Recent analyses on the whole-genome scale of differently adapted clonal animals and plants demonstrated that epigenetic mechanisms such as DNA methylation, histone modifications and non-coding RNAs are among the molecular pathways supporting phenotypic plasticity and that epigenetic variation is used to stably adapt to different environments. Case studies revealed habitat-specific epigenetic fingerprints that were maintained over subsequent years pointing at the existence of epigenetic ecotypes. Environmentally induced epimutations and corresponding gene expression changes provide an ideal means for fast and directional adaptation to changing or new conditions, because they can synchronously alter phenotypes in many population members. Because microorganisms inclusive of human pathogens also exploit epigenetically mediated phenotypic variation for environmental adaptation, this phenomenon is considered a universal biological principle. The production of different phenotypes from the same DNA sequence in response to environmental cues by epigenetic mechanisms also provides a mechanistic explanation for the “general-purpose genotype hypothesis” and the “genetic paradox of invasions”. Full article
(This article belongs to the Special Issue Environmental Epigenomes)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Scheme of environmentally induced change of gene and phenotype expression by epigenetic mechanisms. Environmental signals trigger gene expression change via hormones, second messengers, and environment-sensitive DNA methylation modifying enzymes (DME) and histone modifying enzymes (HME). DNA methylation readers (DMRe), histone modification readers (HMRe) and transcription factors recruit the DMEs and HMEs to specific sites in the chromatin and DNA. Histone modifications such as acetylation (filled squares) and deacetylation (open squares) help to shape chromatin structure and access to the DNA, and methylation (filled circles) and demethylation (open circles) of CpG dinucleotides in the DNA modify gene expression, resulting in different variants of a phenotypic trait. Adapted from Vogt [<a href="#B9-epigenomes-07-00001" class="html-bibr">9</a>].</p> Full article ">Figure 2
<p>Phenotypic, genetic and epigenetic differences between differently adapted populations of marbled crayfish, <span class="html-italic">Procambarus virginalis</span>. (<b>A</b>) Examples of strikingly different marbled crayfish habitats. From Vogt et al. [<a href="#B104-epigenomes-07-00001" class="html-bibr">104</a>], Tönges et al. [<a href="#B105-epigenomes-07-00001" class="html-bibr">105</a>,<a href="#B108-epigenomes-07-00001" class="html-bibr">108</a>] and Andriantsoa et al. [<a href="#B107-epigenomes-07-00001" class="html-bibr">107</a>]. (<b>B</b>) Genetic differences between representatives from several European populations as determined by whole-genome sequencing. A descendant of the oldest known marbled crayfish aquarium lineage was used as a reference. G, Germany. Adapted from Maiakovska et al. [<a href="#B101-epigenomes-07-00001" class="html-bibr">101</a>]. (<b>C</b>) Maximum body size of laboratory raised and wild specimens from Lake Moosweiher (Germany), showing 30% bigger total length (TL) in the lake. From Vogt et al. [<a href="#B104-epigenomes-07-00001" class="html-bibr">104</a>]. (<b>D</b>) Chelipeds of specimens from the laboratory and Lake Moosweiher, showing bigger and sharper spines (arrows) in the wild specimen. From Vogt et al. [<a href="#B104-epigenomes-07-00001" class="html-bibr">104</a>]. (<b>E</b>) Comparative analysis of 697 variably methylated genes in the hepatopancreas and abdominal musculature of specimens from the laboratory (L), Lake Moosweiher (M) and a rice field in Moramanga, Madagascar (Ma). The heatmap shows differences in methylation patterns between individuals, particularly in the hepatopancreas. Adapted from Tönges et al. [<a href="#B105-epigenomes-07-00001" class="html-bibr">105</a>]. (<b>F</b>) Principal component analysis of samples from the laboratory and Lake Singliser See based on the average methylation of 361 variably methylated genes, showing clear separation of the populations. Adapted from Tönges et al. [<a href="#B105-epigenomes-07-00001" class="html-bibr">105</a>]. (<b>G</b>) Differences in population structure between pond, pristine mountain river and polluted lowland river in Madagascar and an acidic lake in Germany. Adapted from Andriantsoa et al. [<a href="#B107-epigenomes-07-00001" class="html-bibr">107</a>] and Tönges et al. [<a href="#B108-epigenomes-07-00001" class="html-bibr">108</a>]. (<b>H</b>) Principal component analysis of methylation of 122 genes separating four populations from rivers and lakes in Madagascar and Germany. Adapted from Tönges et al. [<a href="#B105-epigenomes-07-00001" class="html-bibr">105</a>]. (<b>I</b>) Persistent DNA methylation fingerprints of populations from Andragnaro River (A), Ihosy River (I), Lake Reilinger See (R) and Lake Singliser See (S) in consecutive years (1 and 2), exemplified for a small genic region of the hepatopancreatic DNA. The samples were collected at intervals of 12–21 months and analysed with two different methods. Adapted from Tönges et al. [<a href="#B105-epigenomes-07-00001" class="html-bibr">105</a>].</p> Full article ">Figure 3
<p>Variation of DNA methylation between and within differently adapted Chinese populations of clonal alligator weed, <span class="html-italic">Alternanthera philoxeroides</span>. Populations are indicated by two-letter code. The principal coordinate analysis shows samples from the field collected in subsequent years and the same samples after transfer to a common environment and then to a culture chamber. Zoom-in demonstrates that some of the DNA methylation differences between populations persisted for 10 asexual generations. Adapted from Shi et al. [<a href="#B116-epigenomes-07-00001" class="html-bibr">116</a>].</p> Full article ">Figure 4
<p>Genetic and epigenetic variation in genetically impoverished, sexually reproducing populations. (<b>A</b>) Dependence of DMRs on SNPs in CG, CHG and CHH contexts in 263 inbred genotypes of maize, <span class="html-italic">Zea mays</span>, showing that more than 60% of the epigenetic variation is uncoupled from genetic variation. Adapted from Xu et al. [<a href="#B119-epigenomes-07-00001" class="html-bibr">119</a>]. (<b>B</b>) Negative correlation of genetic and epigenetic variation in invasive populations of house sparrow, <span class="html-italic">Passer domesticus</span>, from seven Kenyan cities. Genetic variation was determined by microsatellite analysis and epigenetic diversity by MSAP. <span class="html-italic">h</span>, haplotype diversity; <span class="html-italic">Ho</span>, heterozygosity; p, probability value; r, Pearson correlation coefficient. Adapted from Vogt [<a href="#B33-epigenomes-07-00001" class="html-bibr">33</a>], compiled with data from Liebl et al. [<a href="#B120-epigenomes-07-00001" class="html-bibr">120</a>].</p> Full article ">Figure 5
<p>Scenario of speciation in asexually reproducing organisms via epigenetic phenotypes and epigenetic ecotypes. Different epigenetic ecotypes arise from a genetically uniform source population by invasion of different ecosystems, the generation of habitat-specific phenotypes by environmentally induced epigenetic changes, and the transgenerational inheritance and selection of these phenotypes. Under favourable conditions, the epigenotypes may be genetically integrated, and the epigenetic ecotypes may thus transform into classical, genetically diverse ecotypes, which can finally evolve to different species.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-8
Previous Issue
Next Issue
Back to TopTop