Diet-Epigenome Axis
2020; Wolters Kluwer; Volume: 13; Issue: 4 Linguagem: Africâner
10.1161/circgen.120.003129
ISSN2574-8300
Autores Tópico(s)Circadian rhythm and melatonin
ResumoHomeCirculation: Genomic and Precision MedicineVol. 13, No. 4Diet-Epigenome Axis Free AccessEditorialPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyRedditDiggEmail Jump toFree AccessEditorialPDF/EPUBDiet-Epigenome Axis Carsten SkarkeMD Carsten SkarkeCarsten Skarke Correspondence to: Carsten Skarke, MD, University of Pennsylvania, Institute for Translational Medicine and Therapeutics (ITMAT), 3400 Civic Center Blvd, Smilow Center for Translational Res 10-101, Philadelphia, PA 19104. Email E-mail Address: [email protected] https://orcid.org/0000-0001-5145-3681 McNeil Fellow in Translational Medicine and Therapeutics, Institute for Translational Medicine and Therapeutics (ITMAT), University of Pennsylvania Perelman School of Medicine, Philadelphia. Originally published19 Aug 2020https://doi.org/10.1161/CIRCGEN.120.003129Circulation: Genomic and Precision Medicine. 2020;13:e003129This article is a commentary on the followingWhole Blood DNA Methylation Signatures of Diet Are Associated With Cardiovascular Disease Risk Factors and All-Cause MortalityEpigenetic modulation through processes such as DNA methylation, post-translational histone modifications, or noncoding RNAs is a mechanism that affects biological fitness by adapting gene activity. Environmental factors and lifestyle are known to prompt changes in epigenetic marks. Human monozygotic twin studies illustrate that epigenetic marks modify gene expression and trigger variant phenotypic presentation over time.1 The NASA twin study is a recent example. Epigenetic changes that occurred during one brother's year of space flight were associated with an enrichment in inflammatory pathways compared with his earthbound twin brother.2See Article by Ma, Rebholz, Braun, Reynolds & Aslibekyan et alMounting evidence suggests that dietary intake introduces dynamic changes in the epigenetic landscape. One vivid illustration of diet-induced phenotypic differentiation comes from the honeybees. Initially, all larvae are nursed with a secretion rich in proteins, fatty acids, amino acids and other nutrients, coined royal jelly. The fate of each of these genetically identical bees, however, is determined by its continued diet: A switch to pollen and nectar produces worker bees, while staying on the royal jelly will develop adult queens. The MRJP1 (major royal jelly protein 1), along with its downstream pathway, has been implicated as a candidate in epigenetically regulating the phenotypic switch.3 Turning to mice as a model system, the phytochemical dihydrocaffeic acid was shown to alleviate a clinical depression phenotype by decreasing interleukin-6 through inhibition of DNA methylation.4 In humans, a diet-epigenome axis might act as a powerful mechanism for fine-tuning the molecular response to food availability and composition, and therefore, offer insight into how food choices affect human health.Here, Ma et al5 in an ambitious, international, and collaborative effort, produce evidence that a set of DNA methylation loci quantified in peripheral blood monocytes associates with diet quality and all-cause mortality. Their epigenome-wide association study in roughly 7000 participants of European ancestry identified a total of 30 methylated cytosine-guanine dinucleotides (CpGs) sites correlated with healthy eating habits as quantified by the Mediterranean-style Diet Score (MDS) and the Alternative Healthy Eating Index (AHEI), both of which score the relative intake of vegetables, fruits, whole grains, nuts, legumes, fish, and other nutrients. For several of these CpGs, a functional role in diet-associated pathways involving fatty acid metabolism and insulin signaling could be demonstrated. In a subsequent genome-wide association study, Ma et al5 identified variants in methylation quantitative trait loci. Several of these were linked to traits associated with food intake, such as lipid levels. Next, the authors leveraged a Mendelian randomization analysis to discern pairs of CpGs and traits for cardiovascular disease risk. In terms of directionality, causal associations were more likely to be identified in linking diet-associated CpGs with traits of cardiovascular disease risk than vice versa, that is, linking cardiovascular disease risk with CpGs. This needs to be further investigated, but a take-away might be that epigenetic regulation by diet may have nuanced effects on modifying cardiovascular disease risk. The study illustrates this by showing that hypermethylation of cg11250194 in FADS2 (a gene involved in the unsaturation of fatty acids) significantly associated with lower triglyceride concentrations. In contrast, hypermethylated cg02079413 in SNORA54 (a small nucleolar RNA related to diseases of alcohol use) and NAP1L4 (encoding a nucleosome assembly protein which interacts with histones and when altered is linked to several types of cancer) associated with a higher body mass index. Furthermore, the authors were able to link several diet-associated CpGs to all-cause mortality. The top candidate, a CpG site, cg18181703, in the SOCS3 gene, hypermethylated over time by a healthy diet might alter inflammatory pathways by inhibiting cytokine signal transduction to guard against disease risk. This paves a way to elucidating the molecular mechanisms by which environmental factors such as dietary preferences drive the epigenetic susceptibility to disease expression.The dietary instruments used in the study, the MDS and the AHEI, are both validated and widely used. The caveat, though, is that these self-reported diet data deviate from ground truth. This is perhaps best exemplified in a small study6 in which registered dietitians and nondietitian controls recorded their food intake over a period of 7 days, and energy expenditure was measured in an unbiased approach by using doubly labeled water. As expected, the difference between self-reported caloric intake and calories burned was much lower on average in the registered dieticians than the nondietician controls. Nevertheless, the participant-level data indicate that even among dietitians, caloric intake can be over- and underestimated by up to 400 and 800 kilocalories/d, respectively. Eventually, technology will save the day. More accurate food intake data can be expected, for example, by involving smartphone applications.7 A more futuristic solution is under development, namely sensors mounted on a front tooth to measure passing nutrients without bias.8 Since humans display a substantial time of day dependent variability in molecular and phenomic outputs, as shown for the human chronobiome,9 the promise is that molecular signals and networks can be integrated with time-specific food intake at high precision to increase signal over noise.In Ma et al,5 the MDS and AHEI scores were calculated from different sets of nutrients. One observation for possible further investigation is that fewer CpGs associated with MDS than with AHEI in the epigenome-wide association study. One of the divergent nutrients assessed was sodium intake, scored for AHEI but not for MDS, possibly tying salt into the epigenetic modulation of blood pressure regulation as a modifiable risk factor.10 Also of note is that both studies5,10 point at cg19693031 in the TXNIP gene, a nutrient sensor which protects cells from oxidative stress. An earlier genome-wide methylation study in DNA from peripheral blood collected in patients with vascular diseases revealed that a lack of methylation at the cg19693031 site in the TXNIP gene correlated with diabetes mellitus. This suggests that hyperglycemia over long periods of time, quantified by the glycated hemoglobin levels, HbA1C, might have epigenetic consequences.11 These observations exemplify opportunities raised by the Ma et al5 data set to examine this in greater detail.The work by Ma et al5 opens up new avenues for research on how food choices shape epigenetic molecular events associated with risk factors and health outcomes. As exciting as this is, discerning the molecular mechanism of nutrients and gauging their clinical relevance remains a challenge. Turning to the honey bees again, regulation along the diet-epigenome axis for phenotypic plasticity might be less a question of singular nutrients and targets than intricate network processes involving, for example, metabolic flux as argued by Maleszka.12A promising feature of the work by Ma et al5 is that the recruitment of multiethnic study participants represents an important step towards generalization of their research findings. This is an important impetus for the research community to foster further diversification of study cohorts.Sources of FundingDr Skarke is supported by the Robert McNeil Fellowship in Translational Medicine and Therapeutics.DisclosuresNone.FootnotesFor Sources of Funding and Disclosures, see page 335The opinions expressed in this article are not necessarily those of the editors or of the American Heart Association.Correspondence to: Carsten Skarke, MD, University of Pennsylvania, Institute for Translational Medicine and Therapeutics (ITMAT), 3400 Civic Center Blvd, Smilow Center for Translational Res 10-101, Philadelphia, PA 19104. Email [email protected]upenn.eduReferences1. Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suñer D, Cigudosa JC, Urioste M, Benitez J, et al.. Epigenetic differences arise during the lifetime of monozygotic twins.Proc Natl Acad Sci U S A. 2005; 102:10604–10609. doi: 10.1073/pnas.0500398102CrossrefMedlineGoogle Scholar2. Garrett-Bakelman FE, Darshi M, Green SJ, Gur RC, Lin L, Macias BR, McKenna MJ, Meydan C, Mishra T, Nasrini J, et al.. The NASA Twins Study: A multidimensional analysis of a year-long human spaceflight.Science. 2019; 364:eaau8650. doi: 10.1126/science.aau8650MedlineGoogle Scholar3. Kamakura M. Royalactin induces queen differentiation in honeybees.Nature. 2011; 473:478–483. doi: 10.1038/nature10093CrossrefMedlineGoogle Scholar4. Wang J, Hodes GE, Zhang H, Zhang S, Zhao W, Golden SA, Bi W, Menard C, Kana V, Leboeuf M, et al.. Epigenetic modulation of inflammation and synaptic plasticity promotes resilience against stress in mice.Nat Commun. 2018; 9:477. doi: 10.1038/s41467-017-02794-5CrossrefMedlineGoogle Scholar5. Ma J, Rebholz CM, Braun KVE, Reynolds LM, Aslibekyan S, Xia R, Biligowda NG, Huan T, Liu C, Mendelson MM, et al.. Whole blood DNA methylation signatures of diet are associated with cardiovascular disease risk factors and all-cause mortality.Circ Genom Precis Med. 2020; 13:e002766. doi: 10.1161/CIRCGEN.119.002766LinkGoogle Scholar6. Champagne CM, Bray GA, Kurtz AA, Monteiro JB, Tucker E, Volaufova J, Delany JP. Energy intake and energy expenditure: a controlled study comparing dietitians and non-dietitians.J Am Diet Assoc. 2002; 102:1428–1432. doi: 10.1016/s0002-8223(02)90316-0CrossrefMedlineGoogle Scholar7. Martin CK, Han H, Coulon SM, Allen HR, Champagne CM, Anton SD. A novel method to remotely measure food intake of free-living individuals in real time: the remote food photography method.Br J Nutr. 2009; 101:446–456. doi: 10.1017/S0007114508027438CrossrefMedlineGoogle Scholar8. Tseng P, Napier B, Garbarini L, Kaplan DL, Omenetto FG. Functional, RF-Trilayer Sensors for Tooth-Mounted, Wireless Monitoring of the Oral Cavity and Food Consumption.Adv Mater. 2018; 30:e1703257. doi: 10.1002/adma.201703257CrossrefMedlineGoogle Scholar9. Skarke C, Lahens NF, Rhoades SD, Campbell A, Bittinger K, Bailey A, Hoffmann C, Olson RS, Chen L, Yang G, et al.. A Pilot Characterization of the Human Chronobiome.Sci Rep. 2017; 7:17141. doi: 10.1038/s41598-017-17362-6CrossrefMedlineGoogle Scholar10. Richard MA, Huan T, Ligthart S, Gondalia R, Jhun MA, Brody JA, Irvin MR, Marioni R, Shen J, Tsai PC, et al.; BIOS Consortium. DNA Methylation Analysis Identifies Loci for Blood Pressure Regulation.Am J Hum Genet. 2017; 101:888–902. doi: 10.1016/j.ajhg.2017.09.028CrossrefMedlineGoogle Scholar11. Soriano-Tárraga C, Jiménez-Conde J, Giralt-Steinhauer E, Mola-Caminal M, Vivanco-Hidalgo RM, Ois A, Rodríguez-Campello A, Cuadrado-Godia E, Sayols-Baixeras S, Elosua R, et al.; GENESTROKE Consortium. Epigenome-wide association study identifies TXNIP gene associated with type 2 diabetes mellitus and sustained hyperglycemia.Hum Mol Genet. 2016; 25:609–619. doi: 10.1093/hmg/ddv493CrossrefMedlineGoogle Scholar12. Maleszka R. Beyond Royalactin and a master inducer explanation of phenotypic plasticity in honey bees.Commun Biol. 2018; 1:8. doi: 10.1038/s42003-017-0004-4CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetailsRelated articlesWhole Blood DNA Methylation Signatures of Diet Are Associated With Cardiovascular Disease Risk Factors and All-Cause MortalityJiantao Ma, et al. Circulation: Genomic and Precision Medicine. 2020;13 August 2020Vol 13, Issue 4Article InformationMetrics Download: 286 © 2020 American Heart Association, Inc.https://doi.org/10.1161/CIRCGEN.120.003129PMID: 32812805 Originally publishedAugust 19, 2020 KeywordsEditorialswhole grainsroyal jellydietbeesvegetablesPDF download SubjectsEditorialOriginal ArticlesRisk FactorsEpigeneticsCardiovascular Disease
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