DNA methylation signatures of duplicate gene evolution in angiosperms

ABSTRACTGene duplications have greatly shaped the gene content of plants. Multiple factors, such as the epigenome, can shape the subsequent evolution of duplicate genes and are the subject of ongoing study. We analyze genic DNA methylation patterns in 43 angiosperm species and 928 Arabidopsis thaliana ecotypes to finding differences in the association of whole-genome and single-gene duplicates with genic DNA methylation patterns. Whole-genome duplicates were enriched for patterns associated with higher gene expression and depleted for patterns of non-CG DNA methylation associated with gene silencing. Single-gene duplicates showed variation in DNA methylation patterns based on modes of duplication (tandem, proximal, transposed, and dispersed) and species. Age of gene duplication was a key factor in the DNA methylation of single-gene duplicates. In single-gene duplicates, non-CG DNA methylation patterns associated with silencing were younger, less conserved, and enriched for presence-absence variation. In comparison, DNA methylation patterns associated with constitutive expression were older and more highly conserved. Surprisingly, across the phylogeny, genes marked by non-CG DNA methylation were enriched for duplicate pairs with evidence of positive selection. We propose that DNA methylation has a role in maintaining gene-dosage balance and silencing by non-CG methylation and may facilitate the evolutionary fate of duplicate genes.

Download Full-text

Epigenetics of Animal Personality: DNA Methylation Cannot Explain the Heritability of Exploratory Behavior in a Songbird

Integrative and Comparative Biology ◽

10.1093/icb/icaa138 ◽

2020 ◽

Vol 60 (6) ◽

pp. 1517-1530 ◽

Cited By ~ 1

Author(s):

Kees van Oers ◽

Bernice Sepers ◽

William Sies ◽

Fleur Gawehns ◽

Koen J F Verhoeven ◽

...

Keyword(s):

Dna Methylation ◽

Exploratory Behavior ◽

Parus Major ◽

Phenotypic Selection ◽

Whole Genome ◽

Nucleotide Polymorphisms ◽

Behavioral Differences ◽

Heritable Variation ◽

Genome Wide ◽

Methylation Patterns

Synopsis The search for the hereditary mechanisms underlying quantitative traits traditionally focused on the identification of underlying genomic polymorphisms such as single-nucleotide polymorphisms. It has now become clear that epigenetic mechanisms, such as DNA methylation, can consistently alter gene expression over multiple generations. It is unclear, however, if and how DNA methylation can stably be transferred from one generation to the next and can thereby be a component of the heritable variation of a trait. In this study, we explore whether DNA methylation responds to phenotypic selection using whole-genome and genome-wide bisulfite approaches. We assessed differential erythrocyte DNA methylation patterns between extreme personality types in the Great Tit (Parus major). For this, we used individuals from a four-generation artificial bi-directional selection experiment and siblings from eight F2 inter-cross families. We find no differentially methylated sites when comparing the selected personality lines, providing no evidence for the so-called epialleles associated with exploratory behavior. Using a pair-wise sibling design in the F2 intercrosses, we show that the genome-wide DNA methylation profiles of individuals are mainly explained by family structure, indicating that the majority of variation in DNA methylation in CpG sites between individuals can be explained by genetic differences. Although we found some candidates explaining behavioral differences between F2 siblings, we could not confirm this with a whole-genome approach, thereby confirming the absence of epialleles in these F2 intercrosses. We conclude that while epigenetic variation may underlie phenotypic variation in behavioral traits, we were not able to find evidence that DNA methylation can explain heritable variation in personality traits in Great Tits.

Download Full-text

Whole-genome DNA methylation patterns and complex associations with gene structure and expression during flower development in Arabidopsis

The Plant Journal ◽

10.1111/tpj.12726 ◽

2014 ◽

Vol 81 (2) ◽

pp. 268-281 ◽

Cited By ~ 47

Author(s):

Hongxing Yang ◽

Fang Chang ◽

Chenjiang You ◽

Jie Cui ◽

Genfeng Zhu ◽

...

Keyword(s):

Dna Methylation ◽

Gene Structure ◽

Flower Development ◽

Whole Genome ◽

Gene Structure And Expression ◽

Methylation Patterns

Download Full-text

Learning retention mechanisms and evolutionary parameters of duplicate genes from their expression data

10.1101/2020.06.19.162107 ◽

2020 ◽

Author(s):

Michael DeGiorgio ◽

Raquel Assis

Keyword(s):

Gene Expression ◽

Gene Duplication ◽

Statistical Learning ◽

Gene Evolution ◽

Functional Divergence ◽

Duplicate Gene ◽

Duplicate Genes ◽

Evolutionary Mechanisms ◽

Retention Mechanisms ◽

Gene Copies

AbstractLearning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. To date, only one method—CDROM—has been developed with this goal in mind. In particular, CDROM employs gene expression distances as proxies for functional divergence, and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the underlying parameters of duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built upon a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents the best available method for classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby also highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.

Download Full-text

Enhanced fixation and preservation of a newly arisen duplicate gene by masking deleterious loss-of-function mutations

Genetics Research ◽

10.1017/s0016672309000196 ◽

2009 ◽

Vol 91 (4) ◽

pp. 267-280 ◽

Cited By ~ 13

Author(s):

KENTARO M. TANAKA ◽

K. RYO TAKAHASI ◽

TOSHIYUKI TAKANO-SHIMIZU

Keyword(s):

Gene Duplication ◽

Gene Dosage ◽

Purifying Selection ◽

Duplicate Gene ◽

Selective Advantage ◽

Duplicate Genes ◽

Segmental Duplications ◽

Loss Of Function ◽

Potential Consequence ◽

Degree Of Dominance

SummarySegmental duplications are enriched within many eukaryote genomes, and their potential consequence is gene duplication. While previous theoretical studies of gene duplication have mainly focused on the gene silencing process after fixation, the process leading to fixation is even more important for segmental duplications, because the majority of duplications would be lost before reaching a significant frequency in a population. Here, by a series of computer simulations, we show that purifying selection against loss-of-function mutations increases the fixation probability of a new duplicate gene, especially when the gene is haplo-insufficient. Theoretically, the probability of simultaneous preservation of both duplicate genes becomes twice the loss-of-function mutation rate (uc) when the population size (N), the degree of dominance of mutations (h) and the recombination rate between the duplicate genes (c) are all sufficiently large (Nuc>1, h>0·1 and c>uc). The preservation probability declines rapidly with h and becomes 0 when h=0 (haplo-sufficiency). We infer that masking deleterious loss-of-function mutations give duplicate genes an immediate selective advantage and, together with effects of increased gene dosage, would predominantly determine the fates of the duplicate genes in the early phase of their evolution.

Download Full-text

Prenatal Alcohol Exposure Is Associated With Adverse Cognitive Effects and Distinct Whole-Genome DNA Methylation Patterns in Primary School Children

Frontiers in Behavioral Neuroscience ◽

10.3389/fnbeh.2018.00125 ◽

2018 ◽

Vol 12 ◽

Cited By ~ 4

Author(s):

Stefan Frey ◽

Anna Eichler ◽

Valeska Stonawski ◽

Jennifer Kriebel ◽

Simone Wahl ◽

...

Keyword(s):

Dna Methylation ◽

Primary School ◽

School Children ◽

Prenatal Alcohol Exposure ◽

Alcohol Exposure ◽

Primary School Children ◽

Whole Genome ◽

Cognitive Effects ◽

Prenatal Alcohol ◽

Methylation Patterns

Download Full-text

Differential DNA methylation patterns in the CD86 gene controls its constitutive expression in keratinocytes

Biochemical and Biophysical Research Communications ◽

10.1016/j.bbrc.2013.07.023 ◽

2013 ◽

Vol 438 (1) ◽

pp. 54-60 ◽

Cited By ~ 5

Author(s):

M.A. Romero-Tlalolini ◽

P. Chávez Olmos ◽

Efraín Garrido

Keyword(s):

Dna Methylation ◽

Constitutive Expression ◽

Methylation Patterns

Download Full-text

Comparison of Whole-Genome DNA Methylation Patterns in Whole Blood, Saliva, and Lymphoblastoid Cell Lines

Behavior Genetics ◽

10.1007/s10519-012-9579-1 ◽

2012 ◽

Vol 43 (2) ◽

pp. 168-176 ◽

Cited By ~ 44

Author(s):

Tara M. Thompson ◽

Duaa Sharfi ◽

Maria Lee ◽

Carolyn M. Yrigollen ◽

Oksana Yu Naumova ◽

...

Keyword(s):

Dna Methylation ◽

Cell Lines ◽

Whole Blood ◽

Lymphoblastoid Cell Lines ◽

Whole Genome ◽

Methylation Patterns

Download Full-text

Inheritance of greenbug, Schizaphis graminum (Rondani), virulence to Gb2 and Gb3 resistance genes in wheat

Genome ◽

10.1139/g89-416 ◽

1989 ◽

Vol 32 (1) ◽

pp. 109-114 ◽

Cited By ~ 37

Author(s):

Gary J. Puterka ◽

Don C. Peters

Keyword(s):

Triticum Aestivum ◽

Resistance Genes ◽

Single Gene ◽

Duplicate Gene ◽

Modifier Gene ◽

Schizaphis Graminum ◽

Sexual Cycle ◽

Duplicate Genes ◽

Polygenic Inheritance ◽

Gene For Gene

The inheritance of greenbug, Schizaphis graminum (Rondani), virulence to wheat, Triticum aestivum L., was investigated. Clones of greenbug biotypes C, E, and F were induced into the sexual cycle, reciprocally crossed, and inbred. The resulting progeny were cloned via parthenogenetic reproduction, so their virulence to resistance genes Gb2 ('Amigo') and Gb3 ('Largo') could be established using diagnostic feeding lesions. The data for both resistance sources fit a duplicate gene – modifier gene inheritance model where avirulence was dominant and virulence was recessive. Virulence to genes Gb2 and Gb3 was conditioned by duplicate genes and a dominant modifier gene epistatic to one of the duplicate genes. Linkage was definite among the genes conditioning virulence to Gb2 and Gb3 when heterozygous males were used in crosses, as a result of achiasmic spermatogenesis. When homozygous males were used in reciprocal crosses, 50% recombination occurred. This unique linkage affinity suggests that the multiple genes conditioning virulence to Gb2 and Gb3 reside on the same chromosomes but are ≥50 map units apart. Specific aphid–host genetic interactions did not fully conform to the gene-for-gene inheritance hypothesis normally associated with host–parasite relationships. Nevertheless, polygenic inheritance of greenbug virulence in wheat could easily be regarded as a gene-for-gene relationship because the duplicate gene – modifier gene mode of inheritance still influences a single gene product in the aphid.Key words: Schizaphis graminum, Triticum aestivum, insect resistance, insect virulence, insect biotypes.

Download Full-text

Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data

Molecular Biology and Evolution ◽

10.1093/molbev/msaa267 ◽

2020 ◽

Author(s):

Michael DeGiorgio ◽

Raquel Assis

Keyword(s):

Gene Expression ◽

Gene Duplication ◽

Statistical Learning ◽

Gene Evolution ◽

Functional Divergence ◽

Duplicate Gene ◽

Previous Method ◽

Duplicate Genes ◽

Evolutionary Mechanisms ◽

Retention Mechanisms

Abstract Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for functional divergence and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the parameters driving duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built on a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents a major advancement in classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.

Download Full-text