Stably integrated transgenes failed to recapitulate their endogenous methylation states in F2 transgenic embryos.

<p>(Left) Native methylation states, at blastula stage, of the homologous genome loci that are differentially methylated between HdrR and HNI medaka strains. The HNI loci, along with their 1.5–2 kb flanking hypermethylated regions (i.e. the whole region in display) were cloned and integrated into drR strain as transgenes. (A & B) Two loci that are hypermethylated in HdrR, but hypomethylated in HNI. (C) A locus that is hypomethylated in HdrR, but hypermethylated in HNI. (Right) Bisulfite PCR sequencing results showing the methylation state of the integrated HNI sequence in the F2 transgenic blastula drR embryos. “core” and “flank (L) / flank (R)” correspond to the endogenously differentially methylated regions and the flanking regions, respectively, shown on the left. The methylation state of the homologous genome regions in the transgenic drR embryos (“Host”) were also shown as reference. Note that the sampled regions (“core”, “flank (L)”, and “flank (R)”) inside the integrated HNI sequences were all poorly methylated regardless of their native states. Mean coverage = 11×.</p

Unlinking the methylome pattern from nucleotide sequence, revealed by large-scale <i>in vivo</i> genome engineering and methylome editing in medaka fish

<div><p>The heavily methylated vertebrate genomes are punctuated by stretches of poorly methylated DNA sequences that usually mark gene regulatory regions. It is known that the methylation state of these regions confers transcriptional control over their associated genes. Given its governance on the transcriptome, cellular functions and identity, genome-wide DNA methylation pattern is tightly regulated and evidently predefined. However, how is the methylation pattern determined <i>in vivo</i> remains enigmatic. Based on <i>in silico</i> and <i>in vitro</i> evidence, recent studies proposed that the regional hypomethylated state is primarily determined by local DNA sequence, e.g., high CpG density and presence of specific transcription factor binding sites. Nonetheless, the dependency of DNA methylation on nucleotide sequence has not been carefully validated in vertebrates <i>in vivo</i>. Herein, with the use of medaka (<i>Oryzias latipes</i>) as a model, the sequence dependency of DNA methylation was intensively tested <i>in vivo</i>. Our statistical modeling confirmed the strong statistical association between nucleotide sequence pattern and methylation state in the medaka genome. However, by manipulating the methylation state of a number of genomic sequences and reintegrating them into medaka embryos, we demonstrated that artificially conferred DNA methylation states were predominantly and robustly maintained <i>in vivo</i>, regardless of their sequences and endogenous states. This feature was also observed in the medaka transgene that had passed across generations. Thus, despite the observed statistical association, nucleotide sequence was unable to autonomously determine its own methylation state in medaka <i>in vivo</i>. Our results apparently argue against the notion of the governance on the DNA methylation by nucleotide sequence, but instead suggest the involvement of other epigenetic factors in defining and maintaining the DNA methylation landscape. Further investigation in other vertebrate models <i>in vivo</i> will be needed for the generalization of our observations made in medaka.</p></div

Strong statistical association between methylation state and genomic sequence in medaka.

<p>(A) Genome browser view of a representative locus (approx. 62 kb) in the HdrR medaka genome showing CpG methylation rate, the called HypoMDs and HyperMDs, the SVM classification results, as well as DNase I hypersensitivity and the called peaks (i.e. DNase I hypersensitive sites, “DHS”). (B & C) Precision-recall curves of the kmer-SVM models trained for binary classification of HypoMDs and HyperMDs (B) without- or (C) with- CpG-masking. HypoMD and HyperMD sequences were assigned to positive and negative classes, respectively. Solid, colored lines are individual precision-recall curves derived from 10-fold cross-validation. The colors represent the cut-off values for binary classification/prediction of the testing pool in each rounds of cross-validation. Area-under-curve (AUC): (B) minimum = 0.83, maximum = 0.84; (C) minimum = 0.53, maximum = 0.56. Random classifier is represented by horizontal dashes at the bottom of both panels and has an AUC of 0.08.</p

Randomly integrated genomic fragments could not autonomously determine their methylation state.

<p>(A) Schematic diagram illustrating the capturing and processing of genomic fragments for the interrogation of their autonomy in methylation state determination. The blue segment represents genomic region that is endogenously hypomethylated. (B) Distributions of the methylation rates of CpGs on the integrated genomic fragments, (left) without- or (right) with- artificial methylation prior to injection. The distributions were displayed separately for CpGs that are endogenously (upper) hypomethylated and (lower) hypermethylated. Bin width = 1%. Note that the histograms in the upper panels (i.e. CpGs that are endogenously hypomethylated) strongly resemble those in the lower panels (i.e. CpGs that are endogenously hypermethylated). “<i>N</i>” denotes the number of CpGs in the corresponding histograms. Mean coverage = (left) 292× and (right) 208×.</p

Artificially conferred methylation states were maintained by full-length HyperMD and HypoMD sequences after being inserted into a gene desert.

<p>(A) Genome browser view of the methylation states (as in HdrR strain) of the genomic locus that contains the landing site for PhiC31-mediated site-specific recombination (approximate location is denoted by the purple triangle). (B) Schematic diagram illustrating the irreversible, site-specific integration of the subcloned, unmethylated HyperMDs and pre-methylated HypoMDs via PhiC31-integrase-mediated site-specific recombination and the expected outcomes depending on whether the integrated sequences can autonomously determine their own methylation state. (C) Methylation state of six HyperMDs at their endogenous loci (with reference to published whole genome bisulfite sequencing dataset; see also <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007123#sec008" target="_blank">Materials and Methods</a>) and at the ectopic location after being cloned and integrated into genome via PhiC31-mediated site-specific recombination. Note that all of the integrated sequences failed to recapitulate their endogenous hypermethylated state. (D) Methylation state of eleven HypoMDs at their endogenous loci and at the ectopic location after being cloned, artificially methylated, then integrated into genome via site-specific recombination. All of the pre-methylated, integrated sequences failed to recapitulate their endogenous hypomethylated state. Complete loss of methylation was only observed in very small number of CpGs in four of the examined sequences: the 9^th CpG of Locus 1, the 1^st CpGs of Locus 4, the 1^st CpG of Locus 6, and the 14^th CpG of Locus 9. Mean coverage = (C) 20× and (D) 15×.</p

HypoMDs could not restore their native methylation state at their endogenous loci after “methylome editing”.

<p>(A) Schematic diagram illustrating the principle of the methylation state editing on the targeted HypoMDs via homology directed repair (HDR) and the use of artificially methylated repair template. HDR was triggered by CRISPR-Cas9 induced DNA double-strand breaks (DSBs) at the targeted loci. The repair template contained the subcloned HypoMD (with substitutions in the spCas9’s PAM sites, from 5’-NGG-3’ to 5’-NGC-3’) along with approximately 800 bp flanking regions that served as homology arms. Note that multiple DSBs were made using a cocktail of sgRNAs that guided spCas9 to six different positions along the targeted HypoMD to enhance DSB, hence HDR, rate. (B & C) Methylation state of two HypoMDs after <i>in vivo</i> methylome editing mediated by CRISPR-Cas9-induced homology-directed repair (HDR) and pre-methylated repair templates. The estimated methylation rates were normalized against the estimated editing rate (see <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1007123#sec008" target="_blank">Materials and Methods</a>). Red triangles: binding positions of the sgRNAs.</p

Japanese Medaka: A Non-Mammalian Vertebrate Model for Studying Sex and Age-Related Bone Metabolism <i>In Vivo</i>

<div><p>Background</p><p>In human, a reduction in estrogen has been proposed as one of the key contributing factors for postmenopausal osteoporosis. Rodents are conventional models for studying postmenopausal osteoporosis, but the major limitation is that ovariectomy is needed to mimic the estrogen decline after menopause. Interestingly, in medaka fish (<i>Oryzias latipes</i>), we observed a natural drop in plasma estrogen profile in females during aging and abnormal spinal curvature was apparent in old fish, which are similar to postmenopausal women. It is hypothesized that estrogen associated disorders in bone metabolism might be predicted and prevented by estrogen supplement in aging <i>O. latipes</i>, which could be corresponding to postmenopausal osteoporosis in women.</p><p>Principal findings</p><p>In <i>O. latipes</i>, plasma estrogen was peaked at 8 months old and significantly declined after 10, 11 and 22 months in females. Spinal bone mineral density (BMD) and micro-architecture by microCT measurement progressively decreased and deteriorated from 8 to 10, 12 and 14 months old, which was more apparent in females than the male counterparts. After 10 months old, <i>O. latipes</i> were supplemented with 17α-ethinylestradiol (EE2, a potent estrogen mimic) at 6 and 60 ng/mg fish weight/day for 4 weeks, both reduction in spinal BMD and deterioration in bone micro-architecture were significantly prevented. The estrogenic effect of EE2 in <i>O. latipes</i> was confirmed by significant up-regulation of four key estrogen responsive genes in the liver. In general, bone histomorphometric analyses indicated significantly lowered osteoblasts and osteoclasts numbers and surfaces on vertebrae of EE2-fed medaka.</p><p>Significance</p><p>We demonstrate osteoporosis development associated with natural drop in estrogen level during aging in female medaka, which could be attenuated by estrogen treatment. This small size fish is a unique alternative non-mammalian vertebrate model for studying estrogen-related molecular regulation in postmenopausal skeletal disorders <i>in vivo</i> without ovariectomy.</p></div

Representative appearance of female (top) and male (bottom) Japanese medaka <i>Oryzias latipes</i> at 8, 10, 12 and 14 months old (Fig. A).

<p>(B) Representative micro-architecture of vertebrae bodies at 8, 10, 12 and 14 months in females (left) and males (right) by 3-dimensional microCT reconstruction. (C) Age-related changes in bone mineral density (BMD) from 8 to 14 months in females (left) and males (right) by microCT analysis. White arrows and arrowheads indicate micro-cracks and thinner arches found in 10–14 months old fish. BMD values labeled by the same letter (a, b, c, d) on the graph are not significantly different from each other (p<0.05).</p

Changes in micro-architecture (A) and bone mineral density (BMD) (B) of vertebrae bodies from 10 months old (baseline) to 11 months old female and male Japanese medaka <i>Oryzias latipes</i> after low and high EE2 treatment for 4 weeks.

<p>White arrows and arrowheads indicate improved micro-architecture and arches in EE2 treated fish. Bars labeled by the same letter (a, b) on the graph are not significantly different from each other (P<0.05).</p

Haematoxylin and Eosin (H and E) staining of sagittal sections from whole adult Japanese medaka <i>Oryzias latipes</i> in (A) female and (B) male at 13 months old.

<p>Fish anterior is to the left. Spinal vertebrae are numbered. The rectangular box showing the vertebrae 15^th–25^th was used for both microCT scanning and histomorphometry. Letters on the sections indicate Br, Brain; Bb, Backbone; E, Eye; G, Gill; Gb, Gall bladder; Gu, Gut; H, Heart; Ha, Hemal arch; K, Kidney; L, Liver; M, Muscle; Na, Neural arch; O, Ovary; Pa, Pancreas; Pg, Pharyngeal gill; RBC, Red Blood Cells, Sb, Swim bladder; Sc, Spinal cord; T, Testis.</p