extfieldsofstudy: Medicine Biology

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Live-cell analysis of DNA methylation during sexual reproduction in Arabidopsis reveals context and sex-specific dynamics controlled by noncanonical RdDM

In this Resource/Methodology, Ingouff et al. developed a new method to investigate DNA methylation reprogramming in plants. Using Arabidopsis, the authors developed sensors that reported CG and non-CG methylation with single-cell resolution.

Cytosine methylation is a key epigenetic mark in many organisms, important for both transcriptional control and genome integrity. While relatively stable during somatic growth, DNA methylation is reprogrammed genome-wide during mammalian reproduction. Reprogramming is essential for zygotic totipotency and to prevent transgenerational inheritance of epimutations. However, the extent of DNA methylation reprogramming in plants remains unclear. Here, we developed sensors reporting with single-cell resolution CG and non-CG methylation in Arabidopsis. Live imaging during reproduction revealed distinct and sex-specific dynamics for both contexts. We found that CHH methylation in the egg cell depends on DOMAINS REARRANGED METHYLASE 2 (DRM2) and RNA polymerase V (Pol V), two main actors of RNA-directed DNA methylation, but does not depend on Pol IV. Our sensors provide insight into global DNA methylation dynamics at the single-cell level with high temporal resolution and offer a powerful tool to track CG and non-CG methylation both during development and in response to environmental cues in all organisms with methylated DNA, as we illustrate in mouse embryonic stem cells.
[Keywords: DNA methylation; sensors; reprogramming; reproduction] Supplemental material is available for this article. Received August 18, 2016;revised version accepted December 28, 2016. Methylation of cytosine residues on the DNA molecule plays important biological functions in many eukaryotes (Law and Jacobsen 2010), including maintenance of genome stability, imprinting, and repression of gene expression. Its faithful propagation is thus critical for proper development. In mammals, DNA methylation is subjected to dramatic reprogramming (resetting) from one generation to the next in both the germline and the early embryo (Feng et al. 2010;Hajkova 2011;Seisenberger et al. 2013;Heard and Martienssen 2014). These repro-gramming phases are critically important for the proper acquisition of totipotency in the zygote and to erase epigenetic marks that might have accumulated during its progenitors' life cycle.
In plants, little is known about DNA methylation dynamics during reproduction (Hsieh et al. 2009;Schoft et al. 2009;Calarco et al. 2012;Jullien et al. 2012;Moreno-Romero et al. 2016), owing largely to the technical difficulty of isolating plant germ cells and the very rapid changes affecting chromatin marks during plant reproduction (Ingouff et al. 2010;She et al. 2013). Contrary to animals, which establish germlines during early embryo development, plant gametes originate in the adult plant from somatic precursors (Kawashima and Berger 2014). In the male (the anthers) and female (the ovules) reproductive organs, somatic cells shift identity to form precursor cells analogous to animal primordium germ cells. In the ovule primordium, typically a single somatic cell differentiates into a megaspore mother cell (MMC), which will undergo meiosis to produce four haploid spores. Only one spore (the functional megaspore) usually survives and produces through three mitoses and differentiates into a haploid embryo sac or female gametophyte containing typically two female gametes (the egg and the central cell) and accessory cells. In the anthers, groups of microspore mother cells (MiMCs) differentiate, each producing four haploid microspores after meiosis. Each microspore undergoes two mitotic divisions to form a mature pollen grain or male gametophyte, two sperm cells enclosed in the vegetative cell. Both sperm cells participate in a double fertilization of the egg and central cells, giving rise to the embryo and the endosperm, respectively.
In plants, cytosine methylation is found in three contexts: CG, but also CHG and CHH (with H = A, T, or C). Current evidence globally suggests long-term stability of methylation patterns in Arabidopsis, particularly for CG methylation. Inheritance of methylated cytosine (mC) has been studied in both experimental and natural Arabidopsis populations as well as in maize (Martienssen and Colot 2001;Regulski et al. 2013). In wild-type Arabidopsis accessions in both natural and laboratory conditions, the results indicate a remarkable level of stability of mC in all three contexts over many generations, with epigenetic changes accumulating in a clock-like manner that matches DNA mutation rates (Becker et al. 2011;Schmitz et al. 2011;Hagmann et al. 2015).
On the other hand, alterations to DNA methylation patterns induced by life history such as environmental stresses are mitotically stable in individual plants but, save for rare exceptions (Weigel and Colot 2012), are usually not meiotically inherited (Dowen et al. 2012;Secco et al. 2015), thus suggesting active resetting of altered methylation marks. Interestingly, epialleles are relatively easy to generate in experimental populations using mutants with hypomethylated genomes (Vongs et al. 1993;Johannes et al. 2009;Reinders et al. 2009). Some of these epialleles are stably inherited once wild-type activity is restored, but a subset undergoes progressive remethylation, reaching wild-type levels after a limited number of generations. This process is dependent on RNAi and occurs during reproduction ), indicating that the Arabidopsis methylome can be actively modified during reproductive development. This was confirmed for male gametophyte development (Calarco et al. 2012), where cell type-specific methylomes (Schoft et al. 2009;Calarco et al. 2012) indicate that, contrary to mammals, sperm cells retain CG and CHG methylation during their differentiation. Although a low level of CHH methylation is detected in the microspore after meiosis and remains in the sperm cells upon mitoses, a higher methylation level is restored by de novo DNA methyltransferase activity in the vegetative nucleus prior to fertilization and then in the embryo after fertilization (Calarco et al. 2012). Thus, there is some evidence that at least CHH methylation, but not CG or CHG, is labile during male gametogenesis. Much less is known about methylation dynamics during female gamete development. Genetic evidence indicates that at least CG methylation is stable during the three haploid divisions leading to the formation of the egg cell (Saze et al. 2003) but is likely lower upon its maturation . However, virtually nothing is known about the dynamics of mC during premeiotic and meiotic development and for non-CG contexts during ovule development. The timing, extent, and mechanisms of such modifications are unclear and remain difficult to dissect with current methods for methylation analysis because they occur in a limited number of reproductive cells deeply embedded in several layers of somatic cells.
In Arabidopsis, mapping of cytosine methylation at single-nucleotide resolution by genome-wide bisulfite sequencing (GWBS) and the integrative analysis of the genomic distribution of DNA methylation, histone modifications, and histone variants have generated a highly refined model for the structure of the epigenome (Cokus et al. 2008;Lister et al. 2008;Roudier et al. 2011;Yelagandula et al. 2014). While the applications of GWBS are rapidly improving, including at the single-cell level (Lau and Bergmann 2015;Clark et al. 2016), they still typically require a significant amount of cells to achieve reasonable coverage and would be challenging to apply on a long developmental series during plant reproduction. Thus, complementary approaches are needed to refine our understanding of mC dynamics in addition to bulk analysis of often heterogeneous cell types. Particularly important is the development of tools to decipher the temporal dynamic of the epigenome during development and in response to exogenous cues during development and growth. Such tools would be well suited to analyze the changes occurring during reproductive development. Here, we developed genetically encoded fluorescent dynamic sensors of DNA methylation (DYNAMETs) that selectively report, with high temporal resolution, CG and CHH methylation in the model plant Arabidopsis thaliana. They provide access to cell-specific information regarding DNA methylation in plant germ cells and allowed us to analyze changes in real time during reproduction. They reveal distinct and sex-specific dynamics for these two contexts of methylation. We further documented the functionality of a mCG sensor in mouse embryonic stem cells (mESCs), suggesting that similar dynamics could be examined during mammalian development.

Evaluation of potential DYNAMETs
To generate DYNAMETs in Arabidopsis, we expanded a strategy relying on a genetically encoded methyl-CpGbinding domain (MBD) fused to a fluorescent protein to report in real time CG methylation states in mammals (Okada et al. 2010;Yamagata 2010). We performed a literature search to find domains or proteins with affinity for mCs in any of three sequence contexts encountered in the plant epigenome (CG, CHG, and CHH). We identified the MBD of Arabidopsis MBD6 that binds specifically to symmetrical mCG in vitro (Zemach and Grafi 2003) as well as SUVH4/KRYPTONITE and SUVH9, two Arabidopsis suppressor of variegation 3-9 homologous (SUVH) proteins that contain SET and RING finger-associated (SRA) domains that bind mCHG and mCHH sequences, respectively, in vitro (Johnson et al. 2007(Johnson et al. , 2008. To test their capacity to report DNA methylation states in planta, we generated DYNAMET cassettes consisting of the MBD of MBD6, the SRA domain of SUVH4 (SUVH4Δ-SRA), and SUVH9 (SUVH9Δ-SRA) fused to a nuclear localization signal (NLS) in-frame with a fluorescent protein (Supplemental Fig. S1A; see the Supplemental Material for details regarding the optimization of the reporters). genic lines for pHTR5:SUVH4Δ-SRA-ECFP or pHTR5: SUVH9Δ-SRA-Venus. We thus used the entire SUVH4 and SUVH9 proteins fused to fluorescent proteins and driven by the HTR5 promoter (Supplemental Fig.  S1A). Fluorescence of both DYNAMET fusions was similarly detectable in transient assays in tobacco cells (Supplemental Fig. S1D,E), but only the SUVH9-Venus fusion protein could be detected in stable transgenic plants (Fig. 1B). Transgenic lines expressing MBD-Venus and SUVH9-Venus showed no detectable phenotypic alterations, suggesting that the DYNAMET protein fusions are not toxic. In conclusion, we obtained two sensors putatively targeting CG (MBD-Venus) and non-CG (SUVH9-Venus) methylation but not CHG.
DYNAMETs report context-specific DNA methylation states at CG and CHH sites To validate the sensors, we first analyzed the subnuclear distribution of MBD-Venus and SUVH9-Venus in transgenic plants. As expected, the fluorescence of both DYNAMETs colocalized with DAPI-stained constitutive heterochromatin (Fig. 1A,B), where most cytosine methylation accumulates in Arabidopsis (Zhang et al. 2006;Lister et al. 2008). To confirm colocalization of MBD-Venus with heterochromatin, we introgressed the reporter in a ddm1 (decrease in DNA methylation 1) mutant background. This led to a diffuse pattern of fluorescence (  1A), consistent with the global loss of mCG in heterochromatin in ddm1 (Lippman et al. 2004;Stroud et al. 2014).
To determine the affinity of the DYNAMET reporters for the different methylation contexts in planta, we performed chromatin immunoprecipitation (ChIP) followed by sequencing (ChIP-seq). Genome-wide distribution of MBD-Venus and SUVH9-Venus in wild-type plants accurately recapitulated CG and non-CG (CHG/CHH) methylation patterns, respectively, as defined previously by GWBS (Fig. 1C,D; Supplemental Fig. S3; Cokus et al. 2008;Lister et al. 2008). To evaluate the specificity of the DYNAMET sensors, we then used published GWBS data sets for all three mC contexts and analyzed correlations with the binding of both reporters in transposable elements (TEs) and genes (Supplemental Fig. S4). We found that while MBD-Venus shows strong enrichment for CG gene body methylation (Supplemental Fig. S4J), we could not detect any enrichment of SUVH9-Venus on the same loci (Supplemental Fig. S4D). This indicates a limited affinity of the SUVH9-Venus reporter for mCG sites. Conversely, while SUVH9-Venus is significantly enriched for the small set of CHH (but not CG) methylated genes (Supplemental Fig. S4F), there was no enrichment of MBD-Venus for these loci (Supplemental Fig. S4L), suggesting that the latter has very limited affinity for mCHH. We then evaluated DYNAMET binding quantitatively. The data for SUVH9-Venus over TEs (Supplemental Fig. S4C) suggest a good correlation between CHH methylation levels and binding intensity (R 2 = 0.68). For MBD-Venus, while stronger enrichment in highly methylated TEs is highly significant, we observed enrichment even for low levels of methylation (<10%, Supplemental Fig. S4G). The same was true for gene body methylation. Thus, SUVH9-Venus is a better quantitative tool than MBD-Venus.
To complement these analyses, we then estimated the correlation between the ChIP data for MBD-Venus and MeDIP-seq (methylated DNA immunoprecipitation [MeDIP] followed by sequencing) data from Arabidopsis (Supplemental Fig. S5), which is technically similar to our ChIP experiment. The comparison with MBD-Venus is relevant, as MeDIP recognizes all mCs, with mCG methylation being predominant in Arabidopsis. The results indicate a high correlation (R 2 = 0.91) between both data sets.
In Arabidopsis, DNA methylation is initially catalyzed by a 24-nucleotide (nt) siRNA-dependent DNA methylation (RdDM) pathway involving the DOMAINS REAR-RANGED METHYLASEs (DRM1 and DRM2). CG and CHG methylation are then maintained by DNA METHYLTRANSFERASE1 (MET1) and CHROMOME-THYLASE3 (CMT3), respectively (Law and Jacobsen 2010). CHH methylation is maintained either through the RdDM pathway (Matzke et al. 2015) or by the CMT2 DNA methyltransferase (Zemach et al. 2013;Stroud et al. 2014). To further define the methylation specificity of SUVH9-Venus, we analyzed its genome-wide distribution in different DNA methyltransferase mutants by ChIP-seq. Enrichment in TEs was reduced in the drm2 mutant (Supplemental Fig. S6A,C) and in the cmt2 mutant on both short TEs and for the central domain of longer TEs (Supplemental Fig. S6B), both of which are known targets of CMT2 (Zemach et al. 2013). Enrichment was eliminated in the drm1 drm2 cmt2 cmt3 quadruple mutant (Supplemental Fig. S6D), which abolishes CHH methylation . These experiments collectively indicate that SUVH9-Venus recognizes DRM2and CMT2-dependent CHH methylation.
We next tested whether MBD6-Venus and SUVH9-Venus DYNAMET lines could monitor changes in methylation states in planta. We thus analyzed DYNAMET fluorescence during male gametogenesis, where GWBS data are available (Calarco et al. 2012). Similarly, we observed in living cells that MBD-Venus patterns were stable throughout male gametogenesis, with a slight reduction in the vegetative nucleus ( Fig. 1E-H) likely reflecting active DNA demethylation (Calarco et al. 2012;Ibarra et al. 2012). In contrast, SUVH9-Venus reported rapid and massive demethylation after the differentiation of the functional microspore, followed by remethylation upon the first mitotic division (Fig. 1I-L). These results are in agreement with the methylome data from microspores and cell types of mature pollen (Calarco et al. 2012;Ibarra et al. 2012) and further suggest that SUVH9-Venus reports specifically CHH methylation but not CHG methylation, which is known to be stable in the microspore. We confirmed these observations using immunolocalization experiments with an anti-YFP antibody as well as DAPI counterstaining to precisely quantify fluorescence against chromatin content ( Fig. 1M; Supplemental Fig. S7). The results are in good agreement with living-cell observations. They notably confirmed the striking difference observed between early and late microspores for SUVH9-Venus.
Next, we evaluated the DYNAMET reporters as livingcell sensors by performing time-lapse imaging. First, we imaged MBD-Venus dynamics during mitosis in root cells over 30 min, with one acquisition every 2 min (Supplemental Movie 1; Supplemental Fig. S8). The re-porter remained tightly bound to segregating chromosomes, which is compatible with CG methylation being a stable epigenetic mark (Law and Jacobsen 2010). We then tracked SUVH9-Venus in male meiocytes during meiotic prophase 1 (Supplemental Movie 2) and early sporogenesis (Supplemental Movie 3) using a multiphoton microscope with time intervals of 30 min. Imaging over a 16-h period revealed highly dynamic chromatin movements in meiocytes and microspores. These experiments showed that the sensors can be used to image DNA methylation over relatively long periods of time.
Altogether, we conclude that DYNAMETs function as live-imaging reporters of DNA methylation states for the symmetrical CG (MBD-Venus) and asymmetrical CHH (SUVH9-Venus) contexts with high temporal resolution. For the sake of clarity, the sensors are referred to as mCG-Venus and mCHH-Venus DYNAMETs here.
We further tested the capacity of our mCG reporter to detect CG methylation in living mESCs in both wildtype cells and cells defective for the Dnmt1 maintenance DNA methyltransferase (Supplemental Fig. S9A-D). The cytological patterns observed in wild-type mESCs (Supplemental Fig. S9B) are consistent with the typical patterns obtained with mC antibodies (Li et al. 2015). In addition, we noted a significant decrease of signal intensity in dnmt1-deficient cells with similar illumination settings (Supplemental Fig. S9C,D), consistent with the partial (∼80%) loss of mCG typically detected in these cells (Li et al. 2015). These observations suggest that the mCG reporter is functional in mammalian ESCs.
Global DNA methylation changes occur during female sporogenesis and gametogenesis and early embryo development We next studied the DNA methylation pattern using DYNAMET lines during the course of male and female germline differentiation. During female sporogenesis, starting from the differentiation of the germ cell precursors and until the formation of the functional haploid spores upon meiosis, we observed a steady nuclear distribution of mCG-Venus ( Fig. 2A-E). Similarly, a stable mCG pattern was observed during all stages of female gametogenesis, from the definition of the haploid megaspore until female gametes are formed (Fig. 3A,B). However, mCG-Venus fluorescence decreased to a lower level in the mature egg cell nucleus when compared with either the surrounding tissues (Fig. 3C) or the functional megaspore (Fig. 3I). As mCG-Venus binds only symmetrically methylated CG in vitro (independently confirmed in Supplemental Fig. S10A, Zemach and Grafi 2003), this suggests either lower CG methylation or significant hemimethylation in the mature egg cell. This latter hypothesis is consistent with a previous report suggesting passive demethylation in this cell due to the absence of the maintenance DNA methyltransferase MET1 ). However, following fertilization, a typical distribution of mCG-Venus fluorescence was restored in the embryo, indicating that symmetrical mCG is rapidly reestablished upon fertilization (Fig. 3D). In contrast to the relative stability of mCG, mCHH-Venus fluorescence became undetectable in the developing female germline (the MMC) at stage 2-I during female sporogenesis (stages as described in She et al. 2013) and prior to DNA replication (cf. Figs. 2G and 3F) and gradually reappeared between stage 2-II and the onset of meiosis (Fig.  2H-J). These dynamic changes correlate with the establishment of a peculiar but transient chromatin structure devoid of the centromeric H3 variant CenH3 and linker H1 histones (She et al. 2013) but still with condensed centromeric 180-base-pair (bp) repeats (Supplemental Fig.  S11).
In contrast, mCHH-Venus patterns appeared stable throughout female gametogenesis (Fig. 3E,F) even in the mature egg cell (Fig. 3G). Although the mature egg cell chromatin structure resembled that of stage 2-I MMCs (lack of linker and centromeric H3 histone and with condensed centromeric repeats) (Supplemental Fig. S12; Ingouff et al. 2007;Yelagandula et al. 2014), it retained a typical mCHH pattern that is also visible after fertilization in the early embryo (Fig. 3H). Finally, we also monitored mCG and mCHH-Venus patterns during male sporogenesis and observed stable levels for both sensors (Supplemental Fig. S13).
To confirm both patterns, we performed immunolocalization experiments with an anti-GFP/YFP antibody and DAPI counterstaining (Figs. 2K and 3I; Supplemental Figs. S14, S15). The results confirmed the live-cell observations, with mCG-Venus remaining broadly invariant during sporogenesis but significantly reduced in the egg cell, and mCHH-Venus being significantly hypomethylated at stage 1-2 during female sporogenesis. Collectively, our data therefore indicate distinct dynamics for both methylation contexts: mCHH is highly labile and reprogrammed by demethylation/remethylation during both male gametogenesis and female sporogenesis, while mCG is globally maintained in male and female gametic precursors and male gametes. Its highly significant decrease in the egg cell might be due to either demethylation or loss of symmetrical methylation in the CG context but is restored following fertilization.

Establishment of CHH methylation in the egg cell relies on a DRM2 and RNA polymerase V (Pol V)-dependent but Pol IV 24-nt siRNA-independent pathway
To identify the DNA methyltransferases controlling mCHH dynamics during female gametogenesis, we analyzed mCHH-Venus expression in genetic backgrounds defective for non-CG DNA methyltransferase activity, including CMT2, CMT3, and DRM2 (Fig. 4). Only a null mutant for DRM2 completely lost mCHH-Venus fluorescence, specifically in the mature egg cell. These observations are in agreement with the fact that DRM1 and DRM2 are the sole DNA methyltransferases detected in the mature egg cell ). Loss of fluorescence in drm2-2 eggs suggested that mCHH-Venus is targeted for degradation when unbound to its targets. Accordingly, treating whole inflorescences with syringolin A, which inhibits proteasome activity (Groll et al. 2008), increased accumulation of mCHH-Venus compared with mock treatment (Supplemental Fig. S16). Consistent with the absence of mCHH-Venus in the egg cell, a maternally provided hypomethylated CHH methylome from a drm2-2 plant led to pleiotropic developmental abnormalities in the young embryo (Supplemental Fig. S17A,B). Interestingly, drm2 mutation transmitted from heterozygous plants showed increased penetrance relative to the expected value, possibly indicating effects from the maternal sporophytic tissues, as described previously for MET1 and CMT3 (FitzGerald et al. 2008;Pillot et al. 2010).
We next analyzed mCHH-Venus in plants deficient for Pol IV (Pol IV/NRDP1) and Pol V (Pol V/NRDPE1) activity that, in somatic tissues, generates the 24-nt siRNA and long noncoding RNA, respectively, which are responsible for targeting DRM2 to RdDM targets. Unexpectedly, the mCHH fluorescence pattern was similar to the wild type in the egg cells of both mutants (Fig. 4A). To better evaluate the effect of these mutations, we performed immunolocalization experiments combined with DAPI counterstaining. Interestingly, quantifications confirmed the observations made on fresh tissues but also revealed a significant fluorescence decrease in the double nrpd1/ nrpe1 mutant but not in the single nrpd1 mutant ( Fig. 4B; Supplemental Fig. S18). Thus, NRPE1, but not NRPD1, likely contributes to DNA methylation maintenance in the egg cell.
To better understand these observations, we monitored the expression pattern of CMT2, of the catalytic subunits of Pol IV (NRPD1) and Pol V (NRPE1), and of their common catalytic subunit NRP(D/E)2A in transgenic lines expressing the corresponding genomic fusions with fluorescent proteins (Fig. 5A-D). Although all protein fusions were detected in the ovule integuments, only the tagged Pol V subunits Venus-NRPE1 and Venus-NRP(D/ E)2A were conspicuously detected in the egg cell (Fig.  5C,D). Most remarkably, tagged CMT2 and the Pol IV catalytic subunit NRPD1 were absent from the egg cell (Fig.  5A,B; Jullien et al. 2012), an observation that we confirmed using a NRPD1-specific antibody (Supplemental Fig. S18). Thus, establishment of mCHH in the egg cell is dependent on DRM2 and Pol V but likely independent of Pol IV.

Discussion
Here, we generated live-imaging DYNAMETs for the CG and CHH sequence contexts. We showed that DYNA-METs can be used to track DNA methylation dynamics in individual cells in Arabidopsis plants. These sensors provide access to a global cell-specific methylation pattern with high temporal resolution. This temporal dimension is particularly critical when studying reproductive cells, where chromatin remodeling occurs rapidly (Ingouff et al. 2010;She et al. 2013).
Our study indicates that DNA methylation reprogramming occurs differently in plants and mammals (Supplemental Fig. S12). While CG methylation is extensively reprogrammed in mammals (Feng et al. 2010;Hajkova 2011;Seisenberger et al. 2013;Heard and Martienssen 2014), numerous genetic and inheritance experiments in Arabidopsis revealed the stability of CG methylation patterns (Paszkowski and Grossniklaus 2011;Schmitz and Ecker 2012;Weigel and Colot 2012;Heard and Martienssen 2014). Our results confirmed that CG methylation is, in general, stable in the male and female germlines in Arabidopsis. The egg cell seems to be the exception, as we observed a significant decrease of signal upon its maturation, which might be due to either demethylation or loss of symmetrical methylation resulting from lack of MET1 activity . The methylome of the egg cell in rice was published recently (Park et al. 2016) and revealed a CG methylation pattern typical of somatic cells. Assuming that these results are also valid in the Arabidopsis egg cell, this might suggest significant hemimethylation rather than demethylation in the egg cell. Importantly, fluctuation of mCG in the mature egg cell is not accompanied by loss of mCHH. In contrast, we observed highly dynamic changes affecting CHH methylation, which undergoes waves of demethylation/remethylation during both female sporogenesis (this study) and male gametogenesis (Calarco et al. 2012;Ibarra et al. 2012;this study). Interestingly, we could not identify cell types that lost both contexts simultaneously. Supplemental Figure S20 provides a summary of our observations.
In the absence of methylome data for egg cells and early embryos in Arabidopsis, it is difficult to assess the relative importance of CG, CHG, and CHH methylation for genome stability in these cells. However, mutants affecting CHG (Pillot et al. 2010) or CHH (this study) methylation and, even more profoundly, CG methylation (Xiao et al. 2006) have embryo patterning defects of varying severity (milder in CHH and highest in CG), suggesting that all three contexts are developmentally important. This is in sharp contrast to somatic tissues, where CG methylation defects severely affect plant development, while non-CG methylation alterations have only limited phenotypic consequences . Thus, proper de novo establishment of CHH methylation patterns in the egg cell is likely important for transcriptional control in the early embryo. Similar observations with respect to H3K9me2 (Autran et al. 2011) suggest a more general role for maternally controlled non-CG methylation for embryo transcriptional control.
We further demonstrated here that the DYNAMETs represent powerful tools for genetic analyses, allowing cell-specific screening. In particular, our genetic evidence showed that global CHH methylation in the egg cell was dependent on DRM2 and Pol V, where both proteins might have cell-autonomous activity, but independent of Pol IV activity, which is responsible for 24-nt heterochromatic siRNA production and is required to guide DRM2 to RdDM targets in somatic cells (Matzke et al. 2015). Accordingly, we observed that Pol IV was absent from the egg cell. This suggests that alternative as yet unidentified sources of small RNAs might be recruited in the egg cell to establish DNA methylation. One possibility is that RNA Pol II substitutes for Pol IV as a source of small RNAs to induce DRM-dependent methylation (Matzke et al. 2015;Cuerda-Gil and Slotkin 2016), acting either cell-autonomously or non-cell-autonomously. Such Pol IV-independent small RNAs could originate from surrounding cells of the maternal sporophyte or accessory cells of the female gametophyte and be transported to the egg cell, as shown between accessory cells and target sperm cells (Martinez et al. 2016). Alternatively, as yet unknown actors might be required, and identifying components of such a pathway will be of major interest.
By revealing the temporal aspects of mC changes, the DYNAMETs significantly expand the toolkit available for DNA methylation research. They reveal highly dynamic patterns of DNA methylation during plant reproduction and suggest that the regulatory pathways controlling methylation in somatic cells are likely distinct in reproductive cells.

Plant material, growth conditions
Arabidopsis mutant alleles and reporter lines used in this study are summarized in Supplemental Table 1. After 2 d at 4°C in the dark, the seeds were germinated and grown on soil in a growth chamber (16 h light/8 h night at 20°C). For mutant analyses, a single transgenic event for SUVH9-Venus was introgressed into the drm2-2, cmt2-3, nrpd1-1, and nrpd1-1/nrpe1-1 mutant stocks.

Cloning of DYNAMET cassettes and genomic fusions
Each DYNAMET contained a methylcytosine-binding domain fused to a nuclear localization signal (NLS) in-frame with a fluorescent protein that was placed under the control of a histone3.3 HTR5 (At4g40040) promoter (pHTR5). The domain targeting mCs in the CG, CHG, and CHH sequence contexts corresponded to the MBD of MBD6 (Zemach and Grafi 2003), the SRA domain of SUVH4, and the SRA domain of SUVH9, respectively (Johnson et al. 2007(Johnson et al. , 2008. The full-length SUVH4 mutated in its SET domain and SUVH9 were also tested to report for CHG and CHH methylation, respectively. All of the DYNAMET constructs were generated by gene synthesis (GenScript). The sequence of each DYNAMET cassette is in the Supplemental Material. The sequence corresponding to SUH4-mSET was cloned by GoldenGate cloning (Engler et al. 2009;Lampropoulos et al. 2013) to generate the pMI108 vector. This binary vector also comprised a second molecular construct with a histone H2B (At5g22880) fused to a RFP mCherry (H2B-mCherry) under the control of the HTR5 promoter. All of the other cassettes were cloned into a pCAMBIA2300 (pCB2300) binary vector to generate the recombinant plasmids pCB2300-pHTR5:MBD-Venus (MBD-Venus), pCB2300-pHTR5:SUVH4Δ-SRA-Venus (SUVH4Δ-SRA-Venus), pCB2300-pHTR5:SUVH9Δ-SRA-Venus (SUVH9Δ-SRA-Venus), and pCB2300-pHTR5:SUVH9-Venus (SUVH9-Venus). Detailed information regarding the optimization of the various DYNAMET constructs is in the Supplemental Material.
The HTR5 promoter was amplified with primers pHTR5-FWDattB4 and pHTR5-REVattB1 and was used in a BP reaction with pDONRP4-P1R (Invitrogen). The resulting entry clone pEN-L4-pHTR5-R1 was subsequently combined with pEN-L1-NF-L2, pEN-R2-S * -L3, and the destination vector pK7m34GW,0 in a three-fragment LR reaction (Karimi et al. 2007). The final vector pHTR5:NLS-GUS-GFP consisted of the HTR5 promoter controlling the expression of a nuclear-localized GFP-GUS fusion protein (NLS-GUS-GFP). The transgenic plants were generated in Col 0 accession by floral dipping (Clough and Bent 1998) and selected on MS solid medium (Duchefa) with the appropriate selective agent.

Immunolocalization
The NRPD1 antibody (Pontier et al. 2005) was provided by T. Lagrange (Centre National de la Recherche Scientifique, University of Perpignan). Immunodetections were performed as described (Pillot et al. 2010;She et al. 2013). Briefly, pistils from stage 1-I to maturity were fixed overnight at 4°C in 2% paraformaldehyde, 1× PBS, and 2% Triton fixative; washed three times in 1× PBS; and dissected to isolate the ovules. The dissected ovules were embedded in acrylamide as described (Bass et al. 1997) in order to facilitate manipulation and maintain the three-dimensional architecture of the tissues. Samples were digested in an enzymatic solution (1% driselase, 0.5% cellulase, 1% pectolyase, 1% BSA [all from Sigma]) for 25 min to 1 h at 37°C depending on the developmental stage, subsequently rinsed three times in 1× PBS, and permeabilized for 2 h in 1× PBS and 2% Tween20. They were then incubated overnight at 4°C with primary antibodies used at the following dilutions: 1:200 for the GFP-Booster chromobody (Chromotek) and 1:200 for NRPD1. The slides were washed day long in 1× PBS and 0.2% Triton. For the GFP-Booster chromobody, which was conjugated to an Atto 488 fluorochrome, slides were then incubated with 1 µg/ mL DAPI in 1× PBS for 1 h, washed for 2 h in 1× PBS, and mounted in Prolong medium (Molecular Probes). For NRPD1, slides were incubated overnight at 4°C with an Alexa fluor 488-conjugated secondary antibody (Molecular Probes) used at 1:400 dilution. After washing in 1× PBS and 0.2% Triton for a minimum of 6 h, the slides were incubated with 1 µg/mL DAPI in 1× PBS for 1 h, washed for 2 h in 1× PBS, and mounted in Prolong.

Sample preparation for microscopy
Imaging of reporter lines during male and female sporogenesis and female gametogenesis was obtained from freshly dissected anthers and carpels and isolated young embryos from freshly dissected seeds. All of the experiments were performed on T2 or T3 generations from at least three independent transgenic lines. To compare fluorescence intensity in the egg cells of mutants, a very potent mCHH-Venus line (H9-11) was introgressed into all of the tested mutants. Microscope settings were first set with the reference mCHH-YFP line (H9-11) and kept identical to study its fluorescent pattern in all of the mutant backgrounds. Image acquisition was performed on homozygous mutants homozygous for CHH-Venus. Fluorescence was imaged immediately after mounting in half Murashige and Skoog (MS) medium with 0.3% (w/v) Phytagel. For imaging roots, sterilized seeds were germinated under an agar block in glass-bottomed dishes (GWSt-5040, WillCoWells) (Larrieu et al. 2015). Roots growing against the glass bottom were selected to perform time-lapse imaging of mCG-Venus in mitotic cells.

Microscopy
Imaging of fluorescence for DYNAMETs and other reporter lines from freshly dissected tissues was performed with an inverted laser scanning confocal microscope (Zeiss LSM780) and a 40× oil immersion objective lens (plan apo, N.A = 1.3; Zeiss) with settings for detection of Venus (excitation, 514 nm; emission, band-pass 515-545 nm) or mCherry (excitation, 561 nm; emission, band-pass 561-621 nm). Fluorescence was acquired with a GaAsP detector with the pinhole set to one Airy unit and optimal sectioning as suggested by the software (Zen). For quantification of the immunostaining experiments, we acquired images using a Zeiss LSM780 inverted confocal scanning microscope with sequential acquisitions for each fluorochrome (excitation, 405 nm for DAPI and 488 nm for Atto 488 and Alexa 488). For each stack, the number of sections was calculated to ensure nonoverlapping contiguous sections in a fluorochrome-dependent manner. For each region of interest (ROI), signal intensity was measured for each individual section and each fluorochrome using the FIJI (Schindelin et al. 2012) measurement module and summed. The raw data were imported into R to calculate means, standard deviations, and statistical significance using R's implementation of the Student's test.

Time-lapse imaging
Time-lapse movies of mCG-Venus in dividing root cells were obtained using an inverted laser scanning confocal microscope (Zeiss, LSM780) with settings as described above and a 20× objective lens (plan apo 0.8; Zeiss). Time-series images were acquired every 2 min. Time-lapse movies of the mCHH-Venus reporter during male sporogenesis and gametogenesis were obtained with an upright multiphoton microscope (LSM880 Examiner Z1, Zeiss) fitted with a coherent laser (Chameleon Ultra II) and an automatable stage (Zen Extension NEce, Ingesym) commuting between the microscope and a vibratome (Microm HM650V, Thermo Scientific). Open flowers and siliques were removed with a dissecting needle from a primary inflorescence of the CHH-Venus line (line H9-11) to conserve only the young buds. The inflorescence was immersed into a homemade embedding cassette filled with an in vitro culture medium (Nitsch medium [Duchefa], 5% [w/v] trehalose, 0.05% [w/v] MES-KOH at pH 5.8, 1× Gamborg's vitamin solution [Sigma]) (Gooh et al. 2015) supplemented with 8% agarose (Sigma). The hardened block containing the inflorescence was placed on the commuting stage and sectioned with the vibratome until the desired stages of microsporogenesis or male gametogenesis were observed. Fluorescence was detected with a nondescanned detector (BiG.2) using the following parameters: 20× water immersion objective lens (plan apo, N.A = 1.0, Zeiss); excitation, 980 nm; and emission, BP500-550. Z-section stacks were performed through the whole depth of anthers with developing meiocytes or male gametophytes at 1-and 1.75-μm intervals, respectively. Time-series images were acquired every 30 min. The images and time-lapse movies were acquired using Zen (Zeiss). Digital image and movie processing was performed with Fiji and Adobe Photoshop/Illustrator software. Image registration for the time-lapse images was performed using Fiji's Trackem2 plug-in.

ChIP
Five-hundred micrograms of inflorescence tissues was collected for each sample and used for ChIP experiments using anti-GFP antibody (Chromotek, GFP-TRAP), which also recognizes YFP. Detailed ChIP protocols are in the Supplemental Material. Indexed libraries were generated using Illumina's TruSeq ChIP sample preparation kit and sequenced on either an Illumina HiSeq 2500 sequencer by Fasteris SA or a NextSeq machine at the Cold Spring Harbor Genomic Core Facility following the manufacturer's instructions. Two replicates were generated for each genotype.

Bioinformatics
Quality filtering and base calling were performed using the CASAVA pipeline. Reads were mapped to the Arabidopsis TAIR10 annotations using Bowtie2, allowing one mismatch. Binning and read counts/coverage relative to annotations were performed using BedTools2 intersectBed. ChIP peak detection was performed using MACS's call peak function, relative to input DNA used as control. Aggregate profiles for exon/intron coverage were performed using ngsplot. Aggregate profiles over TEs were calculated with VAP (Coulombe et al. 2014). Data sets for the Arabidopsis methylome and MeDIP-seq were downloaded from Gene Expression Omnibus (GEO; accession nos. GSM1499351 for methylome data and GSM1326797 for MeDIP data). To estimate correlation coefficients between the MeDIP and MBD-Venus data, reads were binned over 500-bp windows, and correlations were calculated using R. To estimate the correlation between ChIP enrichment and methylation levels, we classified TEs and genes having low, high, and mid methylation levels. Thresholds for methylation levels were adjusted differently for CG, CHG, and CHH to account for the difference in overall methylation between all three contexts and recover sufficient loci in each category for statistical analyses. All file manipulations were performed in R with custom scripts, which are available on request. The statistical significance data in Supplemental Figure S5 were determined in R using ANOVA.

Purification of mCG-Venus-6xHis protein
The sequence of mCG-Venus was amplified and cloned into pET28b expression vector (Novagen). Recombinant histidinetagged mCG-Venus protein production was performed in BL21 (DE3) Escherichia coli strain (New England Biolabs) after addition of 100 mM IPTG. After 4 h at 37°C, cells were pelleted, resuspended in a purification buffer (50 mM Tris at pH 8, 300 mM NaCl, 1 mM TCEP, 10 mM imidazole, protease inhibitor cocktail [Complete, Roche]), and disrupted by sonication (Branson sonicator). Immobilized nickel beads (Sigma) were added to the cleared soluble fraction and incubated for 30 min. Unbound proteins were washed by several cycles of centrifugation/resuspension in fresh extraction buffer. MBD-Venus-6xHis was released from beads by a final resuspension in elution buffer (50 mM Tris at pH 8, 300 mM NaCl, 1 mM TCEP, 250 mM imidazole). Finally, eluted recombinant protein was dialyzed overnight with a buffer containing 50 mM Tris (pH 8), 100 mM NaCl, 1 mM TCEP, and 10% glycerol.

Electrophoretic mobility shift assay (EMSA)
Oligonucleotides used for the EMSA experiments were synthesized as described (Zemach and Grafi 2003) and labeled at their 5 ′ ends with a 6FAM fluorescent dye (Eurogentec). Double-stranded oligonucleotides were generated after annealing of complementary oligonucleotides. DNA-binding assays were performed in 25 mM Tris (pH 7.5), 5% glycerol, 60 mM NaCl, 10 mM MgCl 2 , 2 mM DTT, and 2.5 μg/mL fish DNA as a nonspecific competitor. Recombinant mCG-Venus-6xHis (200 nM) was mixed with 15 nM double-stranded oligonucleotides unmethylated, hemimethylated, or fully methylated in the CG, CHG, or CHH context. Binding of recombinant mCG-Venus-6xHis was further characterized using 200 nM recombinant protein and hemimethylated oligonucleotides in the CG context with concentrations ranging from 5 nM to 200 nM. Interactions between labeled oligonucleotides and recombinant protein were resolved by electrophoresis in 8% acrylamide (19:1) gels containing 5% glycerol and Tris-borate-EDTA buffer and detected in a GE Healthcare Typhoon 9400 scanner.

Proteasome inhibition
The proteasome inhibition protocol was adapted from She et al. (2013). Briefly, 200 mg of whole inflorescences were harvested and incubated for 18 h in half MS medium with mock (water) or 5 µM syringolin A (Groll et al. 2008). After tissue grinding in liquid nitrogen, total proteins were extracted in 200 µL of lysis buffer (125 mM Tris at pH 8, 10 % glycerol, 1 % SDS, 10 mM DTT, protease inhibitor cocktail [Roche]). Samples were homogenized and centrifuged at 15,000 rpm for 10 min. The soluble fraction was then separated on a SDS-PAGE gel and blotted onto Hybond ECL nitrocellulose membrane (GE Healthcare). The DYNAMET fusion proteins were detected by using an anti-GFP antibody (Abcam, Ab6556).

Accession numbers
ChIP-seq data sets have been deposited in GEO under accession GSE70455.


extfieldsofstudy:
Biology
Computer Science
Medicine

text:
Systematic identification of an integrative network module during senescence from time-series gene expression

Background Cellular senescence irreversibly arrests growth of human diploid cells. In addition, recent studies have indicated that senescence is a multi-step evolving process related to important complex biological processes. Most studies analyzed only the genes and their functions representing each senescence phase without considering gene-level interactions and continuously perturbed genes. It is necessary to reveal the genotypic mechanism inferred by affected genes and their interaction underlying the senescence process. Results We suggested a novel computational approach to identify an integrative network which profiles an underlying genotypic signature from time-series gene expression data. The relatively perturbed genes were selected for each time point based on the proposed scoring measure denominated as perturbation scores. Then, the selected genes were integrated with protein-protein interactions to construct time point specific network. From these constructed networks, the conserved edges across time point were extracted for the common network and statistical test was performed to demonstrate that the network could explain the phenotypic alteration. As a result, it was confirmed that the difference of average perturbation scores of common networks at both two time points could explain the phenotypic alteration. We also performed functional enrichment on the common network and identified high association with phenotypic alteration. Remarkably, we observed that the identified cell cycle specific common network played an important role in replicative senescence as a key regulator. Conclusions Heretofore, the network analysis from time series gene expression data has been focused on what topological structure was changed over time point. Conversely, we focused on the conserved structure but its context was changed in course of time and showed it was available to explain the phenotypic changes. We expect that the proposed method will help to elucidate the biological mechanism unrevealed by the existing approaches. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0417-1) contains supplementary material, which is available to authorized users.

senescence and revealed senescence stage-specific gene perturbations [8]. Analysis of the functional enrichment for each stage indicated initial perturbation of cell cyclerelated genes and subsequent perturbation of metabolic, inflammatory, and immune-related genes at the middle stage. At the final stage, genes related to cell death and cell growth regulation were perturbed. Thus, genome-wide time-series analysis can reveal the genotypic signature underlying senescence.
Time-series gene expression data have been widely used to explore the molecular-level events during a phase change such as the senescence process described above, despite of difficulty of culturing cells to full senescence. Typical analytical methods use co-expression patterns to identify functional modules or compare pairwise time points to capture features of the transition or to identify temporally regulated gene expression versus one control sample [9]. Consequently, these approaches yield results based on individual genes or gene sets without considering the connectivity between them. However, cellular processes involve the interactions among several molecules, and these processes can be represented as a biological network with genes or proteins as nodes and their relationships as edges. Thus, interactome data of biophysically interacting proteins are especially useful for analyzing biological processes, but few attempts have been made to integrate time-series gene expression and protein-protein interaction (PPI) data, particularly in senescence and aging.
Recently, a study reported the construction of an agespecific integrative gene network with PPI and topological analysis of the network to reveal the key modules in aging [10]. To construct a network, differentially expressed age-specific genes were selected by following methodology of [11], which had used criterion as follows; 1.5-fold change, 0.01 FDR. The protein interactions mapped with the selected genes become edges of the network. As a result, 37 age-specific networks were obtained and 'ground truth' gene set collected by analyzing brain gene expression data was used to demonstrate whether the networks were significantly related with the aging process or not. The authors revealed that the global topology of the age-specific networks was similar to each other, whereas the local topologies of several genes were significantly different. For the topological comparison among age-specific networks, the similarity measure called graphlet degree distribution agreement [12] was used. It was revealed that the local topologies were significantly changed with age and those genes were associated with age.
We proposed a novel approach to investigate the core modules of a genetic network highly correlated with phenotypic changes from time-series data. To construct the network, we integrated a perturbed gene set with biophysically validated PPIs recently published [13] and identified and analyzed the core sub-gene network. Based on the evidence that continuously perturbed interactions can be used to interpret a gradual phenotypic change with abrupt changes at the beginning and end points, we hypothesized that a sub-gene network with a constant topological structure but gradually altered context (e.g., expression pattern) plays an important role in phenotypic change. This concept can be applied to study cellular senescence because several senescence-related functions, such as the cell cycle, are not enriched at the abrupt phase; actually, a previous study revealed that cell cycle remodeling was nearly continuous during senescence [14]. The proposed approach is distinguished from the previous senescence studies. The previously mentioned study [8] did not consider the interactions among the perturbed genes during senescence and [10] analyzed the network modules topologically changed during aging.
To test our hypothesis, we applied the proposed method to two replicative senescence datasets from human diploid fibroblasts (HDFs) and mesenchymal stem cells (MSCs) and one independent cancer progression dataset from human tissue neoplasia. We performed functional enrichment of the identified core sub-gene network and simple significance tests to confirm whether our findings reflect changes in gene expression that account for the observed phenotypic change.

Data description and system overview
As a proof of concept for our approach, we intensively used recently published time-series gene expression data (GSE41714) measured during replicative senescence in HDFs [8]. This time-series microarray data (GSE41714) was composed of twelve sequential order of senescence stages according to the population doubling time including young and old phenotypes as the first and last time points, respectively. The senescence phenotype for each stage was identified and confirmed by typical determinants such as increased or decreased reactive oxygen species (ROS) levels and high or low level of senescence-associated β-galactosidase activity. We also used time-series microarray data (GSE9593) collected during replicative senescence in MSCs [15]. These data included nine passages from young to old status. Finally, we employed time-series microarray data (GSE15299) for elucidating epithelial cancer progression [16]. This dataset included four time points and was selected to test whether the proposed approach was applicable to processes besides senescence. The series matrix files of these microarray based data were downloaded and if there was no obvious description about normalization process in GEO, the normalization was performed. The array platform of these three datasets was different each other. GSE41714, GSE9593 and GSE15299 used Illumina HumanHT-12 V4.0 expression beadchip, Illumina HumanHT-12 V4.0 expression beadchip and Affymetrix Human Genome U133 Plus 2.0 Array, respectively.
To identify the connectivity between genes, we downloaded a recently published human PPI dataset [13] with 23,124 high-confidence PPIs compiled from systematic screening with high-throughput yeast two-hybrid and literature studies and validated using biological assays. Although the high-confidence PPI set is smaller than the typical PPI set from the I2D database [17][18][19], biological validation allows greater confidence in conclusions based on these PPIs. The proteins in these PPIs were mapped into gene symbols using UniPROT [20].
The entire workflow of the proposed method is shown in Fig. 1. As described above, time-series gene expression dataset and PPIs are integrated and the structurally conserved network is identified. Using this network, phenotypic changes during senescence and cancer progression are analyzed.

Identification of time-specific networks
Before identifying the perturbed gene set, we carry out quantile normalization for the time-series gene expression dataset with R. For normalization of the downloaded dataset which is series matrix format in GEO, we used 'preprocessCore' library provided by Bioconductor.
By normalization, we can expect that the average of expression values and relatively highly expressed genes at certain time points were adjusted. This gives a similar intensity distribution for each time point. After normalization, perturbed gene sets were identified to construct a network for each time point. In our method, the perturbation score of a gene at a time point is calculated as follows: where gene ij indicates gene i at time point j, e ij indicates the expression value of the i th gene at the j th time point, and M is the total number of time points. This formula measures the difference between each expression value and the average for expression value. The perturbation score, p(gene ij ), is calculated for every gene at every time point, and larger values implied that gene i was relatively perturbed at time point j. If adjacent time points with similar phenotypes are grouped, the average perturbation score for the grouped time points was used.
Only genes with perturbation scores above the threshold (minimum cut-off value for significance of perturbation) are selected for each grouped time point. The threshold as determined for a given dataset by assuming the perturbation scores are normally distributed and setting the threshold as the sum of the mean and standard In this example, the final perturbation score is 0 because the average perturbation score is more than lower threshold and less than upper threshold. If the final perturbation score of the gene is not zero, this gene can be included in the perturbed gene set for each time point. To construct a time point-specific network, we identify a set of interactions including at least one perturbed gene from the highconfidence PPI dataset. From these selected interactions for each time-point, time-specific networks are built.

Identification and analysis of common network
From the identified time-specific networks which have different size and include different interactions, we detect topologically conserved sub-network across timepoint. As described in the Introduction, we assumed that sub-networks with constant topological structures play important roles in phenotype change. Topological conservation for a sub-network refers to its continuous perturbation with changing phenotype. As mentioned in the Introduction, we assumed that these network modules and the transition of their information such as perturbation scores were related to phenotypic changes. To prove our assumption, we calculate the average difference of perturbation scores between two time points after identification of common network. For example, in Statistical support for significance of common network We investigate an association between phenotypic changes and variation of perturbation scores with statistical test based on large-sized random sampling. We assume 'average differences of perturbation scores from our Fig. 2 Identification of temporally perturbed genes. In the time-series microarray, each expression value is converted into a perturbation score. After establishing a threshold value from these perturbed scores, filtered perturbed scores satisfying thresholding condition are employed to select interactions common network and random network are same' as a null hypothesis. We tested whether this hypothesis can be rejected or not. The test is composed of three steps. As shown in Fig. 3, first, all interactions identified by the proposed method are collected. From this collection, the interactions are randomly chosen as numerous as the proposed method selected. For example, if 5 interactions were identified by the proposed method at time point 2, the same-sized 5 interactions are randomly selected from the collection about this time point. Consequentially, the random selection only permutes order of time point. Secondly, across time point, common network is detected from randomly selected interaction set. Lastly, we calculate the average difference of perturbation scores between two time points with this common network. These three steps are repeated 10,000 times to generate the distribution. We calculate p-value which indicates whether we can reject the hypothesis or not. Through the result of this test, we demonstrate that context transition explainable phenotypic change can be observed in the conserved network reflecting a time order, i.e. identified by the proposed method. This procedure was systematically implemented by using Java and R.

Results
To demonstrate the effectiveness of the proposed method, we applied it to three time-series gene expression datasets: two senescence datasets and a cancer progression dataset. Although we focused on senescence, we included the cancer progression dataset to test the applicability of our method to other types of datasets. We detected a common network from whole time point-specific networks and demonstrated how the common network can explain the phenotypic change for the three experimental datasets by performing statistical tests and functional enrichment with KEGG pathway database and gene ontology database.
To determine a optimal thresholds for identifying perturbed genes, we used dataset-specific cut-off values. As described in Method section, we calculated the mean and standard deviation for the perturbation scores in given dataset and found that the perturbation scores were not normally distributed: the mean was near zero, and the standard deviation was relatively small. However, we assumed that the distribution was Gaussian because the number of perturbation scores was large enough to apply the central limit theorem. Supporting Fig. 6 shows the distribution about perturbation scores. We used μ ± 1σ as a threshold: perturbation scores Fig. 3 Workflow of computational validation. As many as the number of interactions for each time point identified by the proposed method, interactions are randomly selected without considering their time points to construct random networks. From these random networks, a common network is identified. At two time points which can explain phenotypic change, the average difference of perturbation scores is calculated on common network. This random sampling process is repeated as many as 10,000 iterations. Based on the distribution from 10,000 average differences of perturbation scores, the statistical significance of our average difference of perturbation score is performed using statistical test above μ + 1σ or below μ-1σ were chosen to construct each network. The number of genes selected by using μ ± 2σ or μ ± 3σ as a threshold was too small to construct networks. The experimental results while changing the cut-off threshold were shown in supporting Table 4 and 5 of the additional file.
Significance test in HDF senescence and cancer progression dataset Table 1 summarizes the identified time-specific networks and their common network in the replicative senescence dataset (GSE41714) [8]. Twelve time points based on the population doubling time were grouped into four stages according to the senescence phenotype. The network size was different at each stage, and the proportion of selected interactions was less than 11.2% in general; the common network comprised only~0.409% of all used high-confidence PPIs. We assumed that this network might act as a key module for inducing phenotypic changes despite its small size. Figure 4 shows the identifed common network.
With the common network, we investigated a statistical significance comparing all possible adjacent stages. As shown in Table 2, among three possible adjacent comparisons, only the second comparison (middle-advanced) was significant. From this result, we could suggest that phenotypic change between middle and advanced stages was relatively more influential than other transitions. Actually, it was reported in the previous study [8] that the results of functional analysis in early and middle stage were similar each other. These two stages were highly related with cell cycle and metabolic process. Similary, the results of functional analysis in advanced and very advanced stage were similar. Previous study [8] reported these two stages were related with cell death and NFKB cascade functions. In addition, we performed the significant test for comparison between early and very advanced stage, which indicate the first and last, respectively. The p-value was almost zero for this comparison. The phenotypic changes between the first and last stage was understandably obvious since young cell turned into old cell. This result demonstrated that context transition of the conserved network can reflect and explain the phenotypic changes. Surpporting Fig. 3 shows the four distributions of average difference of  Table 6 shows this results. We investigated how perturbation scores of the common network are changed during transition and whether this changes have direction to the increase or decrease. As shown in Fig. 5, we could find two representative patterns from changing profile of perturbation scores. Up and down fluctuation of these two patterns were almost opposed, mutually. We observed that relatively up-regulated genes in early stage were gradually decreased along with going to very advanced stage. On the contrary, relatively down-regulated genes in early stage were gradually increased along with senescence.
Furthermore, we applied the proposed method to the cancer progression dataset (GSE15299) [16] and performed same experiments as mentioned above. Table 3 shows a summary of the identified networks for each stage. The network size was different at each stage, and the proportion of selected interactions was less than 10.2% in general; the common network comprised only~0.12% of all used interactions. Figure 6 shows the identified common network. We investigated a statistical significance from all possible neighboring stages including a comparison between the first and last stage. As shown in Table 4, statistical test was also performed, we revealed that only one comparison between Day20 and Day 35 was not signficant. We performed statistical test with a significance level of 0.05. In two (early-middle, advanced-very advanced) among three adjacent comparisons, the difference of perturbation score was not significant. However the difference of perturbation score at the comparison between early and very advanced time points was strongly significant a b Fig. 5 Profile on change of perturbation scores of the member genes in the identified common network (HDF senescence dataset). a Change of perturbation scores during senescence process. There were two striking patterns, one of which is gradually decreased pattern and the other is gradually increased pattern. b Comparison for changes of perturbation scores in adjacent two time points and the first and the last time point. We could identify that the changing pattern was reversed at both the first and the last time point. As a statistical result shown in Table 2, this reversely changed pattern of expression is rarely happen. Therefore, we could assume that the common network included information to explain the senescence process To capture the progression of invasive neoplasia, the author made an Ras-inducible human model which can be changed epidermal tissue to squamous cell carcinoma [16].
In this experiments, hyperplasia and disordered tissue polarity were observed that between day 5 and day 10, and fully manifest invasive phenotype was culminated by day 25 to 30. Therefore, until day 20, phenotype was rapidly changed and after day 25, the fully changed phenotype was observed. Owing to this phenotypic change, the average difference of perturbation scores between day 20 and 35 was not significant compared to random case. We demonstrated that the common network constructed by our approach reflects this phenotypic changes. The P-value of comparision between the first and last day was 3.792E-29, which is considerably significant. As shown in Fig. 7, we also observed that the profile of perturbation scores in the common network could support the result of significant test.

Functional enrichment in HDF senescence and cancer progression dataset
Through abovc experiments, we computationally analyzed common network in time-dependent gene expression profile. In addition to the computational validation, we performed two types of functional enrichment test on the common network. Fisrt, we performed gene ontology based enrichment using BiNGO [21] plugin in Cytoscape [22]. Secondly, we carried out pathway enrichment test using KEGG database. Because the identified common network had small size and simple topology, we used the entire common network as an input of functional enrichment test.
On HDF senescence dataset, top 10 pathways and gene ontology terms (P-value < 0.01) were listed in Table 5. As expected, among several pathways, cell cycle and cancer related pathway were significantly enriched. In gene ontolgies, response to several stimulus, cell proliferation and development related processes were importantly enriched. These processes were regarded as an important roles in senescence. On cancer progression dataset, we also selected top ten enriched pathways and gene ontology terms. The result was described in Table 6. In KEGG pathways, bladder cancer pathway was enriched. In gene ontologies, cancer progression associated term was enriched such as anatomical structure development.

Additional analysis with MSC senescence dataset
We performed statistical test and functional enrichment for MSC senescence dataset. The detailed results were described in the Additional file 1 with supporting Table 1    We performed statistical test with a significance level of 0.05. In one (Day 20 -Day 35) among three adjacent comparisons, the difference of perturbation score was not significant. However the difference of perturbation score at the comparison between   We attempted to trace temporal changes in the pattern of perturbation scores for the cell cycle specific common network. To identify this common network from HDF senescence dataset, we only used and focused on 207 known cell cycle-related genes which are annotated in the gene ontology database. We identified a common network comprising 14 nodes and 13 edges as shown in Fig. 8. Among the 207 cell cycle-related genes, only 14 continuously affected senescence while maintaining a constant topology and displaying gradual and directional changes in perturbation score values. During aging, we identified genes known to be up-and down-regulated [23][24][25] during senescence with similar regulations in our analysis. For example, KIF20, a cell cycle controller, was down-regulated in senescence status by activation of p16 via the Rb/E2F pathway [23]; CRABP2 was strongly down-regulated with increased passage number in human amniotic fluid-derived stem cells and might act as a negative regulator to limit cellular senescence [24]; and CCND1, another well-known cell cycle regulator, is down-regulated in HDF senescence [25].
As has been demonstrated by several previous studies [23][24][25], variation of the perturbation score values in the identified common network was generally consistent with expectations for cellular senescence. Interestingly, the gene expressions at the young and senescence stage were completely opposed. Based on this result, we propose that the identified common network can switch cell cycle activity between young and senescence status. Alternatively, transcription factors (TFs) that regulate genes in the identified common network could act as the switch.
In this study, we focused on the former assumption because there may be regulatory relationships among genes of the network. We attempted to identify the regulatory relationships for the identified common network using PathwayStudio 9.0 software (Ariadne Genomics; Rockville, MD, USA) to build a pathway of directed interactions among genes in common network (Fig. 9a), omitting six orphan genes. We included two types of regulation: ProtModification and DirectRegulation. ProtModification indicates a regulator that modifies the target molecule through phosphorylation, glycosylation, etc., and DirectRegulation indicates direct physical interactions that can influence target activity.
Interestingly, we observed a feedback loop composed of the replicative senescence related genes: CDK6, CCND1, CDKN1A, and CDKN1B (Fig. 9a). In the observed regulatory network, including the feedback loop, CDK6 was the most important node because it acted as a hub and could be the genesis of the loop. It has been known that CDK6 regulates DNA replication in G1 and Fig. 8 Visualized dynamic change of the perturbation scores in the identified common network limited to cell cycle-related genes reported to switch cell cycle status from G1 phase to S phase [26]. Furthermore, high expression of CDK6 can form a transcription complex and induce the expression of tumor suppressor p16 [27]. It has been reported that p16 is significantly related with molecular mechanism of senescence [28].
In our feedback loop (Fig. 9b), the subunit regulators of CDK6 (CCND1, CCND2, and CCND3) were downregulated in young status; thus, these four proteins were simultaneously down-regulated in young status. However, expression of CDK6 was increased upon senescence. Along with this expression alteration of CDK6, CDK6 could inhibit CDKN1B by negatively regulating its phosphorylation. CDKN1B translation is also reduced during G1 arrest [29], and CDKN1B down-regulation could inhibit CCND1, which is also positively influenced by CDKN1A. Thus, CDK6 may be consistently upregulated by CCND1 during senescence, allowing maintenance of full senescence status. Much of this process has been previously reported [30][31][32], but our method allowed identification of the regulatory relationship among them with a more integrated view, and we note that our approach yielded a result reflecting the senescence process.
In addition, we investigated TFs which can control this regulatory relationship. We used recently published computational method, iRegulon [33]. Among the results which have high enrichment score (NES > 0.3), we selected TFs inferred by TRANSFAC database widely used to search TFs. Then, we filtered TFs targeting on CDK6. As a result, we can identified seven TFs; STAT5A, ARID3A, POU4F3, DLX5, ZNF35, LMX1A, PAX2. Among them, STAT5A and ARID3A have been revealed to be related with cell cycle process [34,35]. PAX2 has been reported to be mechanistically associated cancer cell proliferation [36]. Through this result, it is possible that the identified regulatory relationship and the related TFs can be regarded as strong candidate which controls cell cycle phase replicative senescence.