VPH1 and minimal CYC1 promoters together with operator binding sites for bacterial  repressor proteins. Moreover, model-driven considerations permitted us to pinpoint a strategy for re-designing gates when a better digital performance is required. Our library of well-characterized Boolean gates is the basis for the assembly of more complex gene digital circuits. As a proof of concepts, we engineered two 2-input OR gates, designed by our software, by combining YES and NOT gates present in our library.In our previous computational work, we showed that gene digital circuits can be automatically designed in an electronic fashion. This demands, first, a conversion of the truth table into Boolean formulas with the Karnaugh map method and, then, the translation of the Boolean formulas into circuit schemes organized into layers of Boolean gates and Pools of signal carriers. In our framework, gene digital circuits that take up to three different input signals  arise from the composition of three kinds of basic Boolean gates, namely YES, NOT, and AND. Here we present a library of YES, NOT, and AND gates realized via plasmidic DNA integration into the yeast genome. Boolean behavior is reproduced via the transcriptional control of a synthetic bipartite promoter that contains sequences of the yeast  E. coli[E. coli[A topic of growing interest in Synthetic Biology is represented by gene digital circuits. Primarily, they find application as biocomputing systems and biosE. coli. BooleanE. coli,6 whereaE. coli,8. TransE. coli. FinallyE. coli and werei[E. coli too.Pools of signal carriers such as transcription factors, small RNAs, and chemicals[On the theoretical side we proposed a computational method for the automatic design of synthetic gene digital circuits. Followihemicals.inducers (transcription or translation activation) and corepressors (transcription or translation inhibition). Inducers bind either active repressors–turning them into an inactive configuration where they are no longer able to bind the DNA–or inactive activators that get activated and start recruiting RNA polymerases for mRNA synthesis. Corepressors bind inactive repressors–enabling them to bind promoters in competition with RNA polymerases–and active activators that become unable to get access to the DNA. At mRNA level, both inducers and corepressors bind riboswitches. Upon chemicals’ arrival to its aptamers, a riboswitch undergoes structural modifications that either produce (corepressor case) or remove (inducer case) a hairpin loop that prevents ribosome binding and translation initiation. Besides this direct control, translation is also negatively regulated by small antisense RNAs that base-pair to mRNA sequences .Recently, we came up with a revised version of our computational tool where gene digital circuit design was simplified by adapting some ideas from to the sThese three mechanisms of transcription and translation control allow the construction of basic Boolean gates such as YES, NOT, and AND that are then assembled into digital circuits. YES gate do not have a counterpart in electronics and simply produce an output in presence of their (single) input. They are used to convert chemicals into transcription factors or small RNAs that carry out their action on other circuit’s gates. NOT gates return an output in absence of their input whereas AND gates take two inputs and produce an output only in presence of both inputs.distributed output architecture. POS formulas can be rearranged with the De Morgan’s laws[final gate architecture). As a result, every digital circuit designed by our software is made of YES, NOT, and AND Boolean gates  genes expressing repressor proteins . Each construct owes its logic behavior to a bipartite promoter[β-estradiol[first completely based on DNA genomic integration.In this work we present a library of YES, NOT, and AND gates engineered in yeast via  promoter,19 regulstradiol . In this way, it is possible to recreate in yeast the same competition, for the promoter sequence, that takes place in bacterial cells between RNA polymerases and repressors. Once bo two TSS) and ano defined). In betces Pombe; a threeces Pombe; a promoerevisiae; and a mind LacI.β-estradiol) and the output is a protein , we think that this parameter on its own cannot provide a comprehensive description of gates’ performance. Higher ρ values may simply mean a better repression rather than a clearer distinction between high and low output. Therefore, besides ρ, we calculated for each gate the signal separation (σ = VL1 - VH0) i.e. the distance, in arbitrary units (AU) of fluorescence, between the minimal 1 and the maximal 0 output at steady state[1-factor (φ = Vc1/Vo1) that corresponds to the ratio of the 1 output of the closed gate– Vc1 i.e. the gene expressing Citrine together with the gene(s) encoding for the repressor(s)–to the 1 output of the open gate . In our work φ has a duplex meaning: one one hand it is an estimation of how effective inducer molecules (such as tetracycline and IPTG) are in inhibiting their target repressors, on the other hand it quantifies repression effects in absence of corepressors (as in the β-estradiol–LexA-HBD system).Although gene Boolean gates are commonly characterized by the sole 1 to 0 output ratio , the repressor binds a synthetic bipartite promoter at one or two operator sites and inhibits Citrine synthesis. Fluorescence is induced by the action of the input chemical that prevents its target repressor from DNA binding.YES gates are devices that produce fluorescence in presence of a single input chemical. In our systems, a repressor protein (TetR or LacI) is expressed under the constitutive μM tetracycline and 40 mM IPTG are required for a full transcription induction  that is almost doubled in the tetOp2 design where, as expected[φ around 1. Therefore, we detected a high affinity between tetracycline and TetR and a moderate affinity between TetR and its operator binding site.Both configurations of the tetracycline-based YES gate show a high signal separation . Indeed, the 1 output corresponds to only about one quarter of the open gate fluorescence and the signal separation (471 AU) is not high enough to determine a precise threshold between 0 and 1 fluorescent signals. According to our digital circuit computational analysis in[σ can be improved by increasing the transcription initiation rate of the promoter that leads to reporter protein production. Here, instead of re-engineering the lacOp2-containing bipartite promoter, we simply double integrated (DI) the transcription unit hosting it in order to mimic an increase of transcription initiation rate. As it is shown in FigureWe found a similar trend in the IPTG-LacI system: a single lac operator (YES lacOp) gives a higher signal separation 4930 AU) whereas two lac operators permit to switch off fluorescence almost completely  in our library. This underlines how this parameter alone cannot fully describe how well a genetic construct approximates a logic behavior. In this case, for instance, it hides a rather low signal separation (882 AU) and very low 1-factor (0.19 versus 1.22 in the single lac operator case). Below, we give a possible theoretical explanation of the different Boolean response with one and two lac operators. Moreover, we show how simulations of the YES lacOp2 and YES lacOp2-DI gates prove a correspondence between multiple integrations and higher transcription initiation rate.Even though a single lac operator is not enough to drastically repress transcription and, as a consequence, its 1 to 0 ratio is slightly lower than 5, this configuration provides high values for both β-estradiol. β-estradiol nullifies the HBD delocalizing action such that LexA can enter the nucleus and repress fluorescence. We chose 500 nM as 1 input level for β-estradiol. This concentration gives a stronger repression than the one registered both with TetR and LacI  and φ slightly lower than 1 (this is probably due only to measure uncertainty rather than to LexA binding in absence of β-estradiol).NOT gates are made of two genes as well. LexA repressor is kept outside the nucleus by the HBD i.e. it is inactive in its ground configuration and gets activated only upon binding to We realized three kinds of two-input AND gates. Every AND gate contains two operators, each one binding a different repressor protein. As previously mentioned, AND gates have been engineered into two configurations by exchanging the positions of the two operators.σ , TetR action is stronger and the difference among the three 0 output fluorescence levels is reduced.Overall, the best performance was obtained with the two "tet AND NOT(estr)" gates . In particular, the design with the lex operator close to the TATA box (AND lexOp-tetOp) gives a better φ = 0.73), σ remains fairly high (2035 AU). In contrast, the AND lacOp-tetOp implementation presents an excess of fluorescence in correspondence to the truth table entry 01 (no tetracycline and 40 mM IPTG). This value is practically indistinguishable from the 1 output level measured for the 11 entry. When this gate is induced with IPTG only (01), the whole promoter regulation is due to the sole TetR whose action, however, is clearly too weak when it binds far from the TATA box. As in the YES lacOp2 case, we managed to engineer a working AND gate with the lacOp-tetOp bipartite promoter configuration via a multiple (triple) integration of the reporter-protein-encoding transcription unit (AND lacOp-tetOp-TI). In this way, the 1 output fluorescence increased highly which permitted to achieve a signal separation (2017.5 AU) comparable to the one measured for the AND tetOp-lacOp configuration .Tet operator positional effects on transcription regulation are even more evident in the "tet AND IPTG" gate " gates. Both LacI and LexA are stronger than TetR and appear only slightly more efficient when they bind close to the TATA box , the latter a too weak down-regulation by TetR. We managed to rescue these two gates with a multiple integration of the transcription unit expressing Citrine. This design was chosen as an alternative to a bipartite promoter with a higher transcription rate, as suggested in. In the eukaryotic composable Parts and Pools[For our deterministic simulations, every gate was realized with nd Pools (see "MomM) fully inhibits LacI action on a single operator but is not effective in the two-operator case. This result might be explained by assuming a cooperative interaction among LacI proteins binding adjacent operators. Rescaling our experimental data to the output of the open YES lacOp2 gate (promoter: pLacOp2), we see that the single-lacOp-containing promoter (pLacOp) is 2.71 fold stronger and the constitutive pAct1 promoter (responsible for LacI production) even 11.25 fold more active than pLacOp2. To reproduce these proportions in silico, we started from the set of parameter values in[kref2 = 0.1s-1). Finally, we tuned the parameter values of reactions that play a role in transcription only since we did not consider any translation regulation mechanism in our gates’ implementation , the IPTG-LacI binding rate constant both at the promoter (γ) and in the nuclear LacI Pool (λ), the pLacOp transcription initiation rate (k2). α turned out to be moderately low  and λ more than three folds lower than γ (i.e. IPTG was more effective on repressors bound to the promoter). This underlines that the bond between lacI and the DNA is not particularly strong. k2 was set about three folds bigger than kref2 to have in silico the same ratio between pLacOp and pLacOp2 strength (i.e. the open gates’ relative fluorescence) as the one measured in vivo. This result is coherent with what we observed on each of the three single-input gates experimentally, namely a reduction of transcription efficiency in conjunction with the insertion of a second operator (i.e. by increasing the distance between the TATA box and the TSS).We reproduced pLacOp experimental behavior by modifying a handful of kinetics parameter values such as the repressor-operator binding rate constant (αw = α). The stronger operator shows, in contrast, an almost 10 fold higher affinity (αs = 7.1 107M-1s-1). When the operator close to the TATA box is taken by LacI, αw is increased the to αs value. Moreover, as a consequence of the stronger bond to the DNA, we had to drastically lower the value of γ (more than 130 folds) used for pLacOp. With this choice of parameter values, a double concentration of pLacOp2 in the closed gate configuration gives results fairly near to the experimental data  alternative to promoter re-engineering.We had to include in our model LacI cooperative interactions in order to mimic YES lacOp2 gate fluorescence levels. In our computational framework we had to assign to the two lac operators different affinities towards LacI molecules. The weaker operator (i.e. the one close to the TSS box) is characterized by the same binding rate constant as in the pLacOp case  to kref2 = 0.1s-1. In this bipartite promoter configuration, the lac operator has an affinity (αlac = 1.75 107M-1s-1) that lies between the αw and αs values previously used. In order to mimic the strong LacI repression effects put in evidence by our experiments, γlac was set to a low value (100s-1). Since the tet operator appeared to be rather inefficient in binding TetR, we had to set αtet value equal to the RNA polymerase binding rate constant. Moreover, both λtet and γtet were given much higher values than in the LacI case. In this way, we managed to approximate the four outputs of the original AND lacOp-tetOp gate implementation well  confirming the result we obtained for the YES lacOp2-DI gate.The experimental data of the AND lacOp-tetOp-TI are more difficult to explain. If, on one hand, it is clear that the Citrine gene was triple integrated, the fluorescence levels associated with each of the truth table entries are too low than the expected ones . In order to reproduce this behavior, we supposed that either a single transcription factor or both of them were accidentally integrated multiply. According to our simulations, the most probable scenario is a triple integration of the LacI gene. However, with this hypothesis we were able to reproduce the gate fluorescence trend only since the computed separation between the 11 and 10 output was just half of the measured one  i.e 4 transcription units, one for each selective marker employed in this work (see Methods below).We implemented two OR gates: "tet OR NOT(estr)" and "tet OR IPTG". Both circuits show a good agreement between measured and computed fluorescence outputs  that represent the inputs for our digital circuits–the output is fluorescence. We characterized every gate with three main parameters: the signal separation (σ) between the  0 and the  1 output, the 1 to 0 gain (ρ), and the 1-factor (φ). σ and ρ quantify the distance between high and low fluorescence outputs and serve to place an unambiguous 0/1 threshold; φ specifies the efficiency of chemicals on their target protein (inducers) or points out the capability of an inactive repressor to bind the DNA in absence of its activating signal (corepressors).In our computational work we showeWith computer simulations we showed that, in order to improve the gate signal separation, multiple integration of the final gate (i.e. the transcription unit encoding for the circuit output) might be a valid alternative to the reengineering of the final gate’s promoter. With thσ values) in order to compose them into bigger distributed-output-architecture-based circuits (e.g. OR gates taking 3 or more inputs). This would permit to quantify how the performance of this design scales with the circuit size and understand possible application limits. Finally, in order to implement a higher number of circuits designed by our software[in silico design and simulations to their wet-lab in vivo implementation.Every gate in our library is a small bio-sensing device and more elaborate biosensors can be built by assembling together some of these gates. New basic gates are, however, necessary in order to realize more complex digital circuits. In particular, a careful analysis of operator positional effects is required to limit promoter leakage and approach complete switch-off states. This feature is of primary importance to build circuits based on the final gate architecture that in this work was not taken into account explicitly. This family of circuits needs the wiring of three layers of Boolean gates. Numerous transcription factors–acting on several, different promoters–will be necessary and unspecific binding  should be avoided. TAL effectors represensoftware, translaE. coli strain DH5 α [F -ϕ 80lacZM15Δ (lacZYA - argF) U169 deoR recA1 endA1 hsdR17. Every construct is a transcription unit (promoter–protein coding region–terminator) cloned into shuttle vectors developed in our lab . They are derivative of the pRS yeast shuttle vectors[ACT1 promoter (FRP337), used to lead the expression of repressor proteins; 2) synthetic bipartite promoters containing both part of the yeast VPH1 (FRP281) and minimal CYC1 promoter (FRP308). They always lead the production of yellow fluorescence, i.e. the circuit output; 3) TetR gene (FRP446); 4) LacI gene (FRP222); 5) LexA-HBD gene (FRP467); 6) Citrine gene (FRP677). It encodes for a reporter protein (yellow fluorescent protein-YFP); 7) CYC1 terminator (FRP332). It represents the end of every transcription unit. Notice that both TetR and LacI are fused to a Nuclear Localization Sequence (NLS). LacI is fused to the HA tag as well. All the transcription units used in this work . The starting strain for Boolean gates’ construction is FRY11 where the red fluorescent protein mKate2[ACT1 promoter. Each Boolean gate has been implemented by integrating the corresponding transcription units into the yeast genome. All the yeast strains used in this work are listed in Additional fileAll yeast strains are derivative of n mKate2 is constμl YPD. Cell solutions were poured into 2.5 ml 96-well plates. They grew at 25° C and 300 rpm for a time variable from 4 up to 8 hours. Cell cultures were then diluted again (approximatively 1:150) in YPD and induced with chemicals into an overall volume variable from 320 up to 500 μl. Cells grew overnight and were re-induced with chemicals in the morning. After this last dilution , cells were let grow for at least 4 more hours. In this way, during every flow cytometry experiment cell solution OD600 was maintained between 0.2 and 2.5 i.e. cells were in the exponential phase. This procedure was followed also to measure gates’ fluorescence in absence of chemical induction (i.e. when the only transcription unit expressing Citrine was integrated into FRY11).In order to perform flow cytometry experiments, yeast cells precultures were prepared in 5 ml YPD solution . They grew overnight into glass tubes at 30°C and 280 rpm. In the morning, cells cultures were approximatively 1:100 diluted into 500 th and the 6th peak. At the beginning of each experiment, we changed the voltage of the 488 nm laser in order to place both peaks as close as possible to fixed, arbitrary positions. The 4th peak was always located between 10080 and 10391 AU  and the 6th peak between 72208 and 74165 AU (2.7%). Moreover, we registered these fluorescence levels also at the end of every analysis to assure that the machine set-up did not change considerably over a single experiment  for yellow fluorescence detection. We used 8 peaks alignment beads to guarantee a constant set-up of the machine in order to compare results from different experiments properly. In particular, we took as a reference the mean fluorescence values of the 4median to quantify the gate output. Coherently, we placed the experimental error bars at the first and third distribution quartile. Finally, both fluorescence median and errors were subtracted the background fluorescence i.e. the the yellow fluorescence signal detected on FRY11 (where the Citrine gene was not integrated).We recorded 10000 events for every sample. All the data were analyzed with R by using the flowcore (Bioconductor) package. Since fluorescence data follow a log-normal distribution, we took the distribution β-estradiol (Sigma-Aldrich). We prepared 10 mM tetracyline stock solution in ethanol, 1M IPTG stock solutions in water, and 10 mMβ-estradiol stocks in ethanol. From their stocks, chemicals were diluted in YPD for flow cytometry experiments.We used three different chemicals in our experiments: tetracycline, IPTG, and I declare I have no competing interests.Supplementary Material.Click here for file


Saccharomyces cerevisiae with high accuracy, namely the histone marks H3K4me3, H3K14ac, H3K36me3, and H3K79me3; and GC content. Previous studies have associated H3K4me3, H3K36me3, and GC content with areas of mitotic recombination. H3K14ac and H3K79me3 are novel predictions and thus represent good candidates for further experimental study. We also show nucleosome occupancy maps produced using next generation sequencing exhibit a bias at DSB hot spots and this bias is strong enough to obscure biologically relevant information. A computational approach using feature selection can productively be used to identify promising biological associations. H3K14ac and H3K79me3 are novel predictions of chromatin marks associated with meiotic DSBs. Next generation sequencing can exhibit a bias that is strong enough to lead to incorrect conclusions. Care must be taken when interpreting high throughput sequencing data where systematic biases have been documented.Meiotic recombination is not distributed uniformly throughout the genome. There are regions of high and low recombination rates called hot and cold spots, respectively. The recombination rate parallels the frequency of DNA double-strand breaks (DSBs) that initiate meiotic recombination. The aim is to identify biological features associated with DSB frequency. We constructed vectors representing various chromatin and sequence-based features for 1179 DSB hot spots and 1028 DSB cold spots. Using a feature selection approach, we have identified five features that distinguish hot from cold spots in  Meiosis is the biological process by which the genome is divided in half to generate daughter cells that can participate in sexual reproduction. In eukaryotes, this process is accompanied by meiotic recombination, which involves pairing of homologous chromosomes and exchanging of genetic material. Meiosis serves to increase genetic diversity in progeny (for review see Saccharomyces cerevisiae showed that regions with a high break frequency had a high G+C content Mapping DSB hot spots Schizosaccharomyces pombe) showing a positive correlation S. cerevisiae. The histone modifications and nucleosome occupancy data used in our analysis were derived from vegetatively growing mitotic cells, which is a different cell state than meiotic cells. Genome-wide epigenetic studies using both mitotic and meiotic states were used to increase the amount of useable data; there is good reason to believe that epigenetic marks found at hot or cold spots in mitotic cells will also be present at those same sites in meiotic cells  in length. Buhler et al. produced a set of peaks representing hot spots with 5-fold and 2-fold enrichment over background. In our analysis, hot spots were defined by centering a 600-bp window at the midpoint of peaks that were enriched 5-fold over background. Cold spots were obtained by finding at least three adjacent probes with a log2 hybridization ratio of less than 0.75, and then centering a 600-bp window at the midpoint of the centermost probe. For each region, we produced a vector of length 350 to represent features such as the chromatin-associated factors “Nucleosome occupancy”, “H3K14ac”, “H3K36me3”, “H3K4me1”, “H3K4me2”, “H3K4me3”, “H3K79me3”, and “H3K9ac”.Buhler et al.et al. Cold spots were defined by a set of non-overlapping 600 bp windows with no reads aligned that did not overlap to any extent simple repeats as downloaded from the UCSC genome browser.Pan et al. used tiling arrays to map histone modifications in S. cerevisiae. We obtained this data from the public database ArrayExpress Archive (http://www.ebi.ac.uk/arrayexpress/) and normalized using MA2C normalization et al.Pokholok In this study, 342 out of 350 features were sequence-based in which each sequence feature represented the normalized frequency of the region for one of the 1–4 possible k-mers. For example, feature 9 for region × would be the number of times the 2-mer “AT” was found in the region divided by the number of k-mers of size 2 found in the region. Hence the feature represents the enrichment of AT relative to all 2-mers found in the region. Similarly, feature 300 for region × would be the number of times “AAGT” was found in the region divided by the number of k-mers of size 4 found in the region. We also included two sequence features “AT content” and “GC content”, reflecting the overall AT and GC content for that region, respectively. It would seem the sequence features could further be reduced by removing the reverse complement of the given k-mer (CG is the same as GC). Whether or not the reverse complement is redundant is based on whether or not strand specific processes are acting at Hot spots. There are examples of strand specific trans-acting factors operating at hot spots Feature selection can be described as finding the subset of features from the set of all possible combinations of features that can best distinguish classes of interest. Because the search space of all possible combinations of features grows exponentially with the number of features, it is rarely feasible to perform an exhaustive search. Instead, various heuristic search methods can be used to identify meaningful feature subsets that can be used to build classifiers with high accuracy. Here we used a genetic algorithm (GA)- based approach total = (A1+A2+A3)/3, in which Ai is defined as the accuracy of the given subset of features using a random forest classifier built utilizing the sub-training dataset i and tested on the validation dataset i and i = {1, 2, 3}. In general, accuracy was defined as the total number of regions classified correctly divided by the total number of regions in the validation dataset. The search space of 350 features was prohibitively large, and running the GA twice on the same training and validation datasets would most likely have yielded two different solutions representing local optima. Thus, a sampling of the fitness landscape was used in which the GA was run 10,000 times on different random divisions of the training dataset into the sub-training and sub-validation datasets. The final solution was obtained by combining the results of these independent runs. Features were ranked according to their frequency of occurrence within the subset of optimal features selected by the GA. Features that were present across many runs were presumed to be more important than those that were selected less often  was first randomly divided such that two-thirds were analyzed by feature selection see  and one-S. cerevisiae, the methyltransferase Set1 is responsible for H3K4 methylation. Set1 mutants exhibit dramatically reduced DSB frequency at well-characterized hot spots et al.cerevisiae. In addition, recent work has associated PRDM9, a sequence-specific DNA binding methyltransferase, with hot spot activity in mammalian meiosis All of the histone modifications used in this study were mapped in vegetatively growing mitotic cells. While the DSB frequency dataset used to map meiotic hot and cold spots was obtained from meiotic cells we address this issue in more detail in the HIS4 in S. cerevisiaeGCN5, which preferentially acetylates H3 histones, leads to decreased recombination at the ade6-M26 hot spot in S. pombeH3K14ac is a histone mark associated with active transcription. Like H3K4me3, H3K14ac is localized primarily to the 5′ end and promoter region of open reading frames and is correlated with the rate of transcription H3K36me3 is a post-translational modification catalyzed by the methyltransferase Set2, and is found primarily in the coding region of genes being actively transcribed Like H3K36me3, H3K79me3 is found primarily within coding regions. Unlike H3K36me3, however, the degree of H3K79me3 presence is not strongly associated with transcription Computational analysis is rarely capable of demonstrating a causal relationship. Feature selection can identify which biological features out of a large number of candidate features are associated with regions of high/low meiotic DSBs. The method cannot identify the reason behind the association. Once an association is discovered it is important to identify potential confounding variables and test whether they may be solely responsible for the correlation of biological features. Such an analysis cannot prove a causal relationship but it is helpful in elucidating uninteresting correlations.An important confounding variable that arises when working with recombination hot spots is their tendency to localize to promoter regions while cold spots localize to coding regions. Many of the histone marks we studied also have a tendency to localize either to the 5′ end of genes or to coding regions. Therefore, it is possible that the results of our analysis reflect this co-localization effect. To explore this, we compared promoter regions of genes with a hot spot within 500 bp upstream of the transcription start site (TSS) (N = 218) to those genes whose TSS is at least 3000 bp away from a hot spot (N = 2491)  compared to its correlation with H3K4me2 which is the next strongest correlation . Even when comparing a large number of histone marks H3K4me3 is inordinately strongly correlated with H3K14ac nd out of 350 features GC content ranks 7th  is greater than that across the entire genome (61.9%).One of the features selected by the feature selection algorithm was a sequence based feature AT content. AT content and GC content measure the same quantity and both were included in the input feature set as a “sanity check” or control. If our computational method is working correctly, then these features should rank similarly. Indeed, this is what was observed AT content ranks 2anks 7th  . Our anTo further explore the relationship between GC content and recombination cold spots we examined the set of cold spots found entirely within coding sequences. Coding sequences in yeast have a GC content of 39.6%, which is GC rich relative to the genome as a whole. The mean GC content of cold spots found entirely within coding sequences was 37.0% compared to the genome average of 38.1% and compared to 36.0% percent GC content calculated for the entire set of cold regions. Cold spots found within otherwise GC-rich regions  still showed reduced GC content contrary to the overall trend of coding regions as a whole. Studies have shown that hot spots are generally absent from protein coding sequences despite their high GC content et al.Of the four biological features included in our analysis with previous evidence from the literature associating them with meiotic DSBs  three were selected by our method . Our method did not identify nucleosome occupancy as an important feature distinguishing hot from cold spots. This is surprising since multiple studies et al. measured nucleosome occupancy using data derived from vegetatively growing mitotic cells. There are examples of hot spots showing a closed chromatin structure during mitosis but an open one in meiosis et al. dataset also mapped nucleosome positions at a high resolution ∼4 bp similar to the 1 bp resolution of the sequencing based studies. Our analysis yielded a discrepancy in the results comparing microarray- and sequencing-based nucleosome occupancy maps. The microarray-based results all show a well-defined valley representing nucleosome depletion centered at hot spots. Based on these results and previously referenced studies, we conclude that the microarray results best approximate what occurs in vivo. On average, nucleosomes are depleted at hot spots for mitotically dividing cells. Contrary to these results, the sequencing-based datasets yielded a small peak of nucleosome occupancy at hot spots  was calculated. This set was further divided according to whether the reads mapped to intergenic or coding regions . An obviComparison of the GC bias between the two control datasets was particularly interesting. The MNase control showed a strong GC bias in mappable reads of 47.6%, which was nearly 10.0% higher than the overall yeast GC content. The sonication control displayed a much lower GC content bias (39.2%) for mappable reads. All of the sequencing-based nucleosome occupancy maps were produced using MNase digestion. Given the clear GC bias calculated for the MNase control, it is possible that much of the GC bias shown by these maps is a product of MNase cleavage bias. Furthermore, our analysis indicates that the bias seen at hot spots occurs regardless of sequencing platform. Nucleosome occupancy maps produced using both Solexa and 454 sequencing exhibited a bias at recombination hot spots. Given the differing nature of these sequencing platforms, the bias may be introduced during sample preparation and not by the sequencing technologies themselves.et al.et al. mapping approach, a similar shallow depletion of read density was observed for the sonication control  were recently shown to be on average nucleosome depleted using FAIRE We do not think this is the case for the genomic loci in question for a number of reasons. One the same set of genomic loci used in our study i.e. using Pan et al. hot spots. Figure is produced as described for (TIF)Click here for additional data file.Figure S2Gene expression comparison in meiotic cells. Panels a-d is comparing gene expression between genes associated with hot spots to genes not associated with hot spots. Height of bars represents the difference in median gene expression for genes associated with hot spots to genes not associated with hot spots (i.e. Median hot gene expression – Median not hot gene expression). Time points represent time after yeast culture is placed in sporulating media. Panels (a) an (c) represent gene expression measured at the given time points for sporulation deficient SK1 and W303 strains these strains do not enter meiosis. Panels (b) and (d) represent gene expression for sporulation-proficient SK1 and W303 strains. An asterisk represents the difference in medians is significant with p-value<0.05, p-value calculated using the Wilcox rank sum test. Panels c-h is as described above except height of bars represents the difference in median gene expression for genes associated with cold spots to genes not associated with cold spots (i.e. Median cold gene expression – Median not cold gene expression). Gene expression is represented by hybridization fluorescence intensities.(TIF)Click here for additional data file.Figure S3Nuclesome occupancy at Buhler et al. hot spots for all sequencing-based datasets. For all datasets, reads were mapped to the yeast genome. Only uniquely aligned reads were retained and the count mapped to each base pair was calculated. The z-score standardized count of reads is plotted using the same procedure as described for (TIF)Click here for additional data file.Figure S4Read density for sequencing controls at Pugh et al. hot spots. (a) Purified DNA digested with micrococcal nuclease (MNase) and sequenced using the Solexa platform. (b) Purified DNA following sonication and sequencing using the Solexa platform. The black line indicates the z-score standardized mapped read density. Data was smoothed using loess smoothing.(TIF)Click here for additional data file.Figure S5Nucleosome occupancy at Pugh et al. hot spots for all sequencing-based datasets. For all datasets, reads were mapped to the yeast genome. Only uniquely aligned reads were retained and the count mapped to each base pair was calculated. The z-score standardized count of reads is plotted at centered Pugh et al hot spots. Plot is produced similar to (TIF)Click here for additional data file.Figure S6Nucleosome occupancy at recombination hot spots obtained at various resolutions. Z-score standardized nucleosome occupancy is shown in 100 bp bins (y-axis). The center of the aligned hot spots is zero on the x-axis. Panels a, b and c represent nucleosome occupancy data measured by ChIP-chip produced by Lee et al.et al.(TIF)Click here for additional data file.Figure S7Sonication and MNase control plotted at Buhler et al. hot spots allowing multimapping reads. Reads for sonicated (a) and MNase-digested controls (b) were mapped allowing multimapping of reads. Read density centered at hot spots is plotted. Data was smoothed using loess smoothing.(TIF)Click here for additional data file.Figure S8Nuclesome occupancy at Buhler et al. hot spots for all sequencing-based datasets following subtraction of the MNase control. Nucleosome occupancy was plotted at hot spots for all sequencing-based nucleosome mapping datasets following subtraction of the MNase control as described in the text. Data plotted similarly to (TIF)Click here for additional data file.


Oncogenic signaling pathways regulate gene expression in part through epigenetic modification of chromatin including DNA methylation and histone modification. Trimethylation of histone H3 at lysine-27 (H3K27), which correlates with transcriptional repression, is regulated by an oncogenic form of the small GTPase Ras. Although accumulation of trimethylated H3K27 (H3K27me3) has been implicated in transcriptional regulation, it remains unclear whether Ras-induced changes in H3K27me3 are a trigger for or a consequence of changes in transcriptional activity. We have now examined the relation between H3K27 trimethylation and transcriptional regulation by Ras. Genome-wide analysis of H3K27me3 distribution and transcription at various times after expression of oncogenic Ras in mouse NIH 3T3 cells identified 115 genes for which H3K27me3 level at the gene body and transcription were both regulated by Ras. Similarly, 196 genes showed Ras-induced changes in transcription and H3K27me3 level in the region around the transcription start site. The Ras-induced changes in transcription occurred before those in H3K27me3 at the genome-wide level, a finding that was validated by analysis of individual genes. Depletion of H3K27me3 either before or after activation of Ras signaling did not affect the transcriptional regulation of these genes. Furthermore, given that H3K27me3 enrichment was dependent on Ras signaling, neither it nor transcriptional repression was maintained after inactivation of such signaling. Unexpectedly, we detected unannotated transcripts derived from intergenic regions at which the H3K27me3 level is regulated by Ras, with the changes in transcript abundance again preceding those in H3K27me3. Our results thus indicate that changes in H3K27me3 level in the gene body or in the region around the transcription start site are not a trigger for, but rather a consequence of, changes in transcriptional activity. Trimethylation of histone H3 at lysine-27 (H3K27) has been associated with silencing of gene expression. Abnormalities of this modification are thought to contribute to the epigenetic silencing of tumor suppressor genes and are regarded as a hallmark of cancer. It has remained unclear, however, whether the production of trimethylated H3K27 (H3K27me3) is the cause or the consequence of gene silencing. To address this issue, we examined the time courses of changes in H3K27me3 level and those in gene transcription induced by an oncogenic form of the Ras protein, the gene for which is one of the most frequently mutated in human cancer. We found that the amount of H3K27me3 was inversely related to transcriptional activity both at the genome-wide level and at the level of individual genes. However, we also found that the Ras-induced changes in H3K27me3 level occurred after those in transcriptional activity. Our results thus demonstrate that changes in H3K27me3 abundance are a consequence rather than a cause of transcriptional regulation, and they suggest that oncoprotein-driven changes in gene transcription can alter the pattern of histone modification in cancer cells. Epigenetic modification of chromatin is a key mechanism for regulation of gene expression Chromatin immunoprecipitation (ChIP) followed by deep sequencing (ChIP-seq) as well as chip-based ChIP have been applied to map precisely the distribution of H3K27me3 across the entire genome. These approaches have also been adopted to elucidate the relation between the distribution of H3K27me3 and transcriptional activity. Such studies have revealed at least two patterns of H3K27me3 enrichment associated with transcriptional repression: a focal enrichment around the transcription start site (TSS) and a broad enrichment encompassing the entire gene. H3K27me3 around the TSS frequently colocalizes with H3K4me3 and is associated with gene repression especially in undifferentiated cells Ink4a, an inhibitor of cyclin-dependent kinases, and this effect is accompanied by a marked decrease in the amount of H3K27me3 at the Ink4a locus in mouse embryonic fibroblasts Fas, Reck, and Par4 transcription concomitant with an increase in DNA methylation The small GTPase Ras controls cell growth and survival in part through epigenetic modification including DNA methylation and histone modification. Ras regulates the activity of downstream signaling pathways including those mediated by mitogen-activated protein kinases (MAPKs) Most of the reported associations between H3K27me3 status and transcription are based on correlation. It has thus remained to be determined definitively whether changes in H3K27me3 distribution are causal with regard to regulation of transcription. To elucidate the biological relevance of H3K27me3, we have now investigated the time courses of Ras-induced changes in H3K27me3 level and in transcription at the genome-wide level in NIH 3T3 cells. Our results indicate that changes in H3K27me3 status follow, rather than precede, transcriptional changes induced by Ras signaling.Fas locus genes including Fas, Acta2, and Stambpl1 (We established mouse NIH 3T3 cells that express a constitutively active mutant (G12V) of human H-Ras or that were infected with the corresponding empty retroviral vector . Expression of the Ras transgene resulted in increased phosphorylation of the MAPK isoforms Erk1 and Erk2  as well Stambpl1 , consistFas locus Fas locus, we performed ChIP-qPCR analysis with antibodies to transcriptionally repressive histone marks including H3K9me2, H3K9me3, and H3K27me3  at time 0. Transcript and H3K27me3 levels were measured by RNA-seq and ChIP-seq, respectively . These results for Itgb5, Adcy7, and Smad6 were confirmed by RT-qPCR and ChIP-qPCR analyses  for Suz12, a subunit of PRC2 that is indispensable for methyltransferase activity at H3K27 ad6 loci –S6C. Deppression , indicate of 4HT , indicate of 4HT –S6F and d Sorcs2 .Itgb5 and Smad6   . Ras sigA levels . These ruCol1a1 and uMink1 transcription also precede those in H3K27me3 level, we examined the respective time courses with cells expressing Raf-ER  or 3 days (uMink1) after 4HT exposure. We obtained similar results for transcripts derived from another intergenic region, uIl33 . Removal of active histone marks by Ras is thus a possible mechanism for Ras-mediated gene silencing. In this case, the repressive H3K27me3 mark might be deposited passively on repressed genes as a result of the loss of acetylation. Further genome-wide and time course analyses of histone acetylation are required to examine this possibility.l-glutamine, 1% MEM–non essential amino acids, and 1% sodium pyruvate .NIH 3T3 cells were obtained from American Type Culture Collection (CRL-1658) and were cultured in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 1% penicillin-streptomycin, 2 mM Complementary DNAs encoding human H-Ras(G12V) or a fusion protein of human Raf-1 and the estrogen receptor (Raf-ER) were subcloned into the retroviral vector pMX-puro 5′-UAAAUUCUCUUCUUCCUGGACGAGU-3′, 5′-UUUGAUUGAGGUCAGGAUUCAAAGG-3′, and 5′-UAUCGUUGGUUUCUCCUGUCCAUCG-3′ for #1, #2, and #3, respectively. The Bmi1 siRNA sequence is 5′-CGUCAUGUAUGAAGAGGAACCUUUA-3′, and the Jmjd3 siRNA sequence is 5′-GGAUGACCUCUAUGCGUCCAAUAUU-3′.Cells were transfected with Suz12, Bmi1, Jmjd3, or control Stealth RNAi duplexes (Life Technologies) with the use of a Neon Transfection System (Life Technologies). The Suz12 siRNA sequences are 3VO4, 400 µM EDTA, 10 mM NaF, and 10 mM sodium pyrophosphate. The lysate was centrifuged at 20,000× g for 10 min at 4°C, and the resulting supernatant was isolated as a cytosolic fraction. The pellet was resuspended in lysis solution for use as a chromatin fraction. Proteins in each fraction were resolved by SDS-polyacrylamide gel electrophoresis and transferred to a polyvinylidene difluoride membrane . Immunoblot analysis was performed with antibodies to H-Ras , to Erk1/2 , to phosphorylated Erk1/2 , to Suz12 , to α-tubulin , to H3K27me3 , and to histone H3 . Immune complexes were detected with horseradish peroxidase–conjugated secondary antibodies and Super Signal West Dura Luminol/Enhancer Solution . The chemiluminescence signals were quantitated with a digital imaging system .Cells were lysed in a solution containing 50 mM Tris-HCl (pH 7.6), 300 mM NaCl, 0.5% Triton X-100, aprotinin (10 µg/ml), leupeptin (10 µg/ml), 1 mM phenylmethylsulfonyl fluoride, 400 µM Na−ΔΔCt method and were normalized by the amount of acidic ribosomal phosphoprotein P0 (Arbp) mRNA. The sequences and gene information of PCR primers are listed in Total RNA was isolated from cells and purified with the use of an SV Total RNA Isolation System (Promega). It was then subjected to RT with the use of a PrimeScript RT reagent kit  followed by real-time PCR analysis with a StepOnePlus Real Time PCR System (Life Technologies) and Fast SYBR Green Master Mix (Life Technologies). Data were analyzed according to the 2Cells were fixed with 0.6 or 1.0% formaldehyde for 10 or 5 min, respectively, at room temperature, after which glycine was added to the medium. The cells were then lysed and stored at −80°C until analysis. The lysates were thawed and subjected to ultrasonic treatment with the use of a Bioruptor  or Covaris S2  instrument in order to obtain chromatin fragments of 200 to 700 bp.Antibodies to H3K27me3 , to H3K9me2 , to H3K9me3 , or to H3 , or normal mouse  or rabbit  immunoglobulin G, were incubated with Protein A Dynabeads or Protein G Dynabeads (Life Technologies) to allow formation of bead-antibody complexes. Chromatin fragments were then subjected to immunoprecipitation with the bead-antibody complexes, after which the beads were washed and immunoprecipitated chromatin fragments were eluted and treated with RNase A and proteinase K. DNA was extracted from the samples with phenol-chloroform and was then precipitated with ethanol and dissolved in TE buffer.−(Ct of IP sample – Ct of input sample) method and are presented as a percentage of input.Quantitative PCR analysis of ChIP DNA was performed as described above. Primer sequences and positions are listed in For comprehensive analysis of Ras-dependent changes in gene expression and H3K27me content, we performed RNA-seq and ChIP-seq analyses at various times after Ras induction. We sampled cells at 0, 2, 4, 7, and 12 days after infection with the H-Ras(G12V) retroviral vector. We sequenced five and six samples for analysis of gene expression and H3K27me3, respectively. ChIP-seq libraries were prepared from ∼40 ng each of ChIP and input DNA with the use of a TruSeq DNA LT Sample Prep Kit . RNA-seq libraries were prepared from 2 µg of total RNA with the use of a TruSeq RNA Sample Prep Kit v2 (Illumina). Two flow cells (16 lanes) of an Illumina HiSeq 2000 instrument were used. Libraries were clonally amplified in a flow cell and sequenced with the use of HiSeq Control Software 1.5 (Illumina) and a 48-nucleotide paired-end sequence. Image analysis and base calling were performed with the use of Real Time Analysis (RTA) 1.13 software. A total of 81,877,304 (RNA-seq) or 1,068,022,370 (ChIP-seq) reads was obtained per sample.For SOLiD sequencing, ChIP-seq libraries were prepared from 20 ng each of ChIP and input samples with the use of a SOLiD Fragment Library Construction Kit with SizeSelect Gels (Life Technologies). For RNA-seq, total RNA (10 µg) was subjected to rRNA depletion  and RNA-seq library construction . The libraries were clonally amplified on SOLiD P1 DNA Beads by emulsion PCR and sequenced with the SOLiD3Plus System (Life Technologies) to generate 50-base single-end reads.Sequencing data of ChIP-seq and RNA-seq are available under the accession number of DRA001075 from DNA Data Bank of Japan Sequence Read Archive (DRA).http://www.bioinformatics.babraham.ac.uk/projects/fastqc) analysis revealed low sequence quality for the last 4 bases of the second read of paired-end reads, and so these bases were trimmed before data analysis. For the sequence data analysis, UCSC mm9 and RefSeq were used as the reference mouse genome and gene model, respectively.FastQC  For H3K27me3 analysis, sequenced reads were mapped to the mouse genome with the use of bwa (ver. 0.5.9) sg and eg represent the TSS and TTS of gene g, isb and ieb are the start and end positions of the ith bin in gene g, and isr and ier are the normalized ith bin positions, respectively. The same normalization procedure was applied to the upstream and downstream regions of each gene, resulting in 600 data points per gene. The methylation signal associated with each bin was then assigned to the normalized positions. The mean and standard deviation of the methylation signal for the 600 data points obtained for each gene were calculated. The methylation signal around the TSS (TSS ±2.5 kb) was analyzed in the same manner.For the purposes of our study, we defined the gene body as the genomic region from the TSS to the TTS. Introns are thus included in the gene body as well as exons. To compare methylation signals associated with genes of different sizes, we calculated the relative position of bins in the gene body as follows:To examine the methylation pattern across the gene body and adjacent regions, we randomly selected 2000 out of 23,232 RefSeq genes. The methylation signal of each selected gene was normalized so that the mean and standard deviation of the signal were equal to 0 and 1, respectively. A hierarchical clustering was performed according to the normalized methylation signal, and the results were visualized as a heat map in which the intensity of the methylation signal was color coded. Gene expression level was also displayed in the heat map .The relation between methylation and gene expression was investigated by comparison of the average methylation signal and FPKM. For this purpose, the average methylation signal in the gene body and in the region around the TSS (defined here as the region from −2.5 to +2.5 kb relative to the TSS) was calculated for each gene at all time points. Gene expression level was categorized into five classes including unexpressed genes (FPKM = 0), and the distribution of the methylation signal in each class is presented as a box-and-whisker plot .The average methylation signal in control (Ras0) cells compared with cells at various times after infection with the H-Ras(G12V) retroviral vector as well as the fold change in the methylation signal were calculated. If the latter fold change was ≥2, then the gene was extracted as a differentially methylated gene. A total of 1027 genes (gene body) or 1230 genes (region around the TSS) fulfilled this criterion, and these genes were further examined by comparison with differentially expressed genes. To examine changes in methylation over time, we performed a hierarchical clustering according to average methylation signal as described above .To examine whether gene expression might be causally related to a change in H3K27me3 level, we defined and calculated “t-half” as shown in Arbp. To calculate the amount of H3K27me3, we split the mouse genome into 1-kb bins and used the number of reads in each bin as the raw methylation signal. This raw signal was normalized as Vec and Ras cells have the same number of reads and was then smoothed with the lowess function. Obtained signals were converted into wiggle (WIG) format and uploaded to the UCSC Genome Browser for visualization.We obtained 130,133,653, 150,914,422, 226,490,377, and 250,941,002 reads from control (Vec) and Ras cells for RNA-seq and from Vec and Ras cells for ChIP-seq, respectively. Sequenced reads were mapped to the mouse genome with the use of the BioScope Map Data program (ver. 1.2). The following analyses were based on the mapping results. Gene expression level was estimated by calculating reads per kilobase of exon model per million mapped reads (RPKM) To identify putative novel transcripts, we mapped sequenced reads to the mouse genome with the use of TopHat (ver. 1.3.3). Given that our SOLiD sequence data included strand information, mapped reads on the Watson and Crick strand were analyzed separately. The number of mapped reads at each genome coordinate was converted to bigWig format and uploaded to the UCSC Genome Browser. Those regions that did not overlap with a RefSeq gene and showed a difference in expression level between Vec and Ras cells were manually inspected.Figure S1A) Number of genes whose expression was found to be affected by Ras signaling. (B) Clustering of time course profiles for Ras-induced changes in gene transcription. Each line represents one of the 933 genes whose transcription is regulated by Ras. The genes are divided into two groups shown in red or blue corresponding to a Ras-induced increase or decrease, respectively, in FPKM value. These two clusters are the source of the expression data in C) Definition of “t-half” as the time corresponding to half of the difference between the H3K27me3 or transcription levels for Ras0 cells and cells expressing H-Ras(G12V) for 12 days.Hierarchical clustering of Ras-induced changes in gene transcription and calculation of t-half. ((TIF)Click here for additional data file.Figure S2A) Venn diagram indicating the number of genes showing Ras-induced changes in expression and in the mean H3K27me3 level in the region around the TSS. (B) Clustering of the temporal profiles of mean H3K27me3 level in the region around the TSS. Each line represents one of 196 genes whose H3K27me3 level in the region around the TSS and expression changed in NIH 3T3 cells during expression of H-Ras(G12V) for the indicated times. Results of hierarchical clustering are depicted on the left with colors of purple, brown, and gray. Changes in expression level (FPKM) of individual genes (as determined in C) Averaged changes in expression and H3K27me3 level for the purple cluster (upper) and the gray cluster (lower) of genes shown in (B). Dashed lines represent t-half. (D) The t-half values for expression and mean H3K27me3 level in the region around the TSS for the purple and gray clusters in (B).Comprehensive analysis of Ras-induced changes in gene transcription and H3K27me3 content in the region around the TSS. ((TIF)Click here for additional data file.Figure S3A) Time course of changes in H3K27me3 level at the Plekha4, Ephx1, Bpifc, and Sorcs2 loci as determined by ChIP-seq analysis of Ras0 cells and cells infected with the retroviral vector for H-Ras(G12V) for 2, 4, 7, or 12 days. The regions for which the mean H3K27me3 level and corresponding t-half were calculated are highlighted in pink. Arrowheads indicate the regions of the genes analyzed by ChIP-qPCR in Figures S7A, S7C and S8B. (B) Changes in gene expression (FPKM) and mean H3K27me3 level for Plekha4, Ephx1, Bpifc, and Sorcs2. The t-half values are indicated by the dashed lines.Additional examples of genes showing immediate changes in expression and delayed changes in H3K27me3 level induced by Ras signaling. ((TIF)Click here for additional data file.Figure S4A) SOLiD sequencing analysis of H3K27me3 level and gene expression at the Itgb5 locus in Ras and Vec cells. The region of increased H3K27me3 level in Ras cells is highlighted in pink. Gene expression is presented as reads per kilobase of exon model per million mapped reads (RPKM). (B) RT-qPCR analysis of Itgb5 expression in Ras cells relative to that in Vec cells. Data are representative of five independent experiments. (C and D) ChIP-qPCR analysis of H3K27me3 (C) and total H3 (D) for the gene body and flanking regions of Itgb5 in Vec and Ras cells. Lowercase letters correspond to the positions indicated in (A). The amount of immunoprecipitated DNA is expressed as a percentage of input DNA. Data are representative of five independent experiments. (E) SOLiD sequencing analysis of H3K27me3 level and gene expression at the Adcy7 locus in Ras and Vec cells. The region of decreased H3K27me3 level in Ras cells is highlighted in pink. (F) RT-qPCR analysis of Adcy7 expression in Ras cells relative to that in Vec cells. Data are representative of five independent experiments. (G and H) ChIP-qPCR analysis of H3K27me3 (G) and total H3 (H) for the gene body and flanking regions of Adcy7 in Vec and Ras cells. Lowercase letters correspond to the positions indicated in (E). Data are representative of five independent experiments.Validation of changes in H3K27me3 content of the gene body and in gene expression induced by Ras signaling. ((TIF)Click here for additional data file.Figure S5Itgb5 and Adcy7. Lowercase letters after the gene names correspond to the positions of Itgb5 (A) and Adcy7 (B) loci shown in ChIP-qPCR analysis of H3K9me2, H3K9me3, and H3K27me3 in the gene body for (TIF)Click here for additional data file.Figure S6Itgb5 and Adcy7 in cells depleted of Suz12 with different siRNA constructs. (A) Distribution of H3K27me3 at the Hoxa3 locus in control (Ras0) cells as revealed by ChIP-seq analysis. The region around the Hoxa10 gene was highly enriched with H3K27me3, whereas that around Hoxa3 contained only a low level of H3K27me3. (B) Knockdown efficiency with Suz12 siRNA #1 in control cells as revealed by RT-qPCR analysis of Suz12 mRNA. (C) Verification of H3K27me3 depletion with Suz12 siRNA #1 in control cells by ChIP-qPCR analysis with primers targeted to the regions indicated by the arrows in (A). The H3K27me3/H3 ratio around Hoxa10 had decreased to a value similar to that for the Hoxa3 gene. (D) NIH 3T3–Raf-ER cells transfected with Suz12 (#2 or #3) or control siRNAs and treated with 4HT or ethanol as in E and F) The cells in (D) were also subjected to RT-qPCR analysis of relative Suz12 (E) or Itgb5 and Adcy7 (F) expression. Data are representative of two independent experiments.Knockdown efficiency and expression of (TIF)Click here for additional data file.Figure S7A) RT-qPCR analysis of gene expression and ChIP-qPCR analysis of the ratio of H3K27me3 to total H3 at gene bodies for the indicated genes at the indicated times after exposure of NIH 3T3–Raf-ER cells to 4HT. The regions of the genes analyzed by ChIP-qPCR are indicated by the arrowheads in B) RT-qPCR analysis of relative gene expression for NIH 3T3–Raf-ER cells transfected with Suz12 or control siRNAs and exposed to 4HT or ethanol (EtOH) vehicle as in C) ChIP-qPCR analysis of H3K27me3 normalized by total H3 at the gene bodies of the indicated genes for cells treated as in (B). Data are means ± SE from two independent experiments.Raf-induced changes in H3K27me3 level at the gene body are not required for those in gene transcription. ((TIF)Click here for additional data file.Figure S8A) of relative Plekha4, Ephx1, Bpifc, and Sorcs2 expression as well as to ChIP-qPCR analysis (B) of H3K27me3 and total H3 at the regions of the genes indicated by the arrowheads in Additional examples of the effects of Ras signal inactivation on gene expression and H3K27me3 level in NIH 3T3–ER-Ras cells. NIH 3T3–ER-Ras cells were exposed to 4HT for 9 days and then incubated in the absence of 4HT as in (TIF)Click here for additional data file.Figure S9A) H3K27me3 ChIP-seq results as well as strand-specific assignment of sequencing reads from RNA-seq analysis for the Il33 locus in Vec and Ras cells. The intergenic region showing a decrease in H3K27me3 content in response to Ras signaling is highlighted in pink. Antisense transcription from the region upstream of Il33 (uIl33) was observed predominantly in Ras cells, with the predicted transcribed region being indicated by the magenta box. Visual inspection suggests that uIl33 might contribute to a novel transcriptional variant of an upstream annotated gene (9930021J03Rik in RefSeq). (B) RT-qPCR analysis of expression as well as ChIP-qPCR analysis of H3K27me3 normalized by total H3 for uIl33 at the indicated times after exposure of NIH 3T3 cells expressing Raf-ER to 4HT. PCR was performed with primer sets targeted to the positions a and b indicated in (A).Identification of novel transcripts derived from an additional intergenic region subject to Ras-induced modulation of H3K27me3 content. ((TIF)Click here for additional data file.Figure S10A) Expression of genes for major PRC2 components in Ras and Vec cells. Gene expression is presented as RPKM as determined by SOLiD sequencing. (B and C) NIH 3T3–Raf-ER cells transfected with Bmi1 (B), Jmjd3 (C), or control siRNAs and treated with 4HT or ethanol vehicle as in Bmi1, Itgb5, and Smad6 (B) or of Jmjd3 and Adcy7 (C). Data are means ± SE from three independent experiments.Effects of Ras signaling on expression of genes for H3K27me3-related enzymes. ((TIF)Click here for additional data file.Table S1Illumina sequencing information. Sequencing throughput and filtering summary of RNA-seq and ChIP-seq analyses of samples at various times after Ras induction.(XLSX)Click here for additional data file.Table S2Primers for RT-qPCR analysis. List of all primer sequences (5′ to 3′) used for RT-qPCR and gene information in this study.(XLSX)Click here for additional data file.Table S3Primers for ChIP-qPCR analysis. List of all primer sequences used for ChIP-qPCR. We represent primer sequences (5′ to 3′) and primer position in mouse reference genome (NCBI37/mm9).(XLSX)Click here for additional data file.


C. elegans factors. We identified 8 human and 3 mouse transcription factors with strong localized enrichment over the mitochondrial genome that was usually associated with the corresponding recognition sequence motif. Notably, these sites of occupancy are often the sites with highest ChIP-seq signal intensity within both the nuclear and mitochondrial genomes and are thus best explained as true binding events to mitochondrial DNA, which exist in high copy number in each cell. We corroborated these findings by immunocytochemical staining evidence for mitochondrial localization. However, we were unable to find clear evidence for mitochondrial binding in ENCODE and other publicly available ChIP-seq data for most factors previously reported to localize there. As the first global analysis of nuclear transcription factors binding in mitochondria, this work opens the door to future studies that probe the functional significance of the phenomenon.Mitochondria contain their own circular genome, with mitochondria-specific transcription and replication systems and corresponding regulatory proteins. All of these proteins are encoded in the nuclear genome and are post-translationally imported into mitochondria. In addition, several nuclear transcription factors have been reported to act in mitochondria, but there has been no comprehensive mapping of their occupancy patterns and it is not clear how many other factors may also be found in mitochondria. Here we address these questions by using ChIP-seq data from the ENCODE, mouseENCODE and modENCODE consortia for 151 human, 31 mouse and 35  It is widely accepted that they arose as the result of an endosymbiotic event The mitochondrial genome in mammals encodes 13 proteins, all of which are components of the electron transport chain, as well as 22 tRNAs and two rRNAs 3 receptor T3Rα1 called p43 has been found to directly control mitochondrial transcription α and ERβ isoforms, and it too has been suggested to bind to the D-loop κB and IκBα have been found in mitochondria and have been proposed to regulate mitochondrial gene expression γ2 transcription factors have been proposed to localize to mitochondria and bind to the genome. In addition to these well-characterized regulators of mitochondrial transcription, multiple reports have suggested that transcription factors that typically act in the nucleus might also have regulatory functions in mitochondrial transcription in vivo chromatin immunoprecipitation evidence for the binding of these factors to mtDNA exists only for CREB However, direct α, CREB, STAT3, p53) revealed no robust binding sites except for enrichment in the D-loop. Resolving the functional significance of the identified occupancy sites in future studies should provide exciting insights into the biology of both mitochondrial and nuclear transcriptional regulation.Here we survey the large compendium of ChIP-seq and other functional genomic data made publicly available by the ENCODE, mouseENCODE and modENCODE Consortia In the course of a study of TFAM occupancy in the mitochondrial and nuclear genomes ftp://ftp.modencode.org, including ChIP-seq data for 151 transcription factors in human cell lines C.elegans (see D. melanogaster below). We also downloaded DNase hypersensitvity (both DNase-seq We downloaded publicly available (as of February 2012) ENCODE and mouseENCODE ChIP-seq and control data from the UCSC Genome Browser and modENCODE data from NUclear MiTochondrial sequences or NUMTs) C. elegans mitochondrial genome is almost completely uniquely mappable  to convert read coverage to signal intensity z-scores reflecting how strongly regions of enrichment stand out compared to the average background read density along the mitochondrial genome for each dataset. We then used the maximum z-scores for each dataset to identify datasets with very strong such enrichment, which we then examined manually in detail.The distribution of read density z-scores for transcription factor ChIP-seq and control datasets in seven ENCODE human cell lines  is shown in These results suggest that while immunoprecipitation is necessary for high enrichment over the D-loop, the enrichment might not be mediated by the proteins targeted by the primary antibody. This does not explain why a large number of factors show little enrichment over the D-loop , 5 and 6β, c-Jun, JunD, MafF, MafK, Max, NFE2 and Rfx5. β: a strong site of enrichment around the 5′ end of the CYB gene, what seems to be two closely clustered sites in the ND4 gene, a weaker site in the ND4L gene, and two other regions of enrichment over CO2 and CO1  of the top 10 nuclear peaks  with that of the putatively mitochondrial binding sites . These observations provide independent corroboration for the mtDNA binding events identified through ChIP-seq.If the observed binding sites in ChIP-seq data are the result of actual association of nuclear transcription factors with mtDNA, then these transcription factors should exhibit mitochondrial localization. We directly tested this by performing immunocytochemistry (ICC) for MafK in HepG2 cells . It is iα, IRF3, NFκB, STAT1, STAT5A and STAT3 were assayed by the ENCODE Consortium. This failure could be attributed to the use of too stringent a z-score threshold when selecting datasets with significant enrichment. We therefore examined available ChIP-seq data against these factors more carefully . However, this is not the case for all of them. One possibility is that many prior studies reporting physical association of transcription factors with the D-loop suffered from the same artifactual read enrichment over that region that we observe, but this would not have been noticeable using the methods of the time. This would not be surprising, as it is only apparent that D-loop enrichment is likely to be artifactual when the high spatial resolution of ChIP-seq is combined with the joint analysis of input and mock immunoprecipitation controls. However, the mitochondrial localization of these factors has been carefully documented in a number of cases We report here the first large-scale characterization of the association of nuclear transcription factors along the entire mitochondrial genome by utilizing the vast ChIP-seq data resource made publicly available by the ENCODE and modENCODE consortia. We find two classes of signal enrichment events, neither of which is detected in high-throughput sequencing datasets that do not involve immunoprecipitation and therefore they are not due to sequencing biases. First, the majority of factors for which we detect strong read enrichment over the mitochondrial genome display high ChIP-seq signal only over the D-loop non-coding region in both human and mouse datasets. However, these signals do not have the characteristics of sequence specific occupancy and are present in a number of mock-immunoprecipitation control datasets. They are thus best explained as experimental artifacts, although it remains possible that they represent real non-canonical association with the D-loop for some factors. Second, for a subset of factors, specific ChIP-seq peaks are observed outside of the D-loop, and these display the additional hallmark characteristics of sequence specific occupancy.Nuclear transcription factors previously reported to localize to mitochondria either did not exhibit significant enrichment in the available ChIP-seq datasets or, when they did, it was over the D-loop region with similar non-specific read distribution shape as other factors. In contrast, applying conservative thresholds we found eight human and three mouse transcription factors (two in common between the two species) that strongly occupy sites outside of the D-loop. They display the strand asymmetry pattern around the putative binding site that typifies true nuclear ChIP-seq peaks. Even more convincing is the fact that the explanatory motif for the factor is usually found under the observed enrichment peaks, further suggesting that they correspond to true in vivo biochemical events.There are three main explanations for our observations. First, it is possible that despite our considerable bioinformatic precautions the observed binding events are in fact nuclear, originating from NUMTs present in the genomes of the cell lines assayed, but absent from the reference genome sequence. We believe that this is very unlikely. An experimental argument against unknown NUMTs comes from the strength of the ChIP-seq signal we see in the mitochondrial genome. These signals are much higher than even the strongest peaks in the nuclear genome for the same factor in the same dataset. This is expected for true mitochondrial genome binding because of the presence of many copies of the mitochondrial genome per cell, in contrast to the presence of only two copies of the nuclear genome. Second, it is possible that mitochondria are sometimes lysed in vivo, with mitochondrial DNA spilling into the cytoplasm where transcription factors could then bind. This cannot be ruled out based on the ChIP data alone but we consider it unlikely, as this would need to happen with a sufficient frequency to explain the remarkable strength of mitochondrial occupancy sites. The third and most plausible interpretation is that these nuclear transcription factors indeed translocate to the mitochondria and interact with the genome, as has been observed for the D-loop in some previous studies for other factors. Indeed, immunocytochemistry experiments in our study confirm the presence of MafK in mitochondria in a majority of HepG2 cells.Several major questions are raised by our results. First, it is not clear how these nuclear transcription factors are targeted to the mitochondria. Mitochondrial proteins are typically imported into the mitochondrial matrix through the TIM/TOM protein translocator complex, and are targeted to the organelle by a mitochondrial localization sequence, which is cleaved upon import. We scanned both human and mouse versions of our factors for mitochondrial target sequences (MTS) with both Mitoprot Second, it is unclear why the same factor binds detectably to the mitochondrial genome in some cell types but not in others. It is certainly possible that different splice isoforms or post-translationally modified proteins are present in different cell types, with only some capable of being imported into mitochondria, or that import into mitochondria only happens under certain physiological conditions only met in some cell lines.Third, the question of the biochemical reality of transcription factor binding at the D-loop remains open. Previous studies understandably focused on the D-loop, given its well-appreciated importance in regulating mitochondrial transcription. As a consequence, the literature supporting a role for some nuclear factors in mitochondria suggests that they do so through binding to the D-loop. Our analysis of ChIP-seq data, which was carried out in an agnostic manner, revealed that dozens of transcription factors – many more than had been studied locally at the D-loop alone – also show high level of enrichment over the D-loop. However, the observed enrichment has characteristics suggesting that these signals are mainly due to experimental artifacts. In support of this judgment, the explanatory motifs for most of these factors were generally not found under the area of strongest enrichment in the D-loop. Therefore a conservative interpretation is that enrichment over the D-loop is an artifact in most cases.Finally, and most importantly, the functional significance of factor occupancy observed by ChIP-seq remains unknown. It is entirely possible that it represents biochemical noise, with transcription factors entering the mitochondria because they have the right biochemical properties necessary to be imported, then binding to mtDNA but with little functional consequence. Alternatively, nuclear transcription factors may in fact be playing a regulatory role in mtDNA. It is difficult to imagine the exact mechanisms through which they might be acting, aside from interactions with the regulatory D-loop. While we do observe pairs of related factor such as c-Jun and JunD, and MafK and MafF binding to the same sites, binding events are overall widely dispersed over the mitochondrial genome and are found outside of the known regulatory regions. Plausible regulatory relationships are therefore not obvious and our results suggest that biological noise should be the working null hypothesis explaining the data. The functional regulatory role of these nuclear transcription factors in mitochondria is a very exciting possibility but it will have to be demonstrated in subsequent studies. Direct functional tests are the golden standard for establishing regulatory relationships, using gain and loss of function experiments and genetic manipulation of putative regulatory sites. The latter is at present not possible for mitochondria while the former are difficult to interpret in the case of the role of nuclear transcription factors in mitochondrial gene regulation, as it is not easy to separate the direct effects of binding to mtDNA from the indirect effects of transcriptional changes in the nucleus. Thus, it may be some time before definitive answers to these questions are obtained. In the meantime, larger compendia of transcription factor ChIP-seq data such as those expected to be generated by the next phase of the ENCODE project will be a primary source of further insight by providing binding data for additional nuclear transcription factors that will clarify allowed or preferred occupancy patterns across the mitochondrial genome.Except for where indicated otherwise, all analysis was carried out using custom-written python scripts.ftp://ftp.modencode.org for modENCODE data D. melanogaster data was mapped against the dm3 version of the fly genome. modENCODE data for C. elegans was mapped against the ce10 version of the worm genome. Reads were mapped with the following settings: “-v 2 -k 2 -m 1 -t –best –strata”, which allow for two mismatches relative to the reference, however for all downstream analysis only reads mapping uniquely and with zero mismatches were considered, to eliminate any possible mapping artifacts.Raw sequencing reads were downloaded from the UCSC genome browser for ENCODE and mouseENCODE N bases were generated starting at each position in the mitochondrial genome. The resulting set of “reads” was then mapped against the same bowtie index used for mapping real data. Positions covered by N reads were considered fully mappable. In this case, N = 36 as this is the read length for most of the sequencing data analyzed in this study.Mappability was assessed as follows. Sequences of length bF percentile of fully mappable position on the mitochondrial chromosome. The estimated parameters were then used to rescale the raw signal over all position to a z-score. This results in datasets with strong peaks receiving low z-scores over most of the mappable mitochondrial genome, and very high z-scores over the regions with highly localized enrichment. We used F = 0.8 for our analysis. As this procedure is sensitive to datasets with very low total read coverage over the mitochondrial genome, we restricted our analysis to datasets with at least 5000 uniquely mappable reads (and with no mismatches to the reference), i.e. ≥10x coverage. We used a z-score cutoff of 20 to select datasets with high enrichment over the mitochondrial genome, as it was the highest z-score observed in sonicated input samplesBecause the number of mitochondria per cell varies from one cell line/tissue to another, direct comparisons between datasets based on the absolute magnitude of the signal in RPM are not entirely valid. For this reason, we normalized the signal as follows. For each dataset, we fit a Gamma distribution over the RPM coverage scores for the bottom The peak calls for human and mouse ENCODE data available from the USCS Genome Browser were used to find de novo motifs for transcription factors from ChIP-seq data. The sequence around the peak summit (using a 50 bp radius) was retrieved for the top 500 called peaks for each factor in each cell line and motifs were called using the MEME program in the MEME SUITE, version 4.6.1 HepG2 cells were grown following the standard ENCODE protocol  and penicillin-streptomycin). Cells were fixed in 10% formalin (Sigma-Aldrich HT501128-4L) for 10 min, permeabilized with 0.1% Triton X-100, and blocked in 5% FBS. Primary antibodies used were MafK  and Hsp60 . Secondary antibodies used were donkey anti-goat AF488 (Invitrogen A11055) and donkey anti-rabbit AF546 (Invitrogen A10040). Imaging on a Zeiss LSM 710 confocal microscope with PlanApochromat 63X/1.4 oil objective, and 0.7 µm optical sections were acquired.Figure S1Distribution of reads over the human mitochondrial genome for STAT1 and STAT5A in ENCODE ChIP-seq data. Reads mapping to the forward strand are represented in black, reads mapping to the reverse strand are represented in yellow. The unique mappability track for the mitochondrial genome is shown in red in the outside track (see Methods for details). Protein-coding, rRNA and tRNA genes are shown as colored bars. The innermost circle shows the motif occurrences in the mitochondrial genome for each factor as black vertical bars. (A) STAT1; (B) STAT5A; The reads per million (RPM) tracks are shown, scaled to the maximum signal level (for both strands) for each dataset. Plots were generated using Circos version 0.60 (PDF)Click here for additional data file.


The processes through which the germline maintains its continuity across generations has long been the focus of biological research. Recent studies have suggested that germline continuity can involve epigenetic regulation, including regulation of histone modifications. However, it is not clear how histone modifications generated in one generation can influence the transcription program and development of germ cells of the next.Caenorhabditis elegans primordial germ cells (PGCs). In mes-4 mutant PGCs, RNA Pol II activation is abnormally regulated and the PGCs degenerate. Genetic and genomewide analyses of MES-4-mediated H3K36 methylation suggest that MES-4 activity can operate independently of ongoing transcription, and may be predominantly responsible for maintenance methylation of H3K36 in germline-expressed loci.We show that the histone H3K36 methyltransferase maternal effect sterile (MES)-4 is an epigenetic modifier that prevents aberrant transcription activity in Our data suggest a model in which MES-4 helps to maintain an 'epigenetic memory' of transcription that occurred in germ cells of previous generations, and that MES-4 and its epigenetic product are essential for normal germ cell development. Chromatin structure is an important determinant of transcriptional activity, and is thought to influence accessibility of the transcriptional machinery to the DNA and/or modulate its productivity, as a component of regulation. The structure of chromatin and its influence on genetic regulation can be heritable, and this heritability forms the basis of epigenetic forms of genome regulation. As the eukaryotic genome is passed between generations, there occurs significant remodeling or re-programming of the gamete epigenomes as they merge in the zygote. An additional round of epigenetic reprogramming also occurs upon establishment of the embryonic germline in many species . The purDrosophila and C. elegans, this quiescence is achieved by interfering with RNA polymerase (Pol) II transcriptional activation. A key mechanism of transcriptional regulation is phosphorylation of serine residues  within a highly repetitious seven amino acid sequence in the C-terminal domain (CTD) of the largest subunit of Pol II . Int. Intama-s Figure , arrows.i Figure . tlk-1 that can recognize the hypo-phosphorylated forms of Pol II  that can,19. As smes  mutants, mes-4 produces the most severe PGC proliferation defects ; LGV: dpy-11(e224), mes-4, DnT1[qIs51]met-1(n4337) is a deletion mutant that lacks the SET domain [mes-2(bn11) is a point mutation that results in a premature stop codon before the SET domain; it produces no detectable protein and very little to no H3K27me3 staining in the PGCs [mes-4(bn85) has an inframe deletion that disrupts the SET domain. mes-4(bn73) has a premature stop codon after amino acid 593 in the middle of the SET domain. mes-4(bn67) has a point mutation in its first PHD finger, which leads to complete dissociation of MES-4 protein from chromosomes [unc-4(e120) and dpy-11(e224) are visible genetic markers.T domain . mes-2 seeded with HT-115 bacteria that had been transformed with an ama-1 dsRNA expression plasmid or control L4440 plasmid. The feeding RNAi was performed at 20°C for ~55 hours, during which the feeding plate was exchanged once after the first 24 hours.RNAi was performed to deplete RNA Pol II/AMA-1, CDK-9 and TLK-1 from embryos by microinjection into parent worms as described previously . EmbryosSamples were fixed using methanol/formaldehyde  for H5 smet-1 or wild type N2 adults were rinsed with M9 buffer and lysed in freshly prepared egg isolation solution  to collect embryos. Isolated embryos were rinsed with M9 buffer and separated from lysed worms and other debris by sucrose flotation as described previously [4, 2 mM CaCl2) containing 1U/ml of chitinase  at 20°C for 40 minutes. The embryos were rinsed with egg buffer and frozen in liquid nitrogen. The frozen embryo pellet (100-150 ul vol) was resuspended in 1 ml phosphate-buffered saline (PBS) containing protease inhibitor cocktail  and homogenized with a Dounce homogenizer. To fix chromatin, formaldehyde was added to the homogenate to a final concentration of 1%, and the tube rocked for 10 minutes at 25°C. After quenching with 125 mM glycine, the nuclei/chromatin fraction was collected by centrifugation at 900g for 1 minute, washed with 1ml PBS three times, and resuspended in 400 μl SDS lysis buffer  containing protease inhibitor cocktail. The fixed material was placed in a sonicator to give sheared chromatin preparations with an average DNA size of ~300 bp. After centrifugation at 13,000g for 10 minutes, the supernatant was collected and diluted 10-fold in ChIP dilution buffer . The DNA concentration of the chromatin preparations was determined and adjusted with LSW buffer  to ~20 μg/ml for H3K36me3 ChIP in met-1, ~10 μg/ml for H3K36me3 ChIP and ~5 μg/ml for pan-H3 ChIP in both genotypes. Aliquots of 2 ml (for H3K36 ChIP) or 1 ml (for pan-H3 ChIP) of chromatin preparation were mixed with 5 μg of monoclonal anti-H3K36me3 ) or 2.5 μg of monoclonal anti-H3  overnight at 4°C on a rotator. Magnetic beads (150 μl) coated with anti-mouse IgG  Carlsbad, CA, USA) were then added to each 1 ml reaction and rotated for 3 hours at 4°C. Beads were washed for 3 minutes with 1 ml of each of the following buffers in succession: LSW buffer, HSW buffer  and TEL buffer , followed by two final washes with TE buffer . Samples were eluted twice with elution buffer  for 15 minutes at 65°C, treated with proteinase K at 55°C for 1-2 hours, then transferred to 65°C overnight to reverse-crosslink. DNA was purified with a commercial kit  and eluted in 40 μl 10 mM Tris-HCl pH 8.0. Eluate DNA was quantified  and adjusted to 1 ng/μl.Synchronized, gravid young eviously . To alloLibraries were prepared from 30 ng of purified immunoprecipitated DNA or input DNA and analyzed on a genome analyzer  at the UNC sequencing facility. Experiments were carried out using two biologically independent samples.C. elegans genome  using MAQ http://www.maq.sourceforge.net using default parameters. The mapped 36 bp reads were extended to 200 bp. For each base pair in the genome, the number of overlapping sequence reads was determined. For all replicates of Input and H3 and H3K36me3 ChIP experiments, the read count per base pair was scaled so that the median read count across the genome was the same. Read counts per base pair were averaged across replicates and visualized in the UCSC genome browser http://genome.ucsc.edu/.The sequenced reads were mapped to the met-1 and N2, we could readily identify a large number of genes with clear enrichment of H3K36me3 in both samples. This allowed us to compare H3K36me3 on genes in particular categories  between the two genotypes.Both met-1 ChIP-seq experiments displayed robust H3K36me3 signal over numerous open reading frames with significantly lower signal in most non-coding regions; we consider the latter to be 'background' or 'noise'. Both individual N2 ChIP-seq experiments had a somewhat lower signal to background ratio. Despite the difference in signal to background and the fact that read counts per base pair are not directly comparable between 10 of the average read counts per gene were plotted for H3K36me3 in met-1 versus N2.Scatter plots Figure  were genmet-1 around transcription start sites (TSS) and transcription end sites (TES) for larval/adult germline-specific and soma-expressed genes are shown in Figure Average profiles of H3K36me3 in http://www.r-project.org; http://www.bioconductor.org were used for analysis of the data and generation of the figures.Custom perl scripts and Bioconductor packages for R C. elegans embryonic transcriptome analysis by Baugh et al. [The gene sets Figure  were defh et al. .The qPCR analyses were performed using a real-time PCR instrument  with supplied reaction mix . Input DNA or DNA samples obtained by ChIP were amplified under conditions of 95°C for 10 min followed by 45 cycles of steps 95°C for 30 seconds, 60°C for 30 seconds and 72°C for 30 seconds. Percentage inputs were calculated by the formula:A file containing the sequences of the primers used is available . Polymerase (Pol) II staining (using the 8WG16 antibody) and DAPI staining  in ama-1 (RNAi) (top) and control embryos (bottom) are shown in grayscale with the merged image (left).Click here for filemes-4 Mutant embryonic germlinesSummary of H3K4me2, H3K36me3 and pSer2 dynamics in wild type and . H3K4me2 (green bars) is maintained in the P-cells of both genotypes, and then largely erased after the primordial germ cells (PGCs) (Z2/Z3) are born. In WT, H3K4me2 levels remain reduced until hatching; in mes-4 embryos, there is strong reappearance observed long before hatching. The RNA polymerase (Pol) II C-terminal domain phosphoepitope detected by the H5 antibody  is not observed in the P-cells of either genotype, but then appears strongly in the PGCs. Whereas in wild type this signal decreases to low levels in later stages, robust levels are maintained in the PGCs. The repression of pSer2 in the P cells is dependent on the maternal CCCH Zn finger proteins, OMA-1 and PIE-1 (black bar). H3K36me3 (blue bars) is maintained at high levels in all germline stages in WT embryos. The H3K36me3 that arrives in gamete chromatin (P0 to P1) is methyltransferase (MET)-1 dependent, as it is present in mes-4 embryos (bottom blue bar), but absent in met-1;mes-4 embryos (not shown). The maintenance of this methylation in the transcriptionally inactive P cells  is completely dependent on MES-4, as it is absent in mes-4. In later mes-4 PGCs, MET-1 dependent H3K36me3 appears in the PGCs, coincident with H3K4me2 (red bar) and the abnormal persistence of robust pSer2 (blue bar).Click here for fileLate stage embryo preparation for chromatin immunoprecipitation (ChIP). (A) Differential interference contras microscopy image showing a field of met-1 embryos after their isolation using the procedure described in Methods. (B) Immunofluorescence microscopy analysis of an estimated 'average' embryo stage of the met-1 embryos purified as in (A), stained for H3K36me3. Red. DAPI; green; H3K36me3; blue, PGL-1 staining in merged image on left. H3K36me3 staining alone is shown in grayscale image on right. Arrow points to a primordial germ cell (PGC). Note that although there is enrichment for H3K36me3 in the PGC, significant levels are still detectable in the somatic nuclei.Click here for filemet-1 embryosQuantitative (q)PCR analysis of H3K36me3 obtained by chromatin immunoprecipitation (ChIP) from mid-late staged . Chromatin obtained via ChIP with anti-H3K36me3 was analyzed by real time PCR using primers specific for genes expressed in germ cells , genes expressed in somatic lineages , a gene expressed in both (rpl-4), an X-linked gene (vha-15) and a non-coding genomic region with no observed activity (non-coding). As was observed in the ChIP sequencing analyses, loci expressed in germ cells showed detectable maternal effect sterile (MES)-4-dependent H3K36me3, whereas those expressed only in soma show little signal above that observed for the non-coding region.Click here for fileInput chromatin H3K36me3 among gene sets analyzed. The gene sets analyzed in Figure 10 (A); germline-enriched genes, (B) ubiquitously expressed genes, (C) soma-specific genes and (D) larval/adult germline-specific genes, did not seem to be enriched for H3K36me3 in input chromatin from either met-1 or wild type late embryos.Click here for filePrimer sequences for quantitative (q)PCR. The primer sequences used for pPCR validation, the genes to which they are targeted, the amplicon sizes and the amplicon coordinates or position within each target are indicated.Click here for file