As organisms adaptively evolve to a new environment, selection results in the improvement of certain traits, bringing about an increase in fitness. Trade-offs may result from this process if function in other traits is reduced in alternative environments either by the adaptive mutations themselves or by the accumulation of neutral mutations elsewhere in the genome. Though the cost of adaptation has long been a fundamental premise in evolutionary biology, the existence of and molecular basis for trade-offs in alternative environments are not well-established. Here, we show that yeast evolved under aerobic glucose limitation show surprisingly few trade-offs when cultured in other carbon-limited environments, under either aerobic or anaerobic conditions. However, while adaptive clones consistently outperform their common ancestor under carbon limiting conditions, in some cases they perform less well than their ancestor in aerobic, carbon-rich environments, indicating that trade-offs can appear when resources are non-limiting. To more deeply understand how adaptation to one condition affects performance in others, we determined steady-state transcript abundance of adaptive clones grown under diverse conditions and performed whole-genome sequencing to identify mutations that distinguish them from one another and from their common ancestor. We identified mutations in genes involved in glucose sensing, signaling, and transport, which, when considered in the context of the expression data, help explain their adaptation to carbon poor environments. However, different sets of mutations in each independently evolved clone indicate that multiple mutational paths lead to the adaptive phenotype. We conclude that yeasts that evolve high fitness under one resource-limiting condition also become more fit under other resource-limiting conditions, but may pay a fitness cost when those same resources are abundant. Microorganisms such as yeast have been used for decades to study adaptive evolution by natural selection. Thirty years ago in now seminal experiments, a strain of yeast was evolved multiple times under carbon limitation. The adaptive changes that gave rise to increases in fitness have previously been studied both phenomenologically and mechanistically but not in detail at the molecular level. To better understand the basis for these strains' fitness increase, we sequenced their genomes and identified putative adaptive mutations. We found that multiple mutational paths lead to these fitness increases. We also determined whether the evolved yeasts' gains in fitness under the original conditions in some cases diminished fitness under other conditions. We therefore evaluated their performance relative to the ancestral strain under the evolutionary and two alternative resource-limiting conditions by determining the ancestral and evolved strains' relative fitnesses and gene-expression levels under all three conditions. We found scant evidence among evolved strains for fitness trade-offs when nutrients were scarce, but discovered a cost was paid when nutrients were plentiful. DrosophilaR.A. Fisher's fundamental theorem of natural selection relates the rate of adaptation by populations of organisms to their genetic variance in fitness at a given time Saccharomyces cerevisiaeIn foundational work, Paquin and Adams monitored the evolution of laboratory strains of the budding yeast et al., discovered that at least one genetic mechanism underlying enhanced glucose transport was tandem duplication of adjacent genes encoding the high-affinity glucose transporters Hxt6 and Hxt7 et al.et al. used these same clones to discover genomic rearrangements that occur during adaptation The clones derived from Paquin and Adams' original experimental evolutions have shown an enduring usefulness over the past 25 years for addressing fundamental questions concerning the nature of adaptive evolution. 15 years after the original experiments, Brown One fundamental question that these strains have not yet been used to address is: “Does evolution of increased fitness under one type of selection cause decreased fitness under another?” - in other words, are there fitness trade-offs? That trade-offs occur and constrain organismal evolution is foundational to much evolutionary theory, theory that extends into ecology where it has guided analyses of how communities are structured in relation to resource availability E. coli serially diluted for 20,000 generations in minimal medium containing glucose as the sole carbon source exhibit reduced fitness on a variety of alternative carbon sources E. coli experimentally adapted to low temperature can show trade-offs at high temperature Chlamydomonas has also revealed trade-offs: strains evolved in the presence of light often grow more poorly than the original ancestor in the dark, and vice versa E. coli populations evolved in a continuous, mixed-sugar chemostat environment , clones evolve most often through either amplification of the lac operon or mutations in the mgl operon. In only one out of thirteen chemostats did a clone evolve having mutations in both of these operons Correlated responses of fitness to selection are conventionally measured in terms of how well an organism performs in an environment different from the one in which it evolved et al.Here we present just such an integrated set of fitness, physiological, and whole-genome sequence data that we use to test whether evolutionary adaptation to one type of carbon limitation diminishes organismal performance under other types of carbon limitation (or non-limitation). Specifically, we asked whether the well-studied Paquin and Adams et al.et al.Paquin & Adams As expected, each evolved clone had a significantly higher relative fitness than the ancestor in the aerobic glucose-limited environment in which all of the original evolutions were performed . In the p<0.05 in two-tailed t-test); strikingly, the relative fitness under acetate-limitation is higher still than under aerobic glucose limitation . These data are consistent with the notion that while the mutations conferring the adaptive advantages for E1 through E5 have an overall net positive effect on fitness when compared to the original ancestor in each environment, some of these mutations might be deleterious or neutral in the anaerobic environment. More specifically, these results indicate that increased glucose transport is still advantageous under anaerobic glucose limitation, but that enhanced respiration provides no benefit in the absence of oxygen. However, enhanced respiration provides obvious benefits under acetate limitation, where oxygen is available.In the above analyses, we compared evolved clones' direct and correlated responses to selection within each environment. To compare the responses to selection between environments, we calculated grand means of all five clones' relative fitnesses within each environment and tested these means for significance differences between each of the three environments (2-tailed t-test). We hypothesized that some of the adaptive mutations due to the original aerobic regime would be deleterious or neutral under anaerobic growth, specifically those that resulted in the “enhanced classical Pasteur effect.” This hypothesis predicts that these same mutations would produce a fitness advantage in aerobic acetate-limited growth (provided they continue to enhance respirative growth). The data in While these trends are important, they are not universally true for all clones. For example, one clone E4,  has the −1), and biomass (g 100 mL−1)—were measured , cell number (cells mLmeasured . The datmeasured . The obs−1 for E2), down to a roughly 2-fold increase in biomass for E5 (−1 for E1 over CP1AB) to no change or perhaps even a slight reduction (cells mL−1 for E5 and OD600 for E1) for these traits ) for the biological replicates were averaged for the subsequent analyses.The “enhanced classical Pasteur effect” described by Ferea et al. .Under aerobic glucose limitation our data  largely HXK2. HXK2 encodes a bifunctional enzyme whose cytosolic form irreversibly commits glucose to metabolism by phosphorylating it HXK1 and itself HXK2 expression was increased 4- to 16-fold in all five evolved clones compared to the parent, a result we provisionally attribute to a 2-fold decrease in expression of the Mig1-Hxk2 regulator SNF1 observed under this condition.Under anaerobic glucose-limiting conditions, glucose-oxidation pathways are repressed (or simply not induced) as would be expected in the absence of oxygen. Contrary to our initial expectations, these same pathways are not highly expressed relative to the ancestor under aerobic acetate limitation. Thus, at least for this set of genes, it appears that evolved clones do not have mutations in one or more global regulators of pathways that result in constitutive up-regulation. Interestingly, under aerobic and anaerobic glucose limitation (but not acetate limitation) we observed up-regulation of the hexokinase gene RAS1RAS1, and the down regulation of the TOR pathway. GO::TermFinder To uncover general expression patterns underlying the direct and correlated responses to selection in these strains, we performed a two-class, unpaired Significance Analysis of Microarrays [SAM] HXT6/7 amplifications CIT1 locus AEP3 gene HXT6/7 amplification The genomes of CP1AB and the five evolved clones E1–E5 have been previously interrogated in a number of different ways including southern blot CAN1 locus) – we would only expect to see a small number of mutations per strain. For E1, we only expect 7–8 neutral mutations, and for E2–E5 we only expect 3–5 mutations. Under the assumption that this is a Poisson process, seeing the observed number of mutations is significant for each clone , L = genome size (bases), and t = time (generations). Using even the most generous estimate of mutation rate  is ∼0.721 and, using clone E1 as an example, the expected number of coding mutations out of 28 observed mutations is between 20 and 21. Given these estimates, our null hypothesis under a Poisson distribution is that we will not observe greater than 20–21 coding mutations out of 28 total mutations. Because 21 of these 28 mutations actually occur in coding regions, we cannot reject the null hypothesis, and thus we do not see more mutations in coding regions than we would expect by chance (p = .085) . SimilarAEP3, which encodes a mitochondrial integral membrane protein that stabilizes mRNA of the ATP synthase complex MIG2, RGT1), as well as glucose and nutrient signaling . In particular, we discovered mutations in the Ras/cAMP signaling pathway that have been previously identified in glucose-limited evolutions IRA2, one of the Ras-GTPase-activating proteins CYR1, which encodes the yeast adenylate cyclase TOR1 and AVO1, a member of the TORC2 complex MUK1, which has no known function, but which has also been the target of selection in independently-evolved, haploid, glucose-limited populations of yeast; the mutation appears not to be adaptive on its own  amplification and ChrXIV rearrangement in E1 (HXT6/7), the ChrVII amplification and ChrXV deletion in E4  have clear implications for adaptation to carbon-limited growth.To confirm the DNA copy number changes and other larger scale genome rearrangements discovered by Dunham, on in E4 , E4, andon in E4 , E5. WhiHXT6/7 and mutations in RGT1 and MIG2 discovered by genome sequencing, an obvious candidate condition in which to test for the presence of trade-offs is glucose-rich medium, as enhanced glucose transport may no longer be advantageous when glucose is abundant, and may even be costly. To test this hypothesis, we grew evolved and ancestral clones under glucose non-limiting conditions in batch culture and found that nearly all showed diminished maximum specific growth rate (μmax), relative to their common ancestor , experimentally evolved these for >100 generations by serial transfer in 2% YEP dextrose medium and then tested for changes in HXT6/7 copy number by quantitative PCR. We discovered that in at least one instance, copy number decreased . We observed that the HXT6/7 amplification resulted in decreased growth rate relative to sister spores containing the “wild-type” HXT6/7 locus , Jansen, et al. Our choice of assay regimes was motivated by a desire to understand the generality, relative magnitude and mechanistic bases of adaptations for acquiring Remarkably, although the clones we investigated evolved in independent populations under aerobic glucose limitation, they performed better than their common ancestor in other carbon limiting environments under both anaerobic and aerobic conditions. The adaptive clones' superior performance is manifest in cell yield and fitness in both the selective and assay regimes. The relative magnitudes of the physiological values associated with fitness differences can be easily explained in terms of the energetics of aerobic vs. anaerobic catabolism The relative magnitude of yield and fitness differences in these two assay regimes reflects the scope for selection: only modest gains are possible in high-affinity glucose transport, whereas much more substantial gains are possible by shifting to and then improving upon the machinery of oxidative metabolism, whose ATP yield is many-fold greater than fermentation MIG2 and RGT1, as well as a tandem duplication of the genes encoding the hexose transporters Hxt6 and Hxt7. Interestingly, the mutations in MIG2 and RGT1 are synonymous, indicating potential functional roles for mutations that do not effect an amino-acid change. By contrast, clone E5 contains an amplification of the entire right arm of chromosome IV, containing the HXT6 and HXT7 loci.Our phenotypic data indicate that five independently evolved clones have converged on growth phenotypes that give them a competitive advantage in selective (and carbon scarce) assay regimes alike. We would not necessarily have predicted this from previous studies HXT6/HXT7 copy number and CIT1 regulation noted previously IRA2, a gene that encodes a Ras-GAP that functions to decrease intracellular cAMP levels. Interestingly, E2 contains a mutation in the same pathway in the gene that encodes yeast adenylate cyclase itself, CYR1. These mutations would be particularly interesting to characterize, as our gene expression data and data from cyr1 mutation would be a gain-of-function mutation that increases intracellular cAMP levels, whereas the ira2 mutation should be a loss-of-function mutation resulting in constitutive Ras signaling and similar increases in cAMP levels.Our microarray results, viewed through the lens of our whole genome sequencing data, suggest other adaptive mechanisms additional to the changes in AVO1, a component of the TORC2 complex, while E5 has mutations in TOR1 itself as well as in MDS3, a putative component of the TOR signaling cascade. Again, our prediction is that these mutations should be gain-of-function that increase signaling through this key regulatory pathway.Gene-expression data also suggest increased signaling through the TOR pathway across all evolved clones, relative to their common ancestor, an observation that is again supported by the presence of novel mutations in this pathway. We found that E2 has a mutation in AEP3, a gene that encodes a mitochondrial protein important for ATP synthase function. Strikingly, the mutations in E1, E2, and E3 all affect the same codon but effect independent amino acid substitutions, and E2 contains 2 additional nucleotide changes that change another amino acid in close proximity to the other mutated codon. We observe additional mutations in E1 that likely affect mitochondrial function, including BNA4 (involved in biosynthesis of nicotinic acid), NGR1 , and TOM70 . It will be illuminating to follow-up these observations with a characterization of their individual or epistatic fitness contributions. These data also provide a hypothesis for observed variation in correlated responses to selection. The whole genome sequence of clone E3 provides no mechanistic basis for enhanced glucose transport, but does have a mutation in the integral mitochondrial protein, AEP3. Significantly, we observed that relative to the ancestor and to other independently evolved clones, E3 exhibited highest fitness under acetate limitation and less of a selective advantage under glucose limitation. A final observation regarding clones E3 is that it appears to have the most distinct gene expression pattern compared to the other five clones under acetate limitation , likely outweighs any cost of uselessly increasing glucose-oxidation ability under anaerobic glucose-limitation. Similarly, although increased glucose transport is unlikely to be adaptive under acetate limitation, any cost imposed thereby is likely offset by an increased capacity to oxidize carbon. More generally, we can use a term defined by Bell & Reboud synclinal – meaning that the direct and correlated fitness responses to this selective regime were positive with respect to the ancestor in all five evolved clones. These conclusions, however, might only apply to carbon (or even particular kinds of carbon) limitation, and in further work it would be appropriate to test these evolved clones under a much more diverse set of environments to determine the breadth of their niche. These types of experiments will be crucial to discerning whether trade-offs exist under other assay regimes and, if so, how mutation accumulation and antagonistic pleiotropy combine to produce them.In retrospect, given the changes we have discovered in strains' physiology, gene expression and genome sequence, the apparent lack of trade-offs under the assay regimes we chose is perhaps not so surprising. An increased capacity to scavenge glucose should produce a fitness advantage in any environment in which glucose is meager; thus the direct genetic evidence we see for this in at least two strains, E1 and E5 . Cultures were stored in 15% glycerol at −80°C.Strains used in this study were −1 for aerobic (0.08% glucose) and anaerobic glucose limitation (0.08% glucose+420 mg mL−1 Tween 80+10 mg L−1 ergosterol) and 0.05 hr−1 for aerobic acetate limitation (10.9 g/L sodium acetate). For aerobic ammonium limited (0.015% (NH4)2SO4), glucose-sufficient (9 g/L glucose) growth, chemostat dilution rate was also set to 0.17 hr−1. Aerobic conditions were achieved by sparging with 25 L h−1 of sterile air and anaerobic conditions by sparging cultures with 25 L hr−1 sterile-filtered, humidified N2 (g). 3 mL samples were taken at time = 0 (immediately following transfer to fresh chemostat) and every 6–8 hours for 2–3 days (∼15 generations). Time and volume of effluent were measured at each sample to determine generations. 1 mL of cells were resuspended in Phosphate Buffered Saline, sonicated for 10 s, and analyzed with flow cytometry to determine relative proportions of fluorescent (reference) to non-fluorescent  strains. 50,000 cells were counted to obtain accurate measurements of relative proportions. Regression analysis of generation time vs. ln was used to calculate per-generation competition coefficients. This method is based on the method worked out by Alex Ward and David Gresham and similar to the method used in 5 cells mL−1 in 10 mL media. Samples were cultured for 24 h (∼6.5 generations) at 30°C on a New Brunswick T-7 roller drum, then diluted to a similar cell density in fresh media and cultured an additional 24 h. Samples for FACS analysis were taken over 3 successive serial dilutions (approximately 20 generations). Pairwise competition experiments were performed in triplicate.Strains for chemostat cell cultures were grown in 1% YEP Dextrose and 1 mL aliquots were frozen in 15% glycerol at −80°C. The entire contents of a single 1 mL frozen aliquot of either the reference strain and evolved or ancestral isolates were used to inoculate an individual chemostat  with working volume set to 400 mL of minimal (SC) media defined by HXT6/7 amplification (haploid segregants GSY2707-2714 were otherwise isogenic from parent diploid GSY1208 that was heterozygous only for the HXT6/7 amplification), multiple independent single colonies of these strains were grown overnight in 2% YEP dextrose and diluted 1∶50 into fresh medium in a 100 µL, 96-well optical plate (Costar), sealed with optical sealing tape (E&K Scientific), and grown for approximately 24 hours in a TECAN plate reader at 30°C. Specific growth rate was defined as the change in ln per hour during exponential growth.To determine specific growth rates in glucose non-limiting batch growth for evolved clones E1–E5, ancestral clone CP1AB, and haploid segregants with or without the HXT7/6 amplification under nutrient-rich conditions, we selected at random five colonies of the parent strain and ten colonies of adapted strain E1 and used these to found fifteen experimental populations. Populations were inoculated at a density of ∼105 cells mL−1 in 10 mL YEPD (2% glucose), cultured at 30°C in a New Brunswick T-7 roller drum, and serially propagated by diluting cells ∼100-fold on a daily basis in fresh media. Experiments were carried out for 15 days (>100 generations); 1 mL of each culture was archived every 25 generations as −80°C 15% glycerol stocks. Population samples from the last time-point of each experiment were spread onto YPD agar. Genomic DNA was prepared from three randomly chosen colonies on each plate using the YeaStar Genomic DNA Kit (Zymo Research); this material was used as template for quantitative PCR assay of HXT7/6 copy number using primers specific for the HXT6/7 locus and control primers on chromosome IV designed against the UBP1 locus  and flash frozen in liquid nitrogen. RNA for gene-expression measurements was isolated using the hot acid-phenol method described by To obtain physiological measurements, cultures were grown to steady state in individual chemostats in each of the three environments described above, under identical conditions. Experiments were performed in triplicate. Biomass estimates were determined by rapidly withdrawing 100 mL from fermentation vessels, and fast-filtering this volume through sterile, tared 47 mm, 0.45 mm Nylon filters (Whatman). Filters were dried overnight in an 80°C oven and weighed the following day. Cell number was estimated by haemocytometry using an aliquot from 1 mL of sample treated with 10 ug mL2 ratios. Following data extraction from the raw images, we averaged the data for both probes for each gene. Raw gene-expression data have been deposited in GEO with accession number GSE25081.To assay relative mRNA abundance, total RNA was isolated as described above. A pooled reference sample was created containing equimolar amounts of each of 36 samples . 325 ng of total RNA from samples or reference pool was used as the input for reverse transcription and labeling with Cy dyes (Amersham) using the Low RNA-input Linear Amplification Kit (Agilent) following manufacturer's instructions except that reaction volumes were halved. 1.5 µg each of labeled sample and labeled reference were hybridized to Yeast Gene Expression Arrays v2 8×15k (Agilent) for 17 hours at 65°C rotating at 10 rpm in a hybridization oven (Shel Lab). Arrays were then washed according to manufacturer's instructions and scanned at 5 µm resolution on an Agilent Scanner. Data were extracted using Agilent Feature Extraction v9.5.3.1, which uses linear-Lowess normalization and calculates logDNAcopyCP1AB and E1–E5 were streaked for single colonies from 15% glycerol stock solutions (−80°C) onto 2% YEP Dextrose plates. Single colonies were grown in 2% YEP Dextrose liquid cultures at 30°C and genomic DNA was extracted by spooling as described To determine the average probability of a mutation in a coding region effecting a non-synonymous coding change we wrote a custom perl script that calculates the average probability that a mutation would change the codon to encode a different amino acid. Briefly, for each codon in the genome, every possible mutation was generated (9 changes for each codon), and the fraction of those 9 possible mutations that created a non-synonmous codon was recorded. For example, a four-fold degenerate site at the wobble base of a given codon would yield a probability of 2/3 non-synonymous (6 out of 9 mutations change the codon). We then simply averaged this probability across all codons in the genome.Dataset S1Gene-expression data from (TXT)Click here for additional data file.Dataset S2Gene-expression data from (TXT)Click here for additional data file.Figure S1DNAcopy . Red lines represent segment means determined by copy see .(TIF)Click here for additional data file.Figure S2HXT6/7 locus following serial batch evolution under high glucose. Copy number of the HXT6/7 locus relative to the ancestral parent. Raw values for HXT6/7 locus were normalized to an internal control chrIV locus (UBP1) to give ΔCt values. These values were then normalized to the ancestral parent values (ΔΔCt). Copy number was then determined as 2∧(−(ΔΔCt)). Values are the mean of three technical replicates with error bars showing standard deviation. “P” indicates CP1AB, “E” indicates evolved clone E1. The number (1–5 for P and 1–10 for E) indicates replicate evolved populations, and “a–c” indicate three randomly chosen end-point clones.Copy Number of (TIF)Click here for additional data file.Figure S3HXT6/7 Segregants in Glucose-Rich Environments. Maximal specific growth rates of otherwise isogenic haploid segregants containing either wild type (4 segregants) or HXT6/7 amplification (4 segregants) loci. The parent diploid of these strains was isogenic except for the HXT6/7 amplification, based upon high-throughput sequencing.Specific Growth Rate of (TIF)Click here for additional data file.Table S1Competition Coefficients (Relative to Common Reference Strain) in Three Environments.(XLS)Click here for additional data file.Table S2Grand Means of Relative Fitness within Each Alternative Environment.(XLS)Click here for additional data file.Table S3Steady State Physiological Measurements within Each Alternative Environment.(XLS)Click here for additional data file.Table S4Summary of Illumina Sequence Data.(XLS)Click here for additional data file.Table S5Primers used.(XLS)Click here for additional data file.Table S6Significance Analysis of Mutations in Evolved Clones.(XLS)Click here for additional data file.Table S7Significance Analysis of Mutations in Coding Regions and Non-Synonymous Mutations.(XLS)Click here for additional data file.Table S8Competition Coefficients (Relative to Common Reference Strain) in Glucose-Rich Environments.(XLS)Click here for additional data file.


LeACS2, direct targets of RIN have not been clarified, and little is known about the transcriptional cascade for ripening.During ripening, climacteric fruits increase their ethylene level and subsequently undergo various physiological changes, such as softening, pigmentation and development of aroma and flavor. These changes occur simultaneously and are caused by the highly synchronized expression of numerous genes at the onset of ripening. In tomatoes, the MADS-box transcription factor RIN has been regarded as a key regulator responsible for the onset of ripening by acting upstream of both ethylene- and non-ethylene-mediated controls. However, except for LeACS2, LeACS4, PG, TBG4, LeEXP1, and LeMAN4 and of RIN itself, indicating direct interaction of RIN with their promoters in vivo. Moreover, sequence analysis and genome mapping of 51 cloned IPed DNAs revealed potential RIN binding sites. Quantitative PCR revealed that four of the potential binding sites were enriched 4- to 17-fold in the IPed DNA pools compared with the controls, indicating direct interaction of RIN with these sites in vivo. Near one of the four CArG boxes we found a gene encoding a protein similar to thioredoxin y1. An increase in the transcript level of this gene was observed with ripening in normal fruit but not in the rin mutant, suggesting that RIN possibly induces its expression.Using immunoprecipitated (IPed) DNA fragments recovered by chromatin immunoprecipitation (ChIP) with anti-RIN antibody from ripening tomato fruit, we analyzed potential binding sites for RIN (CArG-box sites) in the promoters of representative ripening-induced genes by quantitative PCR. Results revealed nearly a 5- to 20-fold enrichment of CArG boxes in the promoters of RIN expression. ChIP-based analyses identified a novel RIN-binding CArG-box site that harbors a gene associated with RIN expression in its flanking region. These findings clarify the crucial role of RIN in the transcriptional regulation of ripening initiation and progression.The presented results suggest that RIN controls fruit softening and ethylene production by the direct transcriptional regulation of cell-wall-modifying genes and ethylene biosynthesis genes during ripening. Moreover, the binding of RIN to its own promoter suggests the presence of autoregulation for  These changes make fruits attractive for the human diet. For climacteric fruits, autocatalytic ethylene production and an increase in respiration occur during ripening, and ethylene has been well characterized as necessary for the coordination and completion of ripening  increase dramatically during ripening, but their transcriptions are inhibited by the rin mutation, indicating that these genes are responsible for the elevation of ethylene level and for ethylene signaling during ripening  [6GG] [4TAG] [6AG]   compared with the pre-ripening stage [mature green (G)], but not in the rin mutant at the corresponding stages  than that of RIN at the ripening stages (A/T)6(A/G)G, and that the preferential binding sequence is 'CCA(A/T)(A/t)(A/T)ATAG' [LeACS4 contains no typical RIN-binding sequences but does contain three atypical CArG-box sequences, CAAATATAAG (ACS4-a), CAATTTTAAG (ACS4-b) and CTAGTTAAAG (ACS4-c) . Similarly, the promoter of LeEXP1 also contains one atypical CArG-box sequence, CAATTTAAAG. ChIP assays demonstrated that these four atypical sites were enriched within the IPed DNA at a high level comparable to the sites within the promoters of LeACS2 and LeMAN4 that contain the typical binding sequences. This result indicates that in vivo RIN binds to a wider spectrum of CArG-box sequences within the genome than previously expected based on in vitro assay.Our previous in vivo suggests that other factors might be necessary for RIN to target only the ripening stage-specific genes. The binding site selectivity of RIN might be affected by chromatin structure  that controls the interaction of RIN with target DNAs in living cells. As another possibility, the selectivity of RIN might be increased by a tetramer- or higher-order multimer formation with other MADS-box proteins. For instance, in the 'quartet model' of floral organ specification, MADS-box proteins in floral organs form four combinations of tetramers that determine the identity of the different floral organs [cis-elements of LeACS2, LeACS4, TBG4 and RIN contain two CArG-box sites enriched at a significant level by ChIP using the anti-RIN antibody. These results suggest that RIN functions as a component of a tetramer that interacts with two binding sites of the target sites, just like the 'quartet model' transcription factor complexes. RIN possesses the ability to form a homodimer or heterodimers with other tomato MADS-box proteins belonging to the APETALA1/FRUITFULL subfamily  and AG subfamily (TAGL1 and TAGL11) [This ability of RIN to bind with a wide range of CArG-box sequences l organs . In thisl organs . In factl organs . In this TAGL11) ,37. Beca TAGL11) ,38, RIN  TAGL11) .cis-elements is necessary but not sufficient to induce the gene expressions required for fruit ripening, and certain factors are necessary for the specific transcriptional regulation by RIN. An assay using a yeast system revealed that RIN activates the transcriptional activity of an RNA polymerase that binds to the flanking genomic region [In this study, we identified novel RIN-binding sites from a screening of IPed DNA. At the regions flanking those sites, however, we found only one gene that is upregulated during ripening. Although we cannot exclude the possibility that the RINs binding to the other sites might regulate the genes located far from the sites (>5-kb), the bindings are likely not to activate any transcriptions. Similar observations were previously reported in the investigation of transcription factors of SEP3 through ChIP with Solexa sequencing (ChIP-SEQ), which showed that several percent of thousands of peaks are located far from protein-coding gene loci . These fc region , suggestin vivo, strongly suggesting that RIN autoregulates its own expression. The existence of this autoregulatory mechanism can explain how the rapid increase in the mRNA level of RIN at the onset of ripening is controlled [i.e., it is expected that autoregulation apparently controls the expression of RIN in a positive manner during ripening due to the transcriptional activation activity of RIN [DEFICIENS (DEF) and GLOBOSA (GLO), Arabidopsis APETALA3 (AP3), PISTILLATA (PI), AG and embryogenetic AGL15 [TOMATO AGAMOUS-LIKE 1 (TAGL1) is possibly autoregulated [RIN might help to maintain its sufficiently high expression in ripening fruit, and consequently effectively regulate the expressions of direct target genes involved in the ripening process.Our analysis revealed that RIN binds to the CArG-box sites in its own promoter ntrolled ,29  to ACC by ACC synthase is a known rate-limiting step [LeACS2 and LeACS4 regulate a massive increase in ethylene production of fruit associated with ripening, which is defined as system 2 ethylene production in contrast to system 1, which produces ethylene constitutively at a low level within pre-ripening fruits [LeACS2 and LeACS4, both of which are controlled by ethylene-independent developmental factors. Our finding strongly suggests that RIN directly regulates LeACS4 as well as LeACS2, and this finding is consistent with those of previous reports [LeACO1 and ETR3 expression by RIN cannot be confirmed at present due to relatively lower enrichment levels of the CArG boxes in their promoters in this study. In addition, a HD-Zip homeobox protein LeHB-1 directly regulates the expression of LeACO1 during fruit ripening [LeACO1. However, we cannot exclude the possibility that RIN regulates the expression by binding to CArG boxes in the transposable element-like sequence in the LeACO1 promoter that could not be examined in this study. Regardless, our results certainly suggest that RIN contributes to the initiation of ethylene production at the onset of ripening through upregulation of LeACS2 and LeACS4.We also demonstrated here the direct interaction of RIN with the promoters of the genes involved in ethylene synthesis, ing step . LeACS2 g fruits -9. Recen reports ,8,9. Mearipening , suggestPG, TBG4, LeEXP1 and LeMAN4, in vivo using ChIP. This finding strongly suggests the direct transcriptional regulation of these genes by RIN, similar to its regulation of LeACS2 and LeACS4. Previous studies have shown that the expression of PG in tomato fruit is regulated by ethylene and that the promoter contains cis sites highly similar to the ethylene-responsive regions of the E8 and E4 genes [PG expression during ripening is induced in an ethylene-independent manner, consistent with our view that RIN directly regulates the PG expression. These facts suggest that both pathways (ethylene and RIN) are effective in controlling the PG expression during ripening. Furthermore, previous analyses with suppression or overexpression of the four genes revealed that these genes are involved in cell wall modification but not enough to independently soften fruit [We also demonstrated here the direct interaction of RIN with the promoters of ripening-induced genes, E4 genes -48. HoweE4 genes  previousen fruit ,19,50,51RIN-associated expression pattern. This gene encodes a protein similar to Trx y1, which serves as an oxidoreductase and is an activator of plastidal peroxiredoxin Q (Prx Q) [Trx y1 expression. However, the involvement of Trx y1 in the ripening process needs further clarification because no functional evidence of such involvement or of the apparently irregular position of CArG-box 133R as a cis-acting element has been reported. More extensive ChIP-based analysis will lead to comprehensive identification of the target genes of RIN and will thus help clarify the transcriptional regulation of gene expression for fruit ripening.We successfully identified four novel RIN-binding CArG-box sites from ripening tomato fruit by ChIP. This led to the detection of the genes near the CArG boxes. Among these genes, SGN-U571769 is possibly regulated by RIN during ripening due to the  (Prx Q) . Prx Q p (Prx Q) ,54. When (Prx Q) . From thRipening of climacteric fruits is genetically controlled by intricate transcriptional cascades through ethylene and non-ethylene-mediated regulation. In this study, parts of the cascades involving RIN are revealed.PG, LeEXP1, TBG4 and LeMAN4) and system-2 ethylene biosynthesis (LeACS2 and LeACS4) in vivo and in vitro. This suggests that RIN controls fruit softening and ethylene production by the direct transcriptional regulation of the cell-wall-modifying genes and ACS genes, respectively. The control of ethylene production by RIN during ripening means that RIN is also indirectly responsible for ethylene-inducible ripening processes together with an ethylene-mediated control. In addition, the presence of autoregulation of RIN itself is also suggested in this study, and thus explains how a rapid increase in the RIN transcription level occurs at the onset of fruit ripening.qChIP-PCR analysis of ripening tomato fruit and gel retardation assay demonstrated that RIN binds to the CArG boxes in the promoters of the genes involved in cell-wall modification  DNA fragments with anti-RIN antibody were recovered and purified. The resulting IPed DNA pool was used for qChIP-PCR as a template as described later.ChIP experiments were performed as previously described  using riTaq DNA polymerase  and by the ligation of an adaptor . The resulting DNA fragments were amplified by PCR with the L oligonucleotide as a primer. The PCR products were size-fractionated by agarose gel electrophoresis into 0.2-1 kb and cloned into a pBlueScriptII SK(-) vector that was digested with EcoRV and supplemented in advance with thymine (T) at the 3'end, followed by the introduction into Escherichia coli (JM109 strain) cells. The cloned DNA fragments were sequenced with a BigDye Terminator cycle sequencing kit v3.1 and analyzed by an ABI Prism 310 genetic analyzer .The IPed DNA was also partially analyzed by sequencing and mapping as previously described . In brieLeACS2 (accession no. X59139), LeACS4 (M88487), LeACO1 (X58273), ETR , PG (X14074), TBG4 (AF020390), LeEXP1 (U82123), LeMAN4 (AY046588), PSY1 (EF157835), INV (Z12027) and RIN (AF448522). Because the 5' region upstream of the protein-coding regions of PSY1, PG, TBG4, LeEXP1, LeMAN4 and RIN is less than 2 kb, their promoter sequences corresponding to 2 kb were complemented with Whole Genome Shotgun (WGS) data using the BLASTN program [Nucleotide sequences of the representative ripening-induced gene promoters were obtained from public databases:  PSY1 EF1835, INV  LeACO1 X273, ETR in silico on a draft genome sequence (WGS) of tomato released by the International Tomato Genome Sequencing Consortium (http://solgenomics.net/about/tomato_sequencing.pl) using BLASTN [The sequenced IPed DNA clones were mapped g BLASTN . Using Fg BLASTN , CArG-boEnrichment levels of the CArG boxes by ChIP were monitored using quantitative real-time PCR (qChIP-PCR) as described below. The IPed DNA pool was used for qChIP-PCR as a template. The pre-immune serum -treated chromatin DNA pools and the total input chromatin DNA (without ChIP treatment) pools were used as templates for the negative and standard controls, respectively. The nucleotide sequences of the oligonucleotide primers specific to the respective CArG-box sites used in this study are listed in Additional File Actin gene, which is non-bound by RIN and thereby used as a reference [Quantitative ChIP-PCR analyses were performed using Power SYBR green PCR master mix and a 7300 real-time PCR system (Applied Biosystems) according to the manufacturer's instructions. In a 50-μl reaction mixture, 2 μl of the IPed DNA, the PI-treated or the input DNA pool was applied as a template. The PCR conditions were as follows: 95°C for 10 min, 40 cycles of 95°C for 10 sec, and 60°C  [Gel retardation assays were performed as previously described  with a sftp://ftp.sgn.cornell.edu/unigene_builds/) using the BLASTN program [http://linux1.softberry.com/berry.phtml)A sequence similarity search for the genomic ~5 kb flanking the CArG boxes enriched by ChIP was carried out against a tomato expressed sequence tag (EST) database provided by Solanaceae Genomics Network  plant at different stages  and of a rin mutant (rin/rin) plant at periods corresponding to these stages, as previously described [Expression levels of the genes were analyzed by quantitative real-time reverse transcription PCR (qRT-PCR) using oligonucleotide primers specific to each EST or escribed . ComplemCAC; SGN-U314153) as a reference [Quantitative RT-PCR analyses were performed in the same manner as for qChIP-PCR except that 2 μl of cDNA synthesis reaction mixture was applied as a template instead. Copy numbers of the objective transcripts were calculated from the measurements (Cq) using standard curves generated from a series of diluted PCR products for the respective genes. The data were normalized with that of a gene encoding clathrin adaptor complexes medium subunit  of each pair for qChIP-PCR is indicated in the right column.Click here for filePrimer pairs specific to the tomato ESTs found in this study used for expression analysis and results of similarity searches for the ESTs. Primer pairs used in this study. Known proteins with the most significant similarity to proteins encoded by the ESTs are shown.Click here for fileA checklist outlining the DNA to qChIP-PCR quality/methodology as described in [ribed in .Click here for fileA checklist outlining the RNA to qRT-PCR quality/methodology as described in [ribed in .Click here for file


Campylobacter jejuni, and have used this for comparative and predictive transcriptomics in the Epsilonproteobacteria.Gene reshuffling, point mutations and horizontal gene transfer contribute to bacterial genome variation, but require the genome to rewire its transcriptional circuitry to ensure that inserted, mutated or reshuffled genes are transcribed at appropriate levels. The genomes of Epsilonproteobacteria display very low synteny, due to high levels of reshuffling and reorganisation of gene order, but still share a significant number of gene orthologs allowing comparison. Here we present the primary transcriptome of the pathogenic Epsilonproteobacterium C. jejuni NCTC 11168, which consists of 992 transcription start sites (TSS), which included 29 putative non-coding and stable RNAs, 266 intragenic  TSS, and 206 antisense TSS. Several previously unknown features were identified in the C. jejuni transcriptional landscape, like leaderless mRNAs and potential leader peptides upstream of amino acid biosynthesis genes. A cross-species comparison of the primary transcriptomes of C. jejuni and the related Epsilonproteobacterium Helicobacter pylori highlighted a lack of conservation of operon organisation, position of intragenic and antisense promoters or leaderless mRNAs. Predictive comparisons using 40 other Epsilonproteobacterial genomes suggests that this lack of conservation of transcriptional features is common to all Epsilonproteobacterial genomes, and is associated with the absence of genome synteny in this subdivision of the Proteobacteria.Differential RNA-sequencing using 454 sequencing technology was used to determine the primary transcriptome of Both the genomes and transcriptomes of Epsilonproteobacteria are highly variable, both at the genome level by combining and division of multicistronic operons, but also on the gene level by generation or deletion of promoter sequences and 5′ untranslated regions. Regulatory features may have evolved after these species split from a common ancestor, with transcriptome rewiring compensating for changes introduced by genomic reshuffling and horizontal gene transfer. While our appreciation of microbial diversity has been greatly increased by the exponential increase in the availability of genome sequences and by metagenomic approaches ,2, it haEnterobacteriaceae) or too distantly related to allow meaningful comparisons at the evolutionary level.The level of RNA in a cell is usually controlled at the transcriptional and post-transcriptional levels. In bacteria, transcriptional regulation is commonly mediated via control of transcription initiation by RNA polymerase (RNAP) at the promoter . AlternaCampylobacter jejuni and Helicobacter pylori, which belong to the order Campylobacterales-ATP end-labeled oligodeoxynucleotides  gels, containing 8.3 M urea. Each lane contained 10 μg of total RNA, isolated from easy cloning vector  and the nucleotide sequence of the inserts was determined.RNA adapter . The raw sequencing data have been uploaded as 454 SFF files into the Short Read Archive with accession number SRX326863 (http://www.ncbi.nlm.nih.gov/sra/?term=SRX326863). An annotated Artemis entry has been created which contains the information of Additional file C. jejuni NCTC 11168 genome sequence (Accession number NC_002163) and is included with the article as Additional file The dRNA-seq histogram files and associated information have been deposited in the GEO database with accession number GSE49312 ; RNAP: RNA polymerase; dRNA-seq: Differential RNA-sequencing; TEX: Terminator exonuclease; TAP: Tobacco acid phosphatase; Mbp: Million base pairs; nt: Nucleotide(s); RACE: Rapid amplification of cDNA ends; RPKM: Reads per kilobase per million mapped reads; tmRNA: Transfer-messenger RNA; RNase: Ribonuclease; SRP: Signal recognition particle; TPP: Thiamine pyrophosphate; ncRNA: Non-coding RNA; sRNA: Small RNA; pRNA: Product RNA; 5’ UTR: 5’ untranslated region; RBS: Ribosome binding site; ORF: Open reading frame; BAB: Blood agar base 2.The authors declare that they have no competing interests.IP and AHMvV designed the research; IP, MR and BMP performed the experimental research and analysed data; AHMvV, MR and TW performed the bioinformatic analyses, AHMvV wrote the paper, on which all authors commented. All authors read and approved the final manuscript.C. jejuni genes on RNA-seq and microarray, using the Illumina-based quantitative RNA-seq data from Chaudhuri et al. [2(RPKM + 1) values [Comparison of expression levels of i et al. , with an) values  for 1509) values  and normClick here for fileC. jejuni. A) Schematic representation of the different types of transcription start sites, with primary and secondary TSS being located at ≤ 500 nt from the translational startcodon of the respective gene. TSS can have multiple associations, as shown for the primary and internal TSS within the first gene. B) Venn diagram representing the overlap between the different classes of TSS identified for C. jejuni. C) The cj1316c is transcribed from both a primary and secondary TSS (left) whereas the ftsZ (cj0696) gene is transcribed from an internal promoter located in the coding sequence of the upstream ftsA (cj0695) gene (right), allowing intraoperonic differentiation of transcript levels. Translational start codons and putative RBS are underlined, TSS are shown underlined in bold typeface, and extended and normal −10 sequences for σ70 and σ28 are indicated in bold typeface.Identification of transcription start sites in Click here for fileC. jejuni transcription start sites and annotated promoter sequences identified by dRNA-seq.Click here for fileC. jejuni genes with primary and secondary promoters.Click here for fileC. jejuni TSS identified by dRNA-seq with TSS identified by 5′ RACE and primer extension.Comparison of Click here for fileC. jejuni TSS identified by dRNA-seq with those previously published  WebLogo representation of the −50 to +1 sequences of σ70 promoters in C. jejuni  in the Click here for fileC. jejuni 6S RNA. (A) The 6S RNA is encoded directly upstream of the purD (cj1250) gene, and is transcribed on the leading strand from a σ70 promoter upstream of the TSS (shown above the histograms). The product RNA (pRNA) transcribed from the complimentary strand is shown below. (B) Predicted folding of the C. jejuni 6S RNA. The sequence used to transcribe the complementary pRNA is marked by a green box. (C) Transcription of the 185 nt 6S RNA is constitutive during exponential growth and during acid shock, as demonstrated using Northern hybridisation.Identification and characterisation of the Click here for fileC. jejuni putative leader peptides allowing coupling of amino acid availability to downstream expression of the amino acid biosynthetic pathways for tryptophan, methionine and leucine. The sequence of the leader peptide is indicated, with the corresponding regulatory amino acid in red typeface. When similar leader peptides are predicted to be present in other Epsilonproteobacteria (based on location and presence of a ribosome binding site), their sequence is included. The dRNA-seq histograms for C. jejuni are shown, with the red histograms representing the + TEX cDNA library enriched for primary transcripts, and the blue histograms representing the non-enriched -TEX cDNA library.Identification of Click here for fileC. jejuni NCTC 11168.Leaderless mRNAs of Click here for fileEpsilonproteobacterial and other genome sequences used in this study.Click here for fileC. jejuni NCTC 11168 (pages 1–2) and H. pylori 26695 (pages 3–4). The highest scoring ortholog in the pairwise comparison was used if the E-score was > 1 E-06, and used in a scatter plot [E. coli and Thiomicrospora crunogena are included for comparison. An overview of the total number of genes orthologous between these species is given in Additional file Lack of gene order-based genome synteny in the Epsilonproteobacteria. All protein-coding annotated features of 42 species of Epsilonproteobacteria  Schematic overview of conservation of internal promoters in the Epsilonproteobacteria, based on promoter predictions shown in Additional file Click here for fileConservation of antisense promoters in the Epsilonproteobacteria.Click here for filePrediction of putative leader peptides in the Epsilonproteobacteria.Click here for fileOligonucleotides used in this study.Click here for fileArtemis entry file for use with the C. jejuni NCTC 11168 genome sequence (accession number NC_002163). This file contains colour-coded entries of each TSS and promoter sequence from -50 to +1, with annotation as given in Additional file Click here for file


Transcription termination of mRNA genes is intricately coupled to cleavage and polyadenylation, whereas transcription of small nucleolar (sno)/small nuclear (sn)RNA genes is terminated by the RNA-binding proteins Nrd1, Nab3 and Sen1. The existence of an Nrd1-like pathway in humans has not yet been demonstrated. Using the U1 and U2 genes as models, we show that human snRNA genes are more similar to mRNA genes than yeast snRNA genes with respect to termination. The Integrator complex substitutes for the mRNA cleavage and polyadenylation specificity factor complex to promote cleavage and couple snRNA 3′-end processing with termination. Moreover, members of the associated with Pta1 (APT) and cleavage factor I/II complexes function as transcription terminators for human snRNA genes with little, if any, role in snRNA 3′-end processing. The gene-specific factor, proximal sequence element-binding transcription factor (PTF), helps clear the U1 and U2 genes of nucleosomes, which provides an easy passage for pol II, and the negative elongation factor facilitates termination at the end of the genes where nucleosome levels increase. Thus, human snRNA genes may use chromatin structure as an additional mechanism to promote efficient transcription termination  Transcription termination is the final stage of the transcription cycle when the RNA polymerase stops transcribing and disengages from the DNA template. Although little is known about the mechanism(s) by which RNA polymerase II (pol II) is released, several factors are implicated in the termination process . In yeasThe second termination pathway is mainly used during transcription of sno/snRNA genes and occurs independently of RNA cleavage. Three key players have been identified; two RNA-binding proteins, Nrd1 and Nab3, and the DNA/RNA helicase, Sen1 . Nrd1 inIn mammals, transcription of protein-coding genes is terminated by a mechanism analogous to that described in yeast ,22, wherOur results suggest that neither a torpedo-like nor an Nrd1-like mechanism functions to terminate transcription of human snRNA genes. Instead, the integrator complex coordinates snRNA 3′-end processing with transcription termination in conjunction with polyadenylation factors. Both Pcf11 and Ssu72 function primarily as transcription terminators rather than RNA 3′-end processing factors for the snRNA genes. In addition, we show that the transcription unit of both U1 and U2 snRNA genes extends to ∼1 kb and is nucleosome-depleted. A negative elongation factor (NELF) involved in the early elongation checkpoint of protein-coding genes  co-opera6 cells were used per immunoprecipitation. Antibodies used in ChIP experiments were purchased from the following manufacturers: anti-pol II , anti H3 , anti-NELF-A , anti-Pcf11 , anti-Ssu72  and anti-Cstf64 . Anti-PTFγ antibodies were a gift from Robert G. Roeder and Jong-Bok Yoon (Chromatin immunoprecipitation (ChIP) analysis was carried out as previously described . On averBok Yoon . Primer 5 cells were transfected, in duplicate, with 20 pmoles of the siRNA in a 24-well format. The following day, cells were harvested and re-plated into one well of a 6-well format dish. Twenty-four hours later, cells were transfected again with 100 pmoles of siRNA and harvested for ChIP and western blot analysis the following day.SiRNAs were purchased from Dharmacon. Knockdown experiments were performed in HeLa cells with lipofectAMINE 2000 reagent (Invitrogen) according to the manufacturer’s instructions. Typically, 1 × 10et al. (Nuclear run-on (NRO) analyses were carried out as described by Cuello et al.  with 80-et al. .Total RNA was prepared using the Trizol (Invitrogen) method, according to manufacturer’s instructions. Samples were DNase treated, phenol/chloroform extracted, precipitated with ethanol and resuspended in water. RNase protection was carried out as outlined in .Antibodies used in western blot studies were purchased from the following manufacturers: anti-SCAF8 , anti-Senataxin , anti-Int5 , anti-Int9  and anti-Int11 ; anti-Xrn2 was a gift from Natalie Gromak .in vitro and hybridized to filters with probes complementary to transcripts from the U1 snRNA genes. The U1 snRNA gene probes cross-hybridize to all vU1 snRNAs analysed within the well-conserved regions, which include the coding and distal 3′ flanking sequences . W. W32). Wequences B. Importin vivo, the density of pol II across the U1 snRNA gene was measured by ChIP analysis with U1 snRNA gene-specific primers. To ensure the primers designed were specific to the U1 snRNA gene only, they were tested in PCR reactions using genomic clones of U1 and vU1.18 and vU1.2 a snRNA genes as template DNA. Genomic (g)DNA was used as a positive control for each primer pair. Only primer pairs specific to U1 snRNA gene give a positive signal in this PCR analysis . ChIP analysis with an antibody that recognizes both phosphorylated and unphosphorylated forms of pol II indicates that pol II remains associated with the U1 snRNA gene up to 900 bp from the start of transcription . These data indicate that termination of transcription of the U1 snRNA gene does not occur immediately downstream of the 3′ box as previously suggested . Taken together, these data support the idea that the U1 and U2 transcription units are more similar than previously considered.To determine more precisely where termination of transcription of the U1 snRNA gene occurs cription C. The exuggested . Moreoveuggested  also indSupplementary Figure S4) . Altering SCAF8 levels has no effect on pol II levels at the end of the β-actin, U1 or U2 genes , arguing against a role for this factor in termination of transcription in human cells, although this factor may be redundant with other potential Nrd1 homologues, including SCAF4  because it is thought to be a good candidate for the human homologue of Nrd1, as it contains a similar Pol II CTD interacting domain and RNA recognition motif . All thrgure S4) , whereasng SCAF4 .Figure Drosophila cells abolishes proper 3′-end formation of snRNAs (in vivo causes read-through (RT) of the U2 snRNA 3′ box processing element as assessed by RNase protection analysis . These data are consistent with results from a previous study in Drosophila, which showed that all Integrator subunits, apart from Int3, Int10 and Int12, participate in snRNA 3′-end processing . These data demonstrate that factors associated with 3′-end processing of transcripts from human snRNA genes are intimately involved in control of termination of transcription.It is well documented that 3′-end processing is tightly coupled to termination of transcription of the pol II-transcribed protein-coding genes, which prompts the question of whether factors involved in 3′-end processing of transcripts from human snRNA genes also influence termination of transcription. An snRNA gene-specific complex, Integrator, is recruited to human snRNA genes to carry out 3′-end processing directed by the conserved 3′ box RNA processing element located within 19 bases downstream of the snRNA-encoding sequence . This cof snRNAs ,43,44. Tf snRNAs A. The ref snRNAs B confirmSupplementary Figure S6). Reducing Pcf11 and Ssu72 also causes an increase in pol II levels at the end of both snRNA genes. . Moreover, ChIP analysis using antibodies to Pcf11 and Ssu72 confirms that these factors are recruited to the snRNA genes .As components of the integrator complex are similar to components of the cleavage/polyadenylation machinery, we wondered whether any mRNA 3′-end processing factors are involved in termination of transcription of human snRNA genes. Previous data indicate that Pcf11 and Ssu72 are required for termination of transcription of both protein-coding and sno-/snRNA genes in yeast . Ssu72 iSupplementary Figure S8). Cells expressing reduced levels of Pcf11 have a marginal defect in 3′ box-directed processing (a 1.7-fold increase in the ratio of RT/pre-U2 snRNA compared with control), whereas knockdown of Ssu72, Xrn2, SCAF8 or Senataxin has no negative effect on processing. This indicates that Ssu72 is acting only as a termination factor and the effect on termination of transcription is not due to a cleavage defect. Notably, loss of Pcf11, In9 or Int11 leads to similar defects in transcription termination, as assessed by ChIP analysis . This result suggests that Pcf11 has a more prominent role in termination of transcription of snRNA genes than in RNA 3′-end processing.As Pcf11 and Ssu72 are both involved in mRNA 3′-end processing, their requirement for 3′ box-directed 3′-end formation was determined after RNAi-mediated knockdown. As a positive control, cells were treated with the kinase inhibitor KM05382 (KM), which inhibits snRNA 3′-end processing by inhibiting the kinase activity of the CKD9 subunit of the P-TEFb complex . There i compare  and 4, wSupplementary Figure S7), raising the possibility that other polyadenylation factors are involved in transcription of snRNA genes and/or processing of the transcripts.Interestingly, Cstf64 is also associated with both protein-coding and snRNA genes . The decrease in PTF-γ occupancy at the promoter is accompanied by an increase in nucleosome density across both the U1 and U2 snRNA genes -dependent pathway is evolutionarily conserved . Funding for open access charge: Wellcome Trust.Conflict of interest statement. None declared.


Nfe2 and Selp were tested by in vivo transgenesis. The data provides the first example of genome wide Runx1/p300 occupancy in maturating primary FL-MK, unravel the Runx1-regulated program controlling MK maturation in vivo and identify a subset of its bona fide regulated genes. It advances our understanding of the molecular events that upon RUNX1mutations in human lead to the predisposition to familial platelet disorders and FPD-AML.RUNX1 transcription factor (TF) is a key regulator of megakaryocytic development and when mutated is associated with familial platelet disorder and predisposition to acute myeloid leukemia (FPD-AML). We used mice lacking Runx1 specifically in megakaryocytes (MK) to characterized Runx1-mediated transcriptional program during advanced stages of MK differentiation. Gene expression and chromatin-immunoprecipitation-sequencing (ChIP-seq) of Runx1 and p300 identified functional Runx1 bound MK enhancers. Runx1/p300 co-bound regions showed significant enrichment in genes important for MK and platelet homeostasis. Runx1 occupied genomic regions were highly enriched in RUNX and ETS motifs and to a lesser extent in GATA motif. Megakaryocytic specificity of Runx1/P300 bound enhancers was validated by transfection mutagenesis and Runx1/P300 co-bound regions of two key megakaryocytic genes  RUNX1 resides on human chromosome 21 and is often involved in leukemia associated chromosomal translocations including 8;21 acute myeloid leukemia (AML) and 12;21 acute lymphoblastic leukemia (ALL) RUNX1. RUNX1 plays a key role in the development of MK in vivo milieu is lacking, as is the information about RUNX1-direct target genes during the advanced stages of megakaryocytic differentiation.The RUNX1 transcription factor (TF) is a key gene expression regulator in embryonic and adult hematopoiesis Pf4 regulatory region resulting in expression of Cre exclusively in the megakaryocytic lineage The murine platelet factor 4 (Pf4) is a member of the CXC chemokine family. Pf4, also known as CXCL4, is released from the alpha granules of activated platelets and is involved in platelet aggregation in vivoNfe2 and Selpin vivo transgenesis. The data provide the first genome wide analysis of Runx1 target genes in maturating primary megakaryocytes and advance our understanding on the regulatory program controlling MK maturation, platelet production and function in vivo. It furnishes important insights into the molecular events that lead to development of megakaryocytic pathologies upon compromised RUNX1 activity.Using MK cell lines we have recently demonstrated that RUNX1 regulates numerous target genes during megakaryocytic differentiation through sequential cooperation with GATA1, AP-1 and ETS TFs FF//Pf4-Cre mice were created by crossing Runx1LL/ mice Runx1 exon 4 in fetal liver MK (FL-MK) was assessed by Southern blotting using primers listed in −1) and cultured for 7 days prior to colony count. Complete blood count was performed on ∼200 µl blood samples, obtained by bleeding the retro-orbital sinus, using the SYSMEX K-21 blood analyzer. Ethics Statement: All experiments involving mice were approved by the Weizmann Institute Institutional Animal Care and Use Committee (IACUC permit number.06121110-1).Runx1−1) at 37°C and 10% CO2. After 7 days in culture mature MK were isolated by two sequential 2%–4% BSA gradients. FACS analysis was used to confirm over 80% purity of maturating megakaryocytes.Murine FL derived mature MK were produced essentially as previously described 7 mature megakaryocytes was fragmented to an average size of ∼200 bp by 20 cycles of sonication (30sec each) in 15 ml tubes using the Bioruptor UCD-200 sonicator . The fragmented chromatin was immunoprecipitated using 60 µl of in house anti-Runx1 antibodies ChIP was performed essentially as described Runx1−/− and MKL/LRunx1 was analyzed using Affymetrix mouse gene ST 1.0 microarrays, according to manufacturer instructions. Microarrays were scanned by GeneChip scanner 3000 7 G and the data was normalized using RMA. Microarray data will be deposited in a publically available database upon acceptance for publication.RNA and was isolated using the EZ-RNA , according to manufacturer instructions. Purified RNA was reverse-transcribed, amplified and labeled with Affymetrix GeneChip whole transcript sense target labeling kit. Labeled cDNA from mature MKNfe2, Selp, Pde3a and Lrrc32 regulatory elements were generated by PCR amplification of regions spanning the following coordinates: Nfe2: chr-6:103088686–103089226; Selp: chr-1:166065335–166066395; Pde3a: chr-6:141243636–141244353; Lrrc32: chr-7:105682347–105683400. Fragments were then cloned into the Bgl2 site of TK-promoter PGL4.73 vector . TFSEARCH (www.cbrc.jp/research/db/TFSEARCH.htm) identified RUNX motifs were mutated by overlap PCR (OE-PCR) using the following primers: Nfe2: ACCGCA to ATTAGA, Selp: ACCACA to ATTCCA, Pde3a: ACCACA to AGTCTA (two adjacent sites), Lrrc32: TGTGGT to TCACTT. Nfe2 and Selp regulatory elements were further cloned into the Hind3 and Sph1 sites of Hsp68-GFP reporter plasmid . qPCR assays were performed using light cycler 480  with 480 SYBR Green I Master . Genomic DNA fragments of Runx1−/− and MKL/LRunx1 were compared and fold expression difference was defined as the ‘Runx1 Effect’. For computation of global statistics, genes were considered transcriptionally activated or repressed by Runx1 when displaying a minimal Runx1-dependent effect of 1.5-fold or higher. To derive the relationship between Runx1 occupancy and change in gene expression we calculated the density of Runx1 bound loci as a function of the distance from genes transcriptional start site (TSS) averaged across activated, repressed or non-responsive genes, thereby quantifying the enrichment of potential regulatory sites in these groups.Gene expression analyses in MKde-novo for discriminating position weight matrices (PWM) and computed their match to the sequence using their approximated binding energy as defined in Pencovich et al 2011 De-novo motif finding was performed as previously described http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45374).To characterize the sequence preferences of Runx1 bound sites, we searched + MK . Cultured primary cells were gently separated by BSA gradients  to minim+ MK . However+ MK . Using tRunx1 loxP-flanked exon 4 (Runx1LL/) mice + MK and platelets Runx1in these Runx1FF//Pf4-Cre mice was inactivated specifically in advance stages of MK maturation (FF//Pf4-Cre mice (FL-MKRunx1−/−) provided unique means for studying Runx1 function explicitly during terminal stages of megakaryocytic maturation.To assess the role of Runx1 during advanced stages of megakaryopoietic differentiation, we generated megakaryocyte-specific Runx1-deficient mice by crossing turation . Thus, FFF/-Pf4-Cre mice displayed megakaryocytic defects similar to those previously noticed upon abrogation of Runx1 activity in hematopoietic progenitor cells FF//Pf4-Cre mice had a higher proliferative capacity, evidenced by increased percentage of Cd41+ BM cells compared to Runx1LL/ mice   , consistF/F/Pf4-Cre mice, we analyzed FL-MK derived from Runx1LL/ and Runx1FF//Pf4-Cre mice. Comparison of gene expression in FL-MKL/LRunx1 Vs. FL-MKRunx1−/− revealed pronounced changes . Many of the responding genes including Itga2, Itgal, Lrrc32, Mkl, Myl9, Pde3a, Pik3cb/g, Selp and Was are known to play a role in megakaryopoiesis and in platelet biogenesis and function  of annotated genes, revealed that ∼55% were placed more than 10 kb away from any TSS, and ∼20% were in “gene deserts” (above 100 kb from the nearest TSS) (Runx1 ChIP-seq assays were performed using FL-MKest TSS) . More spest TSS) . Integraest TSS) , whereasest TSS) . CollectRunx1−/− compared to FL-MKL/LRunx1. This subset of co-bound non-responsive genes contained several genes with major roles in MK development and platelet biogenesis including the TFs SrfZfpm1/Fog1Gfi1bMyh9Gp1ba) Tln1 We next performed p300 ChIP-seq using maturating FL-MK. Peak-calling analysis applying a similar threshold as for Runx1, revealed p300 occupancy at 12,283 genomic regions. Comparison to genomic distribution of Runx1 binding disclosed significantly higher proportion of peaks at regions 10 kb away from TSS (i.e. ∼75% Vs ∼55%), but only ∼5% (Vs ∼12%), and ∼7% (Vs ∼17%) of the peaks were within promoter regions and exons, respectively . ApproxiTo characterize the sequence specificity of Runx1 occupancy sites in maturating MK we searched for DNA sequence motifs within Runx1 bound regions in comparison to background sequences. The most prevalent sequence was the known RUNX motif TGTGGTT, which is identical to the motif found in early differentiating human megakaryocytic cell lines FOS, FOSB and JUN, we assessed the binding of Runx1 to these loci in the maturating FL-MK. ChIP-seq data revealed that Fos, Fosb and Jun genomic regions, bound by Runx1 in FL-MK  were also cloned into GFP reporter constructs containing the basal promoter of the Hsp68 gene, which by itself is largely silent and does not confer expression in hematopoietic cells Nfe2 and Selp regulatory regions conferred GFP expression specifically in mature, pro-platelet megakaryocytes  and are known to play role in cell proliferation and leukemia, or in megakaryopoiesis and platelet function. PubMed IDs indicating the relevant studies pertained to these genes are indicated.(DOC)Click here for additional data file.Table S2listed are the sequence of the various primers used for PCR analyses.(DOCX)Click here for additional data file.


Transcription factor binding to DNA requires both an appropriate binding element and suitably open chromatin, which together help to define regulatory elements within the genome. Current methods of identifying regulatory elements, such as promoters or enhancers, typically rely on sequence conservation, existing gene annotations or specific marks, such as histone modifications and p300 binding methods, each of which has its own biases.Herein we show that an approach based on clustering of transcription factor peaks from high-throughput sequencing coupled with chromatin immunoprecipitation (Chip-Seq) can be used to evaluate markers for regulatory elements. We used 67 data sets for 54 unique transcription factors distributed over two cell lines to create regulatory element clusters. By integrating the clusters from our approach with histone modifications and data for open chromatin, we identified general methylation of lysine 4 on histone H3 (H3K4me) as the most specific marker for transcription factor clusters. Clusters mapping to annotated genes showed distinct patterns in cluster composition related to gene expression and histone modifications. Clusters mapping to intergenic regions fall into two groups either directly involved in transcription, including miRNAs and long noncoding RNAs, or facilitating transcription by long-range interactions. The latter clusters were specifically enriched with H3K4me1, but less with acetylation of lysine 27 on histone 3 or p300 binding.By integrating genomewide data of transcription factor binding and chromatin structure and using our data-driven approach, we pinpointed the chromatin marks that best explain transcription factor association with different regulatory elements. Our results also indicate that a modest selection of transcription factors may be sufficient to map most regulatory elements in the human genome. Transcription factors are DNA-binding proteins that regulate gene expression by binding to promoter regions proximal to gene transcription start sites (TSSs) or to more distal enhancer regions that regulate expression through long-range interactions -3. TransAlthough the interplay between chromatin environments and transcription factor binding is not straightforward, accessible chromatin generally facilitates association of transcription factors to DNA. Some transcription factors, however, can modify the chromatin landscape around their binding site, which may recruit new transcription factors and chromatin-modifying factors to the region ,4. ChangThe goal of this study was twofold. First, we wanted to investigate whether genomic regions enriched with bound transcription factors can be used to improve the identification of regulatory elements in the human genome. Specifically, we investigated whether such enriched regions concurred with existing genome annotations and data for histone modifications. Furthermore, we used the enriched regions to identify chromatin markers that best correlated with the binding of transcription factors and to evaluate previously used markers for regulatory regions. Second, we wanted to investigate whether the combination of transcription factors associated with the enriched regions differed depending on the type of regulatory element to which the enriched region mapped. Specifically, we wondered whether the transcription factor composition differed between enhancers and TSS proximal promoter elements.Our analysis was based on ChIP-Seq reads for transcription factors from two cell lines: K562 and Gm12878. Totals of 39 and 28 factors, respectively, were mapped in each cell line, and 13 factors were mapped in both cell lines. We used clusters of colocalised transcription factor-binding events as identifiers for regulatory elements involving transcription factors and verified that these clusters generally overlapped with regions of active chromatin. We then used two different strategies to identify four groups of transcription factor clusters with potentially different regulatory roles. First, we examined clusters mapping to previously annotated genes and promoters and separated these into (1) clusters mapping to annotated promoter regions (promoter clusters) and (2) clusters mapping to annotated genes but outside the promoter region (gene clusters). Second, we performed an alternative cluster separation independent of annotations, where clusters mapping to histone modifications closely associated with active transcription  binding: transcript clusters) were separated from clusters with potentially distal regulatory function with respect to transcription (enhancer clusters). This definition represents an additional separation of the regulatory elements normally referred to as 'enhancers'  into eleDrosophila [Binding sites for transcription factors tend to cluster in regulatory modules -27, and osophila  and mousosophila ,22 have Presumably, transcription factors require open chromatin for DNA binding, but it is unclear whether open chromatin by itself is a good predictor of transcription factor binding. To address this question, we investigated the overlap between transcription factor clusters and experimentally defined open chromatin regions (OCRs) (see Methods) or regions containing different histone marks associated with open or closed chromatin. Our first observation was the abundance of OCRs compared to regions showing mono-, di- or trimethylation of H3K4, histone modificationstypically associated with accessible chromatin ,7. AlthoDifferent transcription factors showed a preference for different general chromatin signatures Figure  and 2C. Few factors were associated with repressive modifications. An important exception is NRSF , which, in contrast to the other modifications we analysed, mainly associated with H3K27me3  and their promoters  and correlated this with RNA-Seq gene transcription data (see Methods). On the basis of RNA-Seq, we defined 6,228 genes as being highly transcribed in both cell lines and identified 5,591 and 4,124 genes with zero transcription in K562 and Gm12878, respectively. In accordance with previously reported results ,32, H3K4In transcribed genes, clusters were highly abundant around TSSs, as expected Figure . HoweverTo further investigate differences in function between promoter and gene clusters, we asked whether the groups differ in their use of transcription factors. Furthermore, when correlating the locations of transcription factor clusters to histone modifications, we observed that the gene clusters could be further separated into two groups, depending on their association with H3K4me Figure . We therConsistent with the different regulatory functions of promoter and gene clusters in transcribed genes, these two cluster types showed the largest difference in composition   and that this was true both for gene clusters associated with H3K4me  Figure  and for For silent genes, the compositional differences between the three types of clusters showed some discrepancies between the two cell lines. Especially for K562, the composition of gene clusters associated with H3K4me mostly resembled the composition of promoter clusters (r 0.89), whereas the closest resemblance was observed with gene clusters not associated with H3K4me in Gm12878 (r 0.81) (see Discussion).The enrichment of individual transcription factors in promoters and genes did not always reflect the composition of the different cluster groups. Most factors were present in several groups, but the relative enrichment of each factor in each group was sometimes very different  from clusters not associated with promoters or transcript bodies (enhancer clusters), we used associations with histone modifications H3K36me3 and H3K4me3, together with enrichment of Pol II, as described by Mikkelsen et al. . BecauseThree points must be mentioned with respect to the classification of transcript and enhancer clusters. First, we have used the term 'enhancer clusters' to describe clusters which do not contain the histone modifications and polymerase signatures characteristic of transcription and have indicated that these are more likely to be involved in long-range interactions. However, a recent study  showed tIn K562 and Gm12878, we identified 17,836 and 12,424 transcript clusters, respectively, and 5,914 and 9,056 enhancer clusters, respectively. As expected, most of the transcript clusters and a minority of the enhancer clusters in K562 and Gm12878 mapped to annotated promoters and genes  Figure . To valiWe primarily used Pol II-related transcription to separate transcript clusters from enhancer clusters. Transcripts can be produced by polymerases other than Pol II, and clusters associating with these polymerases should be identified by our model as long as they also associate with H3K36me3 and H3K4me3. However, we did not always observe this association when analysing independent data for Pol III. The overlap between Pol III and H3K36me3 was only 39% and 26%, compared to 72% and 71% for Pol II and H3K36me3, in K562 and Gm12878, respectively. We therefore included the independent Pol III data in our model. On the one hand, we do not know whether other polymerases behave similarly to Pol II or Pol III with respect to H3K36me3 and H3K4me3, so we cannot exclude the possibility that some clusters classified as enhancer clusters may be associated with transcription by other polymerases, such as Pol I. The effect of Pol I transcription may, on the other hand, be most pronounced in genomic repeat regions, which are often excluded when mapping ChIP-Seq data to the genome.A large number of clusters mapped outside annotated genes and promoters  Figure . To valiThough attempts have been made to annotate enhancers ,21,40-42Another marker commonly used for enhancer identification is the transcription factor and histone acetyltransferase p300 ,40,43,44Although we saw poor correspondence between p300 and regulatory elements in Gm12878, p300 could be a better marker in other cell lines . Only apH3K27ac has also recently been used as an identifier for enhancer regions ,45, but There are 1,500 to 2,000 known transcription factors binding to DNA in humans ,47. BecaChIP-Seq data are potentially noisy -50, so iNot all singleton peaks are noise. In fact, some of the singleton peaks are among the high-confidence peaks within their data sets. However, by focusing on peak-overlaps, we could concentrate our analysis on regulatory regions containing several peaks, even if each peak in the region was less confident when evaluated on its own. We also considered the effect of losing a few singleton peaks and candidate regulatory regions preferable to including false-positive singleton peaks, which would lead to many additional false regulatory elements.When mapping transcription factor clusters to annotated genes, our results agreed with the common notion that transcription factors in promoter regions drive the recruitment of the transcription initiation complex leading to gene transcription. Clusters were highly enriched within the typical promoter region  of the TSSs in transcribed genes and depleted in silent genes. We also identified a significant number of clusters within genes, which, compared to promoter clusters, showed little change in abundance and composition between transcribed and silent genes. Several studies have reported transcription factor binding outside the typical promoter region ,52, but The discrepancies observed when we compared cluster composition in the two cell lines may have several explanations. The difference in similarity between promoter clusters in transcribed and silent genes for the two cell lines (r 0.70 in K562 vs 0.37 in Gm12878) may partly be explained by the somewhat higher enrichment of Pol II at silent promoters in K562  for miRNA and the UCSC Genome Browser Database for lincRNA.In this study, we used data from the ENCODE project contained in the UCSC Genome Browser Database  and downet al. ) from otConfident ChIP-Seq peaks for each transcription factor were identified by using an in-house method  based onWithin each chromosome, all peak starts were randomly shuffled. Because the full chromosome cannot be mapped by ChIP-Seq, the length of the chromosome was adjusted by the mappability factor in MACS (0.88 for 25-bp tag lengths). Each peak was then extended to 2,000 bp, and overlaps were identified by using the same procedure as that used for the true peaks.ChIP-Seq data for histone modification data and Pol II were analysed by using the program SICER version 1.03 . The gapGene expression was measured by the average RNA-Seq intensity based on tags mapped to exons for each gene. Nonredundant genes were selected by grouping all genes with overlapping subsets of exons transcribed from the same strand, then only the gene with the highest expression was selected from each group. From among the downloaded list of 30,399 genes, 18,682 nonredundant genes were identified by using this approach. A set of transcribed genes was chosen as the top third of all redundant genes when sorted by expression level, whereas a set of silent genes was chosen as all genes with zero expression. This resulted in 6,228 transcribed genes (both cell lines) and 5,591 and 4,124 silent genes in K562 and Gm12878, respectively. Promoters were defined as extending 2,000 bp upstream and 200 bp downstream of TSSs.http://numpy.scipy.org/).Correlation of transcription factor composition between two types of clusters was calculated in the following way. For both cluster types, the percentage of clusters occupied by each factor was calculated, giving a vector of enrichment for each factor in each cluster type. The Pearson's r correlation between the vector for each cluster type was then calculated by using the corrcoef function in Numpy (miRNA: microRNA; OCR: open chromatin region; TSS: transcription start site; ChIP-Seq: chromatin immunoprecipitation coupled with high-throughput sequencing.MBR, PS, FD and TH conceived the study, and MBR designed the study. MBR analysed the data. TH and PS contributed to data analysis. MBR, PS and FD wrote the manuscript. All authors read and approved the final manuscript.Supplementary figures and Tables S1 to S7.Click here for file


Lolium. In this study, fragile phenotypes of 45S rDNA were observed under aphidicolin (APH) incubation in several plant species. Further actinomycin D (ActD) treatment showed that transcriptional stress might interfere with chromatin packaging, resulting in 45S rDNA fragile expression. These data identified 45S rDNA sites as replication-dependent as well as transcription-dependent fragile sites in plants. In the presence of ActD, a dramatic switch to an open chromatin conformation and accumulated incomplete 5′ end of the external transcribed spacer (5′ETS) transcripts were observed, accompanied by decreased DNA methylation, decreased levels of histone H3, and increased histone acetylation and levels of H3K4me2, suggesting that these epigenetic alterations are associated with failure of 45S rDNA condensation. Furthermore, the finding that γ-H2AX was accumulated at 45S rDNA sites following ActD treatment suggested that the DNA damage signaling pathway was associated with the appearance of 45S rDNA fragile phenotypes. Our data provide a link between 45S rDNA transcription and chromatin-packaging defects and open the door for further identifying the molecular mechanism involved.Our previous study demonstrated that 45S ribosomal DNA (45S rDNA) clusters were chromosome fragile sites expressed spontaneously in  Allium and subgenus Mus chromosomes could free shift from one locus to another, suggesting that 45S rDNA might serve as a transposable element Neurospora, nucleolus organized region (NOR) breakage caused large terminal deletions and such 45S rDNA termini were healed by the addition of telomeric repeats to prevent terminus fusion Lolium rigidum, in vivo 45S rDNA breakage led to chromosome rearrangement and varied position and number of 45S rDNA sites Arabidopsisin vitro on metaphase chromosomes in LoliumEukaryotic genomes contain hundreds of tandemly arranged 45S ribosomal RNA (rRNA) genes (45S rDNAs) which are transcribed by RNA polymerase I to generate 18S, 5.8S and 28S rRNAs. The 45S rDNA region shows some typical characteristics of genetic instability, which often results in variable copy number and distribution among closely related species and even intraspecies. The studies revealed that the whole 45S rDNA repeats in Fragile sites are large and highly sensitive regions that are inclined to form abnormal poor–staining lesions  in metaphase chromosomes In this study, our data identify plant 45S rDNA regions as fragile sites derived from replication-associated as well as transcription-dependent defects, and establish that the ActD-induced transcription stress gives rise to a dramatic switch to an open chromatin conformation and an accumulation of incomplete 5′ETS, accompanied by decreased DNA methylation, decreased levels of histone H3, and increased histone acetylation and levels of H3K4me2. The epigenetic alterations might result in the inability of chromatin fibers to fold into higher-order configurations, which may contribute to the appearance of 45S rDNA fragile phenotypes. Furthermore, γ-H2AX accumulation at 45S rDNA sites suggests that ActD-mediated DNA damage is involved in the transcription-dependent fragile expression process.Lolium species LoliumChromosome fragile sites represent regions which are inclined to form poor–staining constrictions, breaks or gaps in metaphase chromosomes when DNA replication is partially inhibited Our previous study proposed that the failure of chromatin folding at 45S rDNA regions contributed to the occurrence of chromosome lesions in ryegrass ViciaWe next investigated whether transcription activity is related to 45S rDNA fragility in plants by using a transcription inhibitor, actinomycin D (ActD), to block rRNA gene transcription. It was reported that the low concentration of ActD (50 or 100 µg/ml) specifically inhibited rRNA synthesis in in vivo although Pol I elongation was strongly inhibited To demonstrate the link between rRNA transcription and NOR site extension, the AgNOR staining experiment was performed. The AgNOR staining signals appeared as two spots linked by fiber-like signals which co-localized with the decondensed NOR regions from cells treated with 15 µg/ml ActD . These rIn interphase nuclei, active rDNA transcription generates a nucleolus, where rRNA maturation and its assembly with specific nucleolar proteins such as fibrillarin, nucleophosmin and nucleolin occur The plant rRNA genes are in a tandem repetitive cluster that contains 18S, 5.8S, 28S tracts, internal transcribed spacers (ITS) and external transcribed spacers (ETS) . DNA metReversible histone modifications are believed to be epigenetic switches responsible for the rRNA genes on/off state. Thus, to ascertain whether the histone modifications were involved in regulation of 45S rDNA chromatin structures, we performed chromatin immunoprecipitation (ChIP) experiments with antibodies specific to H3, H3K9ac, H3K9me2, H3K4me2, H4K5ac and H4K16ac. Apparently, total levels of histone H3 were reduced to a lower density ranging from nearly 11.1% to 20.5% in all analyzed regions in the presence of ActD . These cγ-H2AX is a phosphorylated histone H2A variant and serves as a hallmark of DNA double-strand breaks (DSBs) Here we demonstrate that the plant 45S rDNA repeats are transcription-dependent as well as replication-dependent fragile sites whose phenotypes resemble those of human chromosome fragile sites and establish that the appearance of 45S rDNA fragile sites are associated with epigenetic alterations and DNA damage.The 45S rDNA arrays have been early defined as the secondary constriction and this concept prohibits us from connecting occasionally-observed constrictions or abnormally decondensed rDNA chromatins to fragile phenotypes. The actual underlying structural organization is completely different between the primary constriction centromeres and the secondary constriction NORs Lolium. Actually, approximately half of rDNA repeats are transcriptionally silent and restricted to a condensed state in normal cells RNU1 and RNU2 fragility in human cells Fragile sites in human cells have been well characterized and fragile phenotypes are generally due to DNA replication-dependent defects. Specifically, site-specific gaps or breaks in chromosomes can be induced and enhanced following partial inhibition of DNA synthesis by treatment with the DNA polymerase inhibitor APH Further analysis demonstrated that the ActD-induced 45S rDNA fragile phenotypes are associated with decreased DNA methylation, decreased levels of histone H3, and increased histone acetylation and levels of H3K4me2. Posttranslational modification of histones dynamically alters chromatin conformation and regulates the transition from interphase chromatin to highly condensed metaphase chromosomes during mitosis Lolium and no two plants had the same pattern of rDNA sites, even within the same root meristem Gossypium had many minor rDNA sites which were not detected in the diploid ancestral species The plant rRNA gene is a tandemly repetitive cluster and its coding regions are highly conserved among species because the 45S rDNA repeats play multifunctional and important roles in eukaryotes. However, the highly repetitive nature makes them as ideal substrates for homologous recombination, so the size, distribution and number of 45S rDNA are much variable among different plants Lolium perenne L. diploid turf type cultivar ‘Player’ (2n = 14) were kindly provided by Turf Seed . Seeds of the Zea mays L. inbred line ‘Nongda 108’ (2n = 20) were obtained from China Agricultural University . Seeds of the Oryza sativa L. indica ‘9311’ (2n = 24) and Hordeum vulgare L. cv. ‘Plaisant’ (2n = 14) were obtained from State Key Laboratory of Hybrid Rice of Wuhan University . Seeds of the Sorghum bicolor L. ‘Moench’ (2n = 20) were provided by Yangzhou University . Seeds were germinated at 25°C on cotton gauzes soaked in water. Seedlings were treated for 48 h at 25°C with or without varying concentrations of APH  or ActD  when the primary roots were approximately 1.0 cm long. 15 µg/ml and 50 µg/ml APH and 5 µg/ml and 15 µg/ml ActD were selected for current research.Seeds of the The procedure for metaphase chromosome preparation was adopted from Li et al. 4, 5 mM KCl, 0.5 mM Hepes, 1 mg/ml dithiothreitol, and 0.25% Triton X-100, pH 7.0) and filtered through a 33 µm nylon mesh filter. The nuclei were centrifuged at 200 g for 10 min at 4°C and resuspended in the same buffer. Then, nuclei were fixed in 4% paraformaldehyde in 1×PBS for 1 h at the room temperature and spread on slides.Pure nuclei were isolated using the method proposed by Li et al. 3 in deionized water: 0.02 g/ml gelatin in 1% (v/v) formic acid] and incubated for 2–5 min at 72°C until the staining mixture turned golden-brown. After staining, the slides were washed with running deionized water and dehydrated in an ethanol series. Images were then captured using a light microscope. Subsequently, the selected slides were subjected to the FISH experiment after washing in 2×SSC and ethanol series.The selective staining of Ag-NOR proteins was carried out according to the protocol proposed by Howell and Black FISH was performed as previously described The immunostaining assay was adopted to detect the distribution of fibrillarin and γ-H2AX. Paraformaldehyde-fixed nuclei were blocked with 3% bovine serum albumin  dissolved in 1×PBS at 37°C for 1 h before washed with 1×PBS for three times, each for 3 min. Then, the slides were incubated with anti-fibrillarin antibody  or anti-γ-H2AX antibody  for overnight at 4°C. The antibodies were diluted with 3% BSA in proportions of 1∶100. After washing with 1×PBS for 5 min, the slides were incubated at 37°C for 1 h with secondary antibodies [fluorescein-conjugated goat anti-mouse IgG for fibrillarin , fluorescein-conjugated goat anti-rabbit IgG for γ-H2AX . Immunostained slides were then washed in 1×PBS and counterstained with DAPI. As mentioned above, images were captured and processed.Total RNA was isolated using the RNAprep pure Plant Kit  and contaminating genomic DNA was removed by DNase I treatment  followed by phenol∶chloroform∶isoamyl alcohol (25∶24∶1) extraction. The purified RNA was reverse-transcribed to cDNA by using RevertAid First Strand cDNA Synthesis Kit . Quantitative real-time PCR was performed using a StepOne Plus real-time PCR system  in the presence of SYBR® Green Real-time PCR Master Mix . The amplification parameters were as follow: 94°C for 2 min, followed by 40 cycles at 94°C for 5 sec, 58°C for 15 sec, and 72°C for 25 sec. Fluorescence data were acquired at the 72°C step and during the melting-curve program. Preliminary experiments were run to ensure the amplification of a single PCR product for each gene. Quantitative real-time PCR were repeated three times for each sample from three independent experiments.Genomic DNA was isolated and modified with sodium bisulfite as described ChIP assays were performed using standard procedures Figure S1Effects of APH on centromeres and knobs in maize. (A) FISH mapping on metaphase chromosomes combined centromere probes (red) with 45S rDNA probes (green) showed that APH induced 45S rDNA lesions but had no effect on centromeres in the same metaphase spread after treatment with 15 µg/ml APH. Bar = 5 µm. (B) FISH image showed that decondensation of 45S rDNA was observed, but all of the centromeric sites remained intact blocks of heterochromatin in the same nucleus after treatment with 15 µg/ml APH. Bar = 10 µm. (C) FISH mapping combined knob probes (red) with 45S rDNA probes (green) showed that APH (15 µg/ml) induced NOR lesions but no visible damage on the knob regions in the same metaphase spread. Bar = 5 µm. (D) FISH image showed that extensive decondensation of 45S rDNA was observed, but all of the knob regions remained intact blocks of heterochromatin in the same nucleus after treatment with 15 µg/ml APH. Bar = 10 µm.(TIF)Click here for additional data file.Figure S2Effects of ActD on centromeres and knobs in maize. (A) FISH image showed that all of the centromeric sites remained intact blocks of heterochromatin in both metaphase chromosome spread and interphase nucleus after treatment with 15 µg/ml ActD. Bar = 10 µm. (B) FISH image showed that all of the knob regions remained dense spots in both metaphase chromosome spread and interphase nucleus after treatment with 15 µg/ml ActD. Bar = 10 µm.(TIF)Click here for additional data file.