HEMATOPOIESIS AND LINEAGE SPECIFICATION
Hematopoiesis consists in a progressive restriction of cell fate capacities from hematopoietic stem cells (HSCs) to mature blood cells. Lineage specification and cell commitment can be achieved by precise activation and/or repression of specific genes. It is now accepted by most that lineage specification does not depend solely on single master regulators. Rather, it appears to result from precise combinations of specific transcriptionfactors, which cooperate to form the so-called transcription factor network (TFN). 130 131 TFs that compose the network will act in a concerted way to regulate geneexpression, thereby providing cellular identity. A good example of TFN complexity and its impact on lineage specification and cell commitment is the roleof Pax5, E2A, EBF, and Ikaros during B lymphopoiesis. 132 133 Although the underlying mechanisms are not yet fully decorticated, these TFs are known to influence B lymphopoiesis in a highly coordinated manner. Pax5 is expressed at low level in multipotent HPCs 134 and can activate B lineage specific genes while repressing myeloid genes. 135 136 For example, Pax5 induces c-fms gene repression by direct interaction and inhibition of PU.1 transcriptional activation. 137 Another example of alternative lineage suppression 138 due to direct TF interactions is provided by the interaction of GATA-1 or -2 with PU.1. GATA factors inhibit PU.1 expression and transactivation, 139 140 and PU.1 acts similarly on GATA proteins (transrepression). PU.1 is essential for myeloid and lymphoid specification, whereas GATA-1 is required for erythroid differentiation. Likewise, PU.1 and C/EBPα antagonize each other during HPCs commitment towards neutrophils or monocytes. 141 142
Transcriptional bursting, whereby a promoter occasionally transitions from a long-lived inactive to a short-lived active state that produces a burst of mRNA, is commonly invoked to explain the observed single-cell variability in mRNA numbers  of noisy genes. In contrast, we demonstrate that large differences in transcriptional activity between G1 and S/G2 that go beyond gene dosage effects drive much of the observed single-cell variability in mRNA numbers. Genome-wide studies in yeast [3,35] have identified noisy promoters as those associated with strong TATA boxes and highly regulated by chromatin remod- eling factors. The tetO promoters, whose core region is derived from the native CYC1 promoter , have similar characteristics. Likewise, constitutive, housekeeping promoters and highly ex- pressed tetO promoters previously associated with low expression variability [2,3,31] exhibit only ,2-fold changes in transcription throughout the cell cycle consistent with gene dosage effects. None of these promoters are classified as cell-cycle regulated . While the cell cycle has been appreciated to be an important source of extrinsic noise, our findings suggest there may be a specific role beyond gene dosage for noisy genes that have not been associated with cell-cycle regulation. Transcriptional bursting may still occur, but it is not needed to explain most of the variability in mRNA levels given the variability in timing of the G1 to S transition [38,39]. The heretofore unexplored connection between noisy geneexpression and large differences in G1 and S/G2 transcrip- tional activity raises fundamental questions concerning its origin and prevalence amongst noisy genes in various organisms and its implications in stable gene network regulation.
KLF11 Plays a Role in Glucose Signaling in Pancreatic Beta Cells.
KLF11 is expressed in human pancreatic islets and in the pancreatic beta cell lines HIT-T15, INS832 兾13, ␤TC3, and Min6 (Fig. 1A and data not shown). Similar to its inducible expression in exocrine cells, KLF11 mRNA is up-regulated by TGF-␤ in beta cells (Fig. 1B). Under these conditions, mRNA insulin levels are significantly higher as compared with those at basal conditions. Moreover, high glucose levels induced KLF11 mRNA expression in beta cells (Fig. 1B). KLF11, as a member of the Sp1兾KLF family, has been predicted to bind to either GC-rich or CACC sequences. Data from random oligonucleotide binding and EMSA define the most prob- able GC-box sequence that binds KLF11 as T 兾G-GGGCGGG- G 兾A (Fig. 1C). Interestingly, such a sequence is present in the promoter region of the insulin gene (Fig. 1D). Both ChIP and luciferase reporter assays showed that KLF11 binds and activates the human insulin promoter in beta cells under high-glucose levels (Fig. 1 E and F). Thus, these data indicate that in pancreatic beta cells KLF11 is inducible by glucose and up-regulates levels of insulin expression. Therefore, KLF11 may be involved in a positive regu- lation loop that is important in glucose homeostasis, raising the question whether aberrant KLF11 function may predispose to diabetes. To address this issue, we analyzed the association of KLF11 gene variants with diabetes.
The biochemical, genetic and geneexpression data in yeast supports the notion that Mediator is universally required for RNA polymerase II-dependent transcription (reviewed in ), and we therefore suggest that Mediator is directly involved in the transcriptionof the adhesin genes. Chromatin immunoprecipita- tion (ChIP) experiments to address occupancy of the ALS gene promoters by Med31 remained inconclusive, as we observed variability between biological replicates, from minimal to large 20– 30 fold enrichments (data not shown). We suspect that this variability could be due to the exact timing of the crosslinking of Med31 to the promoters in respect to the transcriptional activation of the ALS genes in hyphae and/or how uniformly filamentous growth/ALS genetranscription is induced in the population of cells in the culture. ChIP studies in S. cerevisiae have yielded different results between labs in regards to Mediator occupancy, from modest (albeit functionally important) enrichments at some constitutively transcribed genes , no enrichment of Mediator subunits on the majority of transcribed genes [76,77], to detectable enrichment of Mediator subunits upstream of many active, as well as inactive genes, and even in the coding regions of some genes . More prominent enrichment for Mediator subunits is seen on genes that are responsive to stress (e.g. heat shock, or change of carbon source from glucose to galactose) [75–77]. Mediator does not bind directly to DNA, which is likely to be a factor in ChIP experiments. It has also been proposed that the interactions of Mediator with promoters could be transient . Moreover, different Mediator subunits can yield different fold enrichments over the background (for example see [76,77]), and it is therefore possible that a Mediator subunit other than Med31 needs to be assayed to detect consistent Mediator occupancy on promoters in C. albicans. In addition to a direct role for Mediator in adhesin geneexpression, an alternative (and not mutually exclusive) possibility is that Mediator regulates the expressionoftranscriptionfactors, which then in turn regulate the adhesins. The expressionof several transcriptionfactors was lower in med31 DD mutants (Table 1, Dataset S2), for example that of EFG1, TEC1 and CPH1, which have been previously shown to regulate the expressionof Med31- regulated adhesins ALS1, ALS3 and EAP1 [59,79].
(AMIGO2) as shown on gel electrophoresis (Figure 2D). Three other transcripts (DDX6, MZF1 and PSPC1) showed no change. For PDXK and CXCR4, the direction of the changes in expression levels in differentiated NG108-15 cells was opposite to the changes observed in HEK293 cells. The fact that a gene may be differently regulated by expressionof β 4 depending on the cell lines likely reflects the versatility of the protein composition of the β 4 platform in these cell lines. Indeed, β 4 acting through the recruitment oftranscriptionfactors, we surmise that these differences reflect the recruitment of different transcriptionfactors in both cell systems. Although these results show that β 4 dependent regulation of several genes coincides with β 4 nuclear targeting, it is clear that other neuronal differentiation factors may also be involved in change ofgeneexpression during differentiation. Interestingly, many genes of this group are directly related to essential neuronal functions. Indeed, NFκB2 is a transcription factor involved in neuronal plasticity and is activated in several regions of brain during neurogenesis 14, 15 . NFκB2 has been linked to neurodegenerative disorders 16 . Similarly CXCR4 is a G protein-coupled receptor for chemokines that are essential attractants during brain development. Abnormal cerebellar and hippocampal dentate gyrus development were observed in the absence of CXCR4 17, 18 . Finally, pyridoxal kinase (PDXK) is an enzyme that converts vitamin B6 derivates in pyridoxal phosphate, a cofactor in the synthesis of various neurotransmitters. Decrease brain level of this cofactor has been reported to cause epilepsy 19 . However, the roleof β 4 in the regulation of these genes in brain remains to be established. From the analysis of publicly available microarray data set for geneexpression in lh versus wt mice cerebellum we identified 94 genes whose expression is significantly modified in lh
not shown). This expression profile does not fit with that of a differentiation antigen, expressed in a specific lineage and totally absent in other tissues, nor with that of a cancer germline antigen normally expressed in germ cells and trophoblast tissues and aberrantly expressed in a variety of human malignancies . In order to characterize mechanisms involved in meloe transcription, we first defined the minimal promoter region active in melanoma cells and look for TF binding sites that could be relevant in the melanocytic lineage. As shown on Figure 2C, CREB and SOX binding sites appeared essential for promoter activity whereas AP- 1 and ETS binding sites seemed only involved in optimal promoter activation. Conversely, the putative PAX3 binding site (2635) did not seem involved in promoter activity, as well as the two proximal and distal putative MITF binding sites, that were actually E-boxes (CA [T/C]GTG) known to bind factors belonging to the basic helix-loop-helix (bHLH) and bHLH leucine zipper (bHLH-LZ) families. MITF, a bHLH-LZ TF critical for the regulation of melanocyte functions, recognizes an «AGTCA [T/C]GTG» DNA motif termed ‘‘M-box’’, identified on gene promoters regulated by this factor, such as Tyrosinase or TRP-1 [27,28]. It has to be stressed that the two E-boxes found on the analyzed sequence were not in the context of a M-box, thus not promoting MITF binding. In conclusion, these putative binding sites are not involved in promoter activity, but this does not exclude a possible fixation of PAX3 and/or MITF at more distant sites, and their possible role in meloe transcription regulation.
For Review Only
MUC5B is one of the major mucin genes expressed in the respiratory tract. Previous
studies in our laboratory have demonstrated that MUC5B is expressed in human lung adenocarcinomas and during lung morphogenesis. Moreover, in human lung adenocarcinoma tissues, a converse correlation between MUC5B and TTF-1 expression, a lung-specific transcription factor, was established. However, the molecular mechanisms that govern the regulation of MUC5B expression in the lung are largely unknown. In order to better understand the biological roleof MUC5B in lung pathophysiology, we report now the characterization of the promoter region of the mouse Muc5b mucin gene. The promoter is flanked by a TATA box (TACATAA) identical to that in the human gene. Human and murine promoters share 67.5% similarity over the first 170 nucleotides. By RT-PCR, co-transfection studies and gel-shift assays we show that Muc5b promoter activity is completely inhibited by TTF-1 whereas factorsof the GATA family (GATA-4/-5/-6) are activators. Altogether, these results demonstrate for the first time that Muc5b is a target geneoftranscriptionfactors (TTF- 1, GATA-6) involved in lung differentiation programs during development and carcinogenesis and identifies TTF-1 as a strong repressor of Muc5b. The characterization of the structural and functional features of Muc5b mucin gene will provide us with a strong base to develop studies in murine models aimed at identifying its biological role in lung pathophysiology.
Lmx1a and Lmx1b regulate Plxnc1 expressionTranscriptionfactors can positively or negatively regulate the expressionof hundreds of genes. To identify regulated genes by Lmx1a/b during the axonal development of mDA neurons, we performed geneexpression profiling on one-day-old Lmx1a/b cKO mice and control littermates (Fig. 4a,b). We used laser-capture microdissection (LCM) to isolate TH- stained neurons of the SNpc and VTA followed by next-generation RNA-sequencing (RNA-seq) (Fig. 4a) . We found 517 genes differently expressed by at least 2 fold between mutant and control animals (p value ≤ 0,001). Gene Ontology (GO) analysis identified possible enrichment genes in different developmental processes including neuron differentiation, axonogenesis, cell morphogenesis, transmission of nerve impulse, and neuron projection development (Fig. 4b). Among the differently expressed genes, the mRNA for the axon guidance receptor Plxnc1 was found 7 times more abundant in Lmx1a/b cKO mutants than in controls (Fig. 4c). To confirm the mRNA sequencing data, we performed in situ hybridization for Plxnc1on midbrain sections (Fig. 4d). As previously reported in mDA neurons of wild type mice, Plxnc1 expression was found restricted to VTA , with no apparent expression detected in SNpc (Fig. 4d). In contrast, mDA neurons from both VTA and SNpc express significantly higher level of Plxnc1 in Lmx1a/b cKO mice (Fig. 4d). Quantification of relative Plxnc1 mRNA transcript level, precisely in SNpc or VTA, reveals that Plxnc1 is respectively 7 and 11 fold more abundant in these two regions in Lmx1a/b mutant mice at P1 (Fig. 4c). Gain of function experiments by overexpressing Lmx1a or Lmx1b in mDA primary neuron cultures also induce a significant decrease in Plxnc1 mRNA levels, confirming the repressive roleof Lmx1a/b on Plxnc1 (Fig. 4e).
Received February 28, 2011; Revised April 20, 2011; Accepted April 21, 2011
In the central nervous system (CNS), myelin is pro- duced from spirally-wrapped oligodendrocyte plasma membrane and, as exemplified by the debi- litating effects of inherited or acquired myelin abnor- malities in diseases such as multiple sclerosis, it plays a critical role in nervous system function. Myelin sheath production coincides with rapid up- regulation of numerous genes. The complexity of their subsequent expression patterns, along with recently recognized heterogeneity within the oligo- dendrocyte lineage, suggest that the regulatory networks controlling such genes drive multiple context-specific transcriptional programs. Conferring this nuanced level of control likely in- volves a large repertoire of interacting transcriptionfactors (TFs). Here, we combined novel strategies of computational sequence analyses with in vivo functional analysis to establish a TF network model of coordinate myelin-associated genetranscription. Notably, the network model captures regulatory DNA elements and TFs known to regulate oligo- dendrocyte myelin genetranscription and/or oligo- dendrocyte development, thereby validating our approach. Further, it links to numerous TFs with previously unsuspected roles in CNS myelination and suggests collaborative relationships amongst both known and novel TFs, thus providing deeper insight into the myelin gene transcriptional network.
Finally, this report describes the transcript abundance several ethylene receptors and transcriptionfactors across berry development and the impact of one speciﬁc inhibitor of ethylene action on their respective mRNA accumulation. This study in grape berry tissues, a non-climacteric fruit, seems to indicate some similarities in the perception and the integration of the ethylene signalling with what was already observed in climacteric fruits. Moreover, our data suggest that key elements of the transcriptional signalling are developmentally regulated in grape berries. However, further experiments will be needed to extend this ﬁrst set of data to the accumulation of the resulting proteins in order to better understand the mechanisms underlying the roleof ethylene across berry ripening.
FISH demonstrated ERBB2 gene amplification in 11 cases of breast cancer. All of these had an ERBB2 3+ score in IHC. Moreover, out of the 15 ERBB2 3+ cases, 11 were FISH-pos- itive, 2 showed no ERBB2 amplification, 1 was borderline with an average of 4 copies of ERBB2 gene per cell, and 1 was undetermined due to lack of material for FISH testing. Interest- ingly, the 2 cases of ERBB2 3+ immunostaining without gene amplification and the borderline case showed high AP-2α and YY1 levels. On the other hand, all the ERBB2 3+ cases with low levels of both AP-2α and YY1 showed ERBB2 gene amplification. Furthermore, when considering the entire ERBB2 expressing group (1+, 2+, 3+), we observed a signif- icant inverse correlation between ERBB2 FISH status on one hand and AP-2α and YY1 levels on the other hand (p = 0.017 and 0.029, respectively) (Table 4). In particular, 80% of the cases with high levels of both AP-2α and YY1 proteins did not present ERBB2 gene amplification (p = 0.006) (Table 4). These results suggest that when the ERBB2 gene is not amplified, the ERBB2 expression may rely partially on AP-2α and YY1 transcription factor levels. Accordingly, considering only the FISH-negative cases (n = 34), the correlation between ERBB2 and AP-2α levels was higher than in the whole group (r = 0.67, p < 0.001 compared with r = 0.31, p = 0.022 in the whole group). Interestingly, the percentage of ERBB2 2+3+/AP-2α-low cases decreased (12% in the FISH- negative group compared with 24% in the entire group) (Table 5). Similarly, the percentage of ERBB2-positive/AP-2α-low/
describe cellular dynamics. e lack of precision in conventional gene induction systems is a
problem for quantitative biology, because understanding the quantitative dynamics of cellular processes requires the ability to apply precise perturbations to the system [ ]. A classical way of system identi cation is to perturb a system and to monitor the systems response to this per- turbation. For example, biological systems are o en perturbed by genetic knockouts, thereby removing one regulator from the system. Slightly more dynamic perturbations can be applied us- ing inducible promoters, which allow one to express genes conditionally and, to some extent, to gradually change the expression level of a gene. Even more informative are time-varying pertur- bations, because they allow one to explore the dynamic behavior of a system [ ]. In principle, time varying perturbations could be applied by inducible promoter systems, but for several rea- sons this would not be eﬀective in practice. First, it is hard to predict the precise action of an inducer due to cell to cell variability and the inherent stochasticity ofgeneexpression. In addi- tion, such an approach would require a detailed model of the dynamics of the induced protein in order to predict the actual protein level at diﬀerent times. Without monitoring the expression level of the perturbed protein, signi cant deviations between the actual and the desired protein pro le are to be expected. However, it becomes feasible to impose precise, time-varying pertur- bations on the level of a protein by constantly observing the level of the perturbed protein and by adjusting the level of induction based on this observation. In the following I will present such a feedback control system ofgeneexpression, which uses an external feedback loop to circumvent the problem of predicting the precise eﬀect of an inducer.
In the analysed samples, the observed large domains of repressed transcription co-exist with sizeable domains of increased transcription. The increased activity of a large number of transcripts, that has been previously reported (Borovecki et al., 2005; Hodges et al., 2006), can be also explained with the particular chromosomal model adopted here. For example, in the dataset from blood samples of HD patients the repressed regions Chr1p34, Chr17q21 and ChrXp11.2 contain HDAC genes (HDAC1, HDAC5 and HDAC6, respectively) whose inactivation could result in increased acetylation of the relative histones and local increases in transcription. Also, the increased transcription observed in some chromosomes can be explained by the changes in cell population within the affected tissue (i.e. astrocytosis and microglial activation associated with nerve cell loss). For instance, the chromosomal region chr6p, which shows repressed transcription in the blood cells of HD samples is largely active in the HD striatal samples, likely because of the presence of microglial activity in caudate of HD patients (Sapp et al., 2001) and whose transcriptional signature contains MHC molecules, which are encoded by genes on Chr6p (Horton et al., 2004). Individual probe analysis of the microarray data for the caudate samples confirmed up-regulation of MHC Class I molecules but not of MHC Class II nor tumour necrosis factors, interleukins (IL 1-33) or interferon (type 1-3) (data not shown).
non-seed tissues viz., (ES, LE, FL, ST) which is indicative of the distinct seed
maturation program that is occurring in the later stages of embryo development. As the stem peel (PS) did not contain all of the tissues normally present in whole stems (ST), and was enriched for the phloem and phloem fiber cells , the PS geneexpression profile did not cluster with ST, and as expected was distantly placed from the rest of the vegetative tissues and seed tissues. Whole stems (ST) and etiolated seedlings (ES) showed a high degree of similarity, possibly due to their polysaccharide composition. Both whole stems and etiolated seedlings are likely to be particularly enriched in xylem tissues, the secondary walls of which produce polysaccharides different from those found in the pectin-enriched phloem fibers in (PS), seed coats (GC, TC), or the primary walls of developing embryos . Taken together, this analysis showed three distinct patterns of relatedness ofgeneexpression among the 13 tissues: early seed stages, the maturing embryo stages and the juvenile vegetative tissues (ES, ST and LF). Nearly a fifth of the identified transcriptome is apparently unique to flax
This study also revealed that an acute intake of PS juice kept IL-17A at a basal level and indicated a tendency to decrease TNF-alpha levels (p = 0.0645) when compared to PB condition. The IL-17A is a pro-inflammatory cytokine that stimulates neutrophil inflammatory response [ 43 ] and the production of other pro-inflammatory cytokines such as TNF-alpha, IL-1B, and IL-6 [ 44 ], as well as the expressionof adhesion molecules such as Intercellular Adhesion Molecule 1 (ICAM-1) [ 45 ]. Its activities are vastly increased due to synergy with TNF-alpha that promotes the induction of target genes involved in inflammatory processes [ 46 ]. Cyanidin, a key flavonoid present in red berries, has shown the capacity to reduce inflammation in mice through binding with the extracellular domain of IL-17RA and consequently disrupting the IL-17A/IL-17RA complex formation [ 47 ]. Few studies have provided evidence regarding the roleof diet in modulating IL-17 levels in humans. Peluso et al. [ 6 ] observed a drop of this cytokine in the plasma of 14 overweight subjects after a pineapple, blackcurrant, and plum juice consumption. Taken together, this observation suggests that PS consumption could present anti-inflammatory effects during the post-prandial period.
M (t) , A (t) and R (t) asymptotically distributed according to f (M, A, R |Y). However, the dimensions of the factor loading matrix M and the factor score matrix A depend on the unknown number R of signatures to be identified. As a consequence, sampling from f (M, A, R|Y) requires exploring parameter spaces of different dimensions. To solve this dimension matching problem, we include a birth/death process within the MCMC procedure. Specif- ically, a birth, death or switch move is chosen at each iter- ation of the algorithm (see the Appendix and ). This birth-death model differs from the classical reversible- jump MCMC (RJ-MCMC) (as defined in ) in the sense that, for the birth-death model, each move is accepted or rejected at each iteration using the likelihood ratio between the current state and the new state proposed by the algorithm. The factor matrix M, the factor score matrix A and the noise variance σ 2 are then updated, conditionally upon the number offactors R, using Gibbs moves.
Genome-wide analyses of the dhh1 role in metabolic adaptation
To highlight the roleof Dhh1 in the gene regulation associated with carboxylic acids and non fermentative growth conditions, we performed DNA microarray analyses of the transcriptome of yeast wild-type and dhh1 mutant cells, grown in glucose or shifted from glucose to formic acid 0.5%, pH 5.0, for 4 hours. About 920 genes were identified as being significantly up or down regulated in the dhh1 mutant compared with the wild-type, in at least one of the two tested conditions (Fig. 7). The mRNAs which amounts increased in the mutant were mostly involved in proteasomal and vacuolar proteolysis, respiration, oxidative and general stress responses and carbohydrate metabolism. The mRNA which steady-state decreased in the mutant were involved in ammonia and amino acid metabolism (including most of the corresponding transcriptional regulators), DNA topology and the maintenance and silencing of telomeres, aminoacyl-tRNA synthesis, transla- tional elongation and mating (Fig. 7). About 75% of these effects were independent of the carbon source, i.e. they were found both in glucose and formic acid. Among the genes that accumulated independently of the carbon source, we found the previously identified targets of Dhh1: EDC1, COX17 [25,44] and SDH4 . Also, the decrease in expressionof genes involved in mating and in tRNA metabolism is reminiscent of the roles of Dhh1 in Ste12 induction  and tRNA maturation , respectively. Interestingly, several genes involved in mRNA decay were up- (EDC1, EDC2, DCS1, DCS2, PUF3, PUF2) or down- (POP1 and the subunits of the CCR4-NOT complex CAF16, CAF4 and NOT3) regulated in the mutant, suggesting the existence of feedback controls between the activity of Dhh1 and the components of the mRNA degradation pathways. Moreover, some translation regulators exhibited increased (SRO9, PET122, PPQ1, SUI1, CBP6) or decreased (TPA1, RPS31, GCN1, RBG2, MDM38, GCN3, ECM32) expression in the dhh1 mutant. Intriguingly enough, many genes encoding RNA helicases (SLH1, BRR2, DED1, DBP1) and telomeric DNA helicases (Table S1) showed significant expression changes in the mutant.
Chromatin conformation capture assays were performed essentially as described, 52 with only minor modifications. MDA-MB231 cells were transfected with a scrambled control siRNA ( ÿ ), with H2A.Z SMARTpool siRNA ( þ ) (Dharmacon Thermo Scientific), Tip60 siRNA ( þ ) or both siRNAs and cultured in phenol red-free DMEM containing 10% FBS-T for 72 h before cross-linking. The culture medium was removed, and cells were fixed with 1.5% formaldehyde for 10 min at room temperature. Cells were then washed twice with cold phosphate-buffered saline solution, and resuspended in ice- cold lysis buffer (10 mm Tris–HCl, pH 8.0, 10 mm NaCl, 0.2% nonidet P-40, and protease inhibitor mixture). Nuclei were resuspended in 1 ml of buffer B 1.2X buffer (MBI Fermentas, Thermo Fisher Scientific, Rockford, IL, USA) supplemented with SDS 0.3%. Triton X-100 1.8% was added to sequester the SDS and incubated for 1 h at 37 1C. The cross-linked DNA was digested overnight with 400 units of restriction enzyme Csp6I (MBI Fermentas). The restriction enzyme was inactivated by incubation at 65 1C for 20 min The reactions were diluted with ligase buffer (50 mm Tris–HCl, pH 7.5, 10 mm MgCl 2 , 10 mm dithiothreitol, 1 mm ATP and 25 mg/ml bovine serum albumin), supplemented with Triton X-100 (1% final concentration). The DNA was ligated using T4 DNA ligase (New England Biolabs, Ipswich, MA, USA) overnight at 16 1C and an additional 100 units for 2 h at 37 1C. RNase was added for 30 min at 37 1C, and samples were incubated with SDS overnight at 70 1C to reverse the crosslink. The following day, samples were incubated for 2 h at 45 1C with proteinase K, and the DNA was purified by phenol–chloroform extractions and ethanol precipitation. Interaction between chromatin domains was assessed by PCR amplification carried out using similar conditions as for real-time PCR amplification but with four nested primer pairs for each predicted ligation event (four possibilities) as performed by Deschenes et al
regulated genes identified by transcriptome profiling with microarrays. Expression levels of a gene in the ploidy series are normalized to the mean (set to 0) and the standard deviation (set to 1).
In stationary phase, polyploid but not haploid yeast cells fail to arrest their cell cycle and lose viability rapidly, although the transcriptional profile of polyploids resembled stationary phase (Andalis et al., 2004). The loss of viability is partially rescued by heterozygosity at the mating-type loci, which improves the efficiency of mitotic arrest of polyploids in stationary phase. When incubated in water instead of spent medium, post-diauxic polyploid cells arrest cell cycle and maintain viability like haploids. These observations of polyploid growth suggest abnormalities in signaling pathways transmitting nutrient availability to cell cycle regulation, so that depletion of nutrients is not sensed properly and that absolute deprivation from energy source is needed to arrest cell cycle. Consistent with this explanation, in the absence of the G1 cyclin Cln3, mitotic entry is diminished and viability is significantly restored in polyploids (figure 5). In WT haploids, Cln3 protein level is down-regulated to delay mitosis in response to nitrogen (Gallego et al., 1997) and carbon (Hall et al., 1998) depletion. Polyploids likely suffer from an inability to down-regulate CLN3 and thus aberrantly continue cell cycle progression, regardless how unfavorable mitosis is under the growth condition. Deletion of CLN3 could restore viability by enabling a switch from proliferation to quiescence (G0), even though CLN3 appears to be required for optimal metabolic adaption to stationary phase (figure 5).