Proper determination of cell fates depends on epigenetic information that is used to preserve memory of decisions made earlier in development. Post-translational modification of histone residues is thought to be a central means by which epigenetic information is propagated. In particular, modifications of histone H3 lysine 27 (H3K27) are strongly correlated with both gene activation and gene repression. H3K27 acetylation is found at sites of active transcription, whereas H3K27 methylation is found at loci silenced by Polycomb group proteins. The histones bearing these modifications are encoded by the replication-dependent H3 genes as well as the replication-independent H3.3 genes. Owing to differential rates of nucleosome turnover, H3K27 acetylation is enriched on replication-independent H3.3 histones at active gene loci, and H3K27 methylation is enriched on replication-dependent H3 histones across silenced gene loci. Previously, we found that modification of replication-dependent H3K27 is required for Polycomb target gene silencing, but it is not required for gene activation. However, the contribution of replication-independent H3.3K27 to these functions is unknown. Here, we used CRISPR/Cas9 to mutate the endogenous replication-independent H3.3K27 to a non-modifiable residue. Surprisingly, we find that H3.3K27 is also required for Polycomb target gene silencing despite the association of H3.3 with active transcription. However, the requirement for H3.3K27 comes at a later stage of development than that found for replication-dependent H3K27, suggesting a greater reliance on replication-independent H3.3K27 in post-mitotic cells. Notably, we find no evidence of global transcriptional defects in H3.3K27 mutants, despite the strong correlation between H3.3K27 acetylation and active transcription. During development, naïve precursor cells acquire distinct identities through differential regulation of gene expression. The process of cell fate specification is progressive and depends on memory of prior developmental decisions. Maintaining cell identities over time is not dependent on changes in genome sequence. Instead, epigenetic mechanisms propagate information on cell identity by maintaining select sets of genes in either the on or off state. Chemical modifications of histone proteins, which package and organize the genome within cells, are thought to play a central role in epigenetic gene regulation. However, identifying which histone modifications are required for gene regulation, and defining the mechanisms through which they function in the maintenance of cell identity, remains a longstanding research challenge. Here, we focus on the role of histone H3 lysine 27 (H3K27). Modifications of H3K27 are associated with both gene activation and gene silencing . The histones bearing these modifications are encoded by different histone genes. One set of histone genes is only expressed during cell division, whereas the other set of histone genes is expressed in both dividing and non-dividing cells. Because most cells permanently stop dividing by the end of development, these “replication-independent” histone genes are potentially important for long-term maintenance of cell identity. In this study, we demonstrate that replication-independent H3K27 is required for gene silencing by the Polycomb group of epigenetic regulators. However, despite a strong correlation between replication-independent histones and active genes, we find that replication-independent H3K27 is not required for gene activation. As mutations in replication-independent H3K27 have recently been identified in human cancers, this work may help to inform the mechanisms by which histone mutations contribute to human disease. Howeverpression . Thus, mDrosophila, H3K27me3 is catalyzed by the methyltransferase Enhancer of Zeste (E(z)), a core component of the Polycomb Repressive Complex 2 (PRC2)  [29 [29Drosoturnover  31]. H3. H3Drosoturnover .Drosophila, transgenic overexpression of H3.3K27M phenocopies loss of function mutations in Polycomb group proteins [The role of H3.2K27 was recently tested by genetic replacement of the endogenous replication-dependent histone genes with transgenes encoding mutant histones that cannot be modified on lysine 27 (H3.2K27R)  33]. Th. Th33]. proteins , suggestDrosophila to a non-modifiable residue (H3.3K27R). Surprisingly, despite loss of H3.3K27ac, we observe no global defects in steady-state RNA levels in H3.3K27R mutants, indicating that H3.3K27ac is dispensable for transcription. However, Polycomb group target genes become de-repressed at late stages of development in H3.3K27R mutants, indicating that H3.3K27 is required for proper long-term epigenetic silencing by Polycomb.In this report, we use CRISPR/Cas9 to mutate lysine 27 of the replication-independent H3.3 genes in Drosophila genome contains two H3.3 genes that encode proteins with identical sequences , hi, hiH3.3Bpe flies . We concH3.2 RNA .H3.3K27R males could help to explain the mutant phenotype. In particular, we looked for genes that were differentially expressed in both H3.3BK27R and double H3.3K27R mutant males relative to wild type, reasoning that these would be most relevant for understanding H3.3K27 function. Nearly two-thirds (7 out of 11) of genes differentially expressed in H3.3BK27R males are also differentially expressed in H3.3K27R double mutants (S2 Table). Among these are two Polycomb target genes, Psc and Su(z)2, that are more highly expressed in K27R mutants than in wild type . Notably, the levels of Psc and Su(z)2 are increased further in H3.3K27R double mutants relative to H3.3BK27R males . Psc and Su(z)2 encode genes that share homology and function. Both genes are bound by Polycomb group proteins and display high levels of H3K27me3 in vivo , an, anH3.3Kactivity . The rolactivity . Thus, Pactivity . AccordiPsc and Su(z)2, two Polycomb group target genes, suggesting a defect in Polycomb group pathway activity in H3.3K27R mutants. To further explore the potential role of H3.3K27 in Polycomb group target gene regulation, we looked for evidence of genetic interactions between H3.3BK27R and mutants in components of the Polycomb or Trithorax group complexes (Table 1). We reasoned that a reduction in Polycomb activity might enhance the H3.3BK27R mutant phenotype, whereas reduced activity of Trithorax, which functions to promote activation of many Polycomb target genes, might suppress the H3.3BK27R mutant phenotype. This type of genetic interaction approach has been extensively used to identify new Polycomb and Trithorax group genes. Although we did not observe any genetic interactions between Trithorax group genes and H3.3BK27R, we identified two Polycomb group components, Polycomb (Pc) and Sex combs extra (Sce), whose mutation enhanced the H3.3BK27R mutant phenotype. Both Pc and Sce encode core components of the PRC1 complex. The chromodomain of Pc specifically binds H3K27me3 histones  = nos-Cas9.P}ZH-2A w. H3.3AK27R alleles were identified using PCR of genomic DNA  followed by Hpy99I digestion and then the entire 2kb region was sequenced. Four independent H3.3AK27R mutant lines were isolated.Mutagenesis was performed essentially as described previously . BrieflyFig 1C, female yw/FM7 or H3.3BK27R/FM7-GFP or H3.3B0/ FM7-GFP were crossed to male yw, and the number of eclosed flies was counted. Data were plotted as a fraction of the total number of eclosed flies for each indicated sex and genotype, divided by the expected fraction. Greater than 1,500 progeny were counted for each cross. For the longevity experiments presented in Fig 1D, female yw/FM7 or H3.3BK27R/FM7-GFP or H3.3B0/ FM7-GFP were crossed to male yw, and female H3.3BK27R/ FM7-GFP were crossed to male H3.3B0. Newly eclosed adults of the genotype of interest  were moved to fresh vials daily, and were allowed to mate for 2 to 3 days, after which the mates were removed from the vials. The number of surviving adults was recorded for up to two months. Flies were maintained at 25C and flipped onto fresh food every 3 to 4 days. Numbers represent the total number of adult flies from three independent experiments.For the viability experiments presented in H3.3BK27R flies , females of the genotype yw/ FM7-GFP or H3.3BK27R/ FM7-GFP or H3.3B0/ FM7-GFP were crossed to FM7-GFP males. Embryos were collected on apple plates over a 4-hour window and allowed to age at 25C for 16 hours. The total number of embryos was recorded, GFP-negative embryos were counted and transferred to a fresh apple plate. GFP-negative larvae that hatched were counted and transferred to fresh vials. The number of subsequent pupae and eclosed adults was recorded.To determine the lethal phase of male H3.3BK27R female viability , the following 4 crosses were performed:To examine the effect of H3.3 gene copy number on yw / FM7-GFP; Df(2L)BSC110 / CyO x yw; H3.3A2x1/ CyO,H3.3B0/ FM7-GFP; Df(2L)BSC110 / CyO x yw; H3.3A2x1/ CyOH3.3BK27R/ FM7-GFP; Df(2L)BSC110 / CyO x yw; H3.3A2x1/ CyOH3.3BK27R/ FM7-GFP; Df(2L)BSC110 / CyO x H3.3B0; H3.3A2x1/ CyOH3.3BK27R/–; H3.3A–/–adults was confirmed by PCR. H3.3B0 was identified with for- TAAGCATCTAGAATTTTCCTCTTGCCTGCACA and rev- GCTGCCTCCGCGAATTA primers, H3.3A2x1 was identified with for- GGGTCACACTGAGCAGACG and rev- GATGTCCTTGGGCATAATGG primers and Df(2L)BSC110 was identified with for- GAACGAAGCTGATGTGCTATTG and rev- GACATCCGAGTCTTTGCATACT primers.Vials were maintained at 25C and flipped every other day. Data were plotted as observed versus expected genotypic frequency based on Mendelian ratios. The genotype of a subset of H3.3AK27R mutation on viability , females of the genotype yw/yw; H3.3AK27R/ CyO were crossed to males of the genotype yw; H3.3AK27R/ CyO. Vials were maintained at 25C and flipped every other day. The number of heterozygous and homozygous H3.3AK27R offspring was recorded.To determine the effect of H3.3BK27R and dosage compensation mutations , females of the genotype H3.3BK27R/FM7-GFP were crossed to males of the following genotypes:To test for genetic interactions between msl-1γ216, cn, bw / CyO (Bloomington stock number 5870)msl-2227/ CyO (Bloomington stock number 5871)msl-31, red / TM3, Sb, Ser (Bloomington stock number 5872)mle9/ CyO (Bloomington stock number 5873)yw; CyO / Ifyw; TM3, Sb / E934H3.3BK27R and Polycomb or Trithorax group genes , females of the genotype H3.3BK27R/FM7-GFP or H3.3BK27R/FM7-GFP; tg-H3.3BWT were crossed to males of the following genotypes (with origin):To test for genetic interactions between esc5, E(Pc)1/ SM5 (Bloomington Stock Center number 3142)w*; Su(z)124, FRT2A, e / TM6C, Sb (Bloomington Stock Center number 24469)w*; FRT82B, Abd-BMcp-1, Sce1/ TM6C, Sb, Tb (Bloomington Stock Center number 24618)yw; Pc15, FRT2A / TM3, Sb (Bloomington Stock Center number 24468)yw; E(z)731, FRT2A / TM3, Sb (Bloomington Stock Center number 24470)E(z)63/ TM3, Sb (gift of Judy Kassis)yw; FRT42D, Df(2R)Su(z)2-1.b8 / SM6b (Bloomington Stock Center number 24467)brm2, e, ca / TM6B, Sb, Tb, ca (Bloomington Stock Center number 3619)brm2, trxE2, ca / TM6B, Tb, ca (Bloomington Stock Center number 3622)red, e, ash21/ TM6B, Tb (Bloomington Stock Center number 4584)w; PBac–Utxf01321/ CyO (Bloomington Stock Center number 18425)y; FRT82B, trxE2/ TM6C, Sb, Tb (Bloomington Stock Center number 24160)Other Genotypes used (with origin):H3.3B0 (gift of Kami Ahmad)H3.3A2x1 (gift of Kami Ahmad)Df(2L)BSC110 / CyO (Bloomington stock number 8835)H3.3BK27R or yw 16–20 hour embryos were prepared as described [H3.3BK27R or yw pharate adults (96–108 hours after puparium formation) were isolated as described [Protein extracts from male escribed . Nuclei escribed  with theth and 75th percentiles, and whiskers depict 1.5 times the inner quartile range. Outliers were removed for clarity. RNA-seq data from H4K16 mutant male third instar wing discs obtained from [Total RNA was collected as described previously . Brieflyned from . Data arAdult cuticles were mounted on glass slides in Hoyers and imaged using a digital camera mounted on a stereo-dissecting scope. Wing imaginal disc immunostaining and confocal microscopy was performed as previously described . Mouse aS1 FigA) Bar plot of H3.3B and H3.3A RNA-seq signal from S2 cells treated with RNAi’s targeting dosage compensation components Msl-2 (blue) and MOF (orange) (46). X-linked H3.3B decreases in both RNAi treatments, relative to control RNAi, whereas autosomal H3.3A does not decrease. (B) Browser shots of RNA-seq reads aligning to the H3.3A (left) and H3.3B (right) genes from wild type, H3.3BK27R, and double H3.3K27R pharate adult males. Nucleotides in reads matching the wild type reference genome sequence are colored gray. Mismatched nucleotides in reads are indicated in color. The lysine 27 codon has been mutated to arginine, as indicated. (C) Plots of observed versus predicted RNA-seq signals for ERCC spike-in control RNAs from wild type, H3.3BK27R, and double H3.3K27R pharate adult males.((TIF)Click here for additional data file.S1 TableH3.3K27R double mutants relative to wild type.Genes highlighted in red indicate those that are also differentially expressed in the (XLSX)Click here for additional data file.S2 TableH3.3BK27R mutants relative to wild type.Genes highlighted in red indicate those that are also differentially expressed in (XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


In the medicinal plant Catharanthus roseus, the zinc finger Catharanthus transcription factor (ZCT) family represses monoterpenoid indole alkaloid (MIA) biosynthetic gene expression. Here, we report the analysis of the ZCT1 promoter, which contains several hormone‐responsive elements. ZCT1 is responsive to not only jasmonate, as was previously known, but is also induced by the synthetic auxin, 1‐naphthalene acetic acid (1‐NAA). Through promoter deletion analysis, we show that an activation sequence‐1‐like (as‐1‐like)‐motif and other motifs contribute significantly to ZCT1 expression in seedlings. We also show that the activator ORCA3 does not transactivate the expression of ZCT1 in seedlings, but ZCT1 represses its own promoter, suggesting a feedback mechanism by which the expression of ZCT1 can be limited.Cys All three A. thaliana zinc finger proteins are C1 family and C1‐2i subclass members , cold temperatures, high light intensities, oxidative stress, salt stress, water deprivation, and wounding  , methyl jasmonate (MJ), salicylic acid (SA), and abscisic acid (ABA). We report that auxin (1‐NAA) and MJ induce ZCT1 expression in hairy roots. Through promoter analysis experiments in transiently transformed seedlings, we determined that an activation sequence‐1‐like motif (as‐1‐like motif) contributes significantly to the promoter activity of ZCT1. Further elements within the promoter, including a GA‐responsive complex (GARC), also contribute. Overexpression of ORCA3 did not transactivate the expression of ZCT1 or activate ZCT1 promoter–reporter constructs in seedlings. ZCT1 is capable of repressing its own promoter, suggesting a possible feedback mechanism limiting the expression of ZCT1.Here, we have chosen to investigate the 1000‐bp upstream region of the 22.1ZCT1 promoter sequence was obtained with the Universal Genome Walker Kit (Clontech). Approximately 800 bp of the ZCT1 promoter was amplified from C. roseus  genomic DNA using the GSP1 and GSP2 gene‐specific primers (Table ZCT1 coding sequence (GenBank accession AJ632082). This sequence matches the sequence 5′ of the ZCT1 coding region of the published genomes  were further considered. Motifs with low information content  and frequent occurrences in all tested promoters were excluded. The pyrimidine box matches sequences from the literature  was used to amplify the 5′ cDNA ends using the same GSP1 and GSP2 gene‐specific primers. Promoter sequences were analyzed with the Plant Cis‐Acting Regulatory Elements  genomic DNA and cloned into pICH41295  and was purchased from Genewiz in pUC57. The 35S minimal promoter (−46‐ to +6‐bp region) was amplified from pICH51288  serves as a reference. The GA‐responsive complex  and the GARC with the second INDETERMINATE DOMAIN (IDD) binding site (173 bp) were cloned into pICH41233  reporters were cloned together with a constitutively expressed Renilla luciferase (RLUC) for normalization  fragment of the  et al., ). All ne2.4Catharanthus roseus seeds (Little Bright Eye) were surface‐sterilized in 70% (v/v) ethanol for one minute, followed by 10% (v/v) bleach containing 0.1% (v/v) Triton X‐100 for ten minutes. The seeds were rinsed three times in sterile water and soaked in 1% Plant Preservative Mixture (Caisson Laboratories) in sterile water for 24 hr. The seeds were planted in sterile Magenta GA‐7 boxes on the surface of 1/2 strength Murashige and Skoog media . Seedlings were grown in the dark at 25°C for one week and then transferred to a 16 hr of light (Erligpowht 45W LED Red Blue Lights) photoperiod for approximately 6 weeks before infection with Agrobacterium rhizogenes.2.5C. roseus hairy root cultures were generated as previously described  were transformed by pricking the seedling stem above the cotyledons with modified tweezers dipped into the Agrobacterium rhizogenes R1000 liquid cultures containing pSB119  driven by the −914‐bp to +86‐bp ZCT1 promoter fragment (pZCT1::tGFP‐I). Hairy roots emerged from the site of infection approximately ~3 weeks after infection. When hairy roots were longer than 5 mm (~6 weeks after infection), hairy root clusters were excised and cultured first on elimination media to remove Agrobacterium, and then on selection media to select for hygromycin‐resistant transgenics. Hairy root cultures were subcultured in liquid media approximately every 28 days and maintained as previously reported , equipped with a SPOT RT3 CCD camera (Diagnostic Instruments), controlled by SPOT Advanced imaging software (v. 5.0) with Peripheral Devices and Quantitative Imaging modules. A Nikon Intensilight C‐ HGFI 130‐W mercury lamp, shuttered with a Lambda 10‐B SmartShutter (Sutter Instruments), was used for GFP excitation, and a GFP filter set  was used for detection.2.8Hairy roots expressing TurboGFP were quantified in a fluorescence plate reader after a modified protocol from Marillonnet et al., .3PO4‐buffer at pH7.2, 1.0 mM EDTA) with fresh 10 mM β‐mercaptoethanol and 0.1% Triton X‐100. After vortexing and centrifugation , carefully avoiding cell debris, 200 μl of the supernatant was transferred to a black 96‐well plate and fluorescence was detected with a plate reader .Roots were transferred to filter paper to remove excess culture media. Approximately 100 mg of tissue was transferred into 2‐ml screwcap tubes together with ten 3‐mm glass beads (Fisher), and the fresh weight of the roots was recorded. Samples were frozen in liquid nitrogen and stored at −80°C until analysis. Frozen samples were crushed in a Mini‐BeadBeater‐16 (BioSpec Products Inc.) for 20 s and transferred to ice, and 600 μl of extraction buffer was added (50 mM Na2.9Catharanthus roseus seedlings were transformed with the efficient Agrobacterium‐mediated seedling infiltration (EASI) method  strains by vacuum infiltration, and tissue was collected 3 days after infection. For promoter activity studies, A. tumefaciens GV3101 (pMP90) strains, containing the different promoter driving reporter constructs, were adjusted to OD600 = 0.2 for infiltration into C. roseus seedlings . Transactivation assays were performed with the reporter strain at OD600 = 0.06 and the effector strain at OD600 = 0.34 .2.10TM Duo‐Luciferase HT Assay Kit (Genecopoeia) as described in Mortensen et al.,  or ORCA3, or seedlings lacking A. tumefaciens during infiltration, using quantitative real‐time PCR (qRT‐PCR). mRNA was extracted from liquid nitrogen flash‐frozen seedlings , stored at −80°C until needed. While still frozen, the cotyledons were crushed by shaking in the Mini‐BeadBeater‐16 (BioSpec) for 15 s with ten 3‐mm glass beads (Fisher). Afterward, RNA was extracted with RNAzol‐RT (Molecular Research Center) and the Direct‐zol RNA Miniprep Plus Kit (Zymo Research) with on‐column DNAse treatment to remove genomic DNA. RNA integrity was assessed using agarose gel electrophoresis, and concentration and purity were quantified with a NanoDrop . cDNA was synthesized using the SuperScript II First‐Strand Synthesis System (Invitrogen) and oligo‐dT primers with up to 2.5 µg of RNA in a 10 µl reaction, according to the manufacturer's instructions.Expression levels of SAND .cDNA was diluted 1:4, and 1 µl was used in a 10 µl reaction with SYBR Green ROX qPCR Master Mix (Qiagen) on the MX3000P qPCR instrument (Agilent) using the thermocycler protocol previously described with an extension time of 30 s  was determined using 5′ rapid amplification of cDNA ends (RACE). Six clones were sequenced to determine that the TSS is located 86 bp 5′ of the translational start site (ATG). The cDNA ends were the same for 5 of the 6 clones (this is referred to as the TSS in this study), and the 6th included one additional 5′ bp. Our sequence matches the sequence upstream of the ZCT1 coding region in the sequenced C. roseus genomes  and PlantPAN 3.0. A detailed PlantCARE characterization of the ZCT1, ZCT2, and ZCT3 promoters can be found in Figure To begin to characterize the transcriptional regulation of ZCT1, ZCT2, and ZCT3 promoters share several common hormone‐ and stress‐responsive elements. All three promoters contain at least one wounding‐responsive element within the first 200 bp upstream from the translational start site. Interestingly, the promoters of ZCT1 and ZCT2 both contain two W‐boxes (WRKY binding sites), two abscisic acid‐responsive elements (ABRE), and two ethylene‐responsive elements (ERE). The plant‐specific WRKY TFs (named for the conserved amino acids in WRKY domain) were identified in C. roseus, and WRKY1 is involved in regulation of MIA biosynthesis , MJ, SA, ABA, and GA3. As‐1‐like elements are known to be activated by MJ, SA, and auxins . This vector was used to create stable transgenic hairy root lines via Agrobacterium‐mediated transformation as previously described  resulted in no significant GFP fluorescence change  hairy root cultures were treated with MJ, ABA, and GA3 for 7 hr, harvested, and analyzed for ZCT1 transcript levels using qRT‐PCR. We have previously shown that ZCT1 expression is responsive to MJ addition by 7 hr , while the basal level of ORCA3 transcripts is very low . In particular, we identified a region (−350 to −180 bp upstream of TSS) containing a cluster of GA‐responsive elements making up a GA response complex  bound by W‐boxes. The GARC bound by W‐boxes is a regulatory unit associated with the antagonistic regulation of GA and ABA in the amylase promoter in rice  and auxin responsiveness and confer high activity of the promoter such as found in the constitutive cauliflower mosaic virus 35S promoter  provide similar levels of expression , but these do not significantly increase the promoter activity compared to the pZCT1_744 construct under the tested conditions.The two longest as‐1‐like element, a 58 bp nucleotide deletion (pZCT1_744_ Δ(as‐1)) of the as‐1‐like element and a mutant construct (pZCT1_744_mut. as‐1) were expressed using the EASI method. If the as‐1‐like element is mutated or deleted (pZCT1_744_mut. as‐1 or pZCT1_744_ Δ(as‐1)), promoter activity is reduced by approximately 50%. If one IDD binding site as well as one W‐box is removed in addition to the as‐1‐like element (pZCT1_744_ Δ(as‐1 IDD W‐box)), activity is not reduced compared to the deletion of just the as‐1‐like element (pZCT1_744_ Δ(as‐1)), suggesting that this W‐box is not contributing significantly to the as‐1‐like‐driven promoter activity under the given conditions. A 501 bp 5′ deletion (pZCT1_243) also results in a reduction of promoter activity by approximately 50%. This section contains multiple elements, including the GARC. The pZCT1_104 fragment, which contains the TATA‐box and transcription start site alone, confers ~26% of the promoter activity, suggesting that there are additional regulatory elements within this 104‐bp fragment.To test the importance of the as‐1‐like element  significantly increases the promoter activity of the 35S minimal promoter. Deletion of one IDD binding site from this fragment  does not significantly affect the activity of the pMinimal35S:GARC + IDD promoter fragment. These results show that the GARC confers promoter activity under the tested conditions, and that the second IDD binding site does not synergistically enhance promoter activity. However, overall, the promoter activity of the 35S minimal promoter fusions is quite low compared to the pZCT1_744 activity.Next, we investigated the GARC without the overshadowing effect of the t Figure . Fusion as‐1‐like element contributes significantly to the expression of ZCT1 in seedlings under the EASI transformation conditions. Other elements in the promoter, including the GARC, also contribute to ZCT1 expression.Taken together, these results suggest the 3.4ORCA3 expression levels were correlated with increased expression of ZCT1, ZCT2, and ZCT3 in a stable hairy root line, suggesting the regulation of ZCTs by ORCA3  by either one of the transcription factors  or GUS (control) in C. roseus seedlings. The co‐expression of the ORCA3 and the promoter–reporter construct did not result in the activation of the ZCT1 promoter  zinc finger proteins such as ZCT1 are an important class of transcription factors involved in controlling growth, development, and stress‐responsive genes  in hairy root cultures   Figure , as wells Figure .ZCT1 expression with high auxin levels suggests their role in decreasing MIA production under growth‐promoting conditions. The synthetic auxin, 2,4‐dichlorophenoxyacetic acid , repressed the hydroxymethylbutenyl 4‐diphosphate synthase (HDS) gene from the methyl erythritol phosphate (MEP) pathway  are enras‐1‐like element for ZCT1 promoter activity was shown by the promoter deletions studies in seedlings  transcription factors  and MJ. An as‐1‐like element is particularly important for promoter activity and suggests a positive regulation of ZCT1 through TGA transcription factors. In contrast to initial expectations, ORCA3 did not transactivate the ZCT1 promoter, while ZCT1 represses its own promoter. Future investigation into ZCT1 regulation will provide insights into the important biological problem of how phytohormone crosstalk and/or feedback mechanisms are integrated at the level of transcription of regulatory proteins.In summary, we have identified The authors declare that they have no conflict of interest.SM, JW, EJC, and CL‐P conceived and designed the research. SM, SS, JW, LC, and NR performed the experiments. SM, SS, JW, LC, NR, EJC, and CL‐P analyzed the data. SM, JW, EJC, and CL‐P wrote the manuscript. Click here for additional data file. Click here for additional data file.


Genome-wide chromatin accessibility and nucleosome occupancy profiles have been widely investigated, while the long-range dynamics remain poorly studied at the single-cell level. Here, we present a new experimental approach, methyltransferase treatment followed by single-molecule long-read sequencing (MeSMLR-seq), for long-range mapping of nucleosomes and chromatin accessibility at single DNA molecules and thus achieve comprehensive-coverage characterization of the corresponding heterogeneity. MeSMLR-seq offers direct measurements of both nucleosome-occupied and nucleosome-evicted regions on a single DNA molecule, which is challenging for many existing methods. We applied MeSMLR-seq to haploid yeast, where single DNA molecules represent single cells, and thus we could investigate the combinatorics of many (up to 356) nucleosomes at long range in single cells. We illustrated the differential organization principles of nucleosomes surrounding the transcription start site for silent and actively transcribed genes, at the single-cell level and in the long-range scale. The heterogeneous patterns of chromatin status spanning multiple genes were phased. Together with single-cell RNA-seq data, we quantitatively revealed how chromatin accessibility correlated with gene transcription positively in a highly heterogeneous scenario. Moreover, we quantified the openness of promoters and investigated the coupled chromatin changes of adjacent genes at single DNA molecules during transcription reprogramming. In addition, we revealed the coupled changes of chromatin accessibility for two neighboring glucose transporter genes in response to changes in glucose concentration. In eukaryotic organisms, cells are faced with genetic information storage and packaging problems. As the carrier of genetic information, instead of folding into a disorganized yarn ball, DNA strands wrap around thousands of protein cores like “beads on a string.” As the fundamental unit of chromatin, a nucleosome consists of ∼147 bp of DNA wrapping around a histone octamer composed of four core histones  . NucleosCurrent genome-wide methods of nucleosome occupancy and/or chromatin accessibility mapping are mainly based on three types of assays followed by short-read sequencing technologies: (1) the nucleosome's protection of nucleosomal DNA sequences from endogenous and exogenous enzymes  ; (2) chrThese methods can map averaged patterns of nucleosome occupancy and chromatin accessibility in a population of cells, failing in precise identification at the single-cell level. Although the single-cell versions of these methods have been recently developed , the corThe emerging single-molecule long-read sequencing technology  provides unique data features that are capable of filling the gap: (1) 5mC can be directly detected at the single-base resolution at the single-molecule level based on ONT electrolytic current signal dynamics without bisulfite conversion ; (2) unlSaccharomyces cerevisiae cells, where single DNA molecules represent single cells, so it allows the “one-to-one” link between sequencing read  and haploid cell. Thus, each single MeSMLR-seq read can be used to mimic a single cell in a given genomic region, and the heterogeneity can be investigated without single-cell sequencing. With the unique output of MeSMLR-seq, we revealed the chromatin basis of gene transcription.We applied MeSMLR-seq to haploid Supplemental Code) was developed to map the 5mC profile at GpC sites for MeSMLR-seq data.In brief, the experimental approach MeSMLR-seq contains two main steps: (1) methyltransferase (M.CviPI) treatment to convert cytosine to 5mC at GpC sites at naked linker DNA and open chromatin; and (2) ONT sequencing to detect the 5mC profile that is subsequently used to identify nucleosome occupancy and chromatin accessibility . The firSaccharomyces cerevisiae (BY4741 strain), an additional step was applied to digest the cell wall that serves as a barrier against methyltransferase treatment of genomic DNA: Yeast cells were treated with Zymolyase to generate spheroplasts . The longest sequencing molecule was 63.1 kb. In particular, from the target sample where yeast was grown in rich media , we generated 1.4 million sequencing molecules with a median length of 7.2 kb, covering 821× of the yeast genome.Using the ONT GridION platform with R9.4.1 chemistry, we sequenced one flow cell per sample and generated 0.9 million (positive control), 1.2 million (negative control), and 1.3 million (on average for six target samples) reads  separately, which were uniquely aligned to the yeast genome . There was no obvious bias of 5mC methylation calling between the molecules that were aligned to forward and reverse strands, and the areas under the receiver operating characteristic curve (AUC) were both 0.86 (Nucleosome Positioning detection by Single-Molecule Long-Read sequencing (NP-SMLR) as below .We first identified 5mC methylation status for every GpC site on each DNA molecule based on the ONT sequencing current signal (referred to as the event level). Since the previous studies  showed toth 0.86 A. Correloth 0.86 B. TherefX1X2…Xl be a molecule, where Xi is the i-th base. Denote si as the methylation score of Xi, if Xi is the cytosine of the GpC dinucleotide. Suppose that the methylation scores of all GpC sites are independent. Nucleosome occupancy detection refers to finding a path π = π1π2…πl that maximizes the likelihood of signals:Let πi takes the value from {L, N1, N2, …, N147}. L represents the linker region; Nm represents the m-th base within a nucleosome; i1, i2, …, in are the positions of cytosines that belong to GpC dinucleotides. The elements of path π are restricted such that: (1) Nm is followed by Nm+1 (1 ≤ m ≤ 146); (2) N147 is followed by L; and (3) L is followed by L or N1. The problem is essentially an alignment between a sequence of nucleotides and a sequence of nucleosomal statuses. NP-SMLR adopts a dynamic programming algorithm .lgorithm  for soluDue to the lack of more advanced experimental technology to generate a gold standard, we evaluated the accuracy of nucleosome occupancy detection at the single-molecule level by simulation tests. The tests were performed under different settings of nucleosome coverage  and GpC frequency (ranges from 1% to 10%) D. The acSupplemental Methods; DAL  gene cluster, the nucleosome peaks called by MeSMLR-seq and MNase-seq were generally well aligned . For instance, 35–61 nucleosomes (median number 58) were phased at the single molecules covering the DAL gene cluster across a 10-kb genomic region . Next, we evaluated the uniformity of nucleosome spacing within single cells by the variation of the distance between adjacent nucleosomes. In contrast to active genes, the nucleosomes surrounding the TSS of silent genes were more uniformly spaced . For instance, at the bulk-cell level, nucleosomes surrounding the TSS of the lowly expressed gene AUA1 (FPKM = 0) were poorly positioned (EMW1 (FPKM = 77) and a pronounced nucleosome-depletion region (NDR) upstream of the TSS , which represented the ideal occasion for a continuous shift pattern. In addition, the spacing of nucleosomes surrounding the TSS of AUA1 was relatively uniform within single cells , while there was a pronounced NDR upstream of the TSS of EMW1, which disrupted the uniformity of nucleosome spacing . MeSMLR-seq resolves these differential nucleosome organization principles with direct and convincing evidence at a long-range scale from single molecules/cells that are hard to obtain by the bulk-cell and short-read sequencing approaches.We focused on the nucleosome organization surrounding transcription start sites (TSSs), which play an important role in transcription regulation . For eacsitioned D, while  the TSS E. At thefor EMW1 D,E. CompSupplemental Methods; r = 0.80) and DNase-seq (averaged Pearson's r = 0.82) . In addition, >83% (1615/1934) of significantly accessible regions called by MeSMLR-seq were also supported by either ATAC-seq or DNase-seq . For example, 34 MeSMLR-seq molecules fully covered the 9-kb genomic region, Chr II: 370,000–379,000, that encompasses four genes . Based on the 5mC footprint, we identified the chromatin status (“open” or “closed”) of the promoters for four genes on each molecule and thus defined and quantified the coupled chromatin status patterns. In total, these molecules detected 13 out of 16  possible combinatorial patterns of the coupled chromatin statuses of four gene promoters ; therefore, we could examine the long-range chromatin accessibility at the single-molecule/-cell level ; narrowly open (Cluster 2 with 75 molecules); and widely open (Cluster 3 with 53 molecules)  of the cell cycle regulation gene Supplemental Note 1; Supplemental Figs. S5, S6), as well as revealing the dynamics of chromatin status during transcription changes in response to different nutrition conditions .In addition, the MeSMLR-seq data revealed nucleosome occupancy and chromatin accessibility profiles consistent with the previous studies based on bulk-cell short-read data  upstreamSupplemental Fig. S5), the quantitative relationship between promoter openness and gene transcription in a cell population remained unclear. Based on unique MeSMLR-seq data, we were able to calculate the fraction of a cell subpopulation with an open promoter of a given gene. With single-cell RNA-seq data for 2812 yeast cells generated in this study , we also calculated the fraction of cells with expression (read count ≥1) of a given gene (referred to as expression frequency). The expression frequency within a cell population was positively correlated with the fraction of cells with an open promoter   A.P-value <2.2 × 10−16, Wilcoxon rank-sum test)  C. These HXT3 and HXT6, during transcription reprogramming. The transport of glucose across the plasma membrane is the first step of glucose metabolism, and the glucose  transporter genes play essential regulatory roles in glucose sensing, signaling, and utilization in a yeast cell  , we counted MeSMLR-seq molecules to estimate the fractions of cell subpopulations with two opposite coupled chromatin accessibility patterns: “Open-ncreased B. The chχ2 test) . These pIn this study, we showed the consistent bulk-level nucleosome occupancy and chromatin accessibility profiles generated by MeSMLR-seq with existing methods. With the unique output of MeSMLR-seq, we investigated the organization principles of nucleosomes surrounding TSSs and studied the heterogeneity of combinatorial chromatin statuses over multiple genomic regions. Together with single-cell RNA-seq data, the relationship between chromatin accessibility and gene transcription was investigated quantitatively. Finally, we revealed the coupled chromatin changes of adjacent genes during transcription reprogramming.A large number of studies have demonstrated key regulatory roles for nucleosome positioning and chromatin accessibility in eukaryotic gene expression  as well However, it should be noted that the molecule-cell link does not hold in diploid or polyploid organisms, as the molecule populations are a mix of allele-specific and cell-specific events. It leads to challenges and opportunities in the further development of new experimental  and statistical  approaches. Once cell subpopulations can be reconstructed from a molecule population, we could distinguish the allele-specific epigenome precisely from different cell subpopulations and achieve more accurate investigation of how epigenetics events behave differently at different alleles. Regardless of the wide interest in the cell-level study, the characterization of nucleosome positioning and chromatin status at single DNA molecules by MeSMLR-seq will also bring very unique and informative data to reveal the dynamic nucleosome positioning mechanism, such as assembly, disassembly, and sliding.Besides the single-molecule information, the long length of MeSMLR-seq reads, which allows correlation analysis of exogenous and endogenous methylation statuses over different positions, could be informative for some research topics: (1) Correlation of exogenous 5mC events has shown the nucleosome occupancy pattern in this study B, and thhttps://nanoporetech.com/resource-centre/pore-c-using-nanopore-reads-delineate-long-range-interactions-between-genomic-loci), MeSMLR-seq contributes a new technique in the toolkit of single-molecule long-read sequencing to obtain first-hand details of epigenetics at single DNA molecules. More innovative studies with single-molecule long-read sequencing should be explored and expected to advance our studies to discover novel and complex biological insights.From a technical viewpoint, there are relatively few applications of ONT data in epigenetics research, as the corresponding experimental approaches or bioinformatics methods are rarely developed, although numerous applications of ONT data have been published rapidly with improved data quality and cost efficiency. In addition to the previously reported studies of identifying methylation and three-dimensional spatial organization of chromatin  and subjected to MeSMLR-seq, bulk-cell RNA-seq, and single-cell RNA-seq experiments .The Preparation and methylation of yeast spheroplasts were performed as previously described . BrieflyWe denote the above mentioned genomic DNA that undergoes in vivo spheroplast methylation as the target sample of MeSMLR-seq. Native genomic DNA extracted from yeast without M.CviPI treatment was used as the negative control . There is no endogenous 5mC in the yeast genome, as reported in a previous study . GenomicCHA1 gene region , CYS3 gene region , GAL10 gene region , and PHO5 gene region  were amplified for evaluating the methylation efficiency of the positive control. The PHO5 gene region , which was shown in Supplemental Table S5.The efficiency of M.CviPI methylation was evaluated using bisulfite sequencing as previously described . First, Native genomic DNA (negative control), methylated genomic DNA (positive control), and extracted genomic DNA after spheroplast methylation (target sample) were directly submitted to ONT sequencing. In brief, the genomic DNA was fragmented (size = 8 kb) using Megaruptor. A sequencing library was prepared using the 1D Ligation Sequencing kit (SQK-LSK108). ONT sequencing was performed using the GridION platform with R9.4.1 flow cells.The software Albacore developed by ONT was used to perform base-calling for ONT raw signals. The base-called ONT sequencing data were aligned to the sacCer3 reference genome using BWA software (version 0.7.17-r1188)  with theSince we used the ONT 1D sequencing strategy in this study, a DNA molecule from a yeast cell might be sequenced twice . Thus, to achieve the “one-to-one” link between the ONT sequencing molecule and haploid yeast cell, we classified all molecules into two groups based on their aligned genomic strands: forward and reverse.Supplemental Table S1.The information of MeSMLR-seq data  was summarized in k, we trained two models for the event level; one was for unmethylated cytosine and the other for methylated cytosine. We modeled the event level for unmethylated cytosine by a Gaussian distribution k and estimated μ0 and Supplemental Fig. S1A, right panel), we modeled the event level for methylated cytosine by a Gaussian mixture model ρ < 1. The parameters μ1, μ2, ρ were estimated based on the event levels from positive control data by EM algorithm. Denote the probability density functions of the two models as fN (unmethylated cytosine) and fP  (methylated cytosine), respectively. Namely,For each 6-mer pattern that includes cytosine of GpC dinucleotide, represented here by x in the above formulas represents the value of the event level. The area of the overlapped region under the two probability density functions fN  and fP  is calculated. The discrimination of the 6-mer k is defined as 1 − the area of overlap.The Supplemental Fig. S1A, left panel). The 6-mer with >1 GpC site included or >10 aligned event levels from the molecule was excluded for 5mC detection. Among the remaining 6-mers, the one with the maximal discrimination was chosen for the calculation of methylation score. Denote k′ as the selected 6-mer, and x′ as the event level that was aligned to k′. The event level x′ was filtered out if one of logfP  or logfN  was <−10; otherwise, the methylation score of the GpC site was calculated asGiven a sequencing molecule from the target sample, we detected 5mC for all GpC sites. For each of the GpC sites, we listed all the 6-mers on the reference genome that cover the cytosine at the dinucleotide  and fP  were replaced by the product of the multiple likelihood.The score As a cross-validation, we randomly split each of the negative control and positive control data into two halves. One of the halves was used to train the models. Using the trained models, we detected 5mC on the other half. With the real methylation status of the test data being known, we were able to evaluate the detection results. It turned out that the area under the ROC curve was 0.86 A.Supplemental Code).We developed a bioinformatics method, named NP-SMLR, to detect and phase nucleosomes at the single-molecule level  Nm is followed by Nm+1 (1 ≤ m ≤ 146); (2) N147 is followed by L; and (3) L is followed by L or N1.qN (·) and qP (·), respectively . A dummy methylation score si = −1 is added for Xi if it is not a cytosine of GpC dinucleotide. DefineBased on the methylation scores of all GpC sites from all molecules in negative and positive control training data, we can fit two density curves using the “density” command in R  correspond to N1, N2, …, N147, separately. Initialize V = p1(L), and V = p1(Nj−1), 2 ≤ j ≤ 148. Elements in Line i(2 ≤ i ≤ n) are then calculated iteratively. For Column 1, the element V is set as max{V, V}qP(si) if Xi is cytosine of GpC, or max{V, V} otherwise. For Column j (2 ≤ j ≤ 148), V is set as VqN(si), or V otherwise. When updating an element, we record the position of the previous element that leads to the maximal value and store the position as a pointer. After updating all elements, the maximal element in the last line is found , and the nucleosome occupancy detection is completed through the backtracking of pointers. All calculations are performed in log scale to avoid rounding error.Accordingly, gramming C. We staτ, and τ, corresponding to regular nucleosome array and open region with specific biological functions, respectively. We set ν2 > ν1 and qN (·). For GpC sites within linkers, qP (·) was used instead. NP-SMLR was applied on the simulated sequence. Denote Zi as the predicted and real indicators of whether the i-th base locates in nucleosome or not, respectively. The accuracy was defined asl is the length of the simulated DNA sequence. In simulation tests, we set ν1 = 15, γ1 = 5, γ2 = 10, τ = 0.1. We set ν2 as 15, 50, 100, 200, 300, 400, 500, and 600, respectively, to achieve different nucleosome coverage (defined as the proportion of bases covered by nucleosomes). For each parameter setting, the above simulation was carried out 1000 times.We evaluated the accuracy of nucleosome occupancy detection (NP-SMLR) through simulation tests under different nucleosome coverage and GpC frequency D. In detThe genomic coordinates of all nucleosomes predicted by NP-SMLR at the single-molecule level were pooled and subjected to iNPS software (version 1.2.2)  with defSupplemental Figure S5, A and B, and Supplemental Figure S6, D and E. The nucleosome peaks called by iNPS were used for the comparison with MNase-seq . For each molecule/cell, we first defined the nucleosome whose center was located downstream from the TSS and closest to the TSS as the +1 nucleosome. Next, we sorted the distances between the +1 nucleosome and the TSS and removed the upper 10% values for robustness. The standard variance of the remaining values was used to represent the heterogeneity of nucleosome positioning for each gene.The heterogeneity of nucleosome positioning was measured by the variation of the +1 nucleosome positioning relative to the TSS across different cells . For each gene, the molecules that fully covered the region (from 500 bp upstream of to 100 bp downstream from the TSS) were chosen. For each molecule, we calculated the lengths of all linker regions that were located in the region “−500, +100”. Then, we calculated the absolute deviation of linker length pair-wisely. The sum of the deviation values was divided by the number of linker pairs. The obtained value, which described the variation of nucleosome distance, was namely the nucleosome spacing uniformity.The uniformity of nucleosome spacing was measured by the variation of the distance between adjacent nucleosomes   Xi and Xj were adjacent GpC sites; (2) the corresponding methylation scores si and sj were >0.5; and (3) the distance between Xi and Xj was <100 bp. The continuous accessible regions were merged. Given an accessible region, the chromatin accessibility score was defined as the median methylation score among all GpC sites within this region.Based on the methylation scores of all GpC sites per molecule, we detected accessible chromatin regions along the molecule. Given a single molecule Supplemental Figure S4, A and B, Supplemental Figure S5C, Supplemental Figure S6, B and C, and Supplemental Figure S7A.In this study, we only considered the accessible regions with the length ≥100 bp for each molecule. A genome-wide chromatin accessibility profile was generated through merging accessible regions of all molecules. The chromatin accessibility profile was used to generate Gi be the i-th base of the genome. Denote M sequencing molecules that covered Gi, and Gi is a GpC site. Define Gi and Gj as a significantly accessible region if: (1) Gi and Gj were adjacent GpC sites; (2) Gi to Gj was <100 bp. The continuous accessible regions were merged to generate a longer accessible genomic region (referred to as “chromatin accessibility peak”).We defined significantly accessible genomic regions as described in the previous study . Let Gi In this study, we only considered the peaks with the length ≥100 bp. For sequencing molecules aligned to forward and reverse genomic strands, we defined chromatin accessibility peaks separately. The overlapped peaks between the forward and reverse strands were used for the comparison with two existing methods  C.To quantitatively measure the accessibility of genes, we first defined the promoter region for each gene. Briefly, chromatin accessibility peaks (including both forward and reverse strands) were called using MeSMLR-seq data for each biological sample. For each biological sample, the overlapped peaks between forward and reverse strands for MeSMLR-seq were merged together. Next, we combined the merged peaks of MeSMLR-seq from all biological samples and the overlapped peaks between two biological replicates of DNase-seq. For each gene, (1) if there was only one peak that was located within the upstream 500 bp and downstream 100 bp of the TSS , the peak was defined as the promoter region; or (2) if there were multiple peaks that were located in the “−500, +100” region, the peak that had the longest overlap was defined as the promoter region; or (3) if there was no peak locating in the region “−500, +100”, the region “−500, +100” was defined as the promoter region.Supplemental Figure S6A, and Supplemental Figure S7D.At the single-molecule level, the accessibility score of a gene was calculated as the median methylation score among all GpC sites within the promoter region. For all molecules covering the promoter of a given gene, we categorized them into two chromatin statuses: “open” if the accessibility score was >0.5; “closed” otherwise. The defined promoter region and the corresponding accessibility score were used to generate q-value <0.01) (vs. Raf). These differentially expressed genes were used to generate Supplemental Figure S7, B–D. Gene enrichment analyses in Supplemental Figure S7C were performed using DAVID (version 6.8)  (e <0.01)  between ion 6.8) .For the differential chromatin accessibility analyses, we first calculated the bulk-cell-level chromatin accessibility as the ratio of those with “open” status among the molecules that fully covered the gene promoter. For each gene, the differential chromatin accessibility score was computed as the difference of bulk-cell-level chromatin accessibility between two carbon sources .https://www.ncbi.nlm.nih.gov/bioproject/) under accession number PRJNA510813. The bulk-cell RNA-seq and the single-cell RNA-seq data generated in this study have been submitted to the NCBI Gene Expression Omnibus  under accession number GSE131702. The Sanger sequences data generated in this study are summarized in Supplemental Table S5. The public sequencing data used in this study are summarized in Supplemental Table S6. The source code of NP-SMLR is in the Supplemental Material  and is also available at https://github.com/Au-Lab/NP-SMLR.The MeSMLR-seq data generated in this study have been submitted to the NCBI BioProject database (


The development of synthetic biology calls for accurate understanding of the critical functions that allow construction and operation of a living cell. Besides coding for ubiquitous structures, minimal genomes encode a wealth of functions that dissipate energy in an unanticipated way. Analysis of these functions shows that they are meant to manage information under conditions when discrimination of substrates in a noisy background is preferred over a simple recognition process. We show here that many of these functions, including transporters and the ribosome construction machinery, behave as would behave a material implementation of the information‐managing agent theorized by Maxwell almost 150 years ago and commonly known as Maxwell's demon (MxD). A core gene set encoding these functions belongs to the minimal genome required to allow the construction of an autonomous cell. These MxDs allow the cell to perform computations in an energy‐efficient way that is vastly better than our contemporary computers. The ambition of synthetic biology to create a cell from first principles requires identification of the clusters of functions that allow the basic biological unit to be alive. Among other important achievements, genome annotation characterized the genes that direct the construction and operation of a functional cell. Many of these genes, for example those that drive the making of the ribosome and rule their behaviour, have a straightforward function. By contrast, we keep discovering new genes coding for unexpected functions pertaining to the operating system of the cell. We infer their activity as if it were the sequel of previous knowledge. In this context, we often face an omnipresent challenge, that of understanding the role of proteins spending energy apparently in a superfluous way, or proteins that dissipate energy while their obvious function does not entail such waste – e.g. why would a protease require ATP hydrolysis when its action is obviously exothermic? Why do many transporters hydrolyse two ATP molecules rather than only one? A careful analysis of the role of such proteins suggests that we deal, in all these cases, with the challenge of accurate recognition, when, to achieve its function, the protein must not only recognize a correct interaction but discriminate between similar ones and exclude those that are irrelevant. In engineered systems – typically built with hard materials – this is brought about by physically separating the components or endowing them with specific shapes and mechanical properties. But how is this achieved in the milieu of a cell, where so many events coincide in space and time operated by diffusible molecules and soft actors?In life, to belong or not to belong is decisive. Making out this quandary is critical to understand how life proceeds. It is also critical to build up novel synthetic biology constructs that are able to scale up their designed functions. Here, after a preamble meant to clarify some deep properties of information usually less familiar to the life sciences scholar, we detail how discrimination can be achieved with a high level of accuracy by a variety of key proteins that behave as Maxwell's demons, microscopic agents invented by James Clerk Maxwell at the end of the XIXth century [MxDs, , a feature that results from natural selection. This results in a ratchet‐like discriminative process allowing information to accumulate. Under this frame, information capture would be a strong evolutionary drive, as any system with fluctuating components would tend to keep those energy‐neutral events that create information while discouraging those that erase it. Biological systems could thus spontaneously move towards information‐rich hubs in a functional solution space. Furthermore, as the information contents of a system increases, an evolutionary niche for emergence of information‐management devices can be reasonably anticipated. How could they look like? By dissipating energy, the residual memory is reset to a ground state that can be used for further computation, while keeping the result of the computation intact. In biology, this implies that we must identify concrete mechanisms that tell us what should be retained and what should be erased in the course of a particular biochemical pathway or other biological processes. To this end, we must identify processes that would comprise devices behaving as the microscopic agents invented by Maxwell.Yet, this view left open the need to erase only the part of the reversible computation that had produced the correct output. To identify the exact result of the procedure, we further need to use complementary  information that tells us where to look for the information just created, putting aside the information that was used to run the procedure reversibly. Briefly, the MxD must discriminate between what is significant and what is not. This remarkable feature results from the fact that the demon is not a random eraser but the result of natural selection, the process that associates information imbedded in genomes with contextual information. This very fact endowed MxDs with a goal (i.e. ability to take context into account for deciding): only those that behaved as if goal‐oriented survived and could belong to a live progeny of the cell [. Within cells, this matches the idea of generally free diffusion (except across cell membranes), where key‐like substrates explore their environment until they reach the proper lock, often an enzyme. During this process, information is measured as the substrate(drug)–target interaction, with little regard for competing entities. The key/lock metaphor extended to many other biological processes. For example, besides enzymes and their substrates, another common illustration was that of the recognition process allowing control of gene expression via interaction of transcription factors with regulatory molecules and their operator site or proper spatial localization of the object of interest.kT. This value is of the same order of magnitude as that of many hydrogen bonds, creating a considerable background noise. Key/lock recognition systems are too simple to afford the accuracy of 1/10 000 or even greater required in the synthesis of proteins and nucleic acids for example. This physics‐based challenge has been explored experimentally very early on. For example, Cramer and co‐workers illustrated the problem by comparing the input of valine and isoleucine analogues in the corresponding tRNA synthetases. They found that several energy‐consuming processes were implemented during the apparently simple process of ligation of an amino acid to its cognate tRNA , most of what was relevant in the cell metabolism was more or less free to diffuse and spread throughout the cytoplasm without alteration. It was however critical to consider the entropy contribution of components of the cell, which required understanding their exploratory tendency in terms of time‐dependent positions and energy states. Common sense appreciates that, when significant entities are available in small numbers – a routine situation in cells – their relative position and energy states become particularly relevant. When two compounds are spatially next to one another, they can interact, and make functional complexes or non‐functional aggregates, for example. When distant, nothing happens. A relevant function is therefore to manage accurately the positioning of entities of interest. This includes taking symmetry into account, keeping energy states between narrow borders, or separating between intact or altered entities (aged ones must be distinguished from young ones in particular). In short, many critical processes are involving information management. Accurate timing also belongs to the category of events that may fit with the action of MxDs. This is the types of situation we try to clarify in the few examples detailed below.The functions we retained here are meant not only to recognize a target or an event, but, in a noisy background, to discriminate against similar targets, target states and location, or events, so that they can interact in an orderly fashion. Relevant biological interactions have, if we pursue the key/lock metaphor, to be immune against omnipresent picklocks and skeleton keys, and spontaneous, but irrelevant interactions . Natural selection operating on this need for discrimination in addition to recognition has led to functional convergent evolution of a variety of dynamic structures that behave as MxDs. The original MxD was an agent that measured properties of similar entities (gas atoms moving at different speeds) between two chambers separated by a trap opened or closed (gated) at will by the demon. Transporting molecules across membranes, when the cell must discriminate between related molecules and change their internal concentration, illustrates the embodiment of a process that is very similar to the original MxD thought experiment. We begin here with transporters that, while getting rid of waste or take in highly diluted nutrients, must take great care not to empty the cell of its important metabolites.et al., et al., et al., Cells must import nutrients and export waste, toxic compounds or mediators of cell interactions. They must also be able to distinguish what belongs to the cell from what does not belong to it. Transport across membranes is performed via a variety of permeases [for the origin of the concept see , apparently widely differing in their chemical properties. Yet, despite this variety, these transporters must nevertheless efficiently discriminate against the prevailing metabolites of the cell, as, otherwise, they would keep leaking out essential components. A way to do so is to tag the exported substrates with specific chemical groups, an energy‐dissipating step, allowing these transporters to act as regulatory safety valves Danchin, . Thus, wet al., et al., At this stage, the protein's conformational changes will lower the ATP hydrolysis activation barrier, preparing for the hydrolysis step that will occur later on. Subsequently (possibly simultaneously in some cases), ATP hydrolysis is coupled to mechanical changes, exposing hydrophobic residues to water  and opening up the transporter into the substrate release compartment mechanical energy source required by transport , while the second one is involved in the physical management of the material system that processes information. The role of hydrolysis of two ATP molecules has been discussed in a variety of models, in particular, the alternating site hydrolysis model, proposed by Alan Senior in 1995 , the ‘power stroke’ mechanical site would become useless and would degenerate over time. This is substantiated by experimental observations. Indeed, a variety of ABC transporters have their second ATP‐binding site degenerate. They have a role restricted to management of a gating process, which can be seen as the consequence of a ‘broken pump’ where ATP hydrolysis is now purely informational, controlling energy‐dependent gating [for example for ion transport , in a highly specific manner  superfamily of ATPases .Global transcription is another process that needs to be carefully controlled. Sigma factors are a family of subunits of RNA polymerase (RNAP) involved in promoter recognition. In general, there may not be strong constraints to get controls acting beyond recognition of their targets. However, under certain circumstances, turning on transcription of a large fraction of the bacterial genome would require massive deployment of resources for the formation of new gene products. It may therefore be important not only to recognize the cognate promoters, but to discriminate against noise in order to activate them only under relevant conditions, in particular while monitoring energy availability. To be sure, management of gene expression under specific environmental conditions sometimes requires discriminative power to allow the cell to express a large number of genes. One of the most remarkable cases to this end involves prokaryotic promoters dependent on the alternative and somehow unique sigma factor σ70‐dependent promoters for RNAP binding, the σ54‐dependent variants are notable in several respects. First, in the RNAP‐σ54 binding site, most conserved bases are located at positions −12 and −24 with respect to the transcription initiation site, and the region is GC‐rich. It has been generally believed that DNA strand separation required for transcription initiation from these promoters is therefore more energy demanding and thus that an extra energy must be entered in the system to trigger formation of an open complex. Furthermore, in a subset of promoters, the RNAP‐σ54 subunit sits on the cognate sequence forming a transcriptionally dead closed complex. While binding of typical RNAP‐σ70 to the −10 and −35 motifs melts DNA at the promoter to initiate transcription, typical RNAP‐σ54 binds −12 (GC) and −24 (GG) motifs while preventing transcription initiation at the promoter, in the absence of the matching activator. Second, the cognate transcription factors (TFs) attach to locations somewhat far from the RNAP‐σ54 binding site(s). This entails that productive contacts between them and the downstream‐bound polymerase require the upstream transcription factor (TF)‐DNA complex to sharply bend the intervening DNA segment. The general similarity of this scenario with that involving eukaryotic promoters run by RNAP II has popularized naming the attachment sites of bacterial TFs in RNAP‐σ54 complexes as ‘prokaryotic enhancers’. The corresponding proteins have been named prokaryotic enhancer‐binding proteins (EBPs) accordingly.Unlike the typical AT‐rich −10/−35 motifs that abound in the majority of the standard σ54 are often facilitated by the DNA‐binding protein integration host factor (IHF), which in turn imposes a fixed geometry that avoids the accidental activation of the RNAP‐σ54 by non‐cognate EBPs – that can also activate the RNAP‐σ54 holoenzyme directly, in the absence of any significant upstream DNA binding  that become competent for ATP hydrolysis. This process was seen as coupled to interactions with the polymerase (and otherwise dead RNAP‐σ54 complexes) through a conserved sequence in the AAA+ activator, known as the GAFTGA motif. This site appeared to couple ATP hydrolysis to formation of an open complex through the conserved regulatory region I of σ54 formation . Upon binding, the intramolecular repression caused by interaction of the protein module with the RNAP‐σ54 promoters seem to have been selected to control gene expression of highly regulated processes that require no expression in the off‐state, and very high expression in the on‐state. The control process entails that reinitiation of open promoter complex formation is coupled to the ATPase activity of AAA+ activator EBPs. As mentioned above, many of the features found in σ54‐dependent systems might be conserved in all three domains of life, specifically in eukaryotic promoters dependent on RNAP II. In terms of evolution, ancestors of σ54 domains may have been part of an early transcription repression system meant to keep the RNAP in check, even acting globally and in trans to genes before the RpoN domain was co‐opted for promoter‐specific binding and specific gene expression ‐component system protein that binds upstream regions of σ54‐dependent promoters for synthesis of a regulatory small RNA and a second sigma factor, σ24 (RpoE). Associated with QseF, sensor protein QseE binds epinephrine, a metabolite that allows the system to couple the bacterial metabolism to host signals, in particular in pathogens. In this system, energy involvement is more complex than with standard EPBs, in that instead of being an ATP‐binding protein, QseF needs to be phosphorylated by QseE in complex with outer membrane sensor QseG to activate its cognate promoters. However, we witness also an energy‐dependent recycling process in that QseE must be dephosphorylated by a second activity of the QseE protein (phosphatase) to start another round of control  is an informational G protein  was initially supposed to facilitate back‐translocation on the 70S ribosome, allowing the system to correct a possibly deleterious error  and at least one copy in the streamlined genomes of Buchnera aphidicola and Mycoplasma mycoides  have been found to endow cells with antibiotic resistance  exist in es Table . SimilarChromosomal DNA is a long protein that folds back on itself multiple times to be accommodated in the small compartment that makes the cytoplasm (or the nucleus in Eukarya). This implies that many structural features of the molecule must be supervised not only during replication , but also as transcription develops while a variety of accidents are doomed to happen. As cases in point, DNA strands may be broken and this will alter supercoiling with a considerable change in DNA compaction. Broken ends must be repaired, and this implies recombination with intact segments of available DNA molecules. Keeping up with the theme of this article, we now look for processes involving energy in an unexpected or unexplained way to identify such control systems. Important nucleic acid‐binding proteins displaying an enigmatic ATPase activity could indeed now be seen as a MxD that recognizes specific DNA features. We document here two cases, that of the recombination protein RecA, and that of topoisomerases of class II. We also provide hints about further processes that are likely to require the action of MxDs, in particular replication initiation and termination.recA null mutants are viable however, and the protein is dispensable in M. mycoides Syn3.0 , RecA proteins recognizing and self‐aligning to a 3‐nt‐period sequence pattern of TGG  is a large helicase of unknown specificity  through a transient double‐stranded break in another duplex . These enzymes ensure that supercoiling of DNA fits with its requirement as a template for efficient transcription. This implies that topoisomerases measure supercoiling locally, then modify it, if not fitting. The spatial structure of a variety of these enzymes is known and used to infer their mechanism of action  is transferred through the gate, which is subsequently sealed back into a continuous DNA duplex , belonging to the AAA+ superfamily, could illustrate such a function. The MaoP/maoS system  is responsible for constraining DNA mobility in the Ori region, limiting long‐distance DNA interactions with other regions of the chromosome. Macrodomain organization influences the segregation of sister chromatids and the mobility of chromosomal DNA , possibly resulting from convergent evolution. Interestingly, MaoP coevolved with Dam methylase as well as SeqA, MukBEF and MatP that are all involved in the control of chromosome conformation and segregation.With the size of genomes increasing, the urge for MxDs allowing the organization of genomes into domains became evident. As a case in point, the genome of E. coli. Ring formation and translocase activity require cleavage by the recombinase subunits. Yet, similar to the multiplicity of RecA functions, these functions may be accessory to the key function of the protein: forming a hole at the division interface with DNA travelling through the central hole and controlling DNA segregation from the parent to the daughter cell  PtkA, the chromosome segregation protein Soj (ParA), the cell division protein MinD and a transcription regulator, SalA. These proteins appear to have arisen via duplication of an ancestral ATP‐binding domain fused with other functional domains. Despite having very different physiological roles, these four proteins engage in a high number of binary functional interactions allowing them to reach proper locations. MinD attracts Soj and PtkA to the cell pole, and, in addition, activates the kinase function of PtkA. SalA also activates the kinase function of PtkA, and it gets phosphorylated by PtkA as well. The consequence of this phosphorylation is the activation of SalA as a transcriptional repressor. SalA also requires ATP binding to recognize its proper target, ScoC, which represses synthesis of extracellular proteases, and bacilysin synthesis and sporulation  and its receptor FtsY, which deliver inner membrane proteins to either the SecYEG translocon or the SecX(YidC) insertase for membrane insertion. The very fact that this system is involved in recognition of specific signals substantiates its role as an information‐management system. Indeed, the recognition step of the system is triggered before GTP hydrolysis, yet another example of energy‐independent (reversible) creation of information. Efficient targeting relies on membrane interaction of FtsY and heterodimerization with the SRP protein Ffh, in a way that fits well with Landauer's principle, with GTP hydrolysis used to reset the system to its ground state ready to accept a new polypeptide in statu nascendi for membrane insertion  and either the DeaD or SrmB helicases display growth defects and an enhanced accumulation of ribosomal RNA fragments Jain, . RNase Ret al., Akkermansia muciniphila or Spirochetes , an omnipresent chaperone located at the tunnel where the polypeptide in statu nascendi exits the ribosome, channels folding along a path that has been the result of a very long process of natural selection. Tig has to recognize the nascent polypeptide but does not need to discriminate its features against similar features  always simultaneously present: chaperonin GroES/GroEL  and those which are properly folded. Energy is used to place the unfoldase into a conformation prone to bind to a specific class that of misfolded proteins. In this process, the presence of ATP may be required for the initial step, which leads to proper folding in a sequence of out of the equilibrium steps  and short ClpB(beta), which is initiated from an internal GUG start at codon 149. The N‐terminal domain, present only in the ClpB long form, is required for efficient binding to protein aggregates and fully effective chaperone function. Coordinated expression of both long and short forms of ClpB is required for the protection from thermal killing .In the same way, another ubiquitous protease is required for cleaning up defective regulators in a large number of bacteria , an omnipresent mineral that is insensitive to harsh physical conditions. PolyP can substitute for ATP in many important reactions [see Danchin,  for revikTln2. To be sure, in E. coli cells growing on glucose, a value of ≈20 kT was reported for average ATP hydrolysis  into the physical world made of compounds endowed with mass and energy, and developing in space and time. A MxD is an abstract massless entity. Yet, its physical implementation requires that it is made of concrete atoms. Worse, it also has to perform its actions, which, in the initial massless metaphor, does not entail mechanical energy dissipation. By contrast, once made of atoms, mechanical constraints ask for a counterpart that dissipates some energy. The movements of an authentic MxD nanomachine are mechanical, and this cannot be free from energy dissipation. As a consequence, when we say that the demon must be reset to its original state, this implies both an informational reset and a mechanical reset.et al., This is the price to pay to implement an abstract requirement into the world of mass and energy. The consequence is that, if information management is indeed a critical feature, it must be associated with a concrete entity that will use energy well above the constraints of noise. We propose that this is exactly the raison d’être of the use of one ATP molecule in the material implementation of a concrete MxD. The living MxD used an amplification of the basic energy requirement of Landauer's principle, making ATP hydrolysis the basic ‘quantum’ of energy necessary to manipulate information, instead of the abstract physical requirement, which is much lower. In mechanical terms, hydrolysis of one ATP molecule is twice the energy necessary to put into motion a molecular motor that exerts a force of roughly 5 pN over a 10 nm step size, producing work of the order of 50 pN nm  should be explored in priority. The latter might be particularly revealing as they couple signal recognition to their cyclase activity. Evolution of these proteins might have led to functions that use an extra or apparently inessential nucleotide in the elusive function of MxDs.What is Life? most life scientists have explored the thermodynamic behaviour of living cells in terms of entropy control, in general perceived as a fight against disorder. In most approaches, this view was linked to information only via the similarity of the mathematical description of entropy in large systems of equivalent entities and that of information carried by a message as described by Claude Shannon. Briefly, behaviours that could be described in a Σ pi lnpi law were implicitly assumed to result from a common cause, pertaining to concepts that remained fuzzy, such as order or information. Yet, the very nature of information has long been explored with much deeper concepts, such as algorithmic complexity (Kolmogorov, The cell is the atom of life. Constructing living factories requires us to understand the way it dissipates energy. Among its many original features, the cell has to distinguish whether something belongs to it or is of foreign nature. Understanding how this proceeds is key for the future developments of synthetic biology. There is no life without an implementation of a process allowing creation and continuity of an identity. Since the time of the famous book of Erwin Schrödinger et al., et al., et al., et al., A limited number of life scientists have perceived the importance of this view [see in particular (Mommaerts, None declared.