Drosophila model, we performed an in-depth study of the biogenesis of telomeric piRNAs and their function in telomere homeostasis in the germline.Telomeric small RNAs related to PIWI-interacting RNAs (piRNAs) have been described in various eukaryotes; however, their role in germline-specific telomere function remains poorly understood. Using a To fully characterize telomeric piRNA clusters, we integrated the data obtained from analysis of endogenous telomeric repeats, as well as transgenes inserted into different telomeric and subtelomeric regions. The small RNA-seq data from strains carrying telomeric transgenes demonstrated that all transgenes belong to a class of dual-strand piRNA clusters; however, their capacity to produce piRNAs varies significantly. Rhino, a paralog of heterochromatic protein 1 (HP1) expressed exclusively in the germline, is associated with all telomeric transgenes, but its enrichment correlates with the abundance of transgenic piRNAs. It is likely that this heterogeneity is determined by the sequence peculiarities of telomeric retrotransposons. In contrast to the heterochromatic non-telomeric germline piRNA clusters, piRNA loss leads to a dramatic decrease in HP1, Rhino, and trimethylated histone H3 lysine 9 in telomeric regions. Therefore, the presence of piRNAs is required for the maintenance of telomere chromatin in the germline. Moreover, piRNA loss causes telomere translocation from the nuclear periphery toward the nuclear interior but does not affect telomere end capping. Analysis of the telomere-associated sequences (TASs) chromatin revealed strong tissue specificity. In the germline, TASs are enriched with HP1 and Rhino, in contrast to somatic tissues, where they are repressed by Polycomb group proteins.piRNAs play an essential role in the assembly of telomeric chromatin, as well as in nuclear telomere positioning in the germline. Telomeric arrays and TASs belong to a unique type of Rhino-dependent piRNA clusters with transcripts that serve simultaneously as piRNA precursors and as their only targets. Telomeric chromatin is highly sensitive to piRNA loss, implying the existence of a novel developmental checkpoint that depends on telomere integrity in the germline.The online version of this article (10.1186/s13072-018-0210-4) contains supplementary material, which is available to authorized users. Tetrahymena thermophila, in plants, and in Diptera [Drosophila melanogaster [Drosophila model, we performed an in-depth study of the biogenesis and function of the telomeric piRNAs in the germline.Telomere transcription is an evolutionarily conserved feature of eukaryotic telomeres . The bio Diptera –5. Small Diptera , 5, 6. P Diptera , 8. Howenogaster . Using aDrosophila germline produce piRNAs from precursors transcribed by both genomic strands. Distinct chromatin components of the piRNA clusters that couple transcription and RNA transport appear to direct the cluster-derived transcripts into the piRNA processing machinery [The piRNA-mediated pathway provides silencing of transposable elements (TE) in the germline , 9. In cachinery –13. The achinery –16. piRNachinery .Dual-strand piRNA clusters can be classified into several types according to their structure and location: extended pericentromeric TE-enriched regions , individD. melanogaster are maintained by transpositions of specialized telomeric retrotransposons, while the telomerase gene has likely been lost in an ancestor of Diptera [HeT-A, TART, and TAHRE retroelements are organized in tandem head-to-tail telomeric arrays, with HeT-A being the most abundant [HeT-A and related TAHRE elements are extremely sensitive to piRNA pathway disruption showing up to 1000-fold overexpression in contrast to TART, which demonstrates only modest upregulation [HeT-A expression has often been used as a readout of piRNA pathway disruption. However, HeT-A elements are not typical piRNA targets, since they are also the source of piRNAs. Several studies have indicated differences between transcriptional regulation of telomeric and non-telomeric piRNA clusters. Indeed, in contrast to HeT-A, the level of the piRNA precursors transcribed by non-telomeric piRNA clusters decreases upon piRNA pathway disruption [The telomeres of  Diptera . The nonabundant –24. Teloabundant ; thus, tabundant , 25. Vioabundant , 26. It gulation , 28. Thesruption , 15, 29.Drosophila strains carrying P-element constructs in the terminal retrotransposon array have been identified and characterized [HeT-A–TART–TAHRE arrays are active in somatic tissues, which have allowed for selection of such transgenic strains [Drosophila telomere in somatic tissues, namely, transcriptionally active retrotransposon arrays and heterochromatic TAS [Unique mapping of small RNA reads is the major source of information about the genomic origin of piRNAs; however, in the case of telomeres, it is technically challenging. Artificial sequences inserted into the endogenous piRNA clusters serve as unique marks that enable exploration of the highly repetitive genomic loci. Transgenic cterized . In contcterized –33, the  strains . Therefoatic TAS , 31.HeT-A–TART–TAHRE arrays show similar chromatin structure and transcriptional status in the germline and can be related to the piRNA-producing domain. At the same time, we found that piRNA production is not similar between the transgenes integrated in different telomeric retrotransposons. Chromatin and cytological studies provide convincing evidence that telomeric piRNA clusters are highly sensitive to piRNA loss in contrast to the heterochromatic non-telomeric dual-strand piRNA clusters. Moreover, piRNA loss causes telomere translocation from the nuclear periphery toward the nuclear interior. Our data, in combination with the previously observed differences in behavior of telomeric and non-telomeric piRNA clusters [Drosophila germline.Taking advantage of the telomeric transgene insertions in combination with experiments on endogenous telomeric elements, we investigated piRNA production and chromatin structure of different telomeric loci in the ovaries of transgenic flies. It was shown that telomere-specific piRNAs significantly affect the chromatin structure and expression of different telomeric regions in the germline. In contrast to somatic tissues, the TAS and clusters , 29, 34,y1w67c23 (yw) strain background carrying the P{EPgy2} construct in the telomeric retrotransposon arrays [P-element-based vector containing mini-white and yellow genes. The transgene EY08176 was inserted into the GAG ORF of HeT-A-related TAHRE on chromosome 2R. The transgenes EY00453 and EY00802 were integrated into 3′ UTR of TART-B1 on 3L, while the EY09966—into TART-C on the fourth chromosome. All TART insertions were located in the 3′UTR within the long non-terminal repeats containing the promoter region. Insertion sites are located between the sense and antisense transcription start sites [mini-white reporter gene expression [It is well known that transgenes inserted within TASs produce abundant piRNAs and exert piRNA-mediated silencing of the complementary targets –38. Howen arrays . P{EPgy2rt sites , which srt sites . All trart sites . The orapression , 33. Thepression . The insHeT-A, TAHRE, and TART-specific small RNAs are found in yw and transgenic strains . Moreover, most of these small RNAs are 24–29 nt long and demonstrate 5′ terminal uridine bias (1U bias), which is a characteristic of the piRNAs  transgenes produce fewer small RNAs compared to EY08176 (insTAHRE) and EY03383 (insTAS) but apparently more than the EY03241 (control) transgene  and EY03383 (insTAS) strains  of EY08176 (insTAHRE), Rhi binding to TART transgenes was much less pronounced but statistically significant , we genetically combined it with the spnE short hairpin construct because the spnE locus is located on the same chromosome as the transgene insertion site, making it difficult to combine the transgene and spnE mutations. It is remarkable that the chromatin of this transgene is resistant to piRNA loss caused by spnE depletion  mutants these effects are less pronounced  proteins [Drosophila ovaries and observed its enrichment in HP1, H3K9me3, and Rhi , EY00802 (ins1TART-B), and EY00453 (ins2TART-B) transgenic strains but not in EY03383 (insTAS) and EY09966 (insTART-C) strains. Thus, the transcription activity of transgenes located at different positions within the telomere is similar in the germline but differs considerably in the somatic tissues and appears to be defined by the tissue-specific chromatin structure.Next, we compared the expression activity of telomeric transgenes in ovaries. We found that the steady-state RNA levels of ted Fig. c. Low leTAHRE produces considerably more piRNAs and shows stronger enrichment in Rhi than the transgenes located in the promoter region of TART elements. Accordingly, we revealed the low level of Rhi biding to the endogenous TART-B promoter region. Moreover, Rhi immunostaining and TART FISH data also demonstrate that much less Rhi is deposited on TART in comparison with HeT-A, suggesting a lower susceptibility of TART elements to engagement in piRNA production in general. Most likely, TART copies are not equivalent in their capacity for piRNA production and Rhi binding, although the nature of these differences is unclear. Previously published works have already underlined the strong differences between HeT-A and TART in genomic copy number, structure, patterns of transcription, and response to piRNA pathway disruption [TART transcripts are more stable [HeT-A lacking RT. We suggested the intriguing possibility that the transcripts of full-length TART copies might be protected from piRNA biogenesis machinery by an unknown mechanism to ensure encoding of the crucial enzyme for telomere elongation, TART RT, in the germline.In this study, we described telomeres as piRNA clusters using a combination of different approaches. The data on endogenous telomeric retrotransposons show that they produce piRNAs and associate with Rhi. Intriguingly, the piRNA production by individual telomeric transgenes depends on the type of telomeric retrotransposon in which the transgene is inserted. The transgene located within sruption , 54, 55.TART insertions, indicating that Rhi and HP1 do not compete with each other for binding sites at telomeric chromatin. Thus, our data revealed heterogeneity in piRNA production and Rhi deposition within telomeric repeats. TART retrotransposons are less susceptible to Rhi binding than are HeT-A and TAHRE.Telomeric chromatin plays a pivotal role in telomere protection and maintenance. HP1 and H3K9me3 regulate capping, telomeric repeat silencing, and control of their transpositions onto chromosome ends , 47, 56.Drosophila germline. The piRNA clusters, enriched in damaged TE fragments, produce piRNAs that target active TEs [piRNA sources and piRNA targets are mainly represented by different genomic loci in the tive TEs , 29. Thetive TEs , 42. Howtive TEs , 17, 29.TAHRE–HeT-A arrays, as well as from endogenous telomeric retrotransposons and TAS, in contrast to non-telomeric piRNA clusters. Of note, HP1 and H3K9me3 association with the EY00453 transgene (insTART) is not affected by spnE depletion, indicating that the chromatin status of this insertion region or particular TART copies is maintained by piRNA-independent mechanism.In-depth analysis of the telomeric piRNA clusters revealed strong differences in chromatin dynamics between telomeric and non-telomeric piRNA clusters. Using different approaches, we demonstrated that the piRNA pathway mutations induce loss of HP1, H3K9me3, and Rhi from the telomeric transgene located in the 42AB piRNA cluster was far less sensitive to germline depletion of Rhi or HP1a compared to the subtelomeric piRNA clusters and transgene located in this region [It has been shown that maternal and/or zygotic piRNAs were sufficient to induce formation of repressive chromatin at non-telomeric piRNA clusters in early embryogenesis and that this state was maintained during germ cell development, even upon piRNA loss at later developmental stages . In conts region . Thus, t38C1, is initiated not only from internal initiation sites but also from flanking promoters, which differ significantly from canonical promoters [38C1, is resistant to piRNA loss. The TATA box-binding protein (TBP)-related factor 2 (TRF2) and Moonshiner, a paralog of a transcription factor IIA (TFIIA), are required for non-canonical transcription initiation and piRNA production from most of the non-telomeric clusters [HeT-A/TAHRE transcription, which is dispensable for HeT-A small RNA production [We propose that the fundamental difference between Rhi-dependent telomeric and non-telomeric piRNA clusters is due to their different transcriptional regulation. Strong bidirectional promoters drive transcription of the telomeric retroelements , 57, 58.romoters . Accordiclusters . In contoduction . In fissoduction , therebyHeT-A copies, normally positioned at the nuclear periphery, were located more toward the nuclear center following the loss of piRNAs. We suggest that this process is induced by the massive HeT-A overexpression and is related to the expression-dependent nuclear positioning phenomenon described by several groups (for review see [We found that the clustered view see ). The stview see . We suggDrosophila somatic cells [Drosophila oocytes at the pachytene stage of meiosis [HeT-A DNA FISH signals next to the nuclear periphery in the nuclei of polyploid nurse cells; however, it is unclear which particular telomeres are involved in clustering. piRNA loss affects the peripheral localization of telomeres in the germline but does not alter telomere clustering or the assembly of the telomere capping complex.Telomere clustering at the nuclear envelope is a commonly observed but not absolute phenomenon. Telomere clustering in close proximity to the nuclear periphery was observed in the ic cells , 65. How meiosis . We revepiwi depletion is observed for such TE copies [In addition to the telomeric regions, some recently transposed transcriptionally active TE copies inserted into euchromatin produce piRNAs . Strong E copies . The maiHeT-A–TART–TAHRE arrays in the Drosophila germline. Both telomeric regions produce piRNAs, bind Rhi, and are expressed at a similar low level , Montpellier, France. P{EPgy2}Upf3EY03241 (stock #16558) was obtained from the Bloomington Drosophila Stock Centre. Strains bearing spindle-E (spn-E) mutations were ru1st1spn-E1e1ca1/TM3, Sb1es and ru1st1spn-Ehls3987e1ca1/TM3, Sb1es. We used piwi2 and piwiNt alleles [Zuc mutants were zucHm27/Df(2L)PRL trans-heterozygous flies [P{UAS-Dcr-2.D}1, w1118, P{GAL4-nos.NGT}40, Bloomington Stock Center) providing GAL4 expression under the control of the germline-specific promoter of the nanos (nos) gene.Transgenic strains EY08176, EY00453, EY00802, EY09966, and EY03383 carrying the EPgy2 element and inserted within different telomeric regions were described previously  and were alleles . Zuc mutus flies . GLKD (fwhite-specific primers 5′-catgatcaagacatctaaaggc-3′ and 5′-gcaccgagcccgagttcaag-3′ was labeled with a DIG DNA labeling kit (Roche).Fluorescence in situ hybridization (FISH) with polytene chromosomes was performed as previously described . A PCR frp49. Standard error of mean (SEM) for two independent RNA samples was calculated. The primers used are listed in Additional file RNA was isolated from the ovaries of 3-day-old females. cDNA was synthesized using random hexamers and SuperScript II reverse transcriptase (Life Technologies). cDNA samples were analyzed by real-time quantitative PCR using SYTO-13 dye on a Light Cycler 96 (Roche). Values were averaged and normalized to the expression level of the ribosomal protein gene Drosophila melanogaster genome  or transgenes by bowtie [y1w67c23 and transgenic strains EY08176, EY00453, EY00802, EY09966, EY03383, and EY03241 were deposited at the Gene Expression Omnibus (GEO), accession number GSE98981.Small RNAs 19-29-nt in size from total ovarian RNA extracts were cloned as previously described . Librariy bowtie . Small RFor every IP experiment ~ 200 pairs of ovaries were dissected. ChIP was performed according to the published procedure . ChromatTART, cloned fragment of TART-A ORF2 corresponding to 434–2683 nucleotides in GenBank sequence DMU02279; HeT-A, cloned fragment of HeT-A ORF corresponding to 1746–4421 nucleotides in GenBank sequence DMU 06920; TAHRE, PCR fragment corresponding to 5147–6165 nucleotides in GenBank sequence AJ542581. The TART probe was labeled using a DIG DNA labeling kit (Roche), HeT-A by a Bio-Nick labeling system (Invitrogen), and TAHRE by PCR DIG labeling mix (Roche). Probes corresponding to 2R-3R TAS, 2L-3L TAS, and 42AB regions were PCR fragments obtained with primers listed in Additional file The combined evaluation of protein and DNA localization was done according to the previously described procedure . Rabbit HeT-A cluster. FISH spot size is the diameter of a sphere encompassing all of the spots in the XY plane and Z position corresponding to the center of mass. The distance between the center of image masses and the nearest point on the nuclear surface was measured by increasing the radius of a sphere originated from the center of image masses until it intersected with the nuclear surface and later recording the radius as a distance. An independent two-sample t test was used to compare hetero- and trans-heterozygous mutants.Calculations were performed using Imaris 7.4.2 software with manual segmentation of nuclei based on DAPI staining, automatic segmentation of in situ signal spots, and automatic calculation of a center of homogeneous mass corresponding to the main white sense probe contained a cloned PCR fragment amplified using primers 5′-ctcacctatgcctggcacaatatg-3′ and 5′-attcagcagggtcgtctttccg-3′. Hybridization with P32 5′-end-labeled oligonucleotide 5′-actcgtcaaaatggctgtgata-3′ complementary to the miRNA-13b-1 was used as a loading control. The blots were visualized with a phosphorimager Typhoon FLA-9500 (Amersham). Northern blot quantification was done using ImageJ.Northern analysis of small RNAs was performed as previously described . The whiAdditional file 1: Figure S1. Localization of telomeric transgenes. Figure S2. Profiles of telomeric retroelement small RNAs .


Drosophila melanogaster. We find that the majority of active gene-distal enhancers and a considerable fraction of gene promoters are divergently transcribed. We observe quantitative relationships between enhancer potential, expression level and core promoter strength, providing an explanation for indirectly related histone modifications that are reflecting expression levels. Lowly abundant unstable RNAs initiated from weak core promoters are key characteristics of gene-distal developmental enhancers, while the housekeeping enhancer strengths of gene promoters reflect their expression strengths. The seemingly separable layer of regulation by gene promoters with housekeeping enhancer potential is also indicated by chromatin interaction data. Our results suggest a unified promoter architecture of many D. melanogaster regulatory elements, that is universal across Metazoa, whose regulatory functions seem to be related to their core promoter elements.Mammalian gene promoters and enhancers share many properties. They are composed of a unified promoter architecture of divergent transcripton initiation and gene promoters may exhibit enhancer function. However, it is currently unclear how expression strength of a regulatory element relates to its enhancer strength and if the unifying architecture is conserved across Metazoa. Here we investigate the transcription initiation landscape and its associated RNA decay in  Drosophila melanogaster have suggested a distinct reduction in divergent events at gene promoters  and their cell type-restricted activities, the regulatory mechanisms of these genomic regions are not well understood. Careful characterization of the properties of TREs and the determinants of their regulatory activity is crucial to better understand the means by which cells control gene expression. Despite the often adopted view on enhancers and gene promoters as distinct entities with discernible functions and local chromatin characteristics , e.g. diromoters  and lessromoters . These oTranscription of mammalian protein-coding genes into mRNA is coupled with upstream transcription in the reverse orientation . The latD. melanogaster genomic sequences. Assays based on self-transcribing regulatory regions (STARR-seq)   , have reARR-seq) . HoweverARR-seq) . In addis (TADs) ,39 may cs (TADs) .D. melanogaster to assess whether properties  of TREs are conserved across Metazoa and what determines their transcriptional activities. We find that divergent transcription is a common state of D. melanogaster gene promoters and gene-distal enhancers, which is supported by three recent studies using alternative assays  supplemented with 10% FBS  and 1% penicilin/streptomycin . Double-stranded RNAs (dsRNA) to deplete Rrp6 and Dis3 were prepared by in vitro transcription from a PCR template with T7 promoters on both ends using the Megascript RNAi kit  according to the manufacturer’s instructions  in 500 μl media was added dropwise to the cells, the plates were agitated for 30 s and incubated for 6 h at 27°C. Finally, 2.5 ml of media with FBS and penicilin/streptomycin were added. The treatment was repeated 2 days later and the cells were harvested 4 days after the first dsRNA treatment.ructions . DsRNA aD. melanogaster  was performed using Bowtie .The expression of each TC in each individual CAGE replicate was quantified by counting of CAGE 5′ ends falling into their defined genomic regions. In addition, CAGE genomic background noise levels were estimated see . Only TCRrp6 and Dis3.To measure the effect of the knockdowns, the mean expression and standard deviation over the four replicates for the knockdown and control experiments were calculated for TCs annotated to the primary FlyBase TSSs of the genes m oligo dT18 and 5 ng/μl/ μl random primers. qPCRs were performed with Platinum SYBR Green qPCR SuperMix-UDG  in a MX3000P (Agilent technologies) machine. RNA levels were normalized to that of Act5C.Expression levels and exosome sensitivities measured by CAGE were validated using RT-qPCR . The RNADHSs were used as focus points for characterising patterns of transcription initiation events at TREs as described elsewhere , with miUnsupervised clustering of DHSs was performed on the basis of transcriptional properties derived from each replicate CAGE library in a two-step approach see , to guarRpS12 and even skipped core promoters, for housekeeping (hkCP) and developmental (dCP) enhancer potential, respectively. At the summit position, the log2 fold change of STARR-seq signal over STARR-seq input signal was calculated. STARR-seq active regions were defined as those DHSs having a log2 fold change of at least 1.5.Wiggle track STARR-seq data  were useD. melanogaster. DRE, Inr, Trl, E-box motifs were retrieved from DMMPMM D. melanogaster motif collection  equal to 1 and a maximum number of motif occurrences retained in memory at 100 × 106. The FIMO output was filtered to only contain the motif hits with maximum score for each motif occurrence for each DHS strand window. Motif hits were subsequently considered significant if they passed a P-value threshold <0.001. Core promoter element clustering was performed using the R function pheatmap (Ward.D agglomeration) on scaled and centered data for each core promoter element. Core promoter clusters were determined by the cutree R function with k = 10 desired groups.Core promoter element occurrences were scanned around each transcribed DHS using MEME FIMO (v4.11.2) . A genomllection . Finallyllection . Motif PBinarised  modENCODhttp://github.com/kundajelab/). Mapped ChIP-seq signal was then quantified within each 401 bp DHS region. ChIP-seq signal within DHS windows was plotted as a function of housekeeping (hkCP) or developmental (dCP) enhancer potential, using the binned 1−99th percentile STARR-seq signal. ChIP-seq signal within DHS windows was also plotted against the binned 1−99th percentile CAGE TPM expression.ChIP-seq data  for histD. melanogaster Kc167 cells based on 1kb resolution HIC data . To calculate enrichments of TAD boundary vicinities of DHS classes, a cut-off of 1 kb from the nearest TAD boundary was applied to determine inclusion or exclusion of a class element from a boundary region.For each DHS class, the proportion of elements annotated as falling inside of a TAD region, or between (not overlapping a TAD) was calculated. For each TAD, the number of DHSs of each class was aggregated. To calculate the enrichment of elements according to TAD size, TADs were split according to the total number of DHSs that were within them, grouping all TADs with more than six elements, and the proportion of each of the classes calculated per total size. The logStatistics of interactions and co-occurrences within TADs were analysed using generalized linear models see .D. melanogaster, we performed deep Cap Analysis of Gene Expression (CAGE (Dis3 and Rrp6 genes when compared to control (dsRNA against GFP) libraries (To characterize transcription initiation events in on (CAGE ) sequencibraries . The greibraries , indicatibraries  gene TSSibraries . In addiibraries . These oTo quantify the extent and characteristics of divergent transcription in fly cells, we clustered proximally mapped CAGE tags into genomic regions representing CAGE-inferred TSSs (referred to as tag clusters (TCs)). Wide TCs were trimmed and those representing multi-modal peaks were split into narrow single-peak TCs . AlthougD. melanogaster are divergent, and that a fraction of these events are associated with exosomal RNA decay, in accordance with human cells.We next calculated the distance from each plus strand TC to the nearest upstream (non-overlapping) minus strand TC Figure . ∼47% anP < 2.2 × 10−16, odds ratio 13.3, Fisher’s exact test). Only 139 (1.5%) DHSs were associated with convergent transcription. Based on these observed trends, we quantified DHS-associated expression through aggregation of CAGE tags in strand-specific divergently oriented windows of 200 bp immediately flanking DHS center points that maximized CAGE tag coverage  . Overall, DHSs showed preferences for minus strand expression upstream (relative to the genome reference) and plus strand expression downstream of DHS center points in exosome depleted libraries  with annotated unidirectional gene TSSs, consistent with a strong directional expression bias resulting from high expression levels and low exosome senstivity from their major strands. In contrast to unidirectional stable DHSs, DHSs in the unidirectional stable w/ PROMPT category were in addition associated with lowly expressed and highly unstable RNAs from their minor strands, properties reminiscent of human mRNA gene promoters associated with PROMPT transcription . DHSs in this class were enriched  in annotated head-to-head gene TSSs. We collectively refer to these three classes as stable TREs, due to the low exosome sensitivity of RNAs transcribed from their major strands.The clustering of DHSs revealed several interesting relationships between expression levels, transcriptional directionality and exosome sensitivity, and displayed widely different enrichments of annotated gene TSS proximities Figure . Three icription ,31,32. Wunstable TREs, Figure weak bidirectional unstable) gathered DHSs associated with balanced low output of unstable RNAs. Intermediate bidirectional stable DHSs exhibited moderately higher expression on the major strand resulting in a more biased directional transcription. DHSs having close to unidirectional output of unstable RNAs were grouped in the third class of unstable DHSs . All three unstable clusters were highly enriched  in gene TSS-distal regions   around DHSs  in dCP enhancers, with the largest overlap (∼55%) found among weak bidirectional unstable DHSs. Importantly, these results provide external evidence that balanced bidirectional output of exosome-sensitive RNAs is a marker of gene promoter-distal open chromatin TREs with enhancer potential in D. melanogaster, which has previously been established in human cells  and unidirectional stable (∼71%) DHSs. Weak unidirectional unstable DHSs had modest overlap with STARR-seq positive enhancers and displayed no real preference to either housekeeping or developmental core promoters. We observed similar STARR-seq enrichments when restricting our analyses to intergenic regions  and developmental (dCP) core promoter types (ctively) . Only 51ctively)  overlapps Figure . With than cells ,10,13. Ian cells , a large regions , indicatD. melanogaster S2 cells and those identified in human cells  and DNA sequence elements at the core promoters and in regions downstream of TSSs of transcribed DHSs. First, we assessed the frequencies of predicted 5′ splice sites and termination signals (polyadenylation sites) at the locations of minor and major strand CAGE summits and up to 1000 bp downstream  that, based on motif finding, either had clear preferences for expected positions  and E-box (Ohler5), U test P < 1 × 10−20). Overall, TATA, Inr, DPE and MTE elements displayed the strongest positional preferences on major strands among investigated motifs , as well as both strands of bidirectional stable DHSs , but a strong depletion of core promoters of unstable DHSs . In contrast, Trl elements (cluster 9) were mostly found in core promoters of unstable DHSs. Other core promoter clusters were not mainly associated with the stability of produced RNAs. In particular, Inr elements (mostly identified in cluster 2) showed no clear differences between stable and unstable DHSs, but rather a preference for major over minor strands. Interestingly, cluster 8 (associated mainly with DRE) was enriched with unidirectional stable and bidirectional stable DHSs  but not unidirectional stable w/ PROMPTs DHSs. In general, we found that bidirectional stable and unidirectional stable major strands showed similar enrichment within core promoter clusters, while unidirectional stable w/ PROMPTs major and minor strands showed higher similarities with unstable DHS groups . This revealed a complex combination of core promoter elements across DHSs, for which many core promoter elements were restricted to only a subset of DHS regions Figure , reflectnogaster ,57. Indinogaster . For insS groups .D. melanogaster TREs are frequently associated with core promoter elements regardless of DHS class, but possess strong diversity in core promoter composition that are reflecting RNA stability and transcriptional directionality.Taken together, we conclude that unidirectional stable DHSs. Invertebrate genomes have an unexpectedly high fraction of head-to-head gene pairs, not immediately explained by their more compact genomes compared to mammalian ones  of upstream annotated head-to-head gene TSSs than the major strand TSSs of unidirectional stable w/ PROMPTs DHSs . In support, we found that plus strand TCs (regardless of DHS class) positioned within 500–1000 bp from minus strand gene TSSs were more frequently associated with unidirectional than divergent transcription , while the divergent fraction increased at distances >1000 bp .We next wanted to investigate the nature of absent PROMPT transcription from ian ones . Given tian ones , we comps Figure . Major sp Figure . In contunidirectional stable DHSs compared to those with PROMPTs . Overall, core promoter clusters .At distances >2000 bp to upstream head-to-head gene TSSs, we observed notable sequence differences between s Figure . In parts Figure  defined s Figure . In conts Figure  and the s Figure . Interess Figure , while Ts Figure . In addis Figure . In agreundaries  than unis Figure   between H3K4me3 and H3K4me1 levels and enhancer strengths . In line with this observation, dCP enhancers were less expressed than DHSs not associated with dCP enhancer potential , while hkCP enhancer-associated transcribed DHSs were associated with the highest expression levels . In general, we observed a striking positive correlation between hkCP signal and the major strand CAGE expression level of DHSs regardless of attributed class, indicating that the stronger the housekeeping enhancer potential, the more transcription is observed from the DHS . These results argue that the observed chromatin mark enrichment over DHS clusters and enhancer classes might reflect local core promoter strength. Indeed, the strength of DRE elements (as determined by motif match score) was positively correlated with hkCP enhancer potential , suggesting that dCP enhancer function is incompatible with strong promoter function. Importantly, the overall trends observed for H3K4me1, H3K4me3 and CAGE expression levels versus housekeeping enhancer potential were consistent both for DHSs that were proximal and those that were distal to FlyBase gene TSSs , while on the contrary, unstable DHSs were strongly enriched within the TADs themselves  to calculate the odds of a partnering TRE of each DHS class appearing within the same TAD . Overall, the s Figure . SimilarD. melanogaster, we clustered the TADs according to their memberships of transcribed DHSs , generating a total of 7 clusters . In addition, some TADs predominantly contained stable TREs (e.g. clusters 6 and 7). The clustering of TAD memberships also revealed other combinations of DHS classes, not apparent from the GLM analysis, e.g. cluster 5, which had a tendency to contain a mixture of classes, in particular combinations of unidirectional stable elements with unstable DHSs. This cluster might reflect cases in which enhancer-core promoter preferences do not follow our generalized DHS class attributions.To investigate if these results reflect a general property or a consequence of multiple layers of regulatory architectures occurring within the genome of clusters . In agrestable classes interacted preferentially with DHSs possessing hkCP enhancer potential, but unstable classes did not show such a preference  are RNAPII promoters. Our findings also agree with recent observations ,42 that Data generated in this study have been deposited in GEO under accession number GSE109588.Supplementary DataClick here for additional data file.


Polycomb group (PcG) proteins are transcriptional repressors that play important roles in regulating gene expression during animal development. In vitro experiments have shown that PcG protein complexes can compact chromatin to limit the activity of chromatin remodeling enzymes and access of the transcriptional machinery to DNA. In fitting with these ideas, gene promoters associated with PcG proteins have been reported to be less accessible than other gene promoters. However, it remains largely untested in vivo whether PcG proteins define chromatin accessibility or other chromatin features. To address this important question, we examine the chromatin accessibility and nucleosome landscape at PcG protein-bound promoters in mouse embryonic stem cells using the assay for transposase accessible chromatin (ATAC)-seq. Combined with genetic ablation strategies, we unexpectedly discover that although PcG protein-occupied gene promoters exhibit reduced accessibility, this does not rely on PcG proteins. Instead, the Polycomb repressive complex 1 (PRC1) appears to play a unique role in driving elevated nucleosome occupancy and decreased nucleosomal spacing in Polycomb chromatin domains. Our new genome-scale observations argue, in contrast to the prevailing view, that PcG proteins do not significantly affect chromatin accessibility and highlight an underappreciated complexity in the relationship between chromatin accessibility, the nucleosome landscape, and PcG-mediated transcriptional repression. This diverse group of proteins form large multiprotein complexes that bind gene regulatory elements and modify chromatin to establish what is thought to be a transcriptionally repressive chromatin state . PcG proDrosophila and mammalian PRC1 complexes which were capable of compacting nucleosomal arrays and inhibiting the activity of nucleosome remodeling complexes . We also examined the relationship between PcG chromatin domains and promoter accessibility in other mouse tissues and cell lines. We found that reduced chromatin accessibility at PcG promoters was widespread . Although PcG complex enrichment is usually associated with gene promoters in mammalian cells, we also examined the subset of distal elements bound by PcG complexes and compared them to distal elements that lacked PcG complexes . In contrast to gene promoters, PcG-bound distal elements showed little difference in their accessibility when compared to distal elements without PcG binding. This suggested that PcG complexes may specifically limit chromatin accessibility at gene promoters.We considered three different measures of chromatin accessibility in mouse embryonic stem cells: the assay for transposase accessible chromatin (ATAC-seq), DNase I hypersensitivity (DNase-seq), and formaldehyde-assisted isolation of regulatory elements (FAIRE-seq). ATAC-seq and DNase-seq measure accessibility by interrogating the digestion frequency of chromatin by Tn5 transposase or DNase I, respectively . Alternaan cells . This dian cells E. FurtheSupplemental Fig. S3A), as well as at PcG-bound distal regulatory elements . Together, these observations indicate that PcG target sites in ESCs exist in a nucleosome-rich state with more closely spaced nucleosomes than PcG-free regions.PcG promoters have been proposed to exist in a more nucleosome-enriched state compared to non-PcG promoters in mammalian cells . We wereRing1−/−;Rnf2fl/fl conditional ESCs  which expresses a doxycycline-sensitive Eed4 transgene (Eed4TG) in an Eed−/− background , in agreement with a previous study examining chromatin accessibility in the Ring1−/−;Rnf2fl/fl ESCs . In wild-type fibroblasts PcG-occupied promoters exhibited lower levels of chromatin accessibility compared to PcG-free promoters. In agreement with our analysis of PRC1-null ESCs, this disparity between chromatin accessibility of PcG-bound promoters and PcG-free promoters remained after deletion of PRC1 in the Ring1−/−;Rnf2fl/fl fibroblasts , although we did observe modest and nonspecific increases in accessibility more generally. Therefore, we conclude that although PcG-bound promoters are associated with reduced accessibility compared to PcG-free promoters, PcG systems do not directly create this lack of accessibility.Our characterization of the chromatin landscape at PcG-occupied promoters is consistent with previous reports implicating PcG complexes in the compaction of nucleosome arrays to create inaccessible chromatin. However, whether PcG complexes themselves define these features in vivo has yet to be interrogated satisfactorily. We therefore set out to examine the chromatin landscape of PcG-occupied promoters in cells lacking normal PcG complex activity . To achinal ESCs  in whichll cells A. As expll cells B. Alternckground . In the  complex , and los complex D. We perll state E. There /fl ESCs . This waSupplemental Fig. S6A) coupled with subtle increases in inter-nucleosomal spacing  and examined ATAC-derived nucleosome features in Ring1−/−;Rnf2fl/fl mouse embryonic fibroblasts . These analyses revealed similar reductions in nucleosome occupancy and altered nucleosome spacing in the absence of PRC1, although this was less apparent in mouse embryonic fibroblasts. Importantly, these effects were not observed or considerably less dramatic at non-PcG promoters, indicating that this effect was specific to the promoters occupied by PcG complexes. To our knowledge, these observations demonstrate for the first time in vivo that PRC1 can influence the nucleosome landscape by altering nucleosome occupancy and spacing, albeit modestly, in a way that does not appear to define overall accessibility at the gene promoters. This suggests that these features are not directly coupled at PcG-occupied gene promoters.Because PcG-occupied gene promoters also show elevated nucleosome occupancy and closer nucleosome spacing , we were spacing J at PcG-Eed (EED) in the Ring1−/−;Rnf2fl/fl conditional ESCs using CRISPR/Cas9-mediated gene editing . To test this hypothesis, we first considered whether RNA polymerase II-dependent gene transcription contributes to the nucleosome landscape or accessibility of gene promoters in a manner opposing that of PRC1. We used the chemical inhibitor triptolide to acutely inhibit RNA polymerase II initiation and occupancy prior to performing ATAC-seq . This resulted in significant increases in nucleosome occupancy , demonstrating that promoter chromatin accessibility is not dependent on the transcriptional state and must be established by other mechanisms. When we examined the nucleosome occupancy and spacing at PcG-bound promoters activated in the absence of PRC1, there were larger decreases in nucleosome occupancy and increased distances between nucleosome dyads compared to PcG-occupied promoters whose expression levels remained unchanged  in an Eed−/− background were treated with 1 µg/mL doxycycline (DOX) for 14 d to disrupt PRC2 complex and function, as previously described   lines were grown on gelatin-coated plates in DMEM supplemented with 15% FBS, 10 ng/mL leukemia-inhibitory factor, penicillin/streptomycin, β-mercaptoethanol, L-glutamine, and nonessential amino acids. /fl ESCs  were adaescribed . SV40-ims (MEFs)  were groes: RNF2 , SMARCA4es: RNF2 , and H3 es: RNF2 , H2A  to induce a frameshift in all four EED translation products. The Eed sgRNAs were cloned into pSpCas9(BB)-2A-Puro  using a previously described protocol . Putative Eed−/− clones were confirmed by Western blotting. Ring1−/−;Rnf2fl/fl;Eed−/− cells were treated with 800 nM TAM for 72 h to ablate RNF2 expression.To delete EED in the protocol . Lipofec6 cells/10 cm plate and allowed to grow for 24 h prior to treatment with 500 nM triptolide (TRP) for 50 min, as previously described , 1 mM PMSF, 3 mM DTT) for 1 min at room temperature and washed three times with ice-cold RSB buffer . Next, 5 × 104 nuclei were counted and resuspended in 1× Tn5 reaction buffer  with 2 µL Tn5 transposase (25 µM) made in house according to the previously described protocol , triplicate , or quadruplicate .Chromatin accessibility was assayed using an adaptation of the assay for transposase accessible chromatin (ATAC)-seq , as prevprotocol . Reactioprotocol  and the 7Ring1−/−;Rnf2fl/fl mouse embryonic stem cells with or without TAM treatment with RSB buffer  supplemented with 0.1% NP-40 and 5 mM N-ethylmaleimide. This was followed by digestion for 5 min at 37°C with 16 units MNase (Fermentas) in 1 mL RSB supplemented with 0.25 M sucrose, 3 mM CaCl2, and 10 mM N-ethylmaleimide. After digestions were stopped with 4 mM EDTA, nuclei were pelleted by centrifugation at 1500g and the soluble S1 fraction collected. Pelleted nuclei were then resuspended in 300 µL nucleosome release buffer , incubated for 1 h at 4°C with gentle rotation, and then gently passed through a 27G syringe needle five times. After the insoluble material was pelleted by centrifugation at 1500g, the soluble S2 fraction was collected and combined with the S1 fraction. To prepare material for constructing sequencing libraries, DNA was purified from chromatin corresponding to 5 × 106 cells using ChIP DNA Clean and Concentrator kit (Zymo). The efficiency of MNase digestion was assessed by DNA electrophoresis (1.5% agarose gel). MNase-seq libraries were prepared from 500 ng DNA using the NEBNext Ultra II DNA Library Prep Kit (NEB) according to the manufacturer's protocol. DNA fragment size selection step was included to enrich for mononucleosome size fragments in the final libraries. Libraries were quantified as for ATAC-seq libraries and were sequenced on Illumina NextSeq 500 using 80-bp paired-end reads in biological triplicate.For micrococcal nuclease (MNase)-seq experiments, we used an adaptation of a native ChIP protocol described previously . BrieflyTo purify nuclear RNA, nuclei were isolated as described for ATAC-seq prior to resuspension in TRIzol reagent (Thermo Fisher Scientific) and RNA extraction according to the manufacturer's protocol. RNA was treated with the TURBO DNA-free Kit (Thermo Fisher Scientific), and rRNA was depleted using the NEBNext rRNA Depletion kit (NEB). RNA-seq libraries were prepared using the NEBNext Ultra Directional RNA-seq kit (NEB), and libraries were sequenced on the Illumina NextSeq 500 with 80-bp paired-end reads in biological quadruplicate.For mouse ESC ATAC-seq, MNase-seq, DNase-seq (GSE37074) , FAIRE-shttp://bioconductor.org/packages/release/bioc/vignettes/DiffBind/inst/doc/DiffBind.pdf); for RNA-seq, DESeq2  were overlapped with mouse ESC RNF2 and SUZ12 peak sets previously identified from biological triplicate data with input control using MACS2 . Data were visualized using R (v 3.4.1)   and ggplv 3.4.1) , with scSupplemental Code). Higher median fragment sizes correspond to higher levels of nucleosome-sized Tn5-tagmented DNA, whereas lower fragment sizes correspond to higher levels of nucleosome-free DNA. To complement this approach, we extracted signal corresponding to nucleosome occupancy and positional information within CGI promoters using the NucleoATAC package . Individual nucleosome dyad centers identified in the nucmap_combined.bed file from NucleoATAC or MNase-seq nucleosome calls from DANPOS2 were used to calculate the distance to the nearest neighboring nucleosome dyad center (inter-dyad distance) using the BEDtools “closest” tool . Only nucleosomes within CGI intervals were included for this analysis, and the median inter-dyad distances for each CGI interval were calculated. Median nucleosome fuzziness scores per CGI were calculated from NucleoATAC-derived nucpos.bed files or DANPOS2 MNase-seq nucleosome calls .As a simple measure of nucleosome occupancy at promoters, the fragment sizes of Tn5-tagmented DNA fragments within each promoter interval were extracted from ATAC-seq .bam files and used to calculate the median fragment size per CGI promoter interval  under accession number GSE98403. Custom scripts used for analysis are provided as Supplemental Code and are available at https://github.com/hamishking/gff-annotation-tools.The ATAC-seq, MNase-seq, and RNA-seq data from this study have been submitted to the NCBI Gene Expression Omnibus (GEO;


CBP and p300 are histone acetyltransferase coactivators that control the transcription of numerous genes in humans, viruses, and other organisms. Although two separate genes encode CBP and p300, they share a 61% sequence identity, and they are often mentioned together as p300/CBP. Zhou et al. showed that under hypoxic conditions, HIF1α and the tumor suppressor p53 compete for binding to the limiting p300/CBP coactivator. Jethanandani & Kramer showed that δEF1 and MYOD genes compete for the limited amount of p300/CBP in the cell. Bhattacharyya et al. showed that the limiting availability of p300/CBP in the cell serves as a checkpoint for HIF1α activity. Here, we use the microcompetition model to explain how latent viruses with a specific viral cis-regulatory element in their promoter/enhancer can disrupt this competition, causing diseases such as cancer, diabetes, atherosclerosis, and obesity. With at least 315 different cellular and viral interacting proteins, CBP and p300 are considered the most heavily connected coactivators in the mammalian protein–protein interaction network . Both arp300/CBP is a 300-kDa protein that has a CH2 domain, which contains its acetyltransferase activity, and five protein-binding domains –6 and coUsing differential equations and a dimensionless state variable, Zhou et al.  determinThe α7 integrin is involved in the differentiation of myoblasts and is negatively regulated by δEF1, a zinc finger transcription factor, and positively regulated by MYOD. δEF1 has an NR (negative region) domain that binds the p300/CBP coactivator. Overexpression of δEF1 inhibits muscle cell differentiation and represses the activation of the muscle creatine kinase enhancer. On the other hand, MYOD activates muscle genes by binding p300, and uses p300/CBP histone acetylase activity to allow for transcription .Jethanandani & Kramer  transfecHelicobacter pylori. The results showed an increase in transcription complex formation at the HREs (hypoxia-response elements) of the mcl1 promoter. Then, they observed that the complex included p300, HIF1α, and APE1 (apurinic/apyrimidinic endonuclease 1). Western blotting on whole cell lysates from AGS cells showed that the binding of p300 to the hif1α promoter decreased at higher levels of H. pylori infection, without a decrease in the p300 concentration. Moreover, they found that higher levels of H. pylori infection increased the expression of hif1α, but decreased the expression of the mcl1 promoter, which is transactivated by HIFα. They discovered that at higher H. pylori levels, HIF1α binds to the HIF-binding site (HBS) on the hif1α promoter. Since the HBS is transcriptionally inactive , this binding does not further transactivate the hif1α promoter. However, this binding has a sequestering effect that limits the intracellular availability of the HIF1α•p300 complex to the mcl1 gene, which decreases mcl1 expression.Bhattacharyya et al.  infectedThe observations in the Bhattacharyya et al. study indicate that p300 is limiting, meaning the HIF1α•p300 complex is limiting. They also show that the decrease in HIF1α•p300 binding to the mcl1 promoter, which decreases mcl1 transcription, is due to competition for the limiting HIF1α•p300 by the hif1α promoter itself. Based on their observations, Bhattacharyya et al. concluded that the limiting availability of p300 in the cell is a checkpoint for HIF1α activity.Microcompetition with Foreign DNA and the Origin of Chronic Disease [These studies showed that competition between cellular transcription factors to bind the limiting p300/CBP is an important regulator of transcription. According to the microcompetition model, disrupting this regulation causes many diseases. The microcompetition model was first described in the book  Disease , 8. It cSome common viruses with an N-box are the cytomegalovirus (CMV), Epstein-Barr virus (EBV), herpes simplex virus 1 (HSV-1), human T-cell lymphotropic virus (HTLV), and human immunodeficiency virus (HIV). These viruses are highly prevalent. For instance, an estimated 3.7 billion people worldwide, or around 67% of the global population, are infected with HSV-1 . Since t“It has been noticed that EBV load in tumor tissues or blood is associated with the clinical progression and prognosis in both lymphoma and NPC. Our result verifies this association. We also emphasize the importance to measure the level of gene expression or copy number in the virus study instead of only concerning ‘with and without’.”Our interpretation of the microcompetition model agrees with that of Zuo et al. It is the copy number of the viruses that sequester the limiting GABP•p300/CBP transcription complex and not the ‘infected or not infected’ that determines the fate of the infected individual. Therefore, it should be measured in clinical practice.A recent paper by Zuo et al.  showed tTo conclude, the microcompetition model explains how an increase in the copy number of a latent virus that binds the limiting GABP•p300/CBP transcription complex increases the sequestering of the complex. This disrupts the allocation of the complex to cellular genes that compete to bind the complex. When this disruption is large enough, the host develops a disease.


SND1), also known as Tudor-SN, TSN or p100, encodes an evolutionarily conserved protein with invariant domain composition. SND1 contains four repeated staphylococcal nuclease domains and a single Tudor domain, which confer it endonuclease activity and extraordinary capacity for interacting with nucleic acids, individual proteins and protein complexes. Originally described as a transcriptional coactivator, SND1 plays fundamental roles in the regulation of gene expression, including RNA splicing, interference, stability, and editing, as well as in the regulation of protein and lipid homeostasis. Recently, SND1 has gained attention as a potential disease biomarker due to its positive correlation with cancer progression and metastatic spread. Such functional diversity of SND1 marks this gene as interesting for further analysis in relation with the multiple levels of regulation of SND1 protein production. In this review, we summarize the SND1 genomic region and promoter architecture, the set of transcription factors that can bind the proximal promoter, and the evidence supporting transactivation of SND1 promoter by a number of signal transduction pathways operating in different cell types and conditions. Unraveling the mechanisms responsible for SND1 promoter regulation is of utmost interest to decipher the SND1 contribution in the realm of both normal and abnormal physiology.The staphylococcal nuclease and Tudor domain containing 1 gene ( SND1) gene encodes SND1, an evolutionarily conserved multidomain protein also called Tudor-SN, TSN or p100  and Tudor domain containing 1 , while Snd1 is assigned to chromosome 4 (4q23) in the rat . These elements map at introns 17 and 16, respectively.The genomic landscapes of the three orthologues are partially similar Figure . UpstreaSND1-IT1 has the capacity of encoding an uncharacterized protein called brain and nasopharyngeal carcinoma susceptibility protein NAG8. Low levels of a different noncoding intronic transcript derived from SND1 have been detected in clear cell renal carcinoma  (Snd1 promoter (GenBank ID: AY957585)  , and a rcis-acting elements and potential binding motifs for transcription factors relevant in regulating SND1 transcription under basal and activated cellular states. We identified features characteristic of housekeeping genes, such as the absence of the canonical TATA box and the presence of CCAAT boxes and a large number of GC-sites  stress through a crosstalk between NF-κB, ATF6 and XBP1 signaling pathways . In respG2 cells . We encoND1 gene . ATF6 isGiven that the cellular processing of ATF6 activation is analogous to that undergone by sterol regulatory element binding proteins (SREBPs) in response to cholesterol deprivation, parallel activation of ATF6 and SREBP-2 in animal cells  may be eSND1 is a target gene for SREBPs. The activation of SREBP-2 in HepG2 cells cultured in the presence of the cholesterol synthesis inhibitor simvastatin or in a lipoprotein-deficient medium resulted in an increment of SND1 promoter activity and transcript levels  and  and enhances the transcriptional activity  by the ENCODE group at UCSC Genome Browser (GRCh38/hg38). In general, nuclease hypersensitive sites provide information about the active transcriptional regulatory elements (SND1 promoter and their consequences on SND1 expression require further in-depth studies.A set of S Mapper  and QuadS Mapper  we foundelements . It is pelements . WhetherFigure SND1 gene. We know that SND1 mRNA is modulated by microRNAs. Of particular clinical interest is the identification of miR-361-5p, miR-184 and miR-320a targeting the 3′ UTR of the SND1 transcript. It has been illustrated that overexpression of either miR-361-5p, miR-184, or miR-320a decreased cancer cell viability, migration and invasion through direct downregulation of SND1 oncogene in gastric and colorectal cancer (Again, there is limited information relative to the posttranscriptional regulation of l cancer , in malil cancer , 95 and l cancer . These fSND1 and the set of transcription factors that modulate the promoter activity and the level of transcription of SND1. It is relevant that SND1 expression is under the control of transcription factors, such as NF-Y, Sp1, Smads, NF-κB, ATF6, and SREBPs, which operate in a number of physio-pathological molecular mechanisms. Given the close connection of SND1 with the control of cell growth and malignant transformation, SND1 has become a novel candidate for cancer treatment and a valid molecular biomarker for prognosis and diagnosis in some cancers (SND1 gene and SND1 protein is at present the focus of a number of clinical investigations.We are currently lacking a thorough examination about the precise role of SND1 in normal and challenged cells. A detailed map of the SND1 transcriptional regulation is needed. Here we illustrate the promoter architecture of  cancers , 38, 97.in vivo tumor formation (SND1 knockout mouse models. These approaches to SND1 deletion would provide an overview about the biological significance of SND1 and its involvement in oncogenic processes and normal development.A selective inhibitor of the SND1 endonuclease activity, the 3′5′-deoxythymidine bisphosphate, has succeeded in reducing hepatocellular carcinoma cells proliferation and inflammation, as well as ormation , 37. Thiormation  as well SND1 promoter regulation may have implications in the development of therapeutic interventions capable of acting on the transcription of SND1 gene directly. Regulatory elements and transcription factors that transcriptionally upregulate SND1 are commonly involved in oncogenic processes. Their inhibition by genome editing or drugs exerts solid antitumor effects (Aforementioned findings about the  effects , 100. AnSND1. A variety of small ligands have been reported to induce the formation and stabilization of G-quadruplex structures in most oncogenes and might be used as anti-cancer drugs (A particularly promising aspect to challenge as potential targets to downregulate SND1 overexpression in tumor cells is the potential presence of functional G-quadruplex forming motifs in the promoter region of er drugs , 88. HowSND1 marks this gene as interesting for further analysis in relation with the multiple levels of regulation of SND1 protein expression. Analysis of SND1 orthologues using approaches such us gene overexpression, silencing, mutant analysis, transgenic and organ-specific knockout animals, could help to elucidate the role of SND1 in normal cell growth, development and oncogenesis, and to assess whether the distorted SND1 expression level is the cause or the consequence of the oncogenic environment. This does not rule out the possibility that the encoded protein SND1 has protective roles throughout a series of effectors in cells undergoing certain stress types. The reported findings show features supporting both sides of the coin.In conclusion, the functional diversity of MJM, YC, and BO conceived the study and wrote the manuscript. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Polycomb repressive complexes 1 and 2 (PRC1 and PRC2) control cell identity by establishing facultative heterochromatin repressive domains at common sets of target genes. PRC1, which deposits H2Aub1 through the E3 ligases RING1A/B, forms six biochemically distinct subcomplexes depending on the assembled PCGF protein (PCGF1–PCGF6); however, it is yet unclear whether these subcomplexes have also specific activities. Here we show that PCGF1 and PCGF2 largely compensate for each other, while other PCGF proteins have high levels of specificity for distinct target genes. PCGF2 associates with transcription repression, whereas PCGF3 and PCGF6 associate with actively transcribed genes. Notably, PCGF3 and PCGF6 complexes can assemble and be recruited to several active sites independently of RING1A/B activity . For chromatin recruitment, the PCGF6 complex requires the combinatorial activities of its MGA-MAX and E2F6-DP1 subunits, while PCGF3 requires an interaction with the USF1 DNA binding transcription factor.  •PRC1 complexes retain high target specificity with little compensatory functions•PCGF2 defines repressive signatures and PCGF3 and PCGF6 transcription active states•RING1A/B is dispensable for the assembly and recruitment of PCGF3 and PCGF6 complexes•PCGF3 and PCGF6 complexes bind target genes through specific DNA binding activities PRC1 encompasses different sub-complexes with distinct biochemical properties. Scelfo et al. dissect these activities, highlighting their little redundancy and high functional specificity. PRC1 sub-complexes associate specifically with transcriptional repression or activation, can function independently of the core RING1A/B enzymes, and can be recruited to chromatin by specific DNA binding activities. The precise control of specific active and repressed transcriptional states is at the basis of first establishing and then maintaining cellular identity . The PolAlthough core enzymatic activities are conserved, PRC1 and PRC2 form distinct subcomplexes defined by the association of ancillary subunits . In PRC1PRC1.2 and PRC1.4 are also termed the canonical PRC1 complexes , based on H3K27me3 recognition deposited by PRC2 . SpecifiThe activities of non-canonical PRC1 complexes can promote the recruitment and/or stabilization of PRC2 to chromatin . This inhttp://www.mousephenotype.org/data/genes/MGI:1917087). Indeed, none of them independently reproduce the loss of RING1A/B activity, suggesting that distinct PCGF activities may act together to determine RING1A/B biological functions. In this context, several questions still remain unanswered. How are the activities of the distinct PRC1 subcomplexes regulated? Do they act redundantly to control similar pathways, or do they  have specific functional features?Loss of RING1A/B activity results in pre-implantation lethality at the two-cell stage . All disHere, we provide a comprehensive dissection of the functional landscape of PCGF proteins, characterizing the crosstalk among the different complexes, their relationship with PRC2 activity, and the recruitment mechanisms that mediate their interactions with chromatin and DNA. By combining the development of highly specific PCGF1-6 antibodies with the generation of KO mouse embryonic stem cells (ESC) lines, we mapped the physiological genome-wide occupancy of all PRC1 subcomplexes to determine their functional control. We show that PCGF proteins retain high levels of binding specificity, with little crosstalk among the different complexes with the exception of PCGF1 and PCGF2, which displayed extensive functional overlap. We demonstrate that, while PCGF1 and PCGF2 activities are strongly linked with transcriptional repression, PCGF3 and PCGF6 are mainly associated with active transcriptional states, even in the absence of RING1A/B recruitment. Importantly, the PCGF3- and PCGF6-containing complexes did not require RING1A/B for their assembly and recruitment to chromatin, providing evidence that both complexes are recruited to target genes by intrinsic and specific DNA binding modules.We analyzed both transcription levels and mass spectrometry to determine the relative amounts of distinct PRC1 subcomplexes active in ESCs and found that the PCGF6- and PCGF2-containing complexes were the most abundant forms of PRC1 A and S1BPcgf1 and Pcgf6 knockouts (KO) and Pcgf2/4 and Pcgf3/5 double-KO  to an ectopic artificial promoter that controlled the luciferase gene (as a readout). PCGF2 and PCGF4 strongly repressed luciferase expression, while PCGF3 or PCGF5 activated it E, consisGene ontology for PCGF2-associated genes showed a significant enrichment for development-related processes, irrespective of their co-occupancy with other PCGF proteins C. In conPcgf KO ESC lines by ChIP-seq  and SUZ12 (for PRC2), as well as deposition of H2Aub1 and H3K27me3, at each specific group of targets C and S4C KO ESCs C and S4CPcgf1/2/4 and Pcgf3/5/6 triple KO ESCs . RNA-seq analyses showed alterations in expression of genes implicated with various developmental processes, overall highlighting a compromised ESC identity . Treatment with 4-hydroxy tamoxifen (OHT) conditionally deletes Ring1B, leading to complete loss of RING1B expression within 48 hr  and Pcgf6 KO ESCs revealed that, consistently with ChIP-seq results, components of the PRC1.6 remained associated with PCGF6 even in the absence of RING1A/B expression  (de novo motif discovery on PCGF6 peaks and found that MYC (E-box) and E2F sites were the most enriched and represented DNA motifs (MgaΔHLH), PCGF6 binding was only moderately affected  analyses for PCGF6 in nd WDR5) A. We perA motifs B. This sA motifs . Indeed,A motifs  -independent manner. As no evident DNA binding activities were previously associated to the PCGF3 complex , we perf MYC-MAX  . However, it is also possible that the high transcriptional status of these targets per se prevents accumulation of repressive marks. Overall, these data showed that the levels of PRC1.2 at target sites correspond well to transcriptional activity, while recruitment of PCGF1, PCGF3, and PCGF6 complexes may be less dependent on the transcriptional status of their target genes. This relates to the passive models of PRC2 recruitment proposed by the Helin and Bernstein laboratories, showing that PRC2 promiscuously binds unmethylated CpG islands until it is excluded by active transcription . Indeed,While PCGF3 and PCGF5 activated transcription in an artificial assay, PCGF6 behaved as a repressor, in apparent contradiction with its physiological association to actively transcribed targets. Since PCGF6 is recruited to DNA by E-box recognition, we envisioned an antagonistic mechanism between PCGF6 and MYC. MYC shares a large fraction of PCGF6 targets, and, upon loss of PCGF6 functions, a substantial set of transcribed PCGF6 targets increased their transcriptional activity. This suggests that PCGF6 may function as “attenuator” of transcription via yet-uncharacterized mechanisms of regulation that may not involve H2Aub1 deposition. We found that RING1A/B activity was dispensable for both assembly and chromatin recruitment of the PCGF3 and PCGF6 complexes, which could provide insight to a not-yet-identified mechanism regulating the activity of these complexes at target sites in the absence of RING1A/B. Indeed, RING1B recruitment and H2Aub1 deposition were very low at uniquely PCGF6 target sites and were only barely detectable at PCGF3 sites. Based on the crystal structure of the RING1B-PCGF4 (BMI1) heterodimer, PCGF proteins should interact with RING1A/B via dimerization of their respective RING domains . In factOur data showed that the PCGF6 complex’s affinity for DNA comes from the DNA binding activities of its distinct subunits. The complete displacement of PCGF6 from chromatin upon loss of MGA expression agrees with previous reports  but was TCACGTG). This may explain the moderate but specific enrichment of PCGF3 at some PCGF6 sites. We further demonstrate that USF1 interacts with PCGF3 independently of DNA and that its expression was essential for the recruitment of the PCGF3 complex to all its target sites. USF1/2 is functionally linked with active chromatin states  and/or DNA elements . Through genome-wide location analysis, we identified a strong enrichment for the USF1/2 DNA binding motif that corresponds to a variant E-box that contains a thymidine at the 5′ of the canonical CACGTG E-BOX motif recognized by MYC .Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Diego Pasini (All ESC lines (E14 and derivatives) were grown in a 0.1% gelatin-coated dish in GMEM supplemented with 15% FBS (Euroclone), 2 mM L-glutamine, 100 U/mL penicillin, 0.1 mg/mL streptomycin, 0.1 mM non-essential amino acids, 1 mM sodium pyruvate, 50 μm β-mercaptoethanol (GIBCO), leukemia inhibitory factor, 3 μm GSK3β inhibitor , 1 μm MEK1/2 Inhibitor .For knockdown experiments, cells were transduced with 5 μg/mL polybrene and lentivirus particles delivering pLKO.1 vector expressing specific shRNAs for 16 hr; cells were then puromycin-selected (2 μg/mL) and grown for 72 hr prior harvesting.To generate stable Pcgf KO cell lines, 10 μg pX459 2.0 plasmids (Addgene) encoding Cas9 and sgRNAs were transfected using Lipofectamine 2000 (Invitrogen), according to the manufacturer's instruction. Puromycin selection was performed for 30 hr at 2 μg/mL, 2000 cells were seeded into a 15-cm dish, and clones were isolated 10 days later. Clones were screened by PCR for genomic DNA, and Western Blot for protein lysates. PCR from positive clones were Sanger-sequenced to confirm genome editing. For double- or triple KO cell lines, single KO clones were subjected to genome editing following the same procedure.Ring1A–/–;Ring1Bfl/fl;Rosa26::CreERT2 conditional mESC line was described previously  5 × Gal4TK-Luc-neo construct described previously (Pcgf chimera was expressed using by 1 μg/mL doxycycline (Sigma) medium added for 48 hr before collecting for ChIP and luciferase reporter gene assay. For the latter, cells were lysed in passive lysis buffer (Promega) and then quantified with Bradford protein assay (Bio-Rad), and the luciferase assay (Promega) was performed on a GloMax instrument (Promega) according to the manufacturer’s instructions.Gal4-DBD-Pcgf inducible cell lines were obtained by transfecting the specific pCDNA4/TO-eviously . TransfePcgf was generated to produce GST fusion proteins; and pCDNA4/TO-Gal4-Pcgf, for 293TREx Flp-In Gal4-Pcgf. For CRISPR/Cas9 genome editing, sgRNAs were cloned into the pX459 2.0 vector (Addgene). Details for sgRNAs and relative gene target are given in Coding sequences for mouse PCGF proteins were amplified from E14 cDNA and cloned into pCR8/Gateway/TOPO/TA (Invitrogen) following the manufacturer’s protocol and sequenced verified. Different plasmids were subcloned in the desired compatible vectors by Gateway technology using LR recombinase (Invitrogen). pGEX-4T-E. coli (BL21) according to standard protocols, purified from crude lysates through fast-flow glutathione Sepharose 4B resin , and eluted with elution buffer containing 10 mM reduced glutathione. Eluted proteins were dialyzed against PBS, verified by SDS-PAGE Coomassie staining, and used to immunize rabbits (carried out at Eurogentech). Antibodies from crude sera were immunoaffinity purified and tested for specificity.GST-PCGF fusion proteins were produced in Western blots were used to analyze lysates obtained with high-salt lysis buffer . For histone modification analyses, lysates were sonicated by Bioruptor .2, 0.34 M sucrose, 0.25% [v/v] IGEPAL), lysed in high-salt lysis buffer supplemented with EDTA-free cocktail proteases inhibitors (Roche), and incubated for 5 min at 37°C with 25 U/mL benzonase (Merck); 200–500 μg nuclear lysates were incubated with 2 μg of antibody for 2 hr at 4°C, and protein A magnetic beads (Invitrogen) were added for 45 min to recover immunoprecipitated complex. Beads were washed three times in high-salt buffer prior elution with loading sample buffer.For immunoprecipitation experiments, nuclei were purified using nuclear preparation buffer  in 150 mM NaCl-salt buffer mounted on a AKTA chromatography system .For GAL4 immunoprecipitation, 293TRex GAL4-PCGFs nuclear extracts (3 mg) were incubated with 14 μg of anti GAL4 antibody for 3 h at 4 °C and then 40 μl slurry of Sepharose Protein G beads were added for 2 h at 4 °C. IgG were used as negative control. Beads were washed with nuclear extraction buffer and immunocomplexes were eluted in Laemmli buffer and resolved on NuPAGE 4%–12% precast protein gels (Invitrogen).Size-exclusion chromatography of nuclear lysate was performed over a Superose 6 column  in 150 mM NaCl-salt buffer mounted on a AKTA chromatography system .Proteins from GAL4-PCGFs purification were separated by SDS–PAGE, using 4%–12% NuPAGE Novex Bis–Tris gels (Invitrogen) and NuPAGE MES SDS running buffer (Invitrogen) and then stained with Coomassie Blue using InstantBlue Comassie (Expedeon). Bands from gel were cut and digested with trypsin (Promega) and incubated for 16 h at 37°C for protein digestion. Then, peptide extraction was carried out and the resulting peptides mixture were combined, reduced in volume in a vacuum concentrator, desalted and concentrated using StageTip (Proxeon Biosystems) columns, washed with 30 μL of 0,1% Formic acid (FA) and finally eluted with 40 μL of 80% MeCN in 0,1% FA. The samples were concentrated in vacuum concentrator (Eppendorf concentrator 5301) for 5 min and peptides were dissolved in 7 μL of 0,1% FA. Approximately 5 μL of purified peptide mixture were analyzed on a LC–ESI–MS-MS Q Exactive HF hybrid quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific). Full scan MS spectra were acquired in a range of m/z 300–1800.Raw data files were analyzed using the peptide search engine Andromeda integrated into the MaxQuant software environment (version 1.5.2.8) with the following parameters: uniprot_cp_hum_2017_01 as protein database, methionine oxidation, Protein N-term Acetylation as variable modifications and carbamidomethylation as fixed, peptide false discovery rate (FDR) 0.01, minimum peptides 2, at least 1 unique, minimum length peptide 6 amino acids. iBAQ intensity values as calculated by MaxQuant were used to estimate relative abundance of proteins. LFQ ratio values are listed in Drosophila chromatin from S2 cell line was added to each ChIP reaction, while for PCGF1 and PCGF2 a total of 50% of human chromatin from 293T cell line was used and, for PCGF6 ChIP-Rx, a total of 50% of human chromatin from 293T cell line expressing GAL4-PCGF6 fusion protein was added to each PCGF6 ChIP reaction. Spiked chromatins were sheared at a size of 200–300 bp.ChIP assays were carried out as described previously . BrieflyFor re-ChIP assays, immunoprecipitated DNA after the first ChIP was eluted with 50 μL of 10 mM DTT for 30 min at 37°C and then diluted 10 × with ChIP buffer, allowing the second ChIP overnight.http://broadinstitute.github.io/picard/). Ambiguous reads mapping to both mm9 and dm6 or hg38 were discarded. Peaks were called using MACS2 v2.1.1 . Gene counts were calculated using HTseq-count v0.8.0  according to manufacturer’s protocol. RNA was used to generate cDNA by reverse transcriptase PCR using the M-MuLV Reverse Transcriptase (Promega). Relative mRNA expression levels were determined using the Go-Taq SYBR Green (Promega) on a Bio-Rad Real-Time PCR System with selected primer pairs. Expression levels were normalized to eviously .GSE122715ChIP-seq and RNA-seq datasets are available at GEO database this accession number:


Nature has evolved exquisite sensing mechanisms to detect cellular and environmental signals surrounding living organisms. These biosensors have been widely used to sense small molecules, detect environmental cues and diagnose disease markers. Metabolic engineers and synthetic biologists have been able to exploit metabolites-responsive transcriptional factors (MRTFs) as basic tools to rewire cell metabolism, reprogram cellular activity as well as boost cell’s productivity. This is commonly achieved by integrating sensor-actuator systems with biocatalytic functions and dynamically allocating cellular resources to drive carbon flux toward the target pathway. Up to date, most of identified MRTFs are derived from bacteria. As an endeavor to advance intelligent biomanufacturing in yeast cell factory, we will summarize the opportunities and challenges to transfer the bacteria-derived MRTFs to expand the small-molecule sensing capability in eukaryotic cells. We will discuss the design principles underlying MRTF-based biosensors in eukaryotic cells, including the choice of reliable reporters and the characterization tools to minimize background noise, strategies to tune the sensor dynamic range, sensitivity and specificity, as well as the criteria to engineer activator and repressor-based biosensors. Due to the physical separation of transcription and protein expression in eukaryotes, we argue that nuclear import/export mechanism of MRTFs across the nuclear membrane plays a critical role in regulating the MRTF sensor dynamics. Precisely-controlled MRTF response will allow us to repurpose the vast majority of transcriptional factors as molecular switches to achieve temporal or spatial gene expression in eukaryotes. Uncovering this knowledge will inform us fundamental design principles to deliver robust cell factories and enable the design of reprogrammable and predictable biological systems for intelligent biomanufacturing, smart therapeutics or precision medicine in the foreseeable future. Commoentation . The latentation . The traentation , 7, protentation , proteinentation , RNA–RNAentation –13, DNA/entation , 15.Transcriptional factor (TF) based biosensors typically consist of a repressor or activator protein regulating the transcriptional activity of a specific promoter. A cis-regulatory DNA sequence  adjacent to the promoter is the core DNA element that binds with a TF restricting or enhancing the access of RNA polymerase (RNAP) to the promoter. A repressor binds to the operator and prevents RNAP proceeding forward to decrease transcription Fig. a, b; an Repressor or activator protein typically transduces a C-terminal ligand-binding activity to the N-terminal DNA-binding activity. Upon interaction with a small molecule or environmental stress signal, TFs will undergo a conformational change leading to altered binding affinity between RNAP and the regulated promoter. The RNAP is typically designed to drive (actuate) the transcription of a reporter protein that outputs an easily measured optical or biochemical signal  . In prinRecent development in metabolite-responsive transcriptional factor (MRTF) based biosensors have expanded our ability to reprogram gene expression or control metabolic activity –22. Mostd-galactopyranoside) and histochemical staining substrate  has been used for β-glucuronidase (GUS) assay, but the fluorescence substrate could reach a much higher sensitivity and detection limit than the X-gluc straining assay [Photinus pyralis) or sea pansy (Renilla reniformis) luciferase is also a widely used reporter candidate if the other two types of reporter are not readily applicable [Renilla luciferase is an ATP-dependent enzyme with 36 kDa molecular weight, while firefly luciferase is ATP-dependent and is a much larger protein (61 kDa). In addition, firefly luciferase generates yellow light ranging from 550 to 570 nm. By contrast, Renilla luciferase emits blue light at a wavelength of 480 nm. Therefore, these two luciferases are compatible and can be used as a dual- reporter system.Classical optical biosensors are primarily converting a chemical signal into a colorimetric output. For instance, blue-white screening is based on the catalytic property of lacZ , and the expression of lacZ is transcriptionally controlled by inducer IPTG or lactose. The input concentration of the inducer (IPTG or lactose) forms a quantitative correlation with the intensity of the output signal (the blue color), which can be spectrophotometrically measured in the case of β-galactosidase assay. Due to the low sensitivity or unavailability of chromogenic compounds, researchers have turned to develop new chemical entity that could emit fluorescence or luminescence. For example, both the fluorescent substrate  and luciferase are now two most widely used reporters in eukaryotic biosensor system. Compared to luciferase or Nluc, although no substrate is needed, fluorescence protein requires high-energy excitatory light and the background noise is generally strong. The background noises are typically derived from yeast autofluorescence, metabolic heterogeneity, and light emission or reflection due to internal organelles or thick cell wall. The increasingly complex interior structure of eukaryotic cell demands more sensitive imaging instrument (i.e. fluorescence microscopy or flow cytometry) to scan a population of cell and determine the mean fluorescence Intensity (MFI) of the output signal. In addition, fluorescence proteins are sensitive to pH, metal ions and oxygen levels. For these reasons, new reporter protein, ATP-independent Nanoluc luciferase, has emerged as the promising substitute to probe transient transcriptional activity in eukaryotes. Not to mention the extremely low background signal, luciferase could be also used as whole-cell assay, thanks to the cell-permeable furimazine substrate and the intensive luminescence emission. To minimize leaky/basal expression and eliminate background noise, it is generally an imperative practice to wash the tested cell with PBS saline buffer. A time-course of luciferase reading with different levels of effector molecules could be continuously recorded with a plate reader. Then a dose–response curve between input effector concentration and the output reporter could be established, where dynamic response range, sensitivity and specificity could be determined from the dose–response curve.In addition to the choice of reporter genes, the architecture of the promoter for the MRTF and the reporter gene are also crucial for fine-tuning of both the operational and dynamic ranges. Engineering native promoters seems to be the first choice in most reported studies , 33. HowInsertion of additional DNA binding sites into native promoter is an alternative for fine-tuning of sensor output Fig. a. The tyLots of native promoters are transcriptionally responsive to media components. Catabolite repression plays a critical role, especially high glucose concentration, nitrogen starvation or the presence of stress signals may shift the transcriptional dynamics. One would desire to find promoters that are minimally affected by environmental conditions. In terms of consistent transcriptional output, one should also consider chromosomal integration of the sensor construct into the genome, instead of detecting sensor activity from episomal plasmid. Engineering a reliable biosensor with the right dynamic response range, sensitivity and specificity is a challenging task. It would require lots of trial-and-error testing before a predictable and sensitive sensor construct could be obtained.Eukaryotic cells are more complex than prokaryotic cells in terms of transcriptional regulation. In eukaryotic cells, activator along with chromatin-modifying enzymes and basal transcription factors work together to activate gene expression in a precise manner . TranscrIn most of the designs, bacterial transcriptional activators can be directly applied into eukaryotic cells Table . Sigma fB. subtilis is a transcriptional repressor that inhibits many genes involved in fatty acid synthesis. However, when FapR is in-frame fused with a transcriptional activator VP16 to form FapR-VP16, which turns FapR into a transcriptional activator in the absence of malonyl-CoA. By contrast, with increased levels of malonyl-CoA, FapR would dimerize and abolishe the activation function of VP16 [Of interest, the bacterial transcriptional repressor can also be converted into a transcriptional activator and used in eukaryotic cells. FapR from  of VP16 . This st of VP16 .DNA binding domains (DBD) of MRTFs are generally conserved among the same family of TFs. It enables us to find DBD from new species or de novo design the DBD . With thSpecific ligand binding domain (LBD) can also be rationally designed. Modular biosensor could be constructed by fusing a conditionally destabilized LBD with a reporter or a transcriptional activator . BindingDue to the complexity of eukaryotic transcriptional regulation, the repressor-operator ribonucleoprotein complex (i.e. LacI–LacO complex in lac operon) that represses bacterial transcription is not the only transcriptional repression mechanism in eukaryotes. Instead, the repressor prevents the transcription via multiple mechanisms in eukaryotic cells. Generally, eukaryotic transcriptional repression can be classified into three categories: inhibition/destabilization of the basal transcriptional machinery, deactivation of activator and remodeling of chromatin or nucleosome 3-D structure .B. subtilis transcriptional repressor FapR along its cognate DNA-binding site fapO has been engineered to construct a malonyl-CoA sensor in S. cerevisiae. This malonyl-CoA sensor was used to screen a genome-wide overexpression library, and the authors identified promising gene targets to improve intracellular malonyl-CoA and 3-hydroxypropionic acid production in Bakers’ yeast [S. cerevisiae. By tuning the repressor expression, operator position and operator sequence, the authors improved the induction ratio (dynamic response range) and sensitivity with defined dose–response relationship [Despite of these multiple repression mechanisms, bacteria-derived transcriptional repressors have been successfully engineered as biosensors in yeast and mammalian cells . Three rs’ yeast . In a sitionship .S. cerevisiae [FadR repressor was engineered to suppress the transcriptional activity of a yeast synthetic promoter containing FadR‐binding operator. It was observed that only overexpression of FadR under the strong and constitutive TEF1 promoter can trigger the repression of reporter gene [FadR-binding sites in the synthetic promoter were found as general strategies to tune the sensor activity.The amount of repressor protein present in the cell seems to be a critical factor determining the functionality of bacteria-derived MRTFs in yeast Fig. b. This wrevisiae . Bacteriter gene . AlthougY. lipolytica [Nuclease-deficient CRISPR system (dCas9) is also designed to repress gene expression in many eukaryotic cells, however, the efficiency is much lower compared with applications in prokaryotic cells . It is ppolytica .Regardless transcriptional activation or repression, mutagenesis might be an effective strategy to obtain ligand binding domain with higher affinity and specificity. In light of not-fully-characterized effector binding domain, de novo design of small molecule-binding domain is also a promising approach . ComputaUnlike prokaryotic cells, most transcriptional regulation takes place in nucleus in eukaryotic cells. To enable precise gene expression control, the cells have developed mechanism to import and accumulate TFs in nucleus. Theoretically, macromolecules with 60 kDa or below would be freely diffused into nucleus via nuclear pore complex (NPC), which is embedded in the nuclear envelope , 55. OthThe existence of nucleus compartment and membrane effectively separates transcriptional regulation with protein expression, as a result, a NLS is generally required to lead the MRTFs through the nucleus envelope. On the other hand, it is not always the case that the small metabolites or effector molecules are freely permeable to the nucleus membrane. The switch between low (OFF) and high (ON) transcriptional activity require that the transcriptionally active MRTFs should be selectively imported into nucleus and interact with the target promoters to control gene expression. The selective partition of the transcriptionally-active MRTFs into the nucleus necessitates us to understand how basic TFs are imported to the nucleus. Harnessing this knowledge will inform us fundamental design principles to effectively engineer MRTF-based sensors, improve the sensitivity and dynamic response range of MRTFs in eukaryotes.Using reactive oxygen species (ROS) response as an example, we may better understand how the importin and exportin control the nuclear transport of transcriptionally-active Yap1 and Skn7 Fig. . Pse1 Pa, which With this knowledge, we may combine the NLS, effector-binding domain and b-ZIP NES sequence to design and engineer novel MRTFs that could be recognized by Pse1 or Crm1 Fig. a, b. ThiGenetically-encoded sensor-regulator system has proven as the efficient way to optimize cell metabolism and improve chemical manufacturing . As an eACT1, CDC14 or TPK2) for screening of strains with desired characteristics [HIS3) for selection of histidine auxotrophic strains in the presence of small metabolite [Leu2 or HIS3), this sensor-regulator system provides the means to connect a metabolite (i.e. end product of a pathway or an intermediary metabolite) with the growth fitness of the engineered cell. Engineering competitive growth advantage will allow metabolic engineers to develop growth-based screening strategies and selectively enrich the desired phenotype without referring to tedious analytical procedures.MRTF-based biosensors have been applied in various areas including high throughput screening of desired strains , 49, dyneristics , or withtabolite . This wiExcept for engineering various TF-based biosensors to expand the sensing capability, the definition of “reporter” is also greatly expanded. Very recently, a novel allosteric TF-based nicked DNA template-assisted signal transduction system (aTF-NAST) has been developed to transduce the signal of small metabolite via various conventional DNA detection technologies . BrieflyEukaryotic cellular metabolism is complex and carried out by many genes with different levels of controls. Most of investigations focus on the construction of biosensor for the detection of a single small metabolite with improved abilities. As the progress of synthetic biology and protein evolution, fine-tuning the pool size of multiple metabolites via orthogonal TF-based biosensor becomes possible . In anotMRTF consists of both a DNA-binding domain (DBD) and an effector-binding domain (EBD). This molecular architecture confers cell the ability to sense environmental signals and small molecules, and transduce the effector-binding activity to the DNA-binding activity. As an endeavor to engineer intelligent biomanufacturing systems, MRTFs are promising tools to translate a small-molecule sensing capability to a transcriptional output. Indeed, recent metabolic engineering advances have demonstrated that MRTFs are indispensable tools to dynamically allocate cellular resources and optimally control pathway expression Fig. . Most ofDue to the cellular complexity of eukaryotic cells, the choice of reporter gene determines the background noise and the approach of how to characterize the biosensors. Because of the simplicity, high sensitivity and low background noise, ATP-independent luciferase (NanoLuc) is a promising reporter to characterize eukaryotic transcriptional systems. Mutagenesis, directed evolution and computational tools are important to engineer novel DNA-binding and effector-binding domains. Promoter strength, the position and number of activator or repressor-biding sites are critical to tune the dynamic response, the sensitivity and specificity of engineered sensors. Repression-based transcriptional regulation is rare in eukaryotic cells, a transcriptional activation domain is generally required to translate a bacteria repressor to a MRTF sensor in eukaryotic cells. To enhance the sensitivity and the detection limit, one should always consider using a minimal promoter containing only the core TF binding sites and the TATA box. Nuclear import and export of transcriptionally active MRTFs plays a critical role in regulating the sensor activity. The molecular underpinnings and structure of nuclear localization signal (NLS), nuclear export sequence (NES) and transcriptional activation domain were exemplified with a yeast-derived ROS sensor Yap1 and Skn7. Rational design of novel MRTF sensors is possible via computational approach and protein evolution.Taken together, MRTFs have proven as powerful tools to control cell metabolism and deliver robust microbial cell factories. Integrating sensor-regulator systems to build biological devices that sense, respond, and compensate the metabolic activity of the engineered system is a critical step to achieve intelligent biomanufacturing. It is anticipated that novel genetic regulatory tools, including optogenetic regulation, fast dynamics split-biosensors, and genome-editing tools would facilitate the development of more sensitive molecular control device. Uncovering the design principles underlying MRTF and engineering predictable sensor-regulator system would help us encode decision-making function into living cell factories and improve the cost-competitiveness of industrial biomanufacturing beyond conventional process engineering strategies.