HOXA2 and HOXA3 directly cooperate with TALE TFs and selectively target different subsets of a broad TALE chromatin platform. Binding of HOX and tissue-specific TFs convert low affinity TALE binding into high confidence, tissue-specific binding events, which bear the mark of active enhancers. We propose that HOX paralogs, alone and in combination with tissue-specific TFs, generate tissue-specific transcriptional outputs by modulating the activity of TALE TFs at selected enhancers.Gene expression programs determine cell fate in embryonic development and their dysregulation results in disease. Transcription factors (TFs) control gene expression by binding to enhancers, but how TFs select and activate their target enhancers is still unclear. HOX TFs share conserved homeodomains with highly similar sequence recognition properties, yet they impart the identity of different animal body parts. To understand how HOX TFs control their specific transcriptional programs  in vivo, which is central to developmental programs, HOX transcription factors display highly similar sequence recognition properties in vitro. To understand how HOX transcription factors control their specific transcriptional programs in vivo, we compared HOXA2 and HOXA3 binding profiles in the mouse embryo. We find that HOXA2 and HOXA3 occupy a large set of high-confidence, non-overlapping genomic regions, that are also bound by three aminoacid loop extension (TALE) transcription factors. We identify three main determinants of HOX paralog-selective binding: recognition of unique variants of the HOX-PBX motif, differential affinity at shared HOX-PBX motifs and, additionally, contribution of tissue-specific transcription factors. These mechanisms result in high-confidence, cooperative HOX-TALE binding at different genomic locations, which bear the mark of active enhancers. We propose that HOX paralogs operate, alone and in concert with tissue-specific transcription factors, to switch on TALE function at selected enhancers.The different anatomical features that develop along the animal body axis are determined by HOX transcription factors. Each member of the HOX family appears in a well-defined territory of the developing embryo, and instruct a specific gene expression program. In stark contrast to their specificity  Gene expression programs instruct and maintain cell fate in embryonic development and adult tissue homeostasis. Transcription factors (TFs) control gene expression by binding to enhancers ,2. Howevin vitro  and BA2 [(basic helix-loop-helix (bHLH) recognition sites]  are alsothan BA2 . Consistthan BA2 . The higthan BA2 , S6F Figthan BA2 . Synergithe BAs)  Fig 6K)Fox genesThese results identify (direct or indirect) cooperativity with tissue-specific TFs as an additional mechanism for HOX selectivity. By enhancing MEIS binding (residence time on chromatin), tissue-specific TFs also contribute to promote the stability of HOX-MEIS complexes in specific tissues. The distribution of tissue-specific motifs in HOX peaks suggests this mechanism accounts for HOX selectivity at a subset of enhancers, rather than being a general mechanism of HOX enhancer selection . We propHOX TFs contain a HD, which display highly similar sequence recognition properties and is shared by hundreds of TFs, yet they instruct diverse, segment-specific transcriptional programs along the antero-posterior axis of all bilaterian animals. By profiling HOXA2 and HOXA3 binding in their physiological domains, we identify three main determinants of HOX-selective binding across the genome: 1) recognition of unique variants of the HOX-PBX motif; 2) differential affinity at ‘shared’ HOX-PBX motifs and; 3) presence of additional tissue-specific, non-TALE, TFs. These mechanisms (with the possible exception of the first) are expected to generate quantitative  differences in the relative levels of HOX/TALE occupancy on commonly bound regions. Such quantitative changes are a feature of continuous networks , in whicin vivo repertoire of HOX occupied sites, we identify DNA sequence as an additional determinant of HOX-TALE functional specificity in vivo. This finding is consistent with the mechanism of latent specificity described for Drosophila Hox/Exd (PBX) interaction [in vitro observations that HOX TFs bind longer, more specific sequence motifs in the presence of TALE. However, the effects of TALE on HOX binding in vivo go beyond the refinement of HOX binding sites as, at least in the BA context, binding with TALE appears to be a requirement for loading HOX on chromatin. Our observations indicate that HOXA2-A3 overwhelmingly recognize genomic sites that are enriched in HOX-PBX motifs and are also occupied by TALE TFs in vivo. Therefore, TALE provides a platform for HOX to bind; selectivity enables HOX paralogs to preferentially bind different subsets of this common platform. In agreement with our finding that BA-specific chromatin states do not seem to play a role in HOX target site selection, TALE platform is largely similar across BA1-2-PBA.HOX paralog-selective binding occurs in cooperation with TALE. The high degree of HOX and TALE interaction flexibility, mediated by paralog-specific protein signatures, has been proposed to generate paralog-specific functions of HOX TFs . Here, beraction  and in vWhat is the functional significance of HOX-TALE interaction on chromatin and how does it contribute to paralog-specific transcriptional programs? Many examples from animal development indicate that transcriptional regulation is mediated by distinct combinations of TFs. TALE TFs operate as a hub, which assists combinatorial assembly of TF complexes. TALE platform enables HOX function to be modulated by other TFs; in doing so, it integrates positional signals (encoded by HOX) and local inputs (provided by cell type-/tissue-specific TFs) into defined transcriptional outputs. While it is possible that MEIS and PBX facilitate access of diverse TFs to relatively inaccessible chromatin, MEIS TFs differ from conventional pioneer TFs, which function to open chromatin regions but are not directly involved in enhancer activation ,41. RemaBecause high instances of MEIS binding are typically associated with combinatorial TF binding, a precise identification of the critical steps for enhancer activation, and their sequential order, remains problematic. For similar reasons, MEIS and PBX shared genomic occupancy complicates dissecting their respective contributions to enhancer binding and activation. In addition to TALE, numerous other TFs are broadly, if not ubiquitously expressed during development, yet their inactivation results in tissue-specific phenotypes. It is tempting to speculate that similar principles of TF functional connectivity could explain other transcriptional networks, i.e. that cell type- tissue-specific regulators harness the activation abilities of broadly expressed TFs to generate cell type -specific gene expression programs.Experiments using animals were performed in accordance with legislation in the UK Animals (Scientific Procedures) Act of 1986 (PPL 70/8858 to Graham Morrissey). All procedures on zebrafish adults and embryos were approved by the University of Massachusetts Institutional Animal Care and Use Committee (IACUC).CD1 mice were time-mated to obtain BA2 or PBA from E115 embryos. Mouse experiments were carried out under ASPA 1986. Wild type zebrafish were raised in the University of Massachusetts Medical Center Zebrafish Facility. Embryos and adult zebrafish were maintained under standard laboratory conditions. Enhancers were amplified from mouse genomic DNA using the primers (listed in findMotifGenome’ module of the HOMER package was used to detect de novo motif in 200nt summit regions [https://www.bioconductor.org/packages//2.10/bioc/html/ChIPpeakAnno.html) were used to intersect, modify and visualize genomic coordinates. Bioconductor Biostring [ChIP-seq was performed as described  using ra regions . Venn di regions  was used regions  was used regions  was used regions ,30. ggpl regions  was used regions , Biocond regions , and Bioiostring  was usedAll RNA-seq and ChIP-seq datasets are available on ArrayExpress.in silico binding site knockout we trained a CNN model for multitask regression of MEIS and HOX RPKM binding level. The CNN was trained by transfer learning, using convolution parameters from a previously published 1-convolutional layer MEIS RPKM model [MEIS differential sequence features are detected by recently published differential convolutional neural network (CNN) structure . For in KM model . ConvoluKM model , namely KM model  and a PoSingle cell transcriptomic data from  was retrHoxa2, Hoxa3 and Meis2 (isoform 1), cloned into pcDNA3 (Invitrogen); pcDNA3-PBX1a is a gift from Francesco Blasi. Reactions  were mixed by gentle flicking, and incubated at room temperature for 12 minutes before being run on 4% acrylamide gel at 70V in 0.5X TBE. Bands were quantified using ImageJ (version 1.52a). Intensity plots for each lane were generated by selecting the same region across all the lanes. Individual band intensities were plotted as histograms areas and quantified by the software. Values in triplicate were averaged and, unless stated otherwise, adjusted to the band intensity of the probe bound by PBX/MEIS in the absence of any HOX. Corresponding numerical data are provided as supporting information (data summary).Probes were made from primers with 5’ ATO700, and purified with QIAGEN PCR purification kit (Qiagen). Proteins were generated using TnT Quick Coupled Transcription/Translation System (Promega) and the following plasmids: pcDNA3-Hoxa2, pcDNA3-Hoxa3, pcDNA3-Meis2, containing mouse coding sequences for Meis2 and Zfp703 enhancers were amplified from mouse genomic DNA using primers listed in http://n2t.net/addgene:60323RRID:Addgene_60323). Enhancers were co-transfected with pcDNA3, pcDNA3-Hoxa2, pcDNA3-Hoxa3, pcDNA3-Meis2, pcDNA3-PBX1a (described above) and pcDNA3-Hoxd3 generated by GenScript. NIH3T3 cells were grown in DMEM (D6429) supplemented with 10% FBS and 5% penicillin/streptomycin, and seeded in 24-well plates at 100,000 cells in 1ml. Cells were transfected with GeneJuice Transfection Reagent (Novagen), using 250ng luciferase plasmid and 300ng pcDNA3 plasmids per well. Cells were harvested 24 hours after transfection and luciferase measured using Luciferase Assay System and the GloMax Multi-Detection System (Promega). Corresponding numerical data are provided as supporting information (data summary).Hoxb1 and Hoxb2 [HOXA3 and HOXC4 (http://horfdb.dfci.harvard.edu/hv7/) were used to generate mammalian expression vectors for FLAG-HOX (v1899 destination vector) using the gateway technology [Gateway entry vectors for mouse nd Hoxb2 , human Hchnology . Gatewaychnology ,65. HEK2Coding sequences for MEIS1b and MEIS2.1 were cloned in pEnt plasmids, confirmed by DNA sequencing and used to generate pExp mammalian expression vectors for GST-tagged proteins with the pDest-GST N-terminal destination vector using the gateway technology . HEK293 S1 FigHOX-FLAG cDNAs were transfected in HEK293 cells. B. Comparison of HOXA3 ChIP-seq replicates in E11.5 PBA (FE≥10). The replicate with larger genome coverage contains most (80%) of the second replicate’s peaks and is used in all subsequent analyses. C. Top three most significant motifs identified in HOXA3 peaks using de novo motif discovery. D. Comparison of HOXA2 ChIP-seq replicates in E11.5 BA2. The smallest replicate was sequenced using the Illumina platform and is used for subsequent analysis; it largely (73%) overlaps HOXA2 ChIP-seq run on the Solid platform . E. Sequence logo of the top three most significant motifs identified in HOXA2 peaks. F. Frequency of HOX-PBX motifs in HOXA3 and HOXA2 peaks. G. Percentage of HOXA2 (red) and HOXA3 (green) peaks containing TGATNNAT motifs, with no mismatch (triangles) or 1nt mismatch (circles) allowed. Increasing numbers of top HOX peaks, ordered by decreasing FE, are plotted on the x axis. For both HOXA2 and HOXA3, the percentage of peaks containing a perfect match (no mismatch) to TGATNNAT decreases with relaxing FE. The opposite trend is observed for the distribution of TGATNNAT motifs with 1nt mismatch allowed. H, Enrichment of HOXA2 regulated genes (red fraction) in the top biological processes and mouse phenotypes identified by GREAT; the remaining fraction corresponds to genes associated with HOXA2 peaks, but not significantly dysregulated in HOXA2 mutant embryos. The number of genes in each category and the corresponding p value (Fisher’s exact test) are indicated.A. Specificity of HOXA3 polyclonal antibody. Western blot using FLAG and HOXA3 antibodies, as indicated. HOXA3 antibody recognizes HOXA3 and does not cross react with mouse HOXA1, HOXA2 and human HOXB1, HOXB2 and HOXC4. Plasmids containing (PDF)Click here for additional data file.S2 FigSulf2 probe. Complexes are colour-coded according to the presence of HOXA2 (red boxes) and HOXA3 (green boxes). Values (raw data) were obtained from three independent experiments. Binding of HOXA3 to the wild-type Sulf2 probe is significantly higher relative to HOXA2, while no significant difference is detected between HOXA3 and HOXA2 binding to the Sulf2 mutant probe. One-way ANOVA with post-hoc Tukey HSD (Honestly Significant Difference) test: * = p<0.05.A. Percentage of TGATNNAT variants in top 250 or in top 250 to 2000 HOXA2 and HOXA3 peaks, as indicated. HOX-selective binding is more evident in the fraction of high-confidence peaks, suggesting that analysis of whole ChIP-seq experiments may mask effective HOX specificity. B. Distance between TGATNNAT (HOX-PBX) and TGACA (MEIS) in top 250 HOXA2 (red) and HOXA3 (green) peaks. Most TGACA occur at <20 nt from a HOX-PBX site. C. Quantification of MEIS/PBX/HOX (trimeric) complexes binding to the wild-type and mutant (PDF)Click here for additional data file.S3 FigZfp703 locus. Strong HOX and TALE binding is observed in both tissues, with higher acetylation levels in BA2. B. Luciferase activity driven by Zfp703 enhancer co-transfected with Hoxa2 (red bar), Hoxa3 (green bar), Hoxa2-a3HD (red empty bar), Hoxa3-a2HD (green empty bar), Meis2 and Pbx1a (grey bar) expression vectors, alone and in combination, in NIH3T3 cells. Addition of MEIS/PBX amplifies the difference in HOXA2 and HOXA3 activation abilities. Changing the HOX-PBX site (shown in C) reduces HOX-TALE activation to the levels observed with TALE alone (empty bars). Values represent fold activation over basal enhancer activity and are presented as the average of at least two independent experiments, each performed in triplicate. Error bars represent the SEM. One-way ANOVA with post-hoc Tukey HSD test: * = p<0.05; *** = p<0.0005. C. Sequence of Zfp703 wild-type and mutant enhancer encompassing HOX-PBX and MEIS sites. HOX-PBX and MEIS motifs are underlined. Nucleotide substitution in the HOX-PBX site are shown in red. D. Luciferase activity driven by Meis2 enhancer co-transfected with Hoxa2 (red bar), Hoxa3 (green bar), Hoxd3 (green bar), Meis2 and Pbx1a, alone and in combination. When co-expressed with MEIS and PBX, HOXA2 shows higher activation capacity than HOX paralog 3. Statistical analysis was performed as in B. EF. HOXA2  and HOXA3  weakly bind the Meis2 probe. MEIS and PBX bind DNA together (black arrow). HOXA2 (E) or HOXA3 (F) do not bind the Meis2 probe with either MEIS or PBX alone. Addition of HOXA2 and HOXA3 to MEIS/PBX results in a trimeric protein complex (arrowheads in E and F). G. Quantification of MEIS/PBX and HOX binding to Meis2 probe. Binding of MEIS/PBX (dimeric) and MEIS/PBX/HOX (trimeric) complexes is expressed as relative percentage to PBX/MEIS binding to Meis2 probe in the absence of any HOX. Dimeric and trimeric complexes are colour-coded according to the presence of HOXA2 (red boxes), HOXA2-A3HD (dark red boxes), HOXA3 (green boxes) and HOXA3-A2HD (dark green boxes). Values were obtained from at least three independent experiments. HOXA2 binds the Meis2 probe with higher affinity with PBX/MEIS, relative to HOXA3. Accordingly, HOXA2 shifts more of the PBX/MEIS dimer into a trimeric complex, relative to HOXA3. A similar behaviour is observed with chimeric HOX . HI. Quantification of HOX/MEIS/PBX trimeric (H) and MEIS/PBX dimeric (I) binding to Meis2 probe shown in Meis2 probe in the absence of any HOX. The total HOX dose is unvaried and corresponds to 100%; HOXA2 and HOXA3 are translated alone and co-translated with each other at different ratios, as follows: 100% HOXA2; 75% HOXA2 + 25% HOXA3; 50%HOXA2 + 50% HOXA3; 25% HOXA2 + 75% HOXA3; 100% HOXA3. Colour coded % are indicated below the x-axis; HOXA2 is red and HOXA3 is green. HOXA2 forms a significantly stronger trimeric complex than HOXA3 at all comparable concentrations, except 25% (difference not significant). Values were obtained from three independent experiments. I. PBX/MEIS complex is significantly reduced in the presence of all HOX. HOXA2 causes a significantly greater reduction than HOXA3. One-way ANOVA with post-hoc Tukey HSD test: * = p<0.05; **p<0.005; *** = p<0.0005. J. Same experiment as in Meis2 probe. Arrow and arrowheads indicate dimeric and trimerix complexes, respectively; asterisks mark lanes containing a detectable trimeric complex. Representative gels are shown; each independent gel contains PBX/MEIS binding to Meis2 wild-type probe as a reference. Mutant probes are indicated on the top and named as in A. UCSC tracks of HOXA2, HOXA3, PBX, MEIS binding and H3K27 acetylation profiles in BA2 (red) and PBA (green) at the (PDF)Click here for additional data file.S4 FigHoxa2 and Hoxa3. Count of cell expressing Hoxa2 and Hoxa3 in the green and purple clusters shows that the majority of cells co-express Hoxa2 and Hoxa3, more than what is expected by chance (Fisher exact test). C. Quantification of HOX/MEIS/PBX binding to Meis2 probe shown in A. tSNE (T-distributed Stochastic Neighbour Embedding) of E10.5 post-otic cranial neural crest single cell RNA-seq  clusters cells into four main clusters. B. Two clusters  exhibit high-expression of (PDF)Click here for additional data file.S5 FigA. Whole Venn diagram of HOXA3, MEIS and PBX binding overlap, cropped in (PDF)Click here for additional data file.S6 FigA. GREAT analysis of MEIS peaks higher in BA2  and PBA . Fold change ≥5 across BAs is used as a cutoff. Differential MEIS peaks are associated with genes involved in different biological processes (top 10 categories are shown). BC. Fold change (PBA versus BA2) in MEIS binding and H3K27ac levels at HOX and GATA peaks. B. HOXA3 binding in PBA (green) and HOXA2 binding in BA2 (red) is associated with increased MEIS binding and H3K27ac in the corresponding tissues. C. Similarly, GATA6 binding in PBA is associated with increased MEIS and H3K27ac in PBA. DE. HOMER motif search in the vicinity of HOX/PBX sequence features identified by CNN  identifies enrichment of Forkhead motifs in MEIS higher PBA. Other enriched flanking motifs include MEIS and HD motifs. F. Example of HOX and Forkhead features, identified by CNN in a HOX- bound region. The RPKM feature tracks show combined RPKM change caused by mutating each nucleotide to its alternatives. Features of two experimental replicates for HOXA2 in BA2 and two replicates of HOXA3 in PBA are predicted by a jointly trained multitask model, trained by transfer learning from a MEIS RPKM model. Below, normalised cross-correlation of region sequence with HOX and Forkhead position weight matrix (PWM) obtained by k-mer counting is shown. Nucleotides in HOX and Forkhead motifs show increased sensitivity to perturbation, compared to their surrounding sequences. G. Motifs occurrence in top 250 HOXA2 and HOXA3 peaks (200 nt summits). Frequencies of single and co-occurring motifs are expressed as percentages and colour-coded in the bar plot. Square brackets indicate bHLH (CATCTG) in HOXA2 and Forkhead (TACAAA) in HOXA3 peaks. Motif frequency is calculated using IUPAC letters, as indicated.(PDF)Click here for additional data file.S7 FigMACS peaks from MEIS ChIP-seq experiments in BA1, BA2 and PBA with replicates were recentered using DiffBind (see methods), and pairwise fold changes and differential binding regions were computed by edgeR as described . Pairwise differential regions were combined into MEIS BA1 up, BA1 down, BA2 up, BA2 down, PBA up, and PBA down regions. Differential sequence motifs were detected as described . Differential H3K27ac regions and fold changes were computed in the same manner.(PDF)Click here for additional data file.S1 Table(CSV)Click here for additional data file.S2 Table(CSV)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


However, how MED26 engages SEC or LEC to regulate distinct genes is unclear. Here, we provide evidence that MED26 recruits LEC to modulate transcription termination of non-polyadenylated transcripts including snRNAs and mRNAs encoding replication-dependent histone (RDH) at Cajal bodies. Our findings indicate that LEC recruited by MED26 promotes efficient transcription termination by Pol II through interaction with CBC-ARS2 and NELF/DSIF, and promotes 3′ end processing by enhancing recruitment of Integrator or Heat Labile Factor to snRNA or RDH genes, respectively.Mediator is a coregulatory complex that regulates transcription of Pol II-dependent genes. Previously, we showed that human Mediator subunit MED26 plays a role in the recruitment of Super Elongation Complex (SEC) or Little Elongation Complex (LEC) to regulate the expression of certain genes. MED26 plays a role in recruiting SEC to protein-coding genes including  Mediator subunit MED26 was shown to help recruit Super Elongation Complex (SEC) or Little Elongation Complex (LEC) to control the expression of certain genes. Here, the authors provide evidence that MED26 recruits LEC to regulate transcription termination of non-polyadenylated genes, including snRNA and replication-dependent histone (RDH) genes at Cajal bodies. Pol II-dependent transcription proceeds through multiple steps, including transcription initiation, elongation, and termination. Termination is tightly coupled to 3′-end processing of Pol II transcripts and is an essential process in transcript maturation3.Eukaryotic RNA polymerase II (Pol II) is responsible for transcription of protein-coding genes and noncoding RNA genes, including most small nuclear RNAs (snRNAs), small nucleolar RNAs (snoRNAs), microRNAs (miRNA), and enhancer RNAs (eRNAs)4. Although most protein-coding and noncoding RNA genes produce polyadenylated messenger RNAs (mRNAs) after 3′-end processing, some genes, including snRNA and replication-dependent histone (RDH) genes, produce transcripts lacking poly(A) tails using a distinct 3′-end processing machinery2. 3′-End processing of snRNA and RDH transcripts depends on different sets of cis-elements and transcription termination and 3′-end processing factors5.In higher eukaryotes, there are three types of 3′-end processing of transcripts6. The stem loop is bound by stem-loop-binding protein (SLBP), while HDE is recognized by U7 small nuclear ribonucleoprotein (snRNP)7. SLBP and U7 snRNP help to recruit heat-labile factor (HLF), composed of Symplekin, the multisubunit cleavage and polyadenylation specificity factor (CPSF), FLICE-associated huge protein (FLASH), and the 64-kDa subunit of cleavage stimulation factor (CSTF64)9. The CPSF subunit CPSF3 (CPSF73) is an endonuclease responsible for 3′-end cleavage of RDH transcripts10. Knockdown of termination factors results in read-through past normal sites of termination to conserved polyadenylation signals (PASs) just downstream of RDH HDEs and synthesis of polyadenylated RDH mRNAs, raising the possibility that polyadenylation of RDH transcripts could provide a mechanism to prevent Pol II from reading through into regions downstream of the RDH genes. In addition, recent evidence indicates that polyadenylated RDH transcripts contribute to the expression of RDH genes outside of S phase or in terminally differentiated, non-proliferating cells12.The 3′ ends of RDH mRNAs are generated by endonucleolytic cleavage at a site downstream from a conserved stem-loop structure and upstream of a purine-rich histone downstream element (HDE)13. Integrator binds to the 3′ box and mediates 3′-end processing of pre-snRNA near the 3′ box5. Conserved PAS sequences are not found downstream of most snRNA genes, suggesting that during snRNA transcription, termination defects do not always lead to polyadenylation14.snRNA 3′-end formation depends on a 3′-box element ~20 nt downstream from the mature 3′ ends of snRNAs16, consistent with the idea that CBCA links capping and elongation by Pol II to facilitate transcript termination. Furthermore, the negative elongation factor (NELF) and DRB sensitivity-inducing factor (DSIF), which are essential for promoter-proximal pausing by Pol II, interact with CBC and SLBP or Integrator to facilitate termination and 3′-end processing RDH or snRNA transcripts, respectively19. This evidence suggests that pausing by elongating Pol II contributes to termination of relatively short transcripts and facilitates 3′-end processing of non-polyadenylated transcripts.Recently, it has been shown that cap-binding complex (CBC) binds to arsenic resistance protein 2 (ARS2) to form CBC-ARS2 (CBCA) and contributes to termination of both RDH and snRNA transcription21, can regulate Pol II during pre-initiation complex assembly, initiation, and elongation22. Previously, we showed that in higher eukaryotes, Mediator, through the N-terminal domain (NTD) of metazoan-specific Mediator subunit MED26, interacts with transcription elongation complexes designated super elongation complexes (SECs) and little elongation complexes (LECs)24. Members of the eleven-nineteen lysine-rich leukemia  and ELL-associated factor  families of transcription elongation factors (TEFs)27 are shared components of SECs and LECs30. SECs also include positive TEFb and mixed lineage leukemia fusion partners31. MED26 helps to recruit SEC to a number of genes including c-myc and hsp7023, where it regulates post-initiation events including phosphorylation of Rpb1 CTD and transcription elongation30. LECs contain ELL and EAF family members, the interactor of little elongation complex ELL subunit 1 (ICE1), interactor of little elongation complex ELL subunit 2 (ICE2), and are associated with zinc-finger CCCH-type containing 8 (ZC3H8) and ubiquitin-specific peptidase-like 1 (USPL1)33. Intriguingly, MED26 helps to recruit LEC to a subset of snRNA and snoRNA genes24, where it contributes to their optimal expression;32 however, it is an unanswered question why different ELL/EAF-containing elongation complexes are used to regulate different classes of genes. In addition, it remains unclear why LEC-associated ELL/EAF, whose only known function is to enhance Pol II elongation, would be needed for optimal expression of very short transcripts such as snRNAs.Mediator, an evolutionarily conserved coregulatory complex that relays regulatory signals between gene-specific transcription activators and the basal initiation machinery34, are enriched at Cajal bodies, which are thought to be sites not only for pre-mRNA splicing but also for transcription regulation of snRNA and RDH genes7. Based on our findings, we propose the model that (i) MED26 recruits LEC to both RDH and snRNA genes in Cajal bodies, then (ii) LEC binds to CBCA and NELF/DSIF to inhibit Pol II read-through past the normal termination sites on these genes, and finally (iii) LEC promotes the 3′-end processing of the RDH or snRNA genes by enhancing recruitment of HLF or Integrator, respectively.In this report, we present a combination of genomic, proteomic, and molecular genetic evidence that human Mediator subunit MED26 recruits LEC to regulate transcription termination at RDH and snRNA genes and 3′-processing of RDH and snRNA precursors into mature, non-polyadenylated transcripts. In addition, we observe that Mediator subunits, like components of LEC, NELF, and HLFTo identify genes regulated by MED26, we performed RNA-sequencing (RNA-seq) of polyA-selected libraries prepared from HEK293T cells transfected with either control or MED26 small interfering RNA (siRNA). We identified 74 genes that were upregulated by a log2 ratio of at least 1.5  following knockdown of MED26  expressing MED26 with a C-terminal auxin-inducible degron (MED26-AID), which is degraded upon addition of auxin to culture media  revealed that MED26 is present at both snRNA and RDH genes   Fig. . To addr24. Based on semi-quantitative mass spectrometry of Mediator enriched via its ability to bind the transcriptional activation domain of ATF6α, we estimate that in these cells, mutant MED26 is present in Mediator at 30–50% of the level of its wild-type counterpart  to arrest the cell cycle at the S phase. As shown in Supplementary Fig. To address the effect of LEC recruitment by MED26 on the transcription regulation of the genes, we used a CRISPR -generated, MED26 hypomorphic mutant HEK293T cell line. This cell line expresses mutant MED26 that lacks the NTD required for LEC interaction with Mediator  to compare the location of transcribing Pol II in wild-type and MED26 hypomorphic HEK293T cells. As shown in Fig. lls Fig. . In contlls Fig. .Fig. 4InTo address the possibility that the apparent increase in read-through transcription is simply a consequence of an increase in actively transcribing Pol II that reaches the 3′-end of the transcript coding sequence, we calculated a PRO-seq read-through ratio, defined as “the sum of reads from 500 to 1000 bp downstream of the 3′-end processing site (TES) divided by the sum of reads from TES to 50 bp upstream of TES.” As shown in Fig. 14. Taken together, our results are consistent with the idea that MED26 plays a role in transcription termination of both RDH and snRNA genes. Although we cannot exclude the possibility that some of the observed effects were caused by a reduction in the absolute amount of MED26 in the mutant cells, our findings are consistent with the model that recruitment of LEC to RDH and snRNA genes via the MED26 NTD suppresses Pol II read-through and enhances normal transcription termination at both snRNA and RDH genes.Of note, although we consistently observed increases in snRNA read-through transcripts in the MED26 mutant cells, following knockdown of MED26, we observed increased levels of just a few snRNA/snoRNA transcripts in our polyA-selected RNA-seq dataset  super-resolution microscopyads Fig. . These bads Fig. . We alsoads Fig. , consistads Fig. . Taken tThe copurification of LEC with 3′-end processing factors for RDH and snRNA genes raised the possibility that LEC contributes to the recruitment of 3′-end processing factors to these genes. To examine this possibility, we investigated whether knockdown of ICE1 affected the occupancy of HLF at RDH genes and Integrator at snRNA genes. Because we found that the HLF component CPSF3 and Integrator component INTS9 copurified with FLAG-tagged ICE1 Figs.  and 6a, Drosophila42. In addition, our results are consistent with the model that LEC recruited to the vicinity of RDH gene promoters by MED26-containing Mediator helps to regulate termination and 3′-processing of RDH transcripts, raising the possibility that these processes might be influenced by promoter sequences. To address this possibility, we examined how replacement of the HIST1H1C promoter with the cytomegalovirus (CMV) promoter, a well-characterized viral promoter that normally directs transcription of a polyadenylated transcript, affects 3′-end processing of an RDH transcript. For this, we performed a plasmid-based assay to determine the effects of promoter exchange in cells. As shown in Supplementary Fig. HIST1H1C gene region from TSS to 100 bp downstream of TES, including the HIST1H1C stem loop and PAS. In addition, we added a FLAG-tag sequence at the C-terminus of the HIST1H1C coding region to distinguish plasmid-encoded histone H1 from endogenous protein. We transiently transfected the plasmids and compared the levels of total transcript, unprocessed read-through transcript, and protein when transcription is driven by the HISTH1C or the CMV promoter. As shown in Supplementary Fig. 43.Because the snRNA promoter was shown to be required for proper transcription termination and 3′-processing of snRNA genes in 47. Degradation of SLBP by the ubiquitin-proteasome pathway outside S phase is associated with disappearance of histone mRNAs outside the S phase48. Considering that SLBP also copurified with ICE1  MED26 recruits LEC to snRNA and RDH genes and plays a role in transcription processes, including initiation and elongation, then (ii) LEC binds to CBCA-NELF-DSIF and cooperatively inhibits reading through by Pol II at the genes, and finally (iii) LEC promotes 3′-end processing of the RDH genes and snRNA genes through the recruitment of 3′-end processing factors for RDH genes or Integrator complex, respectively Fig. .Fig. 10M24. Proteomic analysis revealed that LEC copurifies with CBCA, NELF and DSIF. Several reports have demonstrated that CBCA and NELF/DSIF play roles in terminating the transcription of snRNA and RDH genes19. In addition, we found that LEC copurified with HLF and Integrator, which are 3′-processing factors for the snRNA and RDH genes, respectively. Consistent with these results, we observed that components of Mediator, LEC, NELF, and HLF are colocalized at Cajal bodies, which are involved in transcription regulation of snRNA and RDH genes49. Super-resolution imaging revealed that Mediator and LEC formed similar structures in Cajal bodies, consistent with our proposal that Mediator and LEC play a role in transcription termination of non-polyadenylated genes at Cajal bodies. Of note, knockdown of ICE1 in cells decreased the recruitment of HLF to RDH genes and Integrator to snRNA genes. Based on our findings, we propose a model in which MED26 and LEC regulate termination of snRNA genes and RDH transcripts. In this model, LEC is recruited by MED26 to the gene. At the termination step of transcription, LEC binds to CBCA, NELF, and DSIF and inhibits the reading through of Pol II at the genes. Then, LEC recruits HLF or Integrator to the RDH or snRNA genes, respectively, to promote their 3′-end processing. Our results indicate that MED26 plays a role in the recruitment of LEC to specifically regulate the genes encoding non-polyadenylated transcripts. Considering that MED26 is a metazoan-specific subunit of the Mediator complex and conserved from Drosophila melanogaster to humans, but is not present in yeast, we expect that the mechanism uncovered in our study machinery is metazoan-specific.In this report, we present evidence that MED26-containing Mediator recruits LEC through interaction with the NTD of MED26 and regulates the transcription termination and 3′-end processing at a subset of genes that produce non-polyadenylated snRNA and RDH transcripts. We show evidence that both MED26 and LEC are present at snRNA and RDH genes. Intriguingly, we observed that transcription termination at snRNA and RDH genes is defective in cells expressing MED26 lacking its NTD, which we have shown previously is required for recruitment of LEC to snRNA genes50 and proper transcription termination at several genes. Our results showing that MED26 also promotes transcription termination and 3′ processing at snRNA and RDH genes, through the recruitment of LEC, provides further support for the idea that Mediator can modulate transcriptional events distal from the promoter.Prior studies have provided evidence that yeast Mediator subunit MED18 contributes to recruitment of the cleavage and polyadenylation factors Rna15 and Pta24. It is conceivable that other components of SEC or LEC also contribute to determination of the target genes of SEC or LEC. A recent study showed that ENL, a component of SEC, functions as a chromatin reader and binds to H3K27ac and H3K9ac of the promoter via YEATS domain of ENL51. It is thought that MED26 and ENL cooperatively contribute to SEC recruitment to the target genes. It is also possible that an interactor or component of LEC functions as a chromatin reader and helps MED26 recruit LEC to its targets, including snRNA and RDH genes.This study and our previous research raise the possibility that NTD of MED26 plays a role in the recruitment of SEC to a subset of genes encoding mRNAs containing poly(A) tails and LEC to a subset of genes encoding mRNAs lacking poly(A). However, this hypothesis does not completely explain the mechanisms by which target genes of SEC and LEC are determined because EAF directly interacts with NTD of MED26 and is a shared component of both SEC and LEC7. Considering that SLBP and U7 snRNP can help to recruit HLF to genes8, it is speculated that LEC, SLBP, and U7 snRNP cooperatively recruit HLF to RDH genes. It has been shown that histone mRNAs are only stable during the S phase, because SLBP, which is only present during the S phase, binds to the stem-loop structure of RDH transcripts to prevent RDH mRNAs from undergoing degradation during the S phase7. Degradation of SLBP by the ubiquitin-proteasome system outside the S phase is associated with disappearance of histone mRNAs outside the S phase48. Furthermore, our plasmid-based assay revealed that replacement of a non-polyadenylated gene promoter (HIST1H1C promoter) with a polyadenylated gene promoter (CMV promoter) resulted in increased levels of read-through RDH transcripts and proteins. Considering that SLBP copurified with ICE1  MED26 recruits LEC to snRNA and RDH genes and plays a role in transcription processes, including initiation and elongation, (ii) LEC binds to CBCA and NELF/DSIF to increase Pol II termination, and (iii) LEC promotes the 3′ processing of RDH or snRNA genes through the recruitment of HLF or Integrator, respectively.2 at 37 °C in Dulbecco’s modified Eagle’s medium  supplemented with 10% (v/v) fetal bovine serum (Invitrogen), 55 μM β-mercaptoethanol , 2 mM l-glutamine, penicillin (10 U ml−1), and streptomycin (0.1 mg ml−1). No mycoplasma contamination in these cell lines was confirmed using a PCR Detection Kit . Flp-In 293 cells stably expressing FLAG-tagged ICE1 and AF9 were generated previously24. The MED26 hypomorphic HEK293T cell line D2G8 was generated by applying two small guide RNAs (sgRNAs) complementary to regions of exon 3 in the MED26 gene to induce random sequence insertions and/or deletions. Both strands (sense and antisense) of sgRNA sequences targeting MED26 were chemically synthesized, annealed, phosphorylated using T4 polynucleotide kinase , and ligated into the BbsI site downstream of the U6 promoter in pX458/459 plasmids . Oligonucleotide sequences corresponding to the guide RNAs are included in the list of oligonucleotides in Supplementary Information. CRISPR-mediated mutagenesis was performed essentially as described52. The resulting MED26 hypomorphic clonal cell line, D2G8, contains an allele with a 140 bp deletion that removes sequences encoding MED26 NTD helix 4, which is critical for interaction with SEC and LEC components EAF1 or EAF253. It also contains alleles with frameshift deletions in exon 3 that prevent expression of full-length MED26 but that contain in-frame CUG codons, which can support cryptic CUG/leucine initiation54, upstream of the most N-terminal MED26 peptide identified in Mediator enriched from these cells , and Glutamax. HCT116-MED26-AID cells were additionally supplemented with puromycin (10 μg ml−1) and hygromycin (100 μg ml−1). The HCT116-MED26-AID cell line was established as described55. Briefly, HCT116 cells expressing auxin-dependent F-box protein TIR1 (HCT116-OsTIR1 cells) were generated by introducing OsTIR1 into the AAVS1 safe harbor locus in HCT116 cells by co-transfecting pMK232 (CMV-OsTIR1-PURO) and AAVS1 T2 CRISPR in pX330. After isolation of puromycin-resistant clones, OsTIR1 expression was confirmed by immunoblotting. The AID-hygromycin cassette in pMK287 (mAID-Hygro) was modified by inserting a DNA fragment encoding a linker sequence (EPTTEDLYFQSDNSSPGSGAGA) at its 5′ end to create linker-AID-hygromycin cassette. Standard molecular biology methods were used to generate a donor fragment for CRISPR-mediated homologous recombinational insertion of the linker-AID-hygromycin cassette at the 3′ end of the MED26 coding sequence in chromosome 19. HCT116-OsTIR1 cells were co-transfected with the donor fragment and pX459 derivative encoding Med26 gRNA (GUCGAUCCGCACGGCGACGAC). After hygromycin clonal selection, correct insertion of the linker-AID-hygromycin cassette into both Med26 alleles was confirmed by PCR and sequencing. pMX232 , AAVS1 T2 CRISPR in pX330 , and pMX287  were gifts from Masato Kanemaki, and pX459 (Addgene #62988) was a gift from Feng Zhang. To synchronize the cell cycle at the S phase, we cultured wild-type and MED26 hypomorphic HEK293T cells in medium containing 3 µg ml−1 APH  for 24 h. The cells were subsequently released from the S phase by exchanging the medium and harvested at specified times.Human embryonic kidney 293T (HEK293T) cells and derivatives, HeLa S3 cells and their derivatives, and Flp-In 293 cells  and their derivatives were cultured under an atmosphere of 5% CO5 cells per well) or 10-cm dishes (~2 × 106 cells per dish) were transfected with 50 nM siRNAs targeting human MED26 , siRNA targeting human ICE1 , siRNA targeting human AFF4 , siRNA targeting human CDK9 , or with 50 nM siGENOME NON-TARGETING siRNA Pool #2  using Lipofectamine™ RNAiMAX Transfection Reagent (Invitrogen).HEK293T cells in six-well tissue culture plates  were subcloned into pFastBac HTb  and expressed singly or together with the BAC-to-BAC system (Clontech).Anti-FLAG M2 antibodies , anti-HA antibodies , anti-MED26 antibodies , anti-MED26 antibodies , anti-MED1 antibodies , anti-MED23 antibodies , anti-ICE1 antibodies , anti-CBP80 antibodies , anti-CBP20 antibodies , anti-ZC3H18 antibodies , anti-ARS2 antibodies , anti-SLBP antibodies , anti-NELFb antibodies , anti-NELFe antibodies , anti-Symplekin antibodies , anti-CPSF1 antibodies , anti-CSTF50 antibodies , anti-CSTF64 antibodies , anti-CPSF3 (CPSF73) antibodies , anti-INTS4 antibodies , anti-INTS9 antibodies , anti-ZC3H8 antibodies , anti-CDK9 antibodies , anti-nucleolin antibodies , and anti-AFF4 antibodies  were used in western blots. As secondary anibodies, anti-mouse IgG antibodies , anti-rabbit IgG antibodies , anti-goat IgG antibodies , anti-rabbit IgG antibodies , and anti-mouse IgG antibodies  were used.24. Briefly, nuclear extracts and S100 fractions were prepared in the presence of Benzonase® Nuclease , basically in accordance with the method of Dignam et al.56 from parental HeLa cells or HEK293 FRT cells stably expressing FLAG-tagged proteins. Each of the nuclear extracts was incubated with 100 μl of anti-FLAG agarose beads for 2 h at 4 °C. The beads were washed five times with a 100-fold excess of a buffer containing 50 mM HEPES-NaOH (pH 7.9), 0.15 M NaCl, 0.1% Triton X-100, and 10% (v/v) glycerol and then eluted with 100 μl of a buffer containing 0.1 M NaCl, 50 mM HEPES-NaOH (pH 7.9), 0.05% Triton X-100, 10% (v/v) glycerol, and 0.25 mg ml−1 FLAG peptide. Endogenous ICE1-interacting proteins were affinity-purified using anti-ICE1 antibodies #1  or #2 . Nuclear extracts of HEK293T cells were incubated with 10 μg of normal rabbit IgG  or each of the ICE1 antibodies bound to protein A magnetic beads  for 2 h at 4 °C. After washing the beads with a buffer , the immunoprecipitates were eluted from the beads by addition of sodium dodecyl sulfate (SDS) sample buffer and subjected to SDS-–polyacrylamide gel electrophoresis (SDS-PAGE) and western blot analysis.Protein complexes were purified from nuclear extract fractions of cell lines stably expressing FLAG-tagged proteins using anti-FLAG M2 agarose 58. After denaturation, reduction, and alkylation, proteins were digested with endoproteinase LysC followed by trypsin (Promega). Peptides mixtures were analyzed through 10 multidimensional liquid chromatography steps implemented on a quaternary Agilent 1100 series HPLC in line with a Thermo linear ion trap mass spectrometer. Tandem mass (MS/MS) spectra acquired for the analyses of proteins purified by glutathione affinity . To estimate FDRs, each sequence was randomized keeping amino acid composition and length the same, and the resulting “shuffled” sequences were added to the forward sequences and searched at the same time. Mass tolerance for both precursor and fragment ions were set at 3 a.m.u. and 800 p.p.m. in the SEQUEST and ProLuCID searches, respectively, and to account for alkylation by CAM, 57 Da were added statically to the cysteines. The SEQUEST searches were performed without any peptide end requirements and differential modifications, while the ProLuCID searches were set up against a preprocessed database of tryptic peptides with K/R at both ends and with a differential modification of +16 Da on methionine residues. No maximum number of missed cleavages were specified. Peptide/spectrum matches were sorted and selected using DTASelect/CONTRAST (v. 1.9)61 in combination with an in-house software, swallow , to filter spectra, peptides, and proteins at FDRs <0.5%. Combining all runs, proteins had to be detected by at least two such peptides or by one peptide with two independent spectra. To estimate relative protein levels, distributed normalized spectral abundance factors (dNSAFs) were calculated64. The dNSAF for a protein k is proportional to the amount of the protein present in the sample and is calculated by the formula:i (uSpC) divided by the sum of all unique spectral counts for the M protein isoforms that shared peptide j with protein i.FLAG immunoaffinity- Fig.  or glutaity Fig.  were int7) of HEK293T cells grown to 80% confluence were used for immunoprecipitation. The cells were cross-linked with 2 mM DSG Crosslinker  in phosphate-buffered saline (PBS) for 30 min and then 1% formaldehyde in PBS for 20 min at room temperature. Then, the cells were resuspended and lysed in lysis buffer , and were then sonicated with a Bioruptor® Sonicator  20 times for 30 s each at the maximum power setting to generate DNA fragments of ~150–500 bp. For Rpb1 ChIP, cells were cross-linked with 1% formaldehyde in PBS for 20 min at room temperature before quenching with glycine to a final concentration of 0.125 M, washed twice with ice-cold PBS, and resuspended in lysis buffer  and 1:100 Protease inhibitor cocktail (Sigma). Mouse L cells  were added to the human cells as a spike-in control before sonication. Combined cells were sonicated using a Misonix 3000 sonicator at 4 °C using output 2.5 (9 w power) for 10 cycles (10 s ON/60 s OFF) to generate DNA fragments of ~150–500 bp. Sonicated chromatin was incubated at 4 °C overnight with 5–10 μg of normal IgG or specific antibodies. The specific antibodies used were as follows: MED26 , ICE1 , ELL , CPSF3 , INTS9 , and Rpb1 . Then, Dynabeads™ Protein A for Immunoprecipitation  was added and incubated for 2 h at 4 °C. The beads were washed two times with IP buffer , two times with high-salt buffer , once with LiCl buffer , and two times with TE buffer. Bound complexes were eluted from the beads with 100 mM NaHCO3 and 1% SDS by incubation at 50 °C for 30 min with occasional vortexing. Crosslinking was reversed by overnight incubation at 65 °C. Immunoprecipitated DNA and input DNA were treated with RNase A and proteinase K by incubation at 45 °C. DNA was purified using the QIAquick PCR Purification Kit  or MinElute PCR Purification Kit . Immunoprecipitated and input material was analyzed by qPCR. The ChIP signal was normalized to the total input. Three biological replicates were performed for each experiment. Primer sets are detailed in the Supplementary Information.Cells from one 10-cm dish  with default settings. Peaks were called using MACS2 callpeak (version 2.1.1), default parameter, based on the corresponding input samples. Peaks with q-score <1e − 4 were used for downstream analysis.Sequencing reads were acquired through primary Solexa image analysis. Filtered reads were then aligned either to the human genome (hg38) for MED26 ChIP or to an mm10-hg38 combined genome for Rpb1 ChIP using the Bowtie alignment tool. Only those sequences that matched uniquely to the genome with up to two mismatches and mapped to fewer than three locations were retained for subsequent analyses. Sequence reads for each ChIP-sequence dataset and its associated whole-cell extract controls were used for input. ChIP-seq genome browser tracks depict the average sequence reads from duplicate ChIPs. The public data were downloaded from GEO database with accession ID UT − CtCT). The relative unprocessed and total transcript levels were then calculated using 2−∆Ct. Primer sequences are listed in the Supplementary Information. For oligo-dT selection-based RNA-seq analysis or ribo-depleted RNA-seq analysis, 1 μg of total RNA was subjected to oligo-dT selection or depletion of ribosomal RNA using the Ribo-Zero Kit , and libraries were prepared using the TruSeq RNA Sample Prep Kit (Illumina). Raw reads from sequencing were demultiplexed allowing up to one mismatch using Illumina bcl2fastq2 v2.18. Reads were then mapped to human genome hg38 with STAR aligner (version 2.5.3a) default settings, using Ensembl 87 gene annotation models. Transcripts per kilobase million values were then generated using RSEM (version 1.3.0) function rsem-calculate-expression with option –estimate-rspd. Differential gene expression analysis was performed using R (v. 3.22.3) package edgeR (v. 3.5.0). Genes with FDR <0.05 and absolute fold change >1.5 were included for downstream analysis.Total RNA was isolated using miRNeasy Mini Kit . For RT-qPCR, total mRNA was reverse transcribed using the iScript Select cDNA Synthesis Kit . The threshold cycle (Ct) values were determined by real-time PCR reactions using an Applied Biosystems StepOne Real-time PCR System and Power SYBR Green PCR Master Mix (Life Technologies) and normalized by subtracting the Ct value of the CT from the Ct value of the UT  were added to each sample as a spike-in control. The combined cells were resuspended in cold douncing buffer  and 4 U ml−1 RNase inhibitor; SUPERaseIN) and dounced 30 times until 90% of cells were lysed. Isolated nuclei were pelleted, washed with douncing buffer, and resuspended in ice-cold storage buffer  to 2 × 107 nuclei per 100 µl. Nuclear run-on (NRO) assays were performed with biotin-11-NTPs. In all, 2 × 107 nuclei per 100 µl were thoroughly mixed with equal amount of preheated 2 × NRO reaction mixture , 0.8 U µl−1 RNase inhibitor) and incubated at 37 °C for 3 min in a heat block. Nascent RNA was extracted, purified, and fragmented by base hydrolysis in 0.2 N NaOH on ice for 10 min. After neutralization, fragmented nascent RNA was bound to Dynabeads™ M-280 Streptavidin magnetic beads (ThermoFisher Scientific) and incubated for 20 min at 4 °C. The beads were sequentially washed twice in high salt , twice in medium salt , and once in low salt  wash buffers. Biotinylated RNA was extracted from the beads and precipitated in ethanol. 3′ RNA adaptors were ligated to biotinylated RNA and a second round of biotin-streptavidin purification was performed. The mRNA cap was then removed and the reverse 5′ RNA adaptor ligated. After the third round of biotin-streptavidin purification, adaptor ligated nascent RNA was reverse transcribed (RT) into complementary DNA (cDNA) using RP1 primer. cDNA was amplified with index primers and amplicons of 140–350 bp were selected using the Pippen Prep  instrument. Equimolar concentrations of library fractions were then pooled together and sequenced using a high-output flow cell on the Illlumina NextSeq 500 platform. Raw reads from sequencing were demultiplexed allowing up to one mismatch using Illumina bcl2fastq2 v2.18. The adaptor sequence was removed, and reads were trimmed to 36 bp. After reverse complementing, reads were aligned to the dm6-hg38 combined genome using Bowtie2 (version 2.2.4) with default settings. The last base pair of the reads was used to generate BigWig files for visualization in a genome browser and for downstream analyses.Nuclei were isolated from HEK293T cells and mutant cell lines39. All images were deconvolved and arranged using the Huygens software  and Photoshop , respectively. Quantification of the number and signal intensity of the MED26 and ICE1 particles was performed using the ImageJ Fiji software.HeLa cells grown on glass coverslips were fixed for 15 min at room temperature with 2% paraformaldehyde. Then, the cells were incubated for 15 min with PBST containing PBS and 0.5% Triton X-100. After blocking cells with PBS containing 10% normal goat serum, they were incubated at room temperature with primary antibodies to coilin  at 1:2000 dilutions, MED26  at 1:500 dilution, MED6  at 1:100 dilution, MED24  at 1:100 dilution, NELFb  at 1:100 dilution; CSTF64  at 1:100 dilution; AFF4  at 1:100 dilution; and ICE1 (KIAA0947)  at 1:200 dilution in PBST containing 0.1% bovine serum albumin. The cells were then incubated with Alexa 488-labeled goat polyclonal antibody to mouse IgG at 1:1000 dilution, Alexa 555-labeled goat polyclonal antibody to rabbit IgG at 1:1000 dilution or Alexa 555-labeled rabbit polyclonal antibody to goat IgG at 1:1000 dilution (Life Technologies), covered with a drop of Prolong Gold antifade reagent (Invitrogen), and then photographed with a ZEISS LSM 700 Laser Scanning Microscope. Three-dimensional super-resolution images were acquired using a Leica TCS SP8 STED 3X Gated 660 system with a ×100 objective lens (HC PL APO CS2 ×100/1.40 NA OIL). The excitation was provided by a white light laser and the depletion was from a 660 nm STED laser with the three-dimensional slider adjusted to 60%, and the fluorescence signal was acquired using a Leica HyDTM in time-gated modeHIST1H1C gene with the HIST1H1C promoter was generated as follows. We defined minus (−) as before the TSS and plus (+) as after the TSS. The TSS was assigned the position of +1. The HIST1H1C gene region comprising the promoter (from −801 to −1), HIST1H1C gene region (from +1(TSS) to +762(TES)), and downstream region of the gene (from +763 to +881) containing the stem loop and PAS was amplified by PCR using the genome of HEK293T cells as a template. To distinguish the protein from endogenous histone protein, a FLAG-tag sequence was added to the end of the HIST1H1C coding region by PCR and subcloned into pBluescript II KS  to +762(TES)) and downstream region of the gene (from +763 to +881) was amplified by genomic PCR. To distinguish the protein from endogenous histone protein, a FLAG-tag sequence was added to the end of the HIST1H1C coding region by PCR and subcloned into the CMV promoter-containing pcDNA3.1 hygro (−) (Supplementary Fig. A plasmid containing the Further information on research design is available in the Peer Review FileSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs.Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in  S. cerevisiae, as a eukaryotic model organism, has been widely used as a host in synthetic biology because of its clear genetic background, rapid growth, easy cultivation and safety. Currently, yeast cell factories have been developed for the production of recombinant proteins, biofuels, natural and unnatural products, and bulk and fine chemicals [S. cerevisiae [hemicals . The prerevisiae ,3.S. cerevisiae, endogenous promoters can be classified into two types: constitutive promoters and inducible promoters. Constitutive promoters maintain relatively stable transcription levels with little influence from the intracellular or extracellular stimuli. In order to find strong constitutive promoters for genetic engineering, many studies have been performed to characterize endogenous promoters according to their expression strength under different growth conditions. Keren et al. [S. cerevisiae genome under different growth conditions, including various carbon sources. The promoter activities on glucose were comparable to the transcriptome data using single-molecule sequencing [TEF1, PTDH3, PPGK1, PTPI1, PCCW12, and PENO2—often show slightly different strengths in various studies, which may due to an inconsistent sampling time and experimental conditions [In n et al.  measuredquencing . Commonlnditions ,7,8.S. cerevisiae, these stimuli, or inducers, range from carbon sources, including glucose, galactose, sucrose, maltose, glycerol, acetate and ethanol, to environmental factors such as the temperature, pH, stress and light, to others, such as metabolites, amino acids, metal ions and hormones [GAL1, PGAL2, PGAL7 and PGAL10, are considered to be strong promoters and have been used to drive a higher enzyme expression level for the efficient production of desired products [Inducible promoters can initiate a dramatic change in the transcriptional level of their corresponding genes upon the presence or absence of specific stimuli. In hormones ,10,11. Gproducts . By usinproducts  construcproducts  achievedGAL1 is often used repeatedly for the expression of different enzymes in the construction of metabolic pathways [The construction of metabolic pathways often involves the expression of multiple genes whose expression levels span several orders of magnitude. The fine regulation of these genes makes it challenging to select the proper promoters. It is usually difficult for endogenous promoters to meet the requirements for the rational design and optimization of metabolic flux. This is mainly because the number of well-characterized promoters is inadequate, their dynamic ranges are poor, and they are often not orthogonal to endogenous regulations. For instance, Ppathways , so the pathways , and thepathways . Therefopathways ,16,17. AS. cerevisiae promoters have multiple essential elements for the accurate transcriptional regulation of genes, including a core promoter region, an upstream activator sequence (UAS), an upstream repressor sequence (URS) and nucleosome-disfavoring sequences, such as poly(dA:dT) sequences, as shown in Saccharomyces cerevisiae promoter database) [atabase) ,19,20,21S. cerevisiae.The core promoter is the nucleotide sequence that directly interacts with RNA polymerase II (pol-II) and other general transcription factors to form the pre-initiation complex (PIC) to initiate transcription. In metazoans, several conserved functional elements have been identified, including the TATA box, transcriptional start site (TSS), motif 10 element (MTE), downstream promoter element (DPE), and TFIIB recognition element (BRE), while only the TATA box and the TSS region have been identified in S. cerevisiae contain TATA boxes. Interestingly, previous studies demonstrated that while TATA-less promoters also require TBPs for PIC assembly [HIS3 activity and almost all single-base mutations were deleterious to its activity [PDC1 was higher when the TATA box docked between 88 and 66 bp upstream of the TSS, compared to between 65 and 39 bp upstream of the TSS. The TATA box was not functional when it was located 29, 19 or 9 bp upstream of the TSS, or at 19 or 9 bp downstream of the TSS [PDC1 promoter activity, ranging from 24% to 132%. These results demonstrated the impacts of TATA sequences, their location and flanking bases, on the regulation of gene expression, indicating that TATA boxes can be an effective element for fine-tuning promoter activities.TATA boxes are the recognition sites of the TATA-binding protein (TBP), a general transcription factor, with a consensus sequence of TATA(A/T)A(A/T)(A/G) . Howeverassembly , TATA-coassembly ,24,25. Tassembly ,27. For activity . Besidesrich)5NPyA(A/T)NN(Arich)6 presenting in both TATA-containing and TATA-less promoters, and the transcription is often initiated at the underlined adenosine site [S. cerevisiae, the location of the TSS varies from 40 bp to 120 bp downstream of the TATA box [S. cerevisiae [GAL1 and PGAL10 have different lengths between the TATA box and TSS, which are 84 bp and 114 bp, respectively, while both of their PIC regions span about 20 bp downstream of the TATA box [The TSS region is the consensus sequence A is located upstream of the core promoter and serves as a binding site for specific transcription activators. The UAS is a crucial region of promoters which enhances gene expression. While the core promoter is responsible for PIC recruitment and assembly, the UAS provides additional stability and regulation of PIC formation . All protivities . The pro000-fold . The ups000-fold . The act glucose ,41,42,43 binding .GAL is characterized by the presence of the CGG triplets at both ends, which are separated by 11 bp nucleotides. This allows for the existence of a total of 411 theoretical UASGAL sites with different binding affinities. It was reported that the four UASGAL sites of S. cerevisiae PGAL1 demonstrated different activities: UASGAL4 had the lowest activity, while UASGAL2 and UASGAL3 showed the highest activities [GAL measured in an in vitro assay were inconsistent with the in vivo data [GAL mutant library must be characterized in vivo to understand the properties of the UASGAL sites. The saturation mutagenesis of the transcription factor Gcn4p’s binding site (5′-ATGACTCTT-3′) within the HIS3 promoter found that almost all mismatch mutants reduced the PHIS3 activity significantly and only one mutant with the sequence 5′-ATGACTCAT-3′ increased the binding affinity of Gcn4p and improved the PHIS3 activity [S. cerevisiae [S. cerevisiae [The binding affinity, quantity, and location of the UAS and URS affects promoter activities. The sequence variations of UAS and URS result in different binding affinities with their corresponding transcription factors and this has been studied in great detail using site-directed mutagenesis ,80,81. Utivities . It was activity . It has activity ,84. It irevisiae . Previourevisiae ,86. ThusGAL1 promoter contains a URSMIG and four UASGAL. URSMIG mainly represses the activity of GAL1 promoters under glucose growth conditions, while UASGAL induces the activity of GAL1 promoters under galactose growth conditions. Under the condition of glucose and galactose fermentation, the regulation of GAL1 promoters correlated to the ratio of glucose and galactose [The coexistence of various UASs or URSs in the same promoter could bring about the combinatorial and dynamic regulation of its transcription. Promoters of many genes related to carbon source metabolism have both URS sites, which are commonly suppressed by glucose, and UAS sites induced by other carbon sources. For example, the alactose . In anotalactose . Even thalactose ,90,91,92Promoters’ chromatin structure plays an essential role in transcription regulation. A low nucleosome occupancy facilitates transcription and improves mRNA abundance, while increasing the nucleosome occupancy of promoters tends to decrease their mRNA levels ,94. PrevHIS4 promoter, but also reduced a nearby nucleosome to increase the accessibility of other transcription factors, including Gcn4p, Bas1p, Bas2p [PHO5 promoter has two binding sites of transcription factor Pho4p. One is a low-affinity site located in a nucleosome-free region and another is a high-affinity site occupied by a nucleosome. Under induction conditions, Pho4p interacts with the low-affinity binding site first rather than the high-affinity binding site [Nature has evolved two main strategies to decrease nucleosome occupancy in order to regulate gene expression. One strategy involves the assistance of transcription factors, such as Rap1p, Reb1p, Abf1p and the SWI/SNF complex, which can release nucleosomes from DNA ,101,102.p, Bas2p ,104. Foring site ,106.S. cerevisiae are controlled by poly(dA:dT) tracts, such as PHIS3, PURA3, PADH2, PRPS28A [RPS28A contains a poly(dA:dT) tract located 7 bp downstream of the transcription factor Abf1p binding site. Mutants of this poly(dA:dT) tract had a lower transcription of the RPS28A gene and the nucleosome moved closer toward the Abf1p binding site [The poly(dA:dT) tract, a homopolymeric stretch of deoxyadenosine nucleotides, is a well-known nucleosome-disfavoring sequence in eukaryotic organisms. Its length ranges from 10 to 20 bp, or is even greater in some cases . The pol PRPS28A ,110,111. PRPS28A ,112,113.Several different methods, such as a random mutagenesis by error-prone PCR, saturated mutagenesis, hybrid-promoter engineering, have been used for promoter engineering and have been reviewed previously ,115,116.Escherichia coli. De Mey et al. [E. coli, demonstrating the prospects of predictive and rational promoter designs. However, the accuracy for the prediction still needs improvement. Artificial neural network (ANN) models can better represent the complex and nonlinear interactions within promoter sequences and have been successfully applied in the rational design of promoters. A series of promoters with different strengths was obtained by randomly mutating the Trc promoter and its ribosome binding site in E. coli, which were then used for training and testing using the ANN mathematical model. Sixteen novel artificial elements were generated in silico and their predicted expression levels showed a good correlation with the experimental results, indicating that the model could be used for synthetic promoter designs with specific properties [de novo and predict their expression levels, and three highly active synthetic promoters were identified in E. coli, among the predicted strong promoters, by experimental validation [S. cerevisiae, promoter libraries were created based on native promoters, including the constitutive PTDH3 and the inducible PZEV, and a reliable prediction model was trained with the CNN based on the promoter sequence-activity data collected from these libraries, and the activity of a synthetic promoter (predicted from the TDH3 promoter) increased by 37% and the activity of a ZEV promoter mutant also increased by β-estradiol induction, and its basal expression was reduced [S. cerevisiae.Currently, machine learning is mainly used for promoter engineering in y et al.  applied operties . The genlidation . In S. c reduced . HoweverS. cerevisiae is about 108, therefore the library capacity will not exceed the transformation efficiency. Hybrid-promoter engineering requires a lot of testing, and it is difficult to achieve a high-throughput selection due to the heavy construction workload. Machine learning could construct quantitative models based on a limited database to analyze the data distribution characteristics of the designed promoter library, helping us better understand the underlying interaction principle. The result of machine learning is to predict a serial of promoters that function as expected. This predictability can reduce the workload and increase the accuracy of testing. Therefore, it is foreseeable that machine learning methods will gain momentum in the near future and fuel the development of accurate and customer-tailored engineered promoters.Libraries created by random or saturated mutagenesis are dependent on the transformation efficiency of the strain. The maximal transformation efficiency of TEF1 promoter and obtained a series of synthetic promoters with a wide range of activities; the best candidate showed a two-fold higher activity than the native PTEF1. These promoters were used to regulate efficient glycerol production by driving the rate-limiting enzyme expression in S. cerevisiae [ENO2 and PPDC2 were also engineered by random mutagenesis. Synthetic PENO2 and synthetic PPDC2 obtained from their corresponding mutagenesis libraries improved the expression of recombinant proteins cellobiose transporter and β-glucosidase for cellobiose degradation by 24.4-fold and 3.0-fold, respectively. The recombinant strain had a higher cellobiose consumption and ethanol production than its parent strain, reported as 6.41-fold and 6.36-fold more, respectively [Multiple enzymes are often introduced and overexpressed in metabolic engineering to boost the yield of the desired product. However, an unbalanced expression of these enzymes would accumulate intermediates and result in unnecessary metabolic burdens or toxicities . Thereforevisiae . Other nectively .TDH3 promoter with three UASCLBs, the 240-bp UAS sequence of the mitotic cyclin (CLB2), and this hybrid promoter enhanced the transcription level 2.5-fold compared to TDH3 promoter. In addition, they built a series of heterozygous galactose-inducible promoters by fusing UASGAL with different core promoters and the resulting synthetic promoters had a continuous strength gradient which could achieve a fine-tuned gene expression. A series of post-diauxic phase inducible synthetic promoters were also constructed by combining core promoters and UASs, and their activities were further improved by the optimization of the UAS number [CYC1, PHIS5, PHXT7, and PTEF1, increased the strength of the resulting synthetic promoters. However, this method did not work on strong promoters such as PTDH3 and PGAL1, which may have been evolutionarily optimized for their nucleosome architecture in nature [The rational combination of different promoter elements is another efficient method to improve the dynamic range of promoters b. BlazecS number . More reS number . The intn nature . Based oS. cerevisiae has a strong homologous DNA recombinant capacity, which has been widely utilized in genome insertion, deletion and replacement. Multiple uses of the same promoters or their elements are prone to generating homologous recombinations and lead to the instability of synthetic pathways in S. cerevisiae. Thus, the development of synthetic promoters with sequence orthogonality to avoid homologous recombination is an attractive research field. Exploiting the naturally evolved diversity of heterologous promoters in S. cerevisiae is an effective strategy. Peng et al. [Saccharomyces species and found that most of them are stronger than ScPGAL1, especially SePGAL2 and SkPGAL2 from Saccharomyces eubayanus and Saccharomyces kudriavzevii, respectively. Recently, the design of artificial promoters without the utilization of a native promoter as a scaffold has been exploited. Based on a computational approach, Curran et al. [CYC1 and PHXT7. These promoters, although still small in number, will be useful for synthetic biology applications, especially for industrial applications which are more dependent on stable gene expression.It is well-known that g et al.  comparedn et al.  predicteE. coli, the lengths of promoters are typically less than 100 bp, whereas native yeast promoters usually span hundreds of nucleotides. The long nucleotide sequences not only decrease the efficiency of biosynthetic pathway construction, but also complicate the regulation of these pathways. The construction of minimal promoters could overcome these limitations. The truncation of endogenous promoters to remove non-essential bases is one strategy for minimal promoter construction  between the TATA box and TSS were selected for saturation mutagenesis, and it was found that only the N30 library generated partially active promoters. Thirteen (13) of these functional core promoters were isolated. Furthermore, minimal constitutive UASs of 10 bp were also selected by a similar process. By combining the minimal core sequences and minimal constitutive UASs, minimal promoters were created and the activity of one of them reached 70% of the strong PTDH3, with approximately 20% of its original length. In addition, minimal galactose-inducible promoters were also developed by using these minimal core promoters and their activities were comparable to wild type PGAL1 [S. cerevisiae and may have strong potential in large-scale synthetic biology applications.In bacteria, such as truction a [131,13of PTEF1 . Howeverof PTEF1 ,134. In pe PGAL1 . SynthetE. coli and Bacillus subtilis and eukaryotic S. cerevisiae and Pichia pastoris, often have certain genetic features which make them suitable hosts for specific genetic circuits and biosynthetic pathways. However, the characterization of these circuits and pathways in different hosts always needs promoter substitution. Thus, the creation of broad-spectrum synthetic promoters for applications in diverse hosts could expand the synthetic biology toolbox to avoid promoter redesign and facilitate host selection [S. cerevisiae and the conserved −35 and −10 boxes from E. coli and B. subtilis were rationally combined to develop Pbs, which could be used to drive gene expression in all three hosts. The activity of Pbs was much stronger than the E. coli strong promoter PJ23119, comparable to the S. cerevisiae minimal promoter Pmin, and approximately 75% of the B. subtilis strong promoter Pcdd. UAS mutations renders this promoter with an activity gradient, which is useful in biosynthetic pathway optimization [S. cerevisiae core promoters, Portela et al. [S. cerevisiae and P. pastoris. The reported broad-spectrum promoters have been used in several hosts, and more promoters of this type with a dynamic activity range are required to simultaneously drive gene expression in more diverse hosts, which would facilitate the examination of synthetic pathways in different hosts.Commonly used microbial hosts in synthetic biology, such as prokaryotic election . The conmization . Based oa et al.  successfTo improve the robustness of biological pathways, it is essential to design a feedback control network for gene expression regulated by metabolic intermediates or other stress factors. Gene circuits with feedback regulation usually have one or more biosensors which can respond to physical or chemical signals to realize the real-time dynamic transcription regulation, and thus automatically adjust the state of gene expression. Two key components of biosensors are transcription factors, which can detect either intracellular or environmental signals, and promoters, which can receive signals from effectors and generate an output. Thus, promoter engineering is a common method in biosensor development and optimization.+ ratios are important to maintain redox homeostasis and cell fitness. The activity of the native TRX2 promoter, which is regulated by the transcription factor Yap1p, can be altered by sensing NADPH/NADP+ ratios, but its sensitivity is low. Increasing the number of the UAS-containing Yap1p binding sites greatly enhanced the cascade response effect, and this novel biosensor was useful at selecting cell populations with higher NADPH/NADP+ ratios [YGP1 and CCW14 promoters by optimizing the core promoter and the number and type of UASs successfully created a set of strong synthetic promoters for sensing low pH; these low-pH sensing promoters improved the production of lactic acid by 10-fold under low-pH fermentation compared to the native TEF1 promoter [Most native promoters have some defects in terms of being a part of an excellent biosensor, therefore, engineering native promoters could increase the sensitivity and the overall performance of biosensors. Cytosolic NADPH/NADP+ ratios . Similarpromoter . In addipromoter  found a S. cerevisiae. The bacterial tetracycline operator (tetO), a DNA binding sequence of tetracycline-inducible repressors (tetR), has been used in numerous biosensor applications. Hybrid promoters created by the fusing of one or more tetO to a yeast native promoter, such as a CYC1 promoter, were controllable under different tetracycline concentrations, and the overexpression levels of β-galactosidase comparable to PGAL1 are reached [tetO locations between the TATA box and TSS found that the closer the tetO was located to the TATA box, the stronger the transcriptional repression; the location and number of the tetO together determined the dose-response curve [CYC1, and a camphor-off switch was successfully constructed, which activated gene expression without camphor and repressed gene expression through the addition of micromolar concentrations of camphor, and this sensor was applied to complement the adenine-auxotrophy in a camphor-dependent manner [fapO DNA binding sites have also been widely used to develop various biosensors for the detection of many metabolites in S. cerevisiae, such as fatty acid intermediates, such as malonyl-CoA [The utilization of heterologous regulatory elements can increase the number and types of biosensors which do not interfere with the native cellular regulation of  reached . The numse curve . Camphort manner . The baconyl-CoA ,146. By CYC1 promoter. A lactose inhibitor LacI binding site was positioned downstream of the TATA box, and the hybrid promoter led to a wide range of dual-mode promoter outputs under the regulation of testosterone and isopropyl β-d-1-thiogalactopyranoside IPTG [FUS1 promoter to sense α-pheromone levels. The ARO9 promoter was responsive to aromatic amino acids in the cultivation medium and was used to drive α-pheromone expression, and thus the pheromone quorum sensing could be fine-tuned by the aromatic amino acid concentration [The rational combination of different regulatory elements enables signal integration to perform the combinatorial effects of biosensors. For instance, five binding sites of the androgen receptor, which respond to steroid hormones, were placed upstream of the TATA box of the ide IPTG . In a sintration . The synIn conclusion, numerous synthetic promoters have been engineered and their characteristics are shown in GAL1. Machine learning provides a new design method for synthetic promoters and helps to further understand the mechanism of genome-wide gene expression regulation. Machine learning models may accurately predict synthetic promoter activities and quickly lead to target characteristics. As such, machine learning will likely become a powerful tool for promoter engineering and synthetic biology as a whole.Although numerous synthetic promoters have been created to fine-tune gene circuits and metabolic pathways for a greater compatibility and production improvements, the complexity of biological systems still require more sophisticated and elaborate artificial promoters. Although studies have focused on understanding promoter architecture, there remains much to be learned about the interactions of multiple promoter elements to make promoter engineering easier. Promoter designs of the past were unpredictable, requiring laborious screening and testing, which is expensive and time-consuming. In addition, although multiple synthetic promoters with stronger activities were created, they did not exceed the strength of P


Trees and their seeds regulate their germination, growth, and reproduction in response to environmental stimuli. These stimuli, through signal transduction, trigger transcription factors that alter the expression of various genes leading to the unfolding of the genetic program. A regulon is conceptually defined as a set of target genes regulated by a transcription factor by physically binding to regulatory motifs to accomplish a specific biological function, such as the CO-FT regulon for flowering timing and fall growth cessation in trees. Only with a clear characterization of regulatory motifs, can candidate target genes be experimentally validated, but motif characterization represents the weakest feature of regulon research, especially in tree genetics. I review here relevant experimental and bioinformatics approaches in characterizing transcription factors and their binding sites, outline problems in tree regulon research, and demonstrate how transcription factor databases can be effectively used to aid the characterization of tree regulons. Arabidopsis thaliana [A. thaliana responds to cold through gene regulation sheds light on how tree species such Malus demestica respond to cold [Research in tree genetics, especially in gene regulation networks, has benefitted much from looking beyond trees. The finding of animal c-MYB family of genes resulted in the discovery of the R2R3-MYB family of transcription factors in thaliana ,2, and tEscherichia coli, but controlled by the same arginine repressor [argR (the E. coli gene encoding the arginine repressor) regulon for arginine biosynthesis, or the CO-FT regulon for flowering timing and seasonal growth in Arabidopsis thaliana [The term regulon was originally proposed  as an exepressor . Since ithaliana  and in tthaliana .Arabidopsis thaliana as a model for research in plant molecular biology has resulted in the discovery and characterization of regulons in Arabidopsis thaliana and related plants, but also in various tree species. Some regulons function similarly, such as the CBF regulon in plant response to low temperature [Arabidopsis thaliana [Arabidopsis thaliana [Populus species. While flowering time is still under the control of the CO-FT regulon [The adoption of perature ,7,8 whicthaliana ,10 and pthaliana . Howeverthaliana , but flo regulon , seasona regulon .Many different experimental and bioinformatics methods have been used to discover and characterize regulons, but the large-scale approach of combining ChIP-seq and RNA-seq technology has been the most frequently used in recent years ,14 evolvA conceptually ideal transcription regulon should have four features: (1) a transcription factor, (2) one or more target genes whose expression is regulated by the transcription factor, (3) a set of conserved transcription factor binding sites (TFBS) on the target genes, and (4) specific biological functions accomplished by altering the expression of the target genes. All regulons I mention in this review are transcription regulons.T1, T2 and a transcription factor B (tfB), that share TFBS X. tfB protein can in turn bind to TFBS Y to control the expression of target genes T4 and T5 that share TFBS Y in their regulatory region. Criterion 3 implies that the tfA-regulon includes target genes T1, T2, and tfB, but not T4 and T5 whose TSBS Y is bound by tfB but not by tfA. In contrast, the term “coregulated genes” would typically include T1, T2, tfB, T4, and T5 as coregulated directly by tfA or indirectly by tfA through tfB.The importance of feature 3 above may be illustrated in MSN2 on chromosome 13 and MSN4 on chromosome 11 of the yeast Saccharomyces cerevisiae [MdMYB124  on chromosome 13 and MdMYB88  on chromosome 16 of the apple [In rare cases, two different TFs represent nearly identical paralogues and feature the same DNA- binding affinity. Examples include revisiae , and MdMhe apple .Regular research aims to characterize the four features of a regulon and to understand its biological function. Among the four features, TFBS is typically the worst characterized, partly through negligence and partly through poor experimental design. For example, the partial regulon network Feature 1 of the regulon definition is essential for us to understand the dynamic nature of regulon operation. A transcription factor does not bind to TFBS of its target genes all the time, but only at specific time under specific environment, e.g., CO-FT regulon in response to seasonal change in photoperiod  or bZIP6HAC1 mRNA is exported into cytoplasm in an unspliced form, and is translated to produce functional proteins only after the HAC1 mRNA is spliced by Ire1 to remove the translation inhibition mediated by its intron binding to its 5’ UTR [HAC1 to regulate the transcription of HAC1. A regulon could have just a single gene. For example, HAC1 is a transcriptionally autoregulated gene with its protein binding to its TFBS forming a positive feedback loop until Ire1 stops splicing HAC1 mRNA [The definition of transcription regulon above does not include gene regulation mediated by mechanisms other than transcription factor binding to TFBSs, i.e., it does not include gene regulation involving posttranscriptional or posttranslational modification, DNA methylation, or histone modification. For example, yeast s 5’ UTR ,27,28 HoAC1 mRNA . Thus, aCO-FT regulon regulating flowering time in Arabidopsis thaliana [Populus species [ICE1–CBF–COR regulon network [CBFs to upregulate their expression, and CBFs bind to TFBS of their downstream cold-regulated genes (CORs) to enhance cold tolerance. The key difference between ICE1-CBF-COR regulon network and the regulon network in CBFs include multiple genes instead of a single tfB.A regulon network involves two or more interacting regulons. The interacting tfA-regulon and tfB-regulon  constituthaliana  and in P species  would be network ,31,32 inA regulon network, as represented in A regulon network (RN) differs from the conventional gene regulation network (GRN) in the basic unit of the network. A basic unit in RN is a regulon with its four features , but that in a GRN is not explicitly defined, although two nodes in GRN connected by a directed or undirected line are assumed to be in regulator–regulatee relationship. An RN highlights which feature of a regulon is missing. For example, Saccharomyces cerevisiae [KAR2 [While some regulons in tree species are well characterized ,6,12, threvisiae ,34. Firsrevisiae ,35,36,37revisiae  serving revisiae , and (2)revisiae ,40, extrrevisiae , and redrevisiae . Third, revisiae ,43,44 anrevisiae ,39,45,46KAR2). The first is to check if TF fails to bind to, and regulate, the target gene after removing or mutating the putative TFBS [Zea mays. The ChIP-Seq data set would allow one to identify putative TFBSs in a large number of putative genes. Transcriptomic data were obtained from the wild type with a function NS1 and an ns1 mutant. A gene is likely NS1-regulated if it not only harbors a putative TFBS, but also differs in gene expression between the wild type and the mutant. The third approach is to compare sequence conservation of orthologous genes among related species. A TFBS should be strongly conserved but its flanking sequences should not. These approaches have been used to characterize the two UPREs above and the target genes of Hac1.Multiple approaches have been used to verify the association between TF  and TFBS of target genes  upon finding a new gene in the same way as we come upon a new word. If the dictionary does not contain the new word, then we make use of the second approach of inferring the meaning of the word based on the context and our grammatical knowledge. This is the approach of de novo discovery of transcription factors based on their shared features, but not their presence in the dictionary. If the new word (gene) turns out to be a transcription factor, then it goes to enrich the regulon dictionary.Arabidopsis thaliana. The rest are mostly from crop species. Thus, the field of tree regulon is essentially a virgin land. Note that these databases do not feature an exhaustive compilation of known TFBSs. They may include TFBS from some large-scale studies, but ignore TFBS detected and verified through conventional painstaking experiments. For example, they include one TFBS of yeast Hac1p [There are now many databases of characterized transcription factors, from the early TRANSFAC  to more st Hac1p ,39,45,46st Hac1p ,39,43,44ijp is the frequency of nucleotide i  at site j, sometimes with pseudocounts to avoid taking logarithm of zero, and ip is background frequencies. The pros and cons of various pseudocount schemes, and of different background frequencies, for PWM have been reviewed and numerically illustrated in detail [In spite of the incompleteness, the regulon databases are useful for two main purposes. The first is to check if one’s gene is homologous to a transcription factor already characterized in the database. The second, which should have been more commonly employed, is to retrieve the compiled binding sites to scan new sequences for binding sites ,60,61, wn detail ,63.miR164a. Ideally, the background frequencies, i.e., ip in Equation (1), should be the nucleotide frequencies of these 2000 nt upstream of miR164a . These frequencies were in fact used to compute the PWM (miR164a starting at site 2001), with a 9mer CAATCATTA. This 9mer matches eight out nine sites in the consensus  may be elucidated. A number of programs can scan sequences for putative TFBSs based on an input PWM, and the performance of four such programs has recently been evaluated [The resulting PWM C can now the PWM C. The hionsensus B and itsonsensus D. The exvaluated .AtHB1 is upregulated in hypocotyls and roots of seedlings in response to short day length, and its expression was claimed to be regulated by PIF1/PIL5 [AtHB1 gene. The closest 6mer one can get is CTCGTG at site 781 (AtHB1 start codon at site 2001) and CACGAG at site 1270. Scanning an additional 1000 nt further upstream does not find better matching. Because TFBS is rarely located more than 3000 nt upstream of a coding sequence, PIL5 is unlikely to be a direct regulator for AtHB1 expression. If the regulation is indirect through another transcription factor, then PIL5 and AtHB1 may belong to the same regulon network but not the same regulon according to the definition laid out in the previous section. This highlights the importance of using well-characterized regulons to build regulon networks.The characterized binding sites in transcription databases may also help us determine the presence or absence of direct regulator–regulatee relationship. For example, IF1/PIL5 . The binIF1/PIL5 . HoweverWhen we encounter a new word, cannot find it in dictionaries, and wish to judge if it is a verb, we have to use our grammatical knowledge. Transcription factors, like verbs, also share certain features that allow them to be identified and classified . All traThe nuclear localization signal (NLS) is typically rich in basic amino acids Lys and Arg. The first NLS is PKKKRKV . The NLSn. Positions a and d are hydrophobic. In leucine zipper transcription factors such as GCN4 in yeast [Arabidopsis thaliana [Transcription factors fall into several major structural classes such as leucine zipper, helix-loop-helix, helix-turn-helix, and many others . Leucinein yeast , and XBPin yeast , the fouthaliana ,24 with thaliana ,39,45,46thaliana ,79 and pthaliana  all havethaliana . This shthaliana .In addition to the basic NLS, transcription factors typically feature a basic DNA-binding domain favoring electrostatic interaction with the negatively charged DNA backbone. The domain could be in the middle as in AtHB1  or at thWhile none of the features above can identify a protein as a TF, the presence of all these features jointly does increase the likelihood of a protein being a TF. If a protein not only has all these features, but its expression in the cell also consistently results in altered expression of a set of genes, then we can conclude with reasonable confidence that the protein is indeed a transcription factor. Unfortunately, there is no software at present that integrates all available information in de novo discovery of TFs.There are computational methods for predicting DNA-binding sites from 3D protein structure ,83,84,85Experimental approaches for large-scale de novo discovery of TFBSs include microarray, ChIP- Seq and its derivatives such as ChIP-exo ,18, ChECMus musculus (BioProject ACCN PRJNA544746) that do not overlap. By mapping them onto mouse chromosome 2 (NC_000068), we learn that they are separated by just a single nucleotide C , sonication was used to generate sequence fragments of 300–700 bp, but the single-read has a length of only 50 bases. This implies that, if TFBS is in the middle of each sequence fragment, then the read of 50 bases from the sequence fragment will not include TFBS, so the signal/noise ratio is 0. However, with MNase or other exonucleases, it is theoretically possible to generate TFBS-containing sequence fragments of only 50 bases or even shorter.The ChIP-Seq approach will sequence all sequence fragments F, each oeotide C C. HoweveWith a large number of sequence reads containing the putative TFBS for a particular TF, there are two categories of bioinformatics approaches to extract TFBS. The first, represented by MACS , is to mChIP-seq also has the shortcoming of spurious cross-linking and large sample size which is needed for us to see the peak in The second method of extracting TFBS is by Gibbs sampler which has been used in the identification of functional motifs in proteins ,90,91 anSome researchers  thoughtRegulon research is a rapidly progressing field with many new experimental protocols, computational algorithms, and databases having been developed in recent years. I present conceptual rationales of these protocols and algorithms and highlight their advantages and disadvantages. Existing literature on regulon research, especially in tree species, were critically reviewed to highlight how better results could have been obtained.