By using SAF/MAZ promoter as a model, we set up an approach to probe how the alternative promoters are regulated in real time. We have constructed the bichromatic fluorescent reporter driven by SAF/MAZ 5′-proximal promoter plasmids from which transactivation status of SAF-1 and SAF-3 alternative promoter could be monitored by EGFP and DsRed expression respectively. The results showed that the SAF-3 expression is regulated by alternative promoters. When the bichromatic fluorescent reporter was driven by −1692/+277 or −1401/+277 SAF/MAZ promoter the dominant expression of SAF-3 would be observed in comparison with SAF-1 expression. We also identified that Elk-1 is an inhibitory transcription factor for SAF-3 expression. The temporal diversity of SAF-1 and SAF-3 expressions can be observed via bichromatic fluorescent reporters. These imply that the bichromatic fluorescent reporter driven by alternative promoter construct might be a useful tool for decoding the temporal regulatory repertoire of alternative promoter in human genes.The large-scale identification of putative alternative promoters study shows more than 52% of human genes are regulated by alternative promoters. The human  The Human Genome Project revealed that there are only 20000–25000 protein-coding genes in human genome . By compMyc-associated zinc finger (SAF/MAZ) gene (Genbank MIM: 600999) is a CysHis2-type transcription factor widely expressed in different tissues in humans. It plays a key role in regulation of inflammation-responsive genes, cell cycle controlling genes as well as genes regulating different elements of tumor microenvironment to promote tumor growth. For instance, transcriptions of serum amyloid A (SAA), γ-fibrinogen, matrix metalloproteinase (MMP) 1 (MMP-1) and MMP14 involved in inflammation processing are regulated by SAF/MAZ protein via binding to promoters of these genes [se genes . In non-se genes .SAF/MAZ gene has multiple TSSs. A previous study on GC-rich and TATA-less promoter of SAF/MAZ gene showed that SAF-3 isoform is a better transcriptional activator and expressed during inflammation . It is rIn the present study, we constructed bichromatic fluorescent reporter  system dThe pGL3-promoter (Promega) plasmid was revised to form SAF1-3 activation promoter (SAF1-3 AP) plasmid B. A pair4 DNA polymerase and ligating by T4 ligase so that the EGFP or DsRed expressions were toggled by SAF-1 or SAF-3 activation, respectively , forward 5′-ATGCGAATTCGTGAGCAAGGGCGAGGAG-3′ and reverse 5′-TAGCATGCAT CTATTACTTGTACAGCTCGTCCAT-3′ (EGFP), and forward 5′-ATGCATGCATC CGCCTCCTCCGAGAACGTC and reverse 5′-AGTCCTTAAGCTACAGGAACAG GTGGTGGCG (DsRed2). Since the SAF-1 and SAF-3 share the same ORF, the GGATCC sequence at the beginning of SAF-3 translational starting site was modified by digesting with BamHI, blunting by Tectively C. For pr5 cells per well in six-well culture plate . Transient transfections of cells were performed by Lipofectamine 3000 based on the protocol provided by Invitrogen. The transfection mixture contained 1.6 μg SAF1-3 AP plasmids, 3.2 μl Lipofectamine 3000, and 3.2 μl p3000. For co-transfection, the ratio of SAF1-3 AP plasmids and pCMV-sp1, pCMV-Elk-1, or negative control plasmids is 2.2 μg: 0.2 μg. After culturing for 48 h, the cells were washed for three times by PBS, treated by pancreatic enzymes, and filtered for flow cytometry analysis . For confocal scanning microscopy (Olympus FV1200) analysis, the cells were plated in four- well glass bottom culture plate (20mm 801001 NEST Biotech. Co. Ltd) specific for confocal scanning microscopy and observed fluorescent cells after 48 h.HeLa cells were cultured in DMEM medium containing high glucose (90%) supplemented with 10% FBS and plated at a density of 2.5 × 10The prediction of transcription factors binding to SAF/MAZ promoter spanning −948 to −1 bp upstream of SAF-1 5′-UTR was performed by PROMO online software.Nuclear extract of HeLa cells were prepared by using nuclear protein extraction kit . Protein concentrations were determined by BCA assay .The Elk-1 DNA probe was designated to correspond to DNA fragments between −504 and −525 nts in SAF/MAZ promoter. The sequences of oligos used for probe were 5′-TGCTCCGCTTCCCCCACCCTCC-3′ and 5′-GGAGGGTGGGGGAAGCGGAGCA-3′. These oligos were labeled by biotin at 3′-OH terminus with Biotin-11-dUTP (Thermo Scientific R0081) catalyzed by terminal deoxynucleotidyl transferase  as follows. Fifty microliters of Biotin labeling mixture containing 5 μl of oligos (1 μM), 5 μl of Biotin-11-dUTP (5 μM), 5 μl of 0.1% BSA, 10 μl of reaction buffer (5×), 0.5 μl of TdT (20 U/μl), 24.5 μl of ultrapure water was incubated in 37°C for 30 min, then at 70°C for 10 min to terminate the reaction. The labeled DNA probe was extracted by phenol/chloroform.The binding reaction was performed in 25 μl of buffer containing 5× binding buffer (Beyotime Biotechnology) 5 μl, recombination Elk-1 protein (1 μg/μl) 12 μl, and biotin-labeled probe (0.045 pmol/μl) 8 μl. After a 10-min incubation at 25°C, the probe was added to reaction mixture and kept on incubation at same temperature for another 20 min. The products were loaded on to a 4.9% non-denaturing polyacrylamide gel in 0.5× TBE buffer. Electrophoresis was performed at 120 V for 4–6 h at 4°C. Then, the gel was equilibrated in 0.5× TBE transfer buffer for 5 min. The equilibrated gel was overlaid with a nylon membrane  and covered filter paper soaked with transfer buffer on both gel and membrane sides. The bands were transferred to nylon membrane semi-dried at 200–240 mA for 1 h. The probe–protein complex signals were visualized by chemiluminescence assay (Beyotime GS009) and recorded by an X-ray film.TRIzol reagent (Invitrogen) was used for RNA extraction. The reverse reaction was performed according to PrimeScript RT Reagent Kit protocols . The sequences of two pair of primers priming SAF-1/DsRed and SAF-3/DsRed mRNA are as following: SAF-1 forward: 5′-CCTCATGAACTCCTTCCCGC-3′, SAF-3 forward: 5′-CATCTTCCAGGGTCACC TCG-3′, and DsRed reverse 5′-CCTTCAGCTTCACGGTGTTG-3′. The qPCR was carried on in Stepone Real-Time PCR System (StepOnePlus ABI) according to the protocols of SYBR Premix ExTaqII . For endogenous SAF-1 and SAF-3 mRNA level testing, we used the primers recommended by Ray et al. . The pri2), capturing images at 12-min intervals. Data were collected from background-corrected time-lapse movies in ImageJ software.HeLa cells were transfected with SAF1-3 AP plasmid. After 27 h, the cells expressing bichromatic EGFP or DsRed were imaged for 48 h. The fluorescent signals in MKN-45 cells were detectable at 41–57 h post-transfection. Wide field epifluorescence live-cell imaging  analysis was performed in an environmentally controlled chamber  amongst over 40000 cells. For SAF-3 expression, the construct that showed high promoter activity were −1692/+277 and 1401/+277 fragments. Removal of 806 upstream of this region led to sharp decrease in promoter activity (−595/+277 and −1692/+277Δ−1395/−595).Although there were statistically meaningful differences amongst these four constructs for SAF-1 expression by flow cytometry analysis, the change of SAF-1 expression was not as obvious as for SAF-3 expression A .These results indicate that SAF-1 and SAF-3 expressions were regulated by two distinct promoters.To test each fluorescent reporter expressed from SAF-1 and SAF-3 ORF, we performed RT-PCR using primers specific for SAF-3/DsRed and SAF-1/DsRed mRNA. The ratio of SAF-3/SAF-1 real-time PCR signal was consistent with flow cytometry assays B.cis-element positioned at nucleotides −521/−517 has been predicted [We next asked whether SAF-1/SAF-3 expressions were regulated by other transcription factors besides Sp1 and SAF/MAZ. We then theoretically searched the transcription binding sites in SAF/MAZ promoter located −948/−1 by using PROMO bioinformatics tool. An ETS-domain containing transcription factors, Elk1 redicted ,15. The cis-elements of Sp1 and SAF-1 are mainly located at nucleotides −599 to +1, upstream of SAF-1 starting transcription site. The plasmids expressing Sp1 were co-transfected with SAF1-3 AP plasmids into HeLa cells. As expected, SAF-3 promoter was inhibited when Sp1 was overexpression . Therefore, we conclude that there at least two distinct promoters regulating SAF-1 and SAF-3 variant expression, respectively. The results also suggest that the regions between −1395 and −595 may contain area for binding positive trans-acting regulating elements on SAF/MAZ promoter, we then performed the cis/trans factor prediction within the SAF/MAZ promoter region spanning −948 to −1 bp using PROMO3.0 online software under the 9% maximum matrix dissimilarity rate condition. Although there were multiple binding sites for some of transcription factors in silico analysis, such as pax-5, p53, C/EBPβ, ETF, GR-α, GCF, E2F-1STAT4, and RXR-α a putative ELK1 cis-element at −518/−510 which is near the silencer element containing region was identified by electrophoretic mobility shift assay (EMSA) unexpectedly .We then observed the endogenous SAF-1 and SAF-3 mRNA under perturbation of Sp1 and ELK-1 expression condition. The alteration of endogenous SAF-1 and SAF-3 mRNA expression are consistent with the change in the alternative SAF/MAZ promoter system by overexpression or knocking down these two The results above shows that our bichromatic fluorescence SAF-1 and SAF-3 expression did not fully reveal the both endogenous transcripts expression status. It hints that there may be more DNA elements for regulation of SAF-1 and SAF-3 transcriptions other than −1692 to +277 MAZ promoter fragment. The regulation mechanisms of SAF-1 and SAF-3 promoter activation under endogenous SAF/MAZ promoter need to be further analyzed .


Epigenetic dysregulation is involved in the etiology and progression of various human diseases. Formalin-fixed paraffin-embedded (FFPE) samples represent the gold standard for archiving pathology samples, and thus FFPE samples are a major resource of samples in clinical research. However, chromatin-based epigenetic assays in the clinical settings are limited to fresh or frozen samples, and are hampered by low chromatin yield in FFPE samples due to the lack of a reliable and efficient chromatin preparation method. Here, we introduce a new chromatin extraction method from FFPE tissues (Chrom-EX PE) for chromatin-based epigenetic assays.During rehydration of FFPE tissues, applying a tissue-level cross-link reversal into the deparaffinized tissue at 65 °C dramatically increased chromatin yield in the soluble fraction. The resulting chromatin is compatible with targeted ChIP-qPCR and genome-wide ChIP-seq approaches. The chromatin prepared by Chrom-EX PE showed a gradual fragmentation pattern with varying incubation temperature. At temperatures below 37 °C, the majority of soluble chromatin is over 1 kb. The soluble chromatin prepared in the range of 45–60 °C showed a typical nucleosomal pattern. And the majority of chromatin prepared at 65 °C is close to mononucleosomal size. These observations indicate that chromatin preparation from FFPE samples can be controlled for downstream chromatin-based epigenetic assays.This study provided a new method that achieves efficient extraction of high-quality chromatin suitable for chromatin-based epigenetic assays with less damage on chromatin. This approach may provide a way to circumvent the over-fixed nature of FFPE tissues for future technology development.The online version of this article (10.1186/s12864-019-5639-8) contains supplementary material, which is available to authorized users. Epigenetic alterations have been well described in the pathogenesis of various human diseases –3. In coThe basic repeating unit of chromatin is nucleosome, and it is associated with proteins and RNAs. Nucleosomes and chromatin include epigenetic information such as DNA-based epigenetic marks, histone tail modifications, association with transcription factors or chromatin proteins or regulatory RNAs, nucleosome positioning, accessibility, and chromatin-chromatin interaction. While chromatin-based epigenetic assays are feasible in FFPE tissues –9, the dIn this study, we introduced a novel chromatin extraction method from FFPE tissues called Chrom-EX PE technology to increase soluble chromatin yield with the possibility of controlled preparation of chromatin from FFPE tissues. This approach includes a tissue-level, cross-link reversal step applied to deparaffinized tissue to overcome the disadvantages of over-fixed chromatin. We achieved approximately 70–90% soluble chromatin yields from mouse FFPE tissues, and the isolated chromatin was compatible with ChIP-seq for histone mark and chromatin-associated protein. Furthermore, the chromatin prepared by Chrom-EX PE showed a gradual fragmentation pattern with varying incubation temperatures. We believe this approach can be used to obtain high quality chromatin for ChIP-seq without exposing the sample to harsh treatment, and is potentially applicable to technology development for other chromatin-based epigenetic assays.The major challenge for chromatin extraction from FFPE tissues is low chromatin yield due to high cross-linking level of the tissue sample during preparation and long storage time of FFPE samples. We hypothesized reducing cross-linking before chromatin preparation would increase chromatin yield from FFPE tissues. To test this hypothesis, we introduced a tissue-level cross-link reversal step by heating before chromatin preparation called Chrom-EXPE Fig. a. As incTo further investigate chromatin yields in various incubation temperatures, we performed the experiment at four different temperatures  using FFPE samples from liver tissue. To accurately measure the impacts of incubation temperature on chromatin yield, we isolated the chromatin by sonication but no MNase treatment after tissue-level cross-linking reversal. The yield of soluble chromatin gradually increases with temperature in 45 – 65 °C range compared with the yield from room temperature   Fig. a. The de01) Fig. . We calcp < 0.001) in liver tissue and between 0.840 and 0.894 (p < 0.001) in spleen tissue. The detailed peak numbers and overlap rate in FFPE and frozen tissues are shown in Table We further investigated whether ChIP-seq dataset generated by Chrom-EX PE is comparable with datasets generated from frozen tissues using a published ChIP-seq protocol. Pearson correlation analysis show uniformly high correlation for three histone marks between datasets generated from FFPE tissues and frozen tissues obtained from liver Fig. b and splp < 0.001) in matched sample pairs  in two pairs of FFPE and frozen mouse liver tissues. As expected, RNA Pol II peaks shown in the IGV browser overlap with H3K4me3 marks in TSSs of actively transcribed genes Fig. b, and peFixation of cells by formaldehyde was introduced in the ChIP assay to increase the stability of interactions in chromatin by making covalent bonds between neighboring amino groups. Consequently, the solubility of chromatin is much lower in fixed cells compared with unfixed cells. Over-fixation in FFPE tissues is believed to be detrimental to chromatin extraction due to the nature of heavily cross-linked chromatin. Harsh sonication was typically introduced to overcome low solubility but this introduces adverse damage in chromatin. We introduced a tissue-level cross-linking reversal before chromatin preparation called Chrom-EX PE. This approach dramatically increases soluble chromatin from FFPE tissue and allows us to produce various degrees of chromatin showing a gradual shift from partial, nucleosomal pattern, to smaller than a mononucleosome after MNase digestion and sonication. We validated here that the chromatin generated by Chrom-EX PE at 65 °C is compatible with the ChIP assay. The chromatin generated in the range of 45–55 °C showed a nucleosomal pattern that is typically produced from frozen tissues and cell lines, and may be compatible with nucleosome positioning along with ChIP assay Fig. c. PartiaBased on chromatin yield and fragmentation pattern, we believe that incubation of FFPE tissue in chromatin stability buffer will cause decross-linking of covalent bonds and the kinetics of decross-linking is dependent on temperature and time. In the range of incubation temperatures around 37–55 °C, decross-linking reaction may be slow but more closely recover the original chromatin structure. At higher temperature, the reaction may be faster and more effective. Published chromatin preparation methods –8 supporThis study provided a new Chrom-EX PE technology that achieves efficient extraction of high-quality chromatin from FFPE tissues. The chromatin generated by this technology shows a gradual fragmentation pattern dependent on temperature. We determined that the chromatin generated at 65 °C is compatible with ChIP assay in targeted and genome-wide approaches. Importantly, the chromatin generated at other temperatures may be suitable for chromatin-based epigenetic assays including accessibility, nucleosome positioning, and chromatin-chromatin interaction. This new technology will provide a better opportunity to perform chromatin-based epigenetic studies in archived FFPE samples.Frozen mouse tissues were collected from healthy mice under the Mayo Clinic IRCUC A00001233–16. The tissues were excised and divided. One piece from each pair was flash-frozen in liquid nitrogen and stored at − 80 °C and the other was fixed in 20-fold excess volume of 10% neutral phosphate-buffered formalin  for 48 h. Fixed tissue was dehydrated and embedded in paraffin using an automated tissue processor. FFPE blocks were stored at RT for at least 2 years. These samples were used to establish and optimize Chrom-EX PE protocol.2) and were incubated in the fresh 150 μL MNase digestion buffer containing proteinase inhibitor cocktails in the presence of 10 gel units of MNase  at 37 °C for 20 min with continuous mixing in thermal mixer. After adding the same volume of sonication buffer , the lysates were sonicated for 15 min (30 s on / 30 s off) using Bioruptor Twin (UCD-400)  and centrifuged at 21,130 x g for 10 min. The supernatant was transferred in a new tube. The pellet was resuspended in 100 ul of 1X FFPE Stop buffer 1  and was subjected to sonication for 15 min (30 s on / 30 s off), and centrifuged at 21,130 x g for 10 min. The supernatant was collected into the previously saved fraction. The pellet was resuspended in 100 ul of 1X FFPE Stop buffer 2  and was subjected to sonication for 15 min (30 s on / 30 s off), and centrifuged at 21,130 x g for 10 min. The supernatant was collected into the previously saved fraction and the combined soluble fraction was served as soluble chromatin. The equal volume of soluble chromatin was mixed with 2X ChIP elution buffer  and incubated at 65 °C overnight. The pellet was resuspended in 1X ChIP elution buffer and incubated at 65 °C overnight. DNAs were purified using Min-Elute PCR purification kit after the treatment of RNase A and proteinase K. DNA amount was measured by the Qubit dsDNA High Sensitivity assay , and DNA size was analyzed by the Fragment Analyzer  using the High Sensitivity NGS Fragment Analysis Kit (Cat. #DNF-486). For the comparison of soluble chromatin yield, the ChIP-IT® FFPE Chromatin Preparation Kit  was utilized according to the manufacture’s instructions.Two 20-μm sections from the FFPE blocks derived from mouse tissues were deparaffinized with xylene substitute and were rehydrated by progressively increasing percentage of water . During the progressive dehydration with different ethanol concentrations, the tissues were resuspended in 0.5 ml of chromatin stabilization buffer  and incubated in different temperatures ranging 25 °C to 75 °C overnight. As a control, the tissues were processed without overnight incubation. After the centrifugation , the supernatant was removed. And 0.25 ml of cell lysis buffer  was added and incubated on ice for 10 min. The lysates were washed with MNase digestion buffer , and TE buffer. Bound chromatins were eluted and reverse-crosslinked at 65 °C overnight. DNAs were purified using Min-Elute PCR purification kit after the treatment of RNase A and proteinase K. ChIP enrichment was validated by performing qPCR in the genomic loci targeting the transcription start sites of an active or inactive gene and an intergenic region. ChIP-seq libraries were prepared using the Ovation ultralow DR Multiplex kit  or the ThruPLEX® DNA-seq Kit V2  according to the manufacture’s instructions. The ChIP-seq libraries were sequenced to 51 base pairs from both ends on an Illumina HiSeq 2000 instrument in the Mayo Clinic Center for Individualized Medicine Medical Genomics Facility.Frozen tissues (25 mg) were homogenized for 30 s in PBS using tissue grinder . Homogenized tissues were cross-linked to final 1% formaldehyde, quenched with 125 mM glycine, and washed with TBS. The fixed homogenates were resuspended in cell lysis buffer and incubated on ice for 10 min. The lysates were washed with MNase digestion buffer and were incubated in the fresh 500 μL MNase digestion buffer containing proteinase inhibitor cocktails in the presence of 1000 gel units of MNase at 37 °C for 20 min with continuous mixing in thermal mixer. After adding the same volume of sonication buffer, the lysates were sonicated for 15 min (30 s on / 30 s off) using Bioruptor Twin and centrifuged at 21,130 x g for 10 min. The supernatants were served as chromatin input. For FFPE tissues, chromatin input was prepared from two 20-μm sections as described in the development of Chrom-EX PE. The chromatin input was incubated with anti-H3K4me3 , anti- RNA pol II antibodies , anti-H3K27Ac (CST 8173 BC) or anti-H3K27me3  overnight. After adding 10–30 μL of protein G-magnetic beads, the reactions were further incubated for 3 h. The beads were extensively washed with ChIP buffer , high salt buffer , LiClReal-time PCR analysis was performed using SYBR Green universal PCR mixes (Bio-Rad). The following primer sequences were used in the experiments: H3K4me3- and RNA pol II-positive control locus: mGAPDH-F: 5′- CTCATCCCCGCAAAGGCGGA -3′, mGAPDH-R: 5′- TCGGACCTGGCGATGGCTCG-3′. H3K27me3-positive control locus: mT1-F: 5′- GAGACGCCGATCCGCCGAAG -3′, mT1-R: 5′- ACTCTCCACTCCCACGCGCT-3′. H3K4me3-, RNA pol II-, and H3K27me3-negative control locus: mIntergenic-F: 5′-CCTGCTGCCTTGTCTCTCTC -3′, mIntergenic-R: 5′-ATGGCCTAGGGATTCCAGCA -3′.https://broadinstitute.github.io/picard/). Peaks were called using the MACS2 algorithm and SICER at FDR < 1%. Fragment size was calculated from properly mapped read pairs. Pearson correlation analysis was performed by our in-house scripts where all datasets were randomly downsized to 25 million pairs of reads. In brief, the whole genome was divided into 5-kb bins for H3K4me3 and H3K27Ac and 100-kb bins for H3K27me3 and RNA Pol II, and the number of mapped reads in each bin was calculated, in which genomic regions that have zero or missing values in all samples are excluded. The read count per bin was normalized to 25 million mapped reads (at least one end uniquely mapped and duplicates removed), or to 25 million mapped reads from non-peak regions . The normalized counts by logarithm log2  were used for pairwise correlation analysis with Pearson coefficient. Here, 1 is a pseudo-count to avoid an undefined error of logarithm of zero. We also profiled the average signal levels across peak center +/− 2 kb (H3K4me3 and H3K27Ac) and peak region +/− 2 kb (H3K27me3 and RNAPII) using ngs.plot [Raw sequencing reads were processed and analyzed using the HiChIP pipeline  to obtaings.plot . For eacAdditional file 1:The yield of soluble chromatin is increased in the range of tested temperatures (25 °C -65 °C). Two 20-μm sections from mouse liver FFPE tissues were processed by Chrom-EX PE at indicated temperatures. To accurately measure the impacts of incubation temperature on chromatin yield, we isolated the chromatin by sonication but no MNase treatment after tissue-level cross-linking reversal. DNAs were purified from soluble fraction and insoluble pellet fraction and were quantified using Qubit dsDNA High Sensitivity assay. The percentage of soluble chromatin was calculated from two independent experiments. (PDF 50 kb)Additional file 2:The genomic location of primer pair along with the peak profiles of H3K4me3 and H3K27me3 marks are visualized for ChIP-seq data generated from frozen and FFPE liver tissues in the Integrative Genomics Viewer. The arrow below the RefGene track indicates the location of primer pair used in the study. The peak profiles indicate the antibodies for H3K4me3 and H3K27me3 marks are specific. (PDF 49 kb)Additional file 3:DNA profiles for chromatin inputs from 4 liver FFPE tissues relative to Fig. Additional file 4:DNA profiles in RNAP II, H3K27Ac, H3K4me3 and H3K27me3 ChIP-seq libraries from mouse liver FFPE and frozen tissues. Lane 1, 2: RNAP II libraries from FFPE and frozen tissues; Lane 3–5: H3K27Ac, H3K4me3 and H3K27me3 libraries from FFPE tissues; Lane 6–8: H3K27Ac, H3K4me3 and H3K27me3 libraries from frozen tissues. (PDF 38 kb)Additional file 5:The overall enrichment of three histone marks and RNA pol II is compared from dataset generated from frozen and FFPE liver samples. The average signal levels of H3K4me3 and H3K27Ac are shown across peak center at the upstream and downstream 2 kb. And the average signal levels of H3K27me3 and RNA Pol II are shown across peak region at the upstream and downstream 2 kb. (PDF 64 kb)Additional file 6:Summary of mapping results from the libraries generated from frozen and FFPE tissues. (PDF 54 kb)Additional file 7:Replicates overlapping in mouse frozen liver tissues. (PDF 35 kb)


DNAs that instruct when and where genes should be transcribed in response to a variety of intrinsic and external signals. They contain a cluster of binding sites for sequence‐specific transcription factors and co‐activators to determine the spatiotemporal specificity of gene activities during development. Enhancers are often positioned in distal locations from their target promoters. In some cases, they work over a million base pairs or more. In the traditional view, enhancers have been thought to stably interact with promoters in a targeted manner. However, quantitative imaging studies provide a far more dynamic picture of enhancer action. Moreover, recent Hi‐C methods suggest that regulatory interactions are dynamically regulated by the higher‐order chromosome topology. In this review, we summarize the emerging findings in the field and propose that assembly of “transcription hubs” in the context of 3D genome structure plays an important role in transcriptional regulation.Transcriptional enhancers are regulatory  Enhancer DNAs play a central role in the spatiotemporal control of gene activities during development. In this review, we summarize the emerging findings in the field and propose that assembly of “transcription hubs” in the context of 3D genome topology is an important feature of enhancer function. Just a few years later, the first eukaryotic enhancers were isolated from the intronic regions of mouse immunoglobulin heavy chain (IgH) and immunoglobulin kappa (IgK) locus  in developing limb buds is driven by the distal ZRS enhancer located 850 kb away from the promoter region   assays has revealed the regulatory landscapes of three‐dimensional genome topology  also concluded that domain configuration optimizes long‐range enhancer–promoter interaction at the Shh locus  and transcription factors at enhancers  locus, stripe enhancers and eve transcription unit are all embedded in a single TAD that is bordered by two insulators, Homie and Nhomie (Figure lacZ reporter was placed in a remote location outside of the eve TAD, the stripe enhancers do not activate lacZ (Fujioka et al., eve and synthetic lacZ simultaneously (Figure trans‐homolog co‐activation occurs only when stable association of homologous chromosomes was induced by pairing of insulators (Figure While it appears that enhancers are intrinsically capable of co‐activating multiple promoters when tested (Fukaya et al., e Figure . When a y Figure . Similars Figure , indicats Figure , again s9While recent live‐imaging methods have suggested that the formation of transcription hub is an important feature of enhancer function (Chen et al.,


Clostridium ljungdahlii. Integration of data from cells grown autotrophically or heterotrophically revealed that pathways critical to carbon and energy metabolism are under strong translational regulation. Major pathways involved in carbon and energy metabolism are not only differentially transcribed and translated, but their translational efficiencies are differentially elevated in response to resource availability under different growth conditions. We show that translational efficiency is not static and that it changes dynamically in response to mRNA expression levels. mRNAs harboring optimized 5′-untranslated region and coding region features, have higher translational efficiencies and are significantly enriched in genes encoding carbon and energy metabolism. In contrast, mRNAs enriched in housekeeping functions harbor sub-optimal features and have lower translational efficiencies. We propose that regulation of translational efficiency is crucial for effectively controlling resource allocation in energy-deprived microorganisms.Control of translation is vital to all species. Here we employ a multi-omics approach to decipher condition-dependent translational regulation in the model acetogen  C. ljungdahlii, and show that dynamic regulation of translational efficiency of metabolic pathways is critical.Microorganisms must regulate allocation of resources in nutrient-limited conditions. Here, the authors combine Ribo-seq, RNA-seq and TSS-seq to study resource allocation in the acetogen  Clostridium ljungdahlii is emerging as a promising cell factory for bioproduction1 as well as a model organism for gaining in-depth knowledge necessary to develop new design strategies for acetogens. C. ljungdahlii is readily cultured heterotrophically in the laboratory in simple media, either on a diverse set of five or six carbon sugars, or autotrophically with CO or H2 as electron donor. Furthermore, metabolic models and genetic manipulation tools already developed and optimized for this organism make C. ljungdahlii an ideal candidate for the study of acetogenesis4. However, in order to harness the full biosynthetic potential, it is important to understand the regulatory mechanisms that orchestrate energy metabolism in C. ljungdahlii. These include, but are not limited to, the Wood−Ljungdahl pathway (WLP), the formate dehydrogenase complex, the hydrogenase complex, and the Rnf complex, all central to energy equilibrium in C. ljungdahlii7. A thorough understanding of all factors that regulate energy metabolism under autotrophic and heterotrophic growth conditions is crucial for the metabolic engineering of acetogens and for optimizing targeted production of desired chemicals.The metabolic versatility of acetogens for the fermentation of a large number of sugars yields great promise for the production of biofuels and commodity chemicals. In particular the ability to grow autotrophically with H11. These approaches directly addressed the genotype−phenotype relationship in bacteria, providing crucial insights into the design strategies for microbial cell factories. Ribosome profiling (Ribo-seq) has recently enabled the determination of the numbers and locations of ribosomes on mRNAs in vivo12 and in combination with RNA-seq, has facilitated the global measurement of translational efficiency (TE) and thus provided new insights into translational regulation14. Translation is a major energy burden especially for cells growing in nutrient-deficient conditions. In these niches optimization of resource allocation becomes increasingly critical for survival. We combined Ribo-seq, RNA-seq, and transcription start site sequencing (TSS-seq) to study how resources are allocated under energy-rich heterotrophic growth and energy-deprived autotrophic growth conditions in the model acetogen C. ljungdahlii. We provide evidence that metabolic pathways involved in carbon and energy metabolism are strongly regulated at the translational level. We find that RAST subsystems belonging to these pathways are significantly enriched in mRNA with optimized 5′-untranslated region (5′UTR) features (i.e. high-affinity ribosome-binding site (RBS), increased AU content upstream of the RBS, and optimal distance of RBS from the translation initiation site) and optimized coding region features (high codon adaptation index (CAI) and low AU content). Optimization of mRNA features increases the affinity of mRNA to ribosomes and hence facilitates the proportional increase of TE in response to increased mRNA expression. In contrast, we find that mRNAs belonging to genes involved in housekeeping functions are significantly enriched with suboptimized features that reduce mRNA affinity to ribosomes. We propose that selective control of TE in key metabolic and energy pathways is critical for thriving in nutritionally deprived niches.In recent years, next-generation omic approaches, such as RNA-seq, proteomics, and metabolomics, have been employed to identify the functionality and organizational structure of acetogenic bacterial genomesC. ljungdahlii grown either on CO or H2:CO2 and heterotrophic cultures grown on fructose. To enable direct comparison between transcription and translation, strand-specific RNA-seq libraries were prepared from the same lysates used for Ribo-seq experiments in biological duplicates. RNA-seq and Ribo-seq libraries were deeply sequenced  the Pearson’s correlations for Ribo-seq are 1.00, 0.97, and 0.97 for CO, fructose, and H2:CO2, respectively  under heterotrophic  was the only differentially translated gene . Overall, the differentially translated subsystems under heterotrophic growth are consistent with functions required for fast growth and effective utilization of fructose.In heterotrophic growth, three differentially upregulated subsystems were related to carbon metabolism  mostly involved energy utilization and carbon fixation   was differentially upregulated in CO compared to H2:CO2. Thus, reflecting intricate metabolic adjustments required for optimum utilization of the energy and carbon sources in CO and H2:CO2.Differentially translated subsystems specific to each of the two autotrophic conditions  are differentially transcribed, differentially translated, and most genes, including the Rnf regulator rseC, exhibit differential TE under autotrophic growth conditions (further discussed below).To gain insight into how TE is differentially controlled under autotrophic and heterotrophic conditions, we analyzed the genes comprising the major carbon and energy subsystems that were significantly enriched Fig. . SpecifirnfC gene is transcribed at a significantly lower level during heterotrophic growth . However, rseC translation is also highly repressed under heterotrophic growth at the translational level comparable to that of rnfC  is highly translationally repressed under heterotrophic growth.The C. ljungdahlii and Clostridium autoethanogenum are two phylogenetically indistinguishable species as they have >98% overall genome similarity25. The only active hydrogenase (Hyd) in C. ljungdahlii is the one orthologous to HytABCDE1E2 in C. autoethanogenum, which has been demonstrated to be the only active hydrogenase during H2:CO2 growth26. In C. ljungdahlii, Hyd catalyzes the reduction of NADP and ferredoxin and the oxidation of H2 under H2:CO2 growth. Additionally, Hyd interacts with formate dehydrogenase (Fdh) and the resulting complex (Hyd-Fdh) catalyzes the reduction of CO2 to formate and the oxidation of H226 . In fact, hydB is at least threefold more translationally efficient than hydN and fdhA , the ribosomal protein S1 (RpsA)30, and the translation initiation factors (IF1-3). RpsA (Clju_c41620), a ribosomal protein weakly associated with the 30S ribosomal subunit, has strong affinity towards AU-rich regions at the 5′UTR32 and interacts with the 5′UTR of mRNA through a 10–15 nt motif to facilitate the initiation of translation33. In addition, RPS1 furnishes the 30S subunit with an RNA chaperone activity that is essential for the binding and unfolding of structured mRNAs, allowing the correct positioning of the initiation codon for translation34. Further, RPS1 competes with RNases for the binding of AU-rich regions, plausibly protecting AU-rich upstream RBS region (URR) from degradation30, which leads to increased TE35. Overall, the AU content and the RBS sequence determine how these factors interact with the 5′UTR to control the efficiency of ribosome binding. Moreover, features present on the mRNA coding region can also have strong influence on TE, including tertiary mRNA structure and codon usage36. Therefore, it is vital to understand how these mRNA features influence TE.The 5′UTR region is an important regulatory center for determining the fate of mRNAP = 1.7e−92, 1.2e−78, 8.2e−81 for CO, H2:CO2, and fructose, respectively) when compared to the transcriptional level , implying strong translational regulation  or high-TE (>80th percentile). The difference between the two sets was strikingly more significant at the translational level  had an increased AU content  showed that the RBS motif conservation increased with TE and those with lowest TE had a hardly recognizable RBS motif, whereas groups with high-TE exhibited optimal RBS motifs , we limited our analysis to promoters that had their TSS at least 15 nt upstream of the URR. Genes associated with transcripts harboring URRs with 100% AU had the highest TE  upstream of the translation initiation site Fig. . In addi45. We currently lack insight into how static mRNA features in bacteria influence TE in a condition-specific manner. To address the link between mRNA level, mRNA features, and TE, we first determined the maximum TE (TEmax) for each gene across the three growth conditions tested and used it as a measure of the maximum affinity of the mRNA towards the ribosome. We split genes into quartile groups based on to their TEmax values  would attract more of the limited number of ribosomes and hence outcompete those with a lower affinity (lower TEmax). By plotting the TE quartiles versus RNA-seq (FPKM) categorized in ten groups, we observed that as the transcription level of highly optimized mRNA increases, the affinity (TE) also significantly increases  < 2e−16]). In contrast, for the low-TE group as mRNA level increases the TE significantly decreases  = 0.2e−06]). The high- and low-TE slopes are significantly different (P (Turkey HSD) < 0.01). Our analysis suggests that mRNA molecules with high TEmax are more competitive substrates for the ribosome. The increased competitive capacity becomes apparent at higher levels of transcription at which the number of ribosomes per mRNA will increase. At the same time mRNAs with suboptimized mRNA will show decreased TE because the number of ribosomes per mRNA will drop. Therefore, mRNA molecules with optimized features are prioritized for translation when their mRNA levels are  increased.Comparing translation to enzyme kinetics in which substrate competition is dependent on the concentration and the affinity of each substrate, we considered RNA-seq (FPKM) to represent the concentration of the substrate and TEP (Fisher exact test) < 0.05) in RAST categories involved in carbon and energy  have higher TE than translation IFs  compared to its value in the two autotrophic conditions, suggests negative auto-regulation by the S6:S18 complex (TE = 0.6 in heterotrophic compared to 0.9 in autotrophic conditions). S6 TE is only 0.1 and is stable in all conditions. In contrast, there are no significant translationally upregulated ribosomal proteins in the two autotrophic growth conditions. It is not clear why these ribosomal genes are differentially translated in heterotrophic growth. However, there are many lines of evidence demonstrating that specialized heterogenic ribosomes could translate a subgroup of mRNA molecules50.Further, we find that ribosomal proteins S6, S18, and S20 are significantly translationally upregulated in heterotrophic growth. S6 was demonstrated to form a complex with S18 and the S6:S18 heterodimer binds to specific motifs in their corresponding mRNAs and autoregulate their translationE. coli51. It is found in most bacterial species and has also been shown to interact with the 5′UTR region by directly interacting with the RBS, effectively controlling translational initiation and mRNA stability29. It activates the translation of the glycolysis genes in E. coli and represses biofilm formation, but it has no effect on the pentose phosphate pathway genes52. A transcriptomics study on the csrA mutant has been reported in Clostridium acytobutylicum53, which demonstrated that the transcription of some central carbon metabolism genes were affected, but since CsrA is a translational regulator, it is difficult to deduce concrete conclusions without ribosome profiling or proteomics analysis. A CsrA ortholog also exists in the C. ljungdahlii genome (Clju_c09540). Two small RNA molecules, CsrB and CsrC, antagonize the binding activity of CsrA in E. coli54; however, no corresponding homologs are detectable in the C. ljungdahlii genome. The csrA gene in C. ljungdahlii is differentially upregulated at the transcriptional level under autotrophic growth, but not at the translational level  with TE of 3374 cytoplasmic proteins. Consistent with previous studies60 that showed significant slowdown in the translation of membrane proteins, C. ljungdahlii membrane proteins have significantly lower TE than cytoplasmic proteins in all growth conditions 2 to compare FBA results with different experimental measurements (see R = 0.73) and Ribo-seq (Pearson’s R = 0.70) with fluxes at the subsystem level  using a genome-scale metabolic model for ents see . As expecis-acting regulatory sequences at the 5′UTR. The rate by which TE increases in response to increased mRNA levels depends mainly on the 5′UTR region. Highly optimized 5′UTRs are significantly enriched in growth condition-specific carbon and energy metabolic pathways, whereas suboptimal 5′UTRs are enriched in housekeeping genes. This prudent assignment of optimized 5′UTRs to carbon and energy pathways ensures faster translational response of urgently required pathways, which is vital when scarce resources are transiently available. By the same token, the assignment of suboptimal 5′UTRs to housekeeping genes (lower TE) ensures stable translation as well as the use of minimal resources to sustain maximal growth. Differential TE of metabolic pathways and genes has been reported previously for both eukaryotes61 and prokaryotes62. The effect of the 5′UTR length and secondary structure on TE and their role in regulation of secondary metabolite translation has been reported in S. coelicolor18. However, the effect of 5′UTR and RNA-expression levels on TE and how this strategy is used to allocate resources efficiently was previously unknown. Here, we illustrate how the interplay between defined features in the 5′UTR and RNA transcription levels determines condition-specific TE of metabolic pathways. Moreover, we show how C. ljungdahlii modulates the TE levels for metabolic pathways in a growth condition-dependent manner. Our work unravels how acetogens utilize the differential TE mechanism to use carbon and energy resources optimally to thrive at the thermodynamic energy limit of life.We took a systems-level approach to understand the overall TE and how it is linked to transcription and We propose that pathways involved in carbon and energy metabolism are specifically controlled through optimizing the TE, allowing for dynamic resource allocation. The findings have broad implications on how microorganisms control and optimize their metabolic networks. The results provide a new framework for metabolic regulation in this model acetogen that can readily be extrapolated to other industrially important microbes. Unraveling of regulatory mechanisms lays the foundation for advanced strain design and engineering efforts.C. ljungdahlii (ATCC 55383) was grown anaerobically without shaking to mid log phase in 125 ml serum bottles containing 100 ml of PETC medium (ATCC medium 1754) supplied with 28 mM fructose at 37 °C. For low-fructose TSS-seq analysis C. ljungdahlii was grown in PETC medium supplied with 4 mM fructose. Autotrophic growth was carried out in 1 l bottles containing 300 ml PETC medium and pressurized with either 1.8 bar of CO gas or H2:CO2 (1:4 vol/vol) gas mix. Cultures for further analysis were collected at mid log phase.For heterotrophic growth, 63 with minor modifications. In brief, cell pellets were collected by centrifugation at room temperature for 5 min at 5000 rcf . The growth medium was removed and cell pellets were immediately snap frozen in liquid nitrogen then kept at −80 °C. Cell lysates were prepared by grinding the pellets in liquid nitrogen together with 350–400 µl lysis buffer containing 50 µg/ml thiamphenicol. The lysates were cleared by centrifugation at 4 °C. In order to enable direct comparison between Ribo-seq and RNA-seq, 100 µl of cleared cell lysates were stored at −80 °C for RNA-seq library preparation with 500 µl of Trizol reagent (Thermo Fisher Scientific) for stabilization. The remaining lysates were treated with MNase to remove free RNA and DNA. The reaction was stopped by adding EGTA as described in the original protocol63. Ribosome-bound mRNA footprints (RBF) were harvested by filtering through Sephacryl S400 MicroSpin Columns  followed by the protocol of small RNA isolation as part of the miRNeasy MINI Kit (Qiagen). The ribosomal RNA was removed from RBF by Ribo-Zero rRNA Removal kit for bacteria (Illumina). Sequencing libraries were made using the NEBNext® Small RNA Library Prep Set for Illumina kit (New England Biolabs).All experiments were performed using two biological replicates. Ribo-seq was performed as described previouslyThe lysates stored during Ribo-seq library preparation were brought to room temperature. 140 µl of chloroform was added to each tube, vortex-mixed and centrifuged at 4 °C for 10 min. The aqueous fraction was isolated and total RNA was extracted using the RNeasy mini kit (Qiagen) followed as recommended by the manufacturer’s instruction with minor modifications. In brief, the volume was brought to 900 µl using RLT buffer and 600 µl of 95% ethanol was added and mixed in order to bind the RNA. Eighty microliters of RNase-free DNase mix (8 µl DNase I (Roche) of 10 U/µl in 80 µl total volume) was added to the RNeasy MinElute columns. The ribosomal RNA (rRNA) was depleted using the Ribo-Zero rRNA Removal kit for bacteria (Illumina). rRNA-depleted RNA was used for RNA sequencing (RNA-seq) and TSS-seq. Strand-specific RNA-seq libraries were prepared using the Stranded mRNA-seq Kit (Kapa Biosystems) as per the manufacturer’s instruction.64 with minor modifications. The rRNA-depleted RNA (see above) was split into two samples for two different libraries: the library of the whole transcriptome and the library of the processed transcriptome, respectively. RNA 5′-polyphosphatase (RPP) was treated only for the library of whole transcriptome. The 5′-RNA adaptor (ACACUCUUUCCCUACACGACGCUCUUCCGAUCU) was ligated to both RPP-treated and untreated samples. Then, cDNA was synthesized using the adaptor with random nonamer for reverse transcription compatible with TruSeq primers (GTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTNNNNNNNNN). TruSeq i7 indexed adaptors were used for amplification to enable sample multiplexing.Strand-specific TSS-seq libraries were prepared using the 5´RNA-seq protocolTM 4000 or MiSeqTM instruments at UCSD IGM genomics center. The Ribo-seq libraries were sequenced using 50 bp-cycle kits, and TSS-seq and RNA-seq libraries were sequenced using 100 bp-cycle kits.The libraries from all experiments were sequenced in either the Illumina HiSeqC. ljungdahlii genome (accession no. NC_014328) using bowtie2 65 with one mismatch allowed. Samtools66 (version 1.4.1) was used to filter out low-quality alignments (q < 10). The number of reads per gene was obtained using Subread package-featureCounts (version 1.5.0-p1)67. RPKM, FKPM and correlation values of biological replicates were calculated using Python (2.7) in-house scripts in jupyter notebook format. We used in-house Perl scripts to generate wig files for genome-wide track viewing. Relative expression ratios were obtained for three different paired samples: (i) fructose versus CO, (ii) fructose versus H2:CO2, and (iii) CO versus H2:CO2. Differentially transcribed and translated genes were identified using DESeq220 package in R (3.3.1), with the following parameters settings: adjusted P value < 0.001, log2-fold change ≥ 1. Correlation values between RNA-seq and Ribo-seq were calculated using the mean value of the biological replicates for each experiment. Translational efficiency was obtained by the ratio between translation and transcription levels per gene. Differential-efficiently translated genes were obtained following the criteria: (i) genes translated in both biological replicates ; (ii) genes transcribed in both biological replicates ; (iii) high-differential TE genes > 80th percentile; (iv) low-differential TE genes < 20th percentile.The adapter sequence was trimmed, and low-quality reads were removed. The reads were aligned to the C. ljungdahlii genome (accession no. NC_014328) was annotated in the RAST database in order to systematically organize genes into categories, subcategories, and subsystems. DESeq2 package in R was used to find significant translation and transcription of individual genes and RAST subsystems20. RPKM and FPKM values for translation and transcription, respectively, were summed for genes per each RAST subsystem. We calculated the total number of sequencing reads that were aligned to each RAST subsystem, regardless of whether genes in a given subsystem are differentially regulated or not. We then calculated the RPKM/FPKM for each subsystem and reported the values of the top 20 subsystems that were significantly differentially transcribed/translated according to DESeq2. We assumed that the amount of resources required for transcription/translation will be similar whether it is assigned to one gene or a group of genes in a given RAST subsystem.The For each growth condition, subsystems were normalized as a percent ratio of the total translation or transcription for subsystems annotated by RAST (i.e. genes that are not in RAST categories were not considered). To directly compare translation and transcription across growth conditions, the normalized percent values described above, were summed for all growth conditions and normalized to percent. TE was calculated by dividing subsystem percent Ribo-seq by subsystem percent RNA-seq.P value < 0.01 when compared to the translation of the CO and the H2:CO2 subsystems, but CO subsystem translation versus H2:CO2 was insignificant with adjusted P value > 0.01. Second, fructose subsystem translation >50th percentile. Third, TE of fructose > mean of CO TE and the mean of H2:CO2 TE. In autotrophic growth, subsystems were considered significantly differentially efficient if three conditions were met. First, both CO and H2:CO2 subsystem translation levels have adjusted P values < 0.01. Second, both CO and H2:CO2 subsystems are above their 50th percentiles. Third, both CO and H2:CO2 subsystem translation levels > the mean of fructose subsystem translation level.Translation at the subsystem level was considered significantly differentially efficient in heterotrophic growth if three conditions were met. First, the fructose subsystem translation has adjusted 2:CO2). For each growth condition, RNA 5′-polyphosphatase (RPP)-treated and untreated samples were sequenced in duplicates. Genomic positions at the 5′-end of uniquely aligned TSS-seq reads were considered to be potential TSSs such that TSS is uniquely associated with RPP-treated samples. At this point we had eight pairs of datasets from four conditions. A TSS was scored if a sequencing read was present in the RPP-treated sample, but absent in the corresponding untreated sample. Thereby, we scored TSS from one to eight, with the former being found in only one dataset and the latter being found in all datasets and in all conditions. Finally, the potential TSSs were visually investigated and compared to corresponding RNA-expression levels in order to have stronger confidence. Identified TSS positions were categorized by genomic location. TSSs located 250 bp upstream of start codon to 50 bp downstream of the start codon were annotated as Primary TSSs (P). TSSs located within coding regions at 50 bp downstream of the start codon to end of the stop codon were annotated as Internal TSSs (I). TSSs located antisense relative to any open reading frame were annotated as Antisense TSSs (A). TSSs that do not fall into any of the categories above were annotated as Orphan TSSs (O). Operon coverage was calculated by multiplying the mean operonic gene content (1.67) by the total number primary TSS (1245) divided by the total number of genes (4182). Operons were predicted using the DOOR tool68.To maximize TSS detection, we carried out TSS-seq in four growth conditions. These consisted of two heterotrophic conditions (fructose and low-fructose (see above)), and two autotrophic conditions . A fasta file containing 25 nt upstream of the translation initiation site of each gene was generated. RNAplot69 was used to calculate the 25-nt regions affinity towards anti-SD sequence (AAGGAGGU) with 10-nt walking windows that scan the entire 25 nt. The window with the lowest −∆G score was considered to be the one harboring the RBS. This approach was validated by calculating the distance of the putative RBS from start codon and ensuring that the 5′-RBS mean distance from the start codon is 13 (3′ of RBS is 8 bp upstream of RBS). Further analyses were carried out using an in-house Python script. The putative RBS binding motifs for each category was generated using MEME70. AT content was calculated in percent for the 15 bp upstream of each putative RBS (URR), such that the TSS is upstream of the 5′-end of the URR.We used a method described by Li et al.Computer code will be provided upon request from corresponding author.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4


Control of gene expression in erythropoiesis has to respond to signals that may emerge from intracellular processes or environmental factors. Control of mRNA translation allows for relatively rapid modulation of protein synthesis from the existing transcriptome. For instance, the protein synthesis rate needs to be reduced when reactive oxygen species or unfolded proteins accumulate in the cells, but also when iron supply is low or when growth factors are lacking in the environment. In addition, regulation of mRNA translation can be important as an additional layer of control on top of gene transcription, in which RNA binding proteins (RBPs) can modify translation of a set of transcripts to the cell’s actual protein requirement. The 5′ and 3′ untranslated regions of mRNA  contain binding sites for general and sequence specific translation factors. They also contain secondary structures that may hamper scanning of the 5′UTR by translation complexes or may help to recruit translation factors. In addition, the term 5′UTR is not fully correct because many transcripts contain small open reading frames in their 5′UTR that are translated and contribute to regulation of mRNA translation. It is becoming increasingly clear that the transcriptome only partly predicts the proteome. The aim of this review is (i) to summarize how the availability of general translation initiation factors can selectively regulate transcripts because the 5′UTR contains secondary structures or short translated sequences, (ii) to discuss mechanisms that control the length of the mRNA poly(A) tail in relation to mRNA translation, and (iii) to give examples of sequence specific RBPs and their targets. We focused on transcripts and RBPs required for erythropoiesis. Whereas differentiation of erythroblasts to erythrocytes is orchestrated by erythroid transcription factors, the production of erythrocytes needs to respond to the availability of growth factors and nutrients, particularly the availability of iron. This requires a tight balance between progenitor proliferation and maturation, in conjunction with erythropoietin (Epo) dependent cell survival. Erythroid homeostasis is controlled by a cascade of transcription factors and by signaling pathways triggered by environmental factors.Erythrocytes circulate through human peripheral blood for approximately 120 days. Every day, the human body produces 10Figure 1). For instance, GATA-1 (GATA-binding factor 1) functions as one of the master regulators of erythropoiesis, inducing for instance (i) the expression of the Epo receptor (EpoR), (ii) commitment to the erythroid lineage, and (iii) transcription of the β-globin locus . For instance, during hypoxic stress, Epo production in the kidneys is dramatically increased (L) to maintain viability during terminal maturation . This review focusses on specificity in control of translation, which is generated by the combination of nucleotide sequences in the 5′ and 3′ untranslated regions , together with expression levels and posttranslational control of constitutive translation factors and RNA binding proteins (RBPs). Control of mRNA translation is mediated by a complex web of overlapping mechanisms, including, but not limited to, regulation of translation initiation and the length of the poly(A) tail, by the presence of short upstream open reading frames (uORFs), and by sequence-specific association with RBPs. In this review, a brief overview of these topics of selective mRNA translation as pertains to erythropoiesis will be presented.Studies on gene expression have historically focused on transcription as the primary regulatory mechanism. However, it has become increasingly clear that protein levels correlate poorly with transcript expression, and that translation is a major determinant of protein abundance in the cell . In othe7mG) cap structure of the mRNA and the cap-binding protein eukaryotic initiation factor 4E (eIF4E) . Concurrently, the ternary complex (TC), consisting of the methionine loaded initiator tRNA (tRNAimet) and the GTP-loaded GTPase eIF2 associates with the 40S ribosomal subunit and with translation factors eIF1, -1A, -3, and -5 to form the 43S pre-initiation complex .Canonically, translation is initiated via an interaction between the 5′ 7-methylguanylate ( (eIF4E) . Interacimet to reform. This mechanism is subject to control by phosphorylation of the α subunit of eIF2, which prevents the GDP-GTP exchange, resulting in the inhibition of cap-dependent translation .Next to eIF4E, the availability of GTP-loaded eIF2 is an important rate limiting factor of translation initiation . Once trThe control of rate-limiting factors eIF4E and eIF2 in erythropoiesis and the corresponding signaling pathways are discussed in the next two paragraphs.in vivo and in vitro, whereas erythroblasts mature to erythrocytes in presence of Epo only . This is in part due to SCF-independent proliferation of erythroblasts, but also due to a block in erythroid differentiation (Stem cell factor (SCF) cooperates with Epo to expand erythroblast numbers Epo only . The cruEpo only . Overexpntiation . Upon rentiation . In addintiation . Thus, Pntiation .immunoglobulin binding protein (IGBP1), which is constitutively expressed in erythroblasts, but selectively translated only upon SCF-induced eIF4E release . Therefore, expression of IGBP1/Alpha4 enhances the effect of low levels mTOR activation on mRNA translation. SCF-induced expression of IGBP1 acts as a positive feedback mechanism in the polysome recruitment of multiple eIF4E-sensitive mRNAs, resulting in SCF-independent proliferation of erythroblasts and attenuation of erythroid differentiation , and expression of its target PPR15a . GADD34 enhances the enzyme activity of the catalytic PP1 subunit PPR15b  with the scanning complex . In cases where the uORFs overlaps with the protein-coding ORF , start codon recognition is mostly in a less favorable Kozak consensus sequence, whereas the presence of multiple uORFs in the 5′UTR typically renders translation dependent on the distance between uORFs and between the uORF and the CDS.Increased sensitivity to translation factors, and particularly to eIF2, is predicted by the presence of uORFs in the 5′UTR of the transcript. The uORFs render mRNA translation hypersensitive to translation initiation factors at the level of (i) start codon recognition, (ii) interaction of the peptide or the uORF sequence with the ribosome, and (iii) reinitiation of the TC  and IFDR1  , PABPC1 (PolyA binding protein c1), and PAPOLA, [poly(A) polymerase alpha], which suggests that eIF2 phosphorylation may result in shortening of mRNA polyA-tails. The response to eIF2 phosphorylation is cell type specific, as indicated by a comparison between ribosome footprints in erythroblasts and HEK293 cells  . Other u93 cells .The analysis of uORF translation in erythroblasts indicates that uORFs are involved in eIF2-dependent translational regulation, but they do not predict eIF2-dependent translational regulation. This characterizes the advances of the field. We increasingly understand how general translation factors act together to control cap-dependent translation in commonly used cell models. For specific transcripts we understand the interplay of general translation initiation factors and specific sequences or structural elements in the 5′UTR of the transcript. However, when this information is used to make general predictions, the predictions are often wrong. For instance, Mfold is a well-known program for making structural predictions . This isHeme Oxygenase-1 (HO1) is regulated by hemin as part of feedback control. Alternative splicing generates a hemin resistant alternative transcript  . While ttrans-activating factors (ITAFs) such as Polypyrimidine Tract Binding protein (PTB)   and CSDEin (PTB) . Some ITk of DBA . The indBAG1 transcript  and Heat Shock Protein 70 (HSP70) cochaperone are required for terminal differentiation of erythroblasts . All thranscript . Bag1 deanscript . shRNA-mRUNX1 gene has two promoters that yield RUNX1 transcripts with two distinct 5′UTRs, one of which contains an IRES . The length of the poly(A) tail is determined by the recruitment of polyadenylating and deadenylating enzymes to the transcript. Polyadenylation initially occurs in the nucleus where mRNA cleavage and polyadenylation are coupled .Almost all protein coding mRNAs (except histones) are polyadenylated at their 3′ end. The poly(A) tail protects from degradation, and the length of the poly(A) tail also affects translation initiation . A longer poly(A) tail increases PABP affinity for target transcripts, which enhances the stabilization of multiple preinitiation scanning complexes and thereby enhances translation initiation efficiency. Additionally, the binding of PABP to the poly(A) tail protects the transcript from deadenylation and degradation  binding proteins (PABPs) to the poly(A) tail. PABPs directly interact with the eIF4G scaffold protein of the eIF4F cap-binding complex . This brradation , a proceThe most common PABP is PABPC1. In erythroid MEL (mouse erythroid leukemia) cells, however, there is a prominent role for Pabpc4 . Pabpc4 Figure 4). Whether binding of CPEBs increases the length of the poly(A) tail or causes deadenylation depends on the interaction with additional RBP such as PUMILIO, or on phosphorylation of CPEB, for instance by Aurora kinase A (AURA) (Among the proteins able to recruit cytoplasmic polyadenylation enzymes are the cytoplasmic polyadenylation element binding proteins (CPEBs). CPEBs, however, are promiscuous proteins able to recruit either cytoplasmic poly(A) polymerases such as the Drosophila GLD2 protein , which promotes mRNA stability and translation, or the deadenylating CCR4/NOT complex (A (AURA) . DeadenyA (AURA) . In partA (AURA) . DeadenyA (AURA) .Of the CPEB family members, CPEB4 is specifically induced during erythroid differentiation . ErythroAlthough CPEBs are generally considered as factors that recruit adenylating or deadenylating enzymes, interaction with other general translation factors may also contribute to regulation of translation. Whereas CPEB4 binds eIF3, CPEB1 is known to increase mRNA stability by binding to PABPC1 and PABPC1L .In addition to CPEB and PABP, cytoplasmic polyadenylation is regulated by MUSASHI-1 and -2 (MSI1 and MSI2). These proteins do not specifically control erythropoiesis, but they are very important for hematopoietic stem- and progenitor cells (HSPC) from which erythropoiesis develops. MSI1 and MSI2 interact with mRNA via the MSI-binding element (MBE) . The MBEThe majority of studies done on MUSASHI-mediated translational repression have been done with MSI1. MSI1 represses the translation of target transcripts by competing with eIF4G for binding with PABP, preventing the formation of the 80S ribosome subunit . TranscrGt/GtMsi2) results in a marked decrease in short term hematopoietic stem cells (ST-HSCs) and multipotent progenitors (MPPs) while the effect on long term hematopoietic stem cells (LT-HSCs) was nominal . They were among the first sequence elements known to affect stability of mRNA (The AAU1/HNRPD family of AU-rich element binding proteins (AUBPs) are oppositely regulated from the MSI proteins . AU-rich of mRNA . Transla of mRNA . AUBPs kTTP family members interact with NOT1 to promote the rapid deadenylation by the CCR4/NOT complex . InteresZFP36L1 transcript binding by ELAV1, resulting in a decreased recruitment of ZFP36L1 to polysomes , mRNAs contain secondary structures and specific sequences that bind RBP to specifically control their translation. An important posttranscriptional mechanism of gene expression is mediated by microRNAs. Argonaut proteins are the miRNA-binding RBPs that associate with RNA. Mice deficient in Argonaut 2 (Ago2) suffer from severe anemia, which is caused by the failure of miR-144/451 to act on its targets . Howeverferritin mRNA via a conserved sequence that forms a hairpin structure in the 5′UTR , storage in ferritin and export via ferroportin . Posttrahe 5′UTR . Bindinghe 5′UTR . The preroportin . Interes control . By cont control . Bindingbundancy .Both IRPs cooperate to control iron homeostasis, yet they are regulated via different mechanisms. IRP1 is a bifunctional protein with both enzymatic and RNA-binding activity. Its possesses the capacity to act as an aconitase in the catalyzation of citrate to isocitrate . The catAlthough IRP2 is 57% homologous with IRP1 in humans, it does not function as an aconitase under iron-rich conditions . Unlike MSL  during sex determination of Drosophila melanogaster  is an RNA-binding protein with five cold-shock domains . CSDE1/Unogaster . Repress complex . Paradox complex .MSL-2  deadenylation. CSDE1 binds to ith PABP . In contCSDE1 also interacts with the 4E-Transporter (4E-T), itself a translational regulator with a wide breadth of functions. 4E-T competitively binds to cap-binding protein eIF4E, preventing its association with scaffolding protein eIF4G , while sPabpc1 mRNA expression was reduced indicating more efficient translation of Pabpc1 followed by negative feedback on mRNA stability . Within this superfamily four different RNA binding domains and several auxiliary domains are discerned, on basis of which 16 independent families are identified by capital letters . The hnRr15-LOX translation by hnRNPK involves association of r15-LOX with DDX6 in P-bodies, and is regulated by SRC kinase through an intricate feed-back control of hnRNPK and SRC (r15-LOX translation. Expression of Non-muscle myosin heavy chain IIA (NmhcIIA), involved in intracellular vesicle transport, is also regulated by hnRNPK (The RBPs hnRNPE1 and hnRNPK are important for late erythroid maturation. Together they control translation of the reticulocyte enzyme 15-lipoxygenase (r15-LOX) . This en and SRC . Translay hnRNPK . Thus, ty hnRNPK .Glutathione Peroxidase 4 (GPX4), and in the transcript encoding Unusual SNARE protein in the ER-1 (USE1)  . Interes1 (USE1) . Translaablished . This suibosomes . This isibosomes . Thus, tTranscripts that are subject to control of translation can be identified by density centrifugation to separate subpolysomal from polyribosomal transcripts. In erythroblasts, this resulted in the identification of numerous transcripts that are dependent on for instance growth factors Epo and SCF for their translation . DetaileFinally, ribosome profiling also showed that ribosomes fail to be recovered in reticulocytes and therefore accumulate in the 3′UTR . This isThis review presents a general overview of distinct mechanisms and major players that control mRNA translation in erythropoiesis. However, it is clear that there is still much we have yet to understand regarding mechanisms in mRNA translation. The advent of novel molecular and biochemical technologies has revolutionized the large-scale identification of RBPs and their transcripts. Technologies such as iCLIP  and ribosome profiling have the exciting potential to rapidly advance the field.After transcription, translation functions as an additional regulatory layer of gene expression. Precise translational control enables coordinated protein synthesis in response to environmental signals, or the differentiation stage of cells. It also enables the dissociation of gene transcription and protein synthesis in time, which is of particular importance once gene transcription has ceased in late erythroblasts and reticulocytes undergoing terminal erythropoiesis. We expect that the coming years will provide exciting new insights in selective translation of mRNA during erythropoiesis.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Very long intergenic non-coding RNAs (vlincRNAs) are a novel class of long transcripts (~50 kb to 1 Mb) with cell type- or cancer-specific expression. We report the discovery and characterization of 256 vlincRNAs from a cohort of 64 primary childhood pre-B and pre-T acute lymphoblastic leukemia (cALL) samples, of which 61% are novel and specifically expressed in cALL. Validation was performed in 35 pre-B and pre-T cALL primary samples. We show that their expression is cALL immunophenotype and molecular subtype-specific and correlated with epigenetic modifications on their promoters, much like protein-coding genes. While the biological functions of these vlincRNAs are still unknown, our results suggest they could play a role in cALL etiology or progression. ETV6/RUNX1] rearrangement, together representing about half of pre-B cALL cases. Both subtypes are associated with a good prognosis [TCF3/PBX1], or t [BCR/ABL1], are associated with intermediate to poor outcomes , t [BCR/ABL1], and unclassified (“Other”) pre-B cALL subtypes when performing hierarchical clustering using vlincRNA normalized expression values  [ETV6/RUNX1] subtype.Since it was previously reported that lncRNA expression profiles can accurately classify pre-B cALL molecular subtypes , 7, we iSome vlincRNAs had expression levels orders of magnitude higher in specific subtypes, suggesting a subtype-specific role -specific vlincRNAs shown in ETV6/RUNX1] cases. Nearly a third  of cALL-specific putative vlincRNA promoters (±10 kb) overlapped active chromatin regions, representing a significant enrichment . Enriched active chromatin region overlaps was positively correlated with vlincRNA expression in t [ETV6/RUNX1] samples: the highest expression quartile has a higher fraction of overlap than the lowest quartile  subtype, while binding motifs for AP-1 subunits JUN and FOS, implicated in apoptosis, proliferation, and differentiation, were significantly enriched in the HHD subtype.To delineate the basis of the vlincRNA expression regulation in cALL, we investigated epigenetic regulation by generating ChIP-seq data of the H3K4me1, H3K4me3, and H3K27ac active chromatin marks from a pool of two additional t12;21) samples, suggesting that vlincRNA expression could be regulated through epigenetic mechanisms  in three ENCODE cell lines: GM12878 (B-lymphocytes), K562 (chronic myeloid leukemia), and Jurkat (pre-T ALL). Interestingly, these cell lines also showed active chromatin enrichment on a fraction of vlincRNA promoters identified in t , which is concordant with previously reported sizes (~50 kb—1 Mb), with a median size of ~80 kb . AlthougETV6/RUNX1] and HHD subtypes, which together account for ~60% of pre-B cALL cases. These results, validated in 35 other primary cALL samples suggest that vlincRNA expression patterns can be used as molecular biomarkers for more accurate disease subtype classification. It would be interesting to see if these results scale with increased cohort size to reach the high cALL subtype classification accuracy demonstrated by Lilljebjorn et al.  subtype by taking the corresponding ChIP-seq peaks of the H3K4me3, H3K4me1, and H3K27ac chromatin marks and using them as input in ChromHMM v.1.11 [+ B cord chromatin regions were obtained from the Roadmap Epigenomics Project [ETV6/RUNX1] putative promoter-active region sizes  [M v.1.11  to distr Project  by keepion sizes . The relot v2.47 . Motif aot v2.47  using thETV6/RUNX1] cases and on one human cord blood CD10+CD19+ control using a previously described procedure  promoters were obtained by splitting the regions into ten equal-size bins. Methylation values of covered CpGs overlapping the bins were averaged. Boxplots and hierarchical clustering of promoter methylation levels were obtained by averaging methylation values of covered CpGs across the promoters. For 450K datasets, beta values of probes overlapping active t [ETV6/RUNX1] promoters  were averaged to obtain one methylation value per promoter per sample.WGBS methylation values upstream (-10kb), downstream (+10kb) and on active t [Whole transcriptome, ChIP-seq and WGBS datasets are available on the Gene Expression Omnibus (GEO) under accession numbers GSE89071 and GSE120677.S1 FigGenome wide tracks illustrating the discovery procedure of vlincRNAs in cALL samples. Blue = forward strand, pink = reverse strand.  Strand specific RNA-seq read coverage of pooled discovery samples . (c) RefSeq gene annotations.  Strand specific protein coding gene coordinates of merged UCSC and RefSeq annotations.  Strand specific bases covered by at least one read using genomeCoverageBed from bedtools.  Strand specific covered bases having more than 75% read density and not overlapping protein coding genes or blacklisted regions. (j) Strand specific transcripts longer than or equal to 50 kb after merging covered bases less than 500 bp apart and merging resulting segments less than 10 kb apart.(TIF)Click here for additional data file.S2 Fig2 normalized expression across discovery and replication samples -HHD sample was not included in this analysis). (d) Normalized rld expression of the top five t-specific vlincRNAs in REH cells. t-specific vlincRNAs were determined as having a minimum of 2 fold change higher expression than in the other subtypes and sorting the fold change in descending order.(a) PCA plot of the replication samples  using the DESeq2 regularized log transform (rld) normalized expression of minimally expressed cALL vlincRNAs (n = 273). (b) Hierarchical clustering of the replication samples using Euclidean distance on vlincRNA normalized rld expression values. Cluster purity = 0.94 using 5 clusters. (c) Subtype-specific Pearson correlations of vlincRNA mean log(TIF)Click here for additional data file.S3 Fig+CD19+ control sample respectively. (h) VlincRNA transcripts (n = 256) discovered in the cALL discovery samples (n = 68). (i) RefSeq gene annotations. (j) Active chromatin regions containing the H3K4me3 mark from ChromHMM. (k) Candidate promoter regions ±10 kb around the 5’ start of vlincRNAs. (l) Redefined promoter coordinates by keeping the largest active chromatin regions overlapping the candidate promoters. A third  of the candidate vlincRNA promoters are defined as active. Overlay of strand specific RNA-seq normalized read coverage of the t Illumina samples .  Normalized read coverage of the H3K4me3, H3K27ac and H3K4me1 ChIP-seq histone marks of the t pool sample.  WGBS methylation levels of the merged t cases (n = 3) and the CD10(TIF)Click here for additional data file.S4 FigPairwise Pearson correlations of 450K methylation levels of t active promoters (n = 34) in four healthy B cell stages  and three cALL subtypes , HHD, ’Other’) from both in-house and public datasets . 450K beta values were used as methylation levels. For each promoter, methylation levels were obtained by averaging the values of all overlapping 450K probes. For more than half of t active promoters , no probes overlapped. Circle size increases as Pearson correlation decreases.(TIF)Click here for additional data file.S5 FigSignificantly enriched motifs  in candidate promoters (±10 kb around the 5’ start of vlincRNAs) of t and HHD high expressed quartiles vlincRNAs .(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.


Prediction of evolutionary trajectories has been an elusive goal, requiring a deep knowledge of underlying mechanisms that relate genotype to phenotype plus understanding how phenotype impacts organismal fitness. We tested our ability to predict molecular regulatory evolution in a bacteriophage (T7) whose RNA polymerase (RNAP) was altered to recognize a heterologous promoter differing by three nucleotides from the wild-type promoter. A mutant of wild-type T7 lacking its RNAP gene was passaged on a bacterial strain providing the novel RNAP in trans. Higher fitness rapidly evolved. Predicting the evolutionary trajectory of this adaptation used measured in vitro transcription rates of the novel RNAP on the six promoter sequences capturing all possible one-step pathways between the wild-type and the heterologous promoter sequences. The predictions captured some of the regulatory evolution but failed both in explaining 1) a set of T7 promoters that consistently failed to evolve and 2) some promoter evolution that fell outside the expected one-step pathways. Had a more comprehensive set of transcription assays been undertaken initially, all promoter evolution would have fallen within predicted bounds, but the lack of evolution in some promoters is unresolved. Overall, this study points toward the increasing feasibility of predicting evolution in well-characterized, simple systems. The historical dichotomy between studying living systems through reductionist molecular biology or holistic evolutionary biology has given way to a unification termed the functional synthesis . This syEscherichiacoli  from the consensus wild-type T7 promoter (GAC) at positions -11 to -9  was added to a concentration of 1 mM before the 60 min of growth (no IPTG addition for the second 50 passages). After 60 min to reach density, 1 µl of previous bacteriophage lysate was added . Bacteriophage was allowed to grow under the same conditions, occasionally allowing the cultures to reach full lysis in order to stimulate recombination.Frozen aliquots of BL21-Gold cells (Agilent) transformed with pLUV-G78-KIRV RNAP . This was sonicated at 50% amplitude for 2 min  in an ice bath. Lysed cells were spun at 10,000 × g for 30 min, then the supernatant was added to a 1-ml Ni-NTA resin column which had been equilibrated with 2× binding buffer. The cells were washed with 6× column volumes of wash buffer  then eluted into 3 ml of elution buffer . These samples were dialyzed into 1.2 l of dialysis buffer  overnight and again into 1.2 l of storage buffer  overnight. The resulting protein was adjusted to 1 mg/ml and added to equal volumes of glycerol.RNAPs were purified via Ni-NTA N-terminal 6xHis methods in a manner similar to elsewhere . Briefly2, 6 mM spermadine, 6 mM each NTP, 10 mM DTT, 0.5 µM T7 RNAP, 0.5 µM DNA template, and 0.17 mg/ml DFHBI in DMSO. Reactions were incubated at 37 °C and were read every minute at an excitation/emission of 469/501 nm for 2 h in a Safire monochromator (Tecan). DNA templates were made by thermal cycling 2 µM forward primer and reverse primers (see In vitro transcription was done in a manner similar to elsewhere . In vitrmers see  online w8 cells/ml), and 150 µl of cells was added to each well of a clear, 96-well plate. The proper concentration of IPTG (where required) was added to each well, as, too, was the proper concentration of bacteriophage (determined by previous titer) in order to give the required multiplicity of infection (MOI) (MOI for lysis curves here = 0.01). The plate was grown in a PowerWave 340 microplate spectrophotometer (BioTek) at 37 °C and OD600 readings were taken every minute, preceded by shaking for 10 s and intensity level 4.Cells were grown in a manner identical to those of the passaging scheme (achieve a density of 108 cells/ml). All fitness assays were done using the same frozen stocks.pLUV-G78-KIRV-RNAP plasmid variants , and 50 mg/ml kanamycin before plating on Kanamycin plates. Plates were incubated for 3 h, top agar was removed with spatula and added to 15-ml falcon tubes, to which 4 ml of 2xYT and 1 ml chloroform were added and vortexed, then allowed to sit at 4 °C for at least 1 h. The tubes were then spun at 4,000 × g for 15 min at 4 °C. The aqueous layer after spinning was 5 ml. The phage titer of the aqueous phase was then measured.Less than 50 fresh plaque forming units (passaged and titered within last 4 days) were added to 4 ml of top agar, 350 µl of bacteria grown for 1 h to an OD600 ∼1.00 (a density of 10V01146.1) using breseq , both the CAG and GGG promoters exhibited substantially higher transcriptional activity than CGC and also higher activity than any of the single-step mutants , on the romoters . These tromoters . Consist mutants . These rThere are 64 triplets that constitute the full genotype space for evolution in this system (neglecting promoter duplications and deletions within the promoter sequence itself). Our characterization of just eight triplets (six intermediates) stemmed from a combination of pragmatism and the prediction that evolution would follow direct, single-step sequence changes between the two parental triplets. Validating and characterizing even a single promoter is laborious, so limiting the study to one-eighth of the comprehensive set was effectively necessary.In initiating the evolution of wild-type T7 in response to the new RNAP (the G78-KIRV RNAP), we first determined whether a phage variant whose genome lacked the wild-type RNAP gene (T7Δ1) could grow with the G78-KIRV polymerase expressed in trans from a plasmid. The deletion in T7Δ1 removes not only the RNAP gene but also the first T7 promoter (φ1.1A); for the purposes of our study, we attribute no significance to this missing promoter as φ1.1B—still present—lies within 100 bases of φ1.1 A and there are no intervening genes. T7Δ1 was complemented by the plasmid, but its plaques were noticeably smaller than plaques of wild-type T7  online, E. coli cells harboring the G78-KIRV RNAP expression plasmid were grown for 1 h with 100 µM IPTG induction, at which point T7Δ1 phage was added at an MOI of 0.01–0.1 and the culture grown until lysis. This protocol allowed expression of the complementing RNAP in all cells prior to phage infection. The parental T7Δ1 lysed the culture within ∼90 min, whereas either wild-type T7 phage or T7Δ1 with the wild-type polymerase provided in trans fully lysed the culture in ∼30 min. Over the course of the first 50 passages the lysis time decreased, ultimately going from 90 to 30 min in the presence of IPTG induction  was dramatically shorter in all four replicates than with wild-type T7 A. Becaus5 reads per sample). We highlight several points before describing details.Across the four lines, substitutions occurred in only 5 of the 16 T7Δ1 promoters: φOL, φ1.5, φ2.5, φ6.5, and φOR . As the No promoter evolved to the expected CGG triplet. Furthermore, no promoter evolved changes at more than two bases.Two single-step mutants were observed. One (GAG) was the predicted first step; the other (GCC) lay outside the six intermediates on direct paths between the two parental triplets.As expected, the two single-step intermediates with low G78-KIRV activity did not evolve.The populations of all four adapted lines were sequenced at two time points, after transfer 50 and after transfer 100  was found in one to two promoters in three lines , and the mutation did not fix in the population, occurring in 46.6% of the phage by passage 50 but only 25.6% by passage 100 , but in another, there was rapid evolution toward a single endpoint, suggesting strong selection for a particular sequence .Individual promoters followed different paths. Most promoters failed to evolve at all, but even those that did evolve often failed to carry the same substitutions as others that evolved. Evolution of the φ2.5 promoter was observed in only one line  and 100 , and also eventually duplicated in lines 2, 3, and 4  was forced to use a laboratory-evolved variant of T7 RNAP (the G78-KIRV RNAP). This G78-KIRV RNAP variant only weakly transcribed wild-type T7 promoters, and its optimal promoter sequence was three bases removed from the consensus T7 wild-type promoter. Growth of T7Δ1 in this environment was expected to favor evolution of possibly all T7 promoters to match the optimum for the G78-KIRV RNAP.At the outset, we formed a prediction matrix  for the E. coli RNAP at position 7555 and one for T7 RNAP immediately after gene 10 at position 24170. Transcription from the three E. coli promoters in the initial 750 bp of the phage genome by the E. coli RNAP produces transcripts of up to 7 kb  most promoters did not evolve, and 2) many triplets that experienced evolution could, with one additional mutation, have evolved yet higher activity but did not, despite a long opportunity for evolution. Thus, several known one-step paths to higher activity were not taken. Our analysis of promoter activity did not include all 64 possible triplets, so it is unknown whether other “expected” paths also exist in the genotype space, but knowing that some other high fitness paths exist would only confirm that the system failed to evolve higher activity in many promoters when they were each one step away.E. coli A2 promoter near the φOL promoter at the 5′ end of the phage genome duplicated in experimental line 4 (In addition to changes in promoter identities, promoters in all four lines experienced duplication events . Most ocl line 4  online.Empirical recapitulations of mutational pathways such as those demonstrated in this work can yield insights to how enzymes and organisms traverse fitness landscapes . As but Assuming that the evolution results speak to natural selection, extensive promoter heterogeneity and duplication in wild phage should be observed. Indeed, single-subunit T7-like RNAPs with wildly different promoter sequences have been identified not just in other bacteriophages but also in eukaryotic mitochondria and plant chloroplasts , consistThese studies of enzyme evolution and regulatory interaction are useful in displaying critical features of evolution, and they thus lead us to the prospect of predicting the evolution of systems. This prediction of evolutionary paths is what we present here. Our choice to use a heterologous T7 RNAP to evolve T7Δ1 phage was due to the fact that the system is well characterized down to the promoter bases involved in polymerase recognition, that different RNAPs were available, and that the phage is easily evolved . OverallSupplementary DataClick here for additional data file.