RNA-binding proteins play a key role in shaping gene expression profiles during stress, however, little is known about the dynamic nature of these interactions and how this influences the kinetics of gene expression. To address this, we developed kinetic cross-linking and analysis of cDNAs (χCRAC), an ultraviolet cross-linking method that enabled us to quantitatively measure the dynamics of protein–RNA interactions in vivo on a minute time-scale. Here, using χCRAC we measure the global RNA-binding dynamics of the yeast transcription termination factor Nab3 in response to glucose starvation. These measurements reveal rapid changes in protein–RNA interactions within 1 min following stress imposition. Changes in Nab3 binding are largely independent of alterations in transcription rate during the early stages of stress response, indicating orthogonal transcriptional control mechanisms. We also uncover a function for Nab3 in dampening expression of stress-responsive genes. χCRAC has the potential to greatly enhance our understanding of in vivo dynamics of protein–RNA interactions. Protein RNA interactions are dynamic and regulated in response to environmental changes. Here the authors describe ‘kinetic CRAC’, an approach that allows time resolved analyses of protein RNA interactions with minute time point resolution and apply it to gain insight into the function of the RNA-binding protein Nab3. RBPs also play a very important role in adapting to dynamic environments, such as those encountered by microbes when exposed to stress. Survival under stress is contingent on the ability to rapidly reprogram gene expression and, while this ability has been largely attributed to the activity of transcription factors, it is becoming increasingly clear that RBPs also play a primary role in shaping gene expression response profiles by modulating RNA processing and decay2. RBPs involved in RNA decay are believed to play an important role during the first few minutes of the adaptation response during which major transcriptional reprogramming events happen4. However, direct measurement of protein–RNA interactions during these early stages has so far proved elusive. Consequently, little is known about the contribution of individual RNA decay factors during rapid rewiring of the gene expression program in response to environmental changes.RNA-binding proteins (RBPs) control almost all aspects of gene expression, including the stability of the RNA, its structure, the rate at which the RNA is processed, how efficiently it is translated and its subcellular localization. Not surprisingly, because of these important functions, RBPs are often found associated with many diverse genetic and somatic diseases, including muscular disorders, autoimmune diseases, and cancer5. UV-irradiation is used to forge covalent bonds (cross-links) between proteins and directly bound RNAs. Proteins of interest are then purified under stringent conditions and high-throughput sequencing of the cross-linked RNA enables mapping of the interaction sites. A number of CLIP-related techniques have been developed over the years, such as CRAC , iCLIP, and PAR-CLIP (photoactivatable ribonucleoside-enhanced crosslinking and immunoprecipitation)8. For CRAC the protein of interest is fused to a tandem affinity purification tag  to enable purification of cross-linked RNAs under completely denaturing conditions6. Recent advancements have enhanced the efficiency of the library preparation, increased the data complexity and improved the resolution of RNA-binding site detection11. Despite such advances, current protocols are ill-suited to quantitatively measure dynamic changes in protein–RNA interactions. Using current commercially available UV-irradiation equipment, the cross-linking step can take up to 30 min to reach the desired dose (depending on organism and wavelength)14. This limitation rules out measurements of the early stress responses, which can happen on the minute time scale4. In addition, due to prolonged UV-irradiation, cells are exposed to major additional stresses, such as DNA damage, which can confound the results and insert a bias toward RNA transcripts that are specific for the irradiation conditions.In recent years, ultraviolet (UV) cross-linking and immunoprecipitation (CLIP) followed by deep sequencing has emerged as the main technology to map protein–RNA interactions in vivoTo tackle these problems, we have improved the original CRAC protocol and developed a UV-irradiation device that cross-links proteins to RNA in vivo in seconds. These advancements enabled us to perform quantitative time-resolved in vivo measurements of direct protein–RNA interactions at 1-min time-point resolution. We refer to this method as kinetic CRAC (χCRAC).Saccharomyces cerevisiae to investigate the dynamic interactions of the RBP Nab3 during the adaptation process. Nab3 is a component of the Nrd1-Nab3-Sen1 (NNS) transcription termination complex that is involved in degradation of diverse classes of lnc-RNAs, such as cryptic unstable transcripts (CUTs), Nrd1 unterminated transcripts, and various messenger RNAs (mRNAs) and in the maturation of snoRNAs16. Depriving yeast of glucose results in significant changes in mRNA levels and a transcriptome-wide redistribution of NNS components20. χCRAC accurately detected these widespread changes in Nab3 binding; importantly, the high temporal resolution enabled us to document transient changes in cross-linking of Nab3 to many transcripts, indicating a potentially pervasive importance of termination factors in the early stages of stress response. We also uncover a role for Nab3 in regulating the expression kinetics of stress-responsive genes and found that Nab3 is required for suppression of retrotransposon transcription during late stages of the glucose deprivation response. This suggests that Nab3 could play an important role in maintaining genome integrity during stress.We have applied χCRAC to glucose-deprived 6-TEV-ProtA) Nab3 that were UV-irradiated in the Vari-X-linker or in the Megatron, which (to the best of our knowledge) is currently the most efficient UV cross-linker on the market for cross-linking cell cultures12. Our tests with the Vari-X-linker’s 254 nm lamps showed that it can cross-link yeast proteins to RNA in seconds, up to tenfold more efficiently than the Megatron  to improve the in vivo protein–RNA cross-linking efficiency CLIP/CRAC experiments on very short time-scales, enabling the measurement of dynamic protein–RNA interactions in living cells with high temporal resolution.22; adjusted p-values < = 0.05). The Vari-X-linker data were more highly enriched for short-lived lncRNA species (Stable Uncharacterized Transcripts (SUTs), Xrn1 Unstable Transcripts (XUTs), Cryptic Unstable Transcripts (CUTs), and anti-sense transcripts) , it also illustrates that long UV-irradiation times increase the likelihood of detecting alterations in transcription that are the result of the activation of the DNA damage response.Differential expression analysis of Nab3 CRAC data generated using the Megatron and the Vari-X-linker revealed significant differences between the two UV-irradiation conditions  Fig. . This suts) Fig. 23, 24. Tata Fig.  were higata Fig. . Additio17. Therefore, to test the feasibility of our χCRAC method, we measured changes in Nab3 cross-linking during glucose deprivation. Because Nab3 co-transcriptionally binds RNA, we also performed χCRAC on RNA polymerase II using a strain expressing an HTP-tagged Rpo21 subunit26. This enabled us to determine how well Nab3 binding correlates with changes in Pol II transcription. To measure changes in steady-state RNA levels, we performed RNA-Seq on ribosomal RNA-depleted total RNA. We devised a simple experimental set-up that would enable us to rapidly shift cells to a new medium . The rest was rapidly harvested by filtration and transferred to a flask with medium lacking glucose. After the shift, cells were cross-linked at various time-points. To control for changes in gene expression caused by the filtration process, we performed experiments where filtered cells were transferred back to glucose containing medium. To accurately quantify differences in cross-linking between time-points, we made several improvements to the original CRAC protocol6 to reduce sequence representation biases  and treated (glucose to no glucose), we fitted a Gaussian process (GP) regression model to both time series29. By tagging Nab3 with the FKBP12-rapamycin-binding (FRB) domain in the anchor-away strain, we were able to rapidly and effectively deplete Nab3 from the nucleus by adding rapamycin to the glucose medium 1 h before shifting the cells to medium lacking glucose ). The largest changes were observed in snoRNAs, protein-coding genes and anti-sense RNAs Fig. . To detey-axis) correlated with changes in the levels of total mRNA as measured by RNASeq at each time point . Only at the 0 (glucose) and late 30 and 40-min (no glucose) time-points a highly positive correlation between changes in total RNA levels and changes in Pol II transcription  was observed. These results suggest that it takes about 30–40 min to adjust total RNA levels to mirror Pol II transcription levels. After about 14 min of glucose deprivation ~60% of the transcriptionally upregulated genes also showed a comparable increase in total RNA levels. This percentage only marginally increased at later time-points,  during glucose deprivation. Using K-means clustering, we divided the Nab3 cross-linking profiles of the differentially bound transcripts into four clusters Fig. . Interest = 0) were highly positively correlated, which is consistent with the co-transcriptional binding of Nab3 to the nascent transcript32. However, 1 min after the shift to medium lacking glucose, the Nab3 and Pol II data decoupled, suggesting rapid changes in Nab3 binding that were independent of alterations in Pol II transcription. Thus, during early stages of glucose deprivation, binding of Nab3 to nascent transcripts might be regulated by additional factors, providing an additional level of control, which is largely orthogonal to transcriptional regulation. At later time-points, however, the correlation between the data sets improved, indicating that the cells have started to adjust to their new environment. Consistent with this, comparison of Nab3 and Pol II χCRAC profiles revealed that at later time-points Nab3-binding profiles generally followed Pol II transcription  clustered near the 5′end of protein-coding genes where Nab3 is known to act34 . A striking example was the enolase (ENO1) gene, an enzyme involved in gluconeogenesis and glycolysis  CRAC37, ChIP-Seq38 and TIF-Seq39 data to identify transcription start sites and transcript isoforms, respectively. All data sets show that transcription can initiate upstream of the ENO1 TATA box  signal  when cells are grown in glucose  were picked up as a result of snoRNA read-through. We identified a number of new genes that are regulated by Nab3-dependent attenuation during glucose deprivation  data for these genes showed a clear accumulation of Pol II around the Nab3 cross-linking sites in the ethanol-treated cells and little transcription downstream, indicative of Pol II pausing and termination , total RNA levels increased quite dramatically , nuclear depletion of Nab3 did not significantly alter total mRNA levels of the control group genes under normal or stress-conditions , focusing on IC2 Fig. ; group 2IC2 Fig. . As posiMD3 Fig. ; group 3lly Fig. . This sucin Fig. . Thus, Pons Fig. .YBR085C-A gene expression levels was surprising given that we detected a strong increase in Nab3 cross-linking near the 5′ end of the transcript   Fig. . Quantition Fig. . Thus, w.We conclude that Nab3 controls the induction kinetics as well as the maximum mRNA expression levels of stress-responsive genes during glucose deprivation42. In line with transposon-abundance, very few reads mapped to Ty5, which was, therefore, not further considered. Consistent with our initial results . This suggests that Nab3 activity is required to suppress transcription of Ty1 transposable elements primarily during late stages of the glucose adaptation response. Consistent with this idea, Nab3 cross-linking to Ty1 was highest at the late time-points  and continued to increase during the glucose deprivation response  at late stages of the adaptation response.For the less-abundant Ty3 and Ty4 retrotransposons the pattern was noisy, however, we could detect an increase in Ty3 Pol II transcription during the last three time-points, indicating that Nab3 is also involved in regulating Ty3 expression  or deprived of glucose for 20 min, with and without rapamycin treatment . Strikingly, Nab3 cross-linked primarily to a single region in Ty2 retrotransposons , which is not fast enough to do time-resolved analyses with minute time-point resolution. Another problem we faced with the Megatron system is that it was not trivial to cross-link cells when the lamps were at full output. As a consequence, cells would not always receive the same level of 254 nm UV intensity during the 100 s of UV-irradiation, resulting in variation in cross-linking efficiencies between samples and noise in the data. It was also not possible to control the temperature inside of the unit. Although the Megatron works well for normal cross-linking studies, these issues made it very difficult to perform time-resolved analyses during short periods. To overcome this, we incorporated a shutter system to allow the lamps to be at full power with stable, repeatable output before exposing the cells. A fan cooling system was installed to minimize thermal shock to the sample. With the Vari-X-linker, the lamps can be left on throughout the experiment and cells will only be exposed when the shutters are opened .The Vari-X-linker incorporates a number of new features that enhance the effectiveness of UV cross-linking. The sample is presented in a controlled 1 cm thick layer contained in a specially constructed UV transparent bag and flanked by two beds of powerful 254 nm (400–550 W) or 365 nm lamps (350 W) that were assembled on trays for easy exchange of the lamps. As far as we are aware, the Megatron is currently the fastest cross-linker available on the market for cross-linking proteins to RNA in actively growing cells600~0.5) at 30 °C. For time-point zero, 1 L of cells were cross-linked in the Vari-X-linker using the high-output 254 nm lamps for 12 s and then harvested by rapidly passing the cells through a 0.8 µm filter (Millipore) using a new vacuum filtration device (see above). The remaining 7 L of cells were harvested on filters and quickly resuspended in S-TRP (no glucose samples) or SD-TRP (glucose control samples) and maintained at 30 °C. For each time-point 1 L of cells were cross-linked in the Vari-X-linker and harvested by filtration as above. This yielded ~1 g of cells for each time-point.For an eight time-points time-course, 8 L of cells were grown in synthetic medium with glucose (SD-TRP) to exponential phase . Extracts were incubated with 250 µl of equilibrated IgG Sepharose beads  for 2 h at 4 °C. Beads were washed three times 5 min with 10 ml of TN1000  and three times 5 min with TN150 .Cells were lysed in 1 V/w of TN150  and 3 V of Zirkonia beads  by vortexing the cells five times for 1 min, with a 1-min incubation on ice between each step. Three milliliter of lysis buffer was added and extracts were clarified by centrifugation  for 1 h before the cells were shifted to medium lacking glucose (but supplemented with 1 µg/µl rapamycin).For the Nab3 anchor-away nuclear depletion experiments2, 0.1% NP-40, 5 mM β-mercaptoethanol, 150 mM NaCl, Roche Midi protease inhibitors; 1 ml per gram of cells). Subsequently, 1 V/w of TMn150 was added containing 1U/ml of RQ1 RNase-free DNAse (Promega) and the suspension was incubated for half an hour on ice to degrade the chromatin.For the Pol II (Rpo21-HTP) χCRAC experiments, cells were lysed in 1 V/w TMn150  for 2 h at 4 °C. Beads were washed three times 10 min with TN1000 and rinsed three times with TN150.For the Hfq cross-linking test Fig. , 0.5 L o2). Beads were subsequently incubated with 80 µl of 1xPNK buffer containing eight units of TSAP alkaline phosphatase (Promega) and 80 units of recombinant RNasin (Promega) for 1 h at 37 °C. After one 500 µl wash with wash buffer I and three 500 µl washes with 1xPNK buffer, the beads were resuspended in 80 µl of 3′ linker ligation mix  and 30 units of T4 polynucleotide kinase (NEB)) for 40 min at 37 °C. ATP (Roche) was added to 1 mM final concentration, followed by another 20-min incubation at 37 °C. Beads were subsequently washed three times with 500 µl of wash buffer I and three times with 500 µl of 1xPNK buffer and incubated with 80 µl of 5′ linker ligation mix  40 units of T4 RNA ligase 1 (NEB) and 5′ adapter  were resuspended in 600 µl of TN150 and incubated for 2 h with 10 µg of home-made GST-TEV protease at 18 °C. The TEV eluates were subsequently incubated with 0.1 unit of RNace-IT (Agilent) for 5 min at 37 °C after which 0.4 g of guanidine HCl (Sigma) was added to the TEV eluates to inactivate the RNAses. NaCl and Imidazole was added to a final concentration of 300 and 10 mM, respectively and the samples were incubated overnight with 50 µl of Nickel agarose beads (Qiagen) at 4 °C. Beads were transferred to a Snap Cap columns (Pierce), washed three times with 500 µl wash buffer I  and three times with 1×PNK buffer  that divides the tasks over multiple processors. The pipeline performs the following steps: demultiplexing of raw fastq files by pyBarcodeFilter.py version 2.3.3 from the pyCRAC tool suite56 (version 1.2.2.6). Flexbar57 then trims the reads and removes 3′ adapters sequences (Supplementary Table www.novocraft.com) version 2.0.7 and those that mapped to multiple genomic regions were randomly distributed over each possible location. PyReadCounters then makes read count and fragments per kilobase transcript per million reads (FPKM) tables for each annotated genomic features. Only genes for which cross-linking could be detected in all time-points were considered. Genomic feature files were obtained from ENSEMBL (version R64-1-1.75). Coordinates for anti-sense transcripts, CUTs, XUTs, SUTs, and retrotransposons59 were obtained from the Saccharomyces Genome Database .Sequencing was performed on IlluminaHiSeq 2500 and HiSeq 4000 machine by our Edinburgh Genomics facility. The complete pipeline for the processing of paired-end kinetic CRAC data is available on PyCalculateFDRs.py was used to find significantly enriched Nab3-binding peaks using default settings. Only peaks with at least five reads were considered and the minimum width of the peak interval was set to 20 nucleotides. Oligo-A reads were identified using blast and in-house perl and python scripts. PyBinCollector.py was used to generated the Nab3 and Pol II cross-linking distribution figures.60. Only profiles were selected that showed a fold-change in FPKM of at least 1.5 and had a mean pairwise correlation over two biological replicates of 0.7.K-means clustering of cross-linking profiles was performed using the STEM clustering program41, we first selected protein-coding genes that had a minimum coverage of 10 FPKM at the indicated time-points . The EI was calculated by summing the nucleotide density in the promoter proximal region  and dividing this number by the nucleotide density of the body region (+251 to 3′ end). Subsequently, we divided the values for the 4 and 18-min time-point by the values for the 0 time-point to calculate the EI. These data were then compared to changes in Pol II transcription, which was calculated by dividing the total normalized nucleotide density of the whole gene from the 18-min sample by the total normalized nucleotide density of the 0-minute sample. We then only selected those genes that showed an increase in transcription of at least 1.5, a coverage of at least 10 FPKM and EI of at least 2. From the resulting list of genes, only genes were selected that (a) had Nab3-binding sites near the 5′ end of the gene and (b) showed reproducible profiles in a replicate experiment.To calculate the Pol II transcription EIWe scaled the FPKM values of all transcripts within each time point by a constant factor such that the sum of FPKMs for all time points and all experimental replicates is the same. This was done for all data sets that were analyzed simultaneously, e.g., Nab3 and Pol II data sets (see below). For all data sets, the same time points were used . Finally, for all analyzed data sets, we divided each time series of each experimental replicate by its steady state value before the imposition of stress (at 0 min after the nutrient shift). This way, all time series start at the same normalized binding value of 1 a.u. before the nutrient shift and the other values for later time points are relative to the background binding signal. We only keep those transcripts for the analysis that have real values for all time points in all experimental replicates after all steps of the normalization procedure.22 in which two Megatron data sets were compared to four Vari-X-linker Nab3 glucose data sets. Only differentially expressed genes were selected that had an adjusted p-value of 0.05 or lower.For the differential expression analyses we used DESeq2fj(t) represent the binding response in condition j (stress or control) at time t, relative to time 0. Our main assumption is that this response, averaged over a population of cells, can be well modeled as a smooth function of time. To capture this assumption, we formulate a probabilistic model for the response function in terms of GPs (see e.g.27). A GP is an infinite-dimensional generalization of the multivariate Gaussian distribution, which provides a suitable prior distribution over a space of functions. Here we enforce the smoothness assumption by modeling correlations between function values at times s and t using a squared-exponential covariance function:To determine whether the imposition of stress results in differential dynamics of RBP binding, we used a Bayesian non-parametric regression approach. Let α, λ that are fitted to the data as described below.This covariance depends on two hyperparameters yj(t) of binding to a specific transcript in condition j at time t  are obtained from the unobserved function fj(t) by addition of zero-mean Gaussian noise with standard deviation σ. These assumptions enable us to marginalize exactly the unobserved function values to obtain an estimate of the data evidence .We assume that observations f(t) describing the dynamics of the system (null hypothesis).H0, all the time series (control and stress) can be explained as noise corrupted observations of a single underlying function fc(t) and ft(t), which generate the observations .H1, control and stress time series result from two distinct underlying dynamics, i.e., there are two functions We then reformulate the testing question as a model selection problem. We consider two competing models:28 in adopting a Bayes factor greater or equal to 10 as strong evidence of one hypothesis over the other. The Bayes factor computation is performed independently for every transcript; it should be noted that, as this is a Bayesian method, no issues of multiple testing arise, since sampling variability is already accounted for in the marginalization process.The ratio of the evidence under the two hypotheses (Bayes factor) quantifies the ratio of posterior probabilities of each model being correct, and hence provides a criterion for selecting one hypothesis over the other. We follow Kass and Rafteryα, λ as well as the observation noise with standard deviation σ are known. Such parameters can also be assigned a prior distribution and marginalized for a fully Bayesian treatment; however, this greatly complicates the computational task of computing the evidence as exact marginalization is not possible. To avoid these additional overheads, we adopted an empirical Bayesian strategy and fixed the hyperparameters in a data-driven fashion. The scaling hyperparameterα2 was defined as 50% of the variance of all binding time series, in control and treatment experimental replicates, for a given transcript. The length scale hyperparameter λ, which determines the number of units the data can be extrapolated away from, was globally set as 1 min. The variance of the observation noise σ2 was chosen to be the variance of the binding time series of a given transcript in control conditions, i.e., under the glucose-to-glucose shift. Notice that the GP models under both hypotheses were provided with the same hyperparameter values, to avoid over fitting to the data.It should be noted that the evidence calculation can only be performed exactly when the covariance hyperparameters 56 used for the data analyses is available on https://bitbucket.org/sgrann/pycrac. The complete data analysis pipeline is available on https://bitbucket.org/sgrann/kinetic_crac_pipeline/. Other python and perl scripts used for isolating oligo-A tailed reads are available upon request.The pyCRAC packagehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?&acc=GSE85545). The data that support the findings of this study are available from the corresponding author upon request.Fastq and processed sequencing data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO) under the accession code GSE85545 (Supplementary InformationSupplementary Figures, Supplementary Tables, Supplementary Notes, Supplementary Methods and Supplementary ReferencesPeer Review FileReviewer reports and authors' response from the peer review of this Article at Nature Communications


FoxO1 gene regulation are largely unknown. We have previously shown that the chromatin factor high-mobility group A1 (HMGA1) plays a key role in the transcriptional regulation of glucose-responsive genes, including some that are involved in FoxO1-mediated glucose metabolism. Here we investigated the impact of HMGA1 on FoxO1 gene expression.The forkhead transcription factor (FoxO1) is a master transcriptional regulator of fundamental cellular processes ranging from cell proliferation and differentiation to inflammation and metabolism. However, despite its relevance, the mechanism(s) underlying Hmga1–/– mice. Reporter gene assays and chromatin immunoprecipitation for binding of HMGA1 to the endogenous FoxoO1 locus were performed in cells overexpressing HMGA1 and in cells pretreated with siRNA targeting HMGA1.FoxO1 protein and gene expression studies were performed by Western blot analysis combined with qRT-PCR of material from human cultured cells and EBV-transformed lymphoblasts, and from primary cultured hepatocytes from wild-type and FoxO1 gene promoter, thereby activating FoxO1 gene transcription. Forced expression of HMGA1 in primary cultured hepatocytes from Hmga1–/– mice and in EBV-transformed lymphoblasts from subjects with reduced expression of endogenous HMGA1 increased FoxO1 mRNA and protein levels.HMGA1 increased FoxO1 mRNA and protein expression in vitro, in cultured HepG2 and HEK-293 cells by binding FoxO1 gene regulation and its role in metabolism.These findings may contribute to the understanding of  PEPCK) and glucose-6-phosphatase (G6Pase), as well as genes involved in glucose counterregulation, including the insulin-like growth factor binding protein 1 (IGFBP1) gene [Forkhead box protein O1 (FoxO1), a member of the forkead family of transcription factors characterized by a highly conserved forkhead DNA-binding domain, regulates the transcription of genes involved in a wide spectrum of fundamental biological processes, like cell proliferation and differentiation, apoptosis and DNA repair, inflamation and stress response, insulin sensitivity and energy metabolism . As a crP1) gene , 11, thuP1) gene –15. ConsP1) gene .FoxO1 gene expression have been poorly investigated and poorly documented, so that the only data available in this regard refer to the upregulation of FoxO1 gene in response to E2F-1, a transcription factor involved in cell cycle progression and apoptosis [FoxO1 mRNA levels were increased in muscle of fasted or calorically restricted rats [FoxO1 gene transcription. However, the transcription factors mediating this activation have not been identified.While insulin regulation of FoxO1 protein is fairly well understood , 18 the poptosis , the hompoptosis , FoxC1 [poptosis , and Foxpoptosis . Also, ited rats , 24, supPEPCK and G6Pase [HMGA1 gene and protein expression have been linked to insulin resistance and T2D in humans and mice [The high-mobility group A1 (HMGA1) protein is a nuclear architectural factor that belongs to the superfamily of nonhistone chromatin-binding proteins. By binding to AT-rich regions of DNA, HMGA1 can transactivate gene promoters through mechanisms that facilitate the assembly and stability of stereospecific DNA–protein complexes , thereby activating or repressing a variety of mammalian genes –27. We pd G6Pase , as welld G6Pase 31–33. Ad G6Pase . Accordiand mice 34–38, wand mice .FoxO1 gene.Taking into account the above considerations, here we explored the possibility that HMGA1 may be involved in the transcriptional regulation of 2 atmosphere at 37 °C. Cryopreserved primary hepatocytes from HMGA1–/– and wild-type mice [HMGA1 gene defects [Human embryonic kidney 293 (HEK-293) cells and HepG2 human hepatoma cells were cultured in Dulbecco's modified Eagle medium and RPMI 1640 medium, respectively, supplemented with 10% fetal bovine serum (FBS) , 2 mM glutamine, penicillin (100 U/ml), and streptomycin (100 µg/ml) in a humidified 5% COype mice  were cul defects , 34, 40, defects , anti-Fo defects .FoxO1 gene promoter region (−1989 to −155 bp relative to the start site of transcription) was amplified from the BAC clone RP11–181D10 , using modified specific primers. After sequence analysis, the resulting product was cloned upstream to the luciferase (Luc) gene into pGL3 basic vector , to obtain the recombinant plasmid FoxO1-Luc. This construct was transiently transfected into HepG2 or HEK-293 cells, using the LipofectAMINE 2000 reagent (Invitrogen), in the presence or absence of an effector vector (pcDNA3HA-HMGA1a) for the HMGA1a isoform protein, as obtained by subcloning the BamH1/EcoR1 fragment from pcDNA1-HMGI into the pcDNA3HA plasmid (Invitrogen) [HMGA1 siRNA plus nonspecific control siRNA with a similar GC content  and maintained in culture for 72 h, after which cells were removed from the culture plates and subjected to a second transfection with the same siRNAs for an additional 72 h. Renilla control vector served as an internal control of transfection efficiency. EBV-transformed lymphoblasts were transfected with expression vector containing the HMGA1 cDNA as described previously [Human itrogen) . Luc acteviously . For theeviously .HMGA1 siRNA or with HMGA1 cDNA, as described previously [FoxO1 gene promoter were used for PCR amplification of ChIP-ed DNA (30 cycles), using PCR ready-to-go beads (Amersham Pharmacia Biotech): human FoxO1 (NT_007819) for 5′-CCCAAGGCTTTGGTCCTATC-3′, rev 5′- GCCGGATTCACTGTATTCTTG -3′. PCR products were electrophoretically resolved on 1.5% agarose gel and visualized by ethidium bromide staining.ChIP was performed in HepG2 and EBV-transformed lymphoblasts, either untreated or pre-treated with eviously , 30, 43.eviously , and theFoxO1 (NM_002015.3) , and mouse FoxO1 (NM_019739) , were designed according to sequences from the GenBank database. qRT-PCR was performed as previously described [For qRT-PCR, total cellular RNA was extracted from cells using the RNAqueous-4PCR kit and subjected to DNase treatment (Ambion). RNA levels were normalized against 18 S ribosomal RNA in each sample, and cDNAs were synthesized from 2 μg of total RNA using the RETROscript first strand synthesis kit (Ambion). Primers for human escribed , using Rt test. P < 0.05 was considered significant. All bar graph data shown are mean ± S.E.M.Statistical significance was evaluated by Student’s HMGA1, both FoxO1 mRNA and protein levels were increased in HepG2 cells overexpressing HMGA1, and were reduced in cells pretreated with siRNA targeting HMGA1 , we performed ChIP coupled with qRT-PCR of ChIP-ed samples, showing that binding of HMGA1 to the endogenous FoxO1 chromosomal locus was manifest in living HepG2 cells naturally expressing HMGA1, and was decreased in cells that were exposed to distamycin A .Based on the above functional observations and the presence of several putative binding sites for HMGA1 in the promoter region of n A Fig. . The redGA1 Fig. , furtherPEPCK and G6Pase, as well as on the transcription of the insulin-target gene IGFBP1, thereby contributing to the maintenance of fasting euglycemia [Hmga1–/– mice was lower than in wild-type animals [FoxO1 gene expression and function also in vivo, in whole animal. This hypothesis has now been further supported in the present work, by additional studies of primary cultured hepatocytes from Hmga1–/– and wild-type mice, showing that the mRNA expression of FoxO1 was lower in Hmga1–/– hepatocytes compared to wild-type hepatocytes  had a significantly lower risk of proliferative diabetic retinopathy, compared to non-carrier patients, and this protective effect was attributed, at least in part, to downregulation of the vascular endothelial growth factor A, a major activator of neovascularization [HMGA1 rs139876191 variant on diabetic retinopathy may also be mediated through a process that involves inactivation of FoxO1 gene and protein expression. Although the relevance of our data to the clinical setting requires further investigation, they provide compelling evidence for the identification of HMGA1 as a novel nuclear activator of FoxO1 gene transcription, which may help explaining noted differences in diabetes phenotypes.From a clinical point of view, the observation that diabetic subjects with rization . In thisrization , 50. ThuFoxO1 gene regulation and might be useful in understanding the molecular basis of clinical phenotypes in those conditions where insulin action is compromised . Understanding these mechanisms could help in identifying novel therapeutic targets for preventing and treating these diseases.In our opinion, these findings offer new biological and mechanistic insights into the mechanism(s) underlying


Doublesex (Dsx) serves as a basis for evolution of sex-determining mechanisms in animals. However, little is known about the regulation of Dsx in environmental sex determination. In the crustacean Daphnia magna, environmental sex determination is implemented by male-specific expression of the Dsx ortholog, Dsx1. Transcriptional regulation of Dsx1 comprises at least three phases during embryogenesis: non-sex-specific initiation, male-specific up-regulation, and its maintenance. Herein, we demonstrate that the male-specific up-regulation is controlled by the bZIP transcription factor, Vrille (Vri), an ortholog of the circadian clock genes—Drosophila Vri and mammalian E4BP4/NFIL3. Sequence analysis of the Dsx1 promoter/enhancer revealed a conserved element among two Daphnia species (D. magna and D. pulex), which contains a potential enhancer harboring a consensus Vri binding site overlapped with a consensus Dsx binding site. Besides non-sex-specific expression of Vri in late embryos, we found male-specific expression in early gastrula before the Dsx1 up-regulation phase begins. Knockdown of Vri in male embryos showed reduction of Dsx1 expression. In addition, transient overexpression of Vri in early female embryos up-regulated the expression of Dsx1 and induced male-specific trait. Targeted mutagenesis using CRISPR/Cas9 disrupted the enhancer on genome in males, which led to the reduction of Dsx1 expression. These results indicate that Vri was co-opted as a transcriptional activator of Dsx1 in environmental sex determination of D. magna. The data suggests the remarkably plastic nature of gene regulatory network in sex determination.Divergence of upstream regulatory pathways of the transcription factor  Doublesex (Dsx). Although how genetic factors on sex chromosomes control Dsx expression has been extensively studied in model organisms such as mouse, fruit fly, and nematodes, little is known about dependence of Dsx regulation on environmental signals. We used the crustacean, Daphnia magna, owing to its advantages for analyzing environmental sex determination: 1) fully sequenced genome, 2) recent advancement of genome engineering and 3) artificial control of sex by juvenile hormone treatment. We found that early male embryos transiently express the bZIP transcription factor, Vrille (Vri), known to be a circadian regulator, before male-specific Dsx1 activation begins. Disruption of a potential Vri-binding site in the Dsx1 regulatory region, and gain- and loss-of-function analyses revealed that Vri regulates male-specific Dsx1 activation in Daphnia. We infer that a novel gene can be co-opted as a regulator of Dsx in environmental sex-determining pathway. Our results would expand our understanding about the diversity and evolution of the sex-determining pathways in animals.Sex is widespread for reproduction of offspring in the animal kingdom. In the sex determination process, through interactions of several genes in a hierarchical manner, an initial cue leads to sex-specific expression of the major effector of sexual differentiation,  The diversity and evolution of sex-determining pathways among animals are fundamental issues in developmental and evolutionary biology. The primary cues to trigger sexual development have been varied across evolution ,2, and cDaphnia magna, has emerged as a model organism for understanding ESD because of its fully sequenced genome [Daphnia normally produces female clones through parthenogenesis, but switches to sexual reproduction when environmental qualities for growth and reproduction decline [Daphnia produces clonal males that allow fertilization of haploid eggs, which results in the production of resting eggs as a survival strategy upon harsh conditions [Doublesex1 (Dsx1) is exclusively expressed in male-specific tissues and regulates the male trait development in D. magna [The crustacean waterflea, d genome ,7 and add genome , ectopicd genome , CRISPR/d genome  and TALEd genome –13. In h decline . In unfanditions . We and nditions ,17. A denditions ,17, suggD. magna , which pDsx1 activation in D. magna, we had previously examined temporal change of its expression during embryogenesis [Dsx1 mRNAs  which differ only at the 5′ UTR, zygotic transcription of Dsx1-α mRNA is largely divided into three phases (Dsx1-pemRNA starts three hours later than Dsx1-s mRNA (around 9-hpo) and thereafter become more abundant in male embryos. We also generated transgenic D. magna to visualize spatiotemporal expression patterns and discovered that male-specific Dsx1 expression starts in a presumptive primary organizer that migrates from the rostral to the caudal side on a ventral region at 11-hpo and thereafter gradually becomes specialized in male traits [Dsx1-α mRNA transcript in a specific population of gastrula cells. However, there is a significant time lag between the critical period of the JH action and onset of up-regulation of Dsx1-α mRNA levels  [To understand mechanisms of JH-dependent ogenesis . Of the e phases , non-sexe traits . These pA levels , suggestnt (MET) , but unkDsx1-α mRNA transcription that starts at 6-hpo. We searched for potential transcription factor binding sites at a region upstream of the transcription start site of Dsx1-α transcript. We found a potential enhancer that contains a consensus sequence of the Dsx binding site and an overlapping element for binding of an ortholog of the bZIP transcription factors, Drosophila Vrille (Vri) and vertebrate E4BP4/NFIL3, which are known to be involved in various general development processes including growth [D. magna, Vri showed male-specific transient expression at 6-hpo. Loss- and gain-of-function analyses showed Vri to be necessary and sufficient for Dsx1 activation. In addition, the disruption of the enhancer suggested Vri-dependent Dsx1 activation. We infer co-option of the transcription factor Vri to the environmental sex-determination cascade.In this study, we aimed to identify the transcription factor responsible for male-specific up-regulation of g growth ,22, circg growth ,24, metag growth , apoptosg growth , and humg growth . In D. mDsx1 male-specific expression from 6-hpo, we analyzed a sequence within 7,899 base pairs upstream from the transcription start site of Dsx1-α mRNA. First, elements similar to known TF binding sites were searched with the TFBIND program [Dsx1 up-regulation and maintenance phases suggest positive feedback regulation of this gene, we investigated consensus binding sites of Drosophila melanogaster Dsx. Of the thousands of potential TF binding sites found in this study, we focused on an element similar to the fat body enhancer of the Drosophila yolk protein gene 1 that contains a Dsx binding site and an overlapping bZIP protein binding site [Daphnia pulex .To find candidate transcription factors (TFs) that activate  program  using thing site . We confia pulex . In thisproteins . We deteproteins . Phylogeg Vrille . TherefoVri by qRT-PCR during embryogenesis Dsx1 exprVri in early embryos is sufficient to activate Dsx1 and trigger male development, we induced transient ectopic expression of Vri in females by delivering capped, polyadenylated mRNAs into ovulated eggs via microinjection. We first attempted to establish a system to mimic transient expression of Vri in early male embryos. We constructed GFP mRNAs harboring the 5′ UTR and 3′ UTR sequences obtained from Xenopus laevis β-globin gene and injected this chimeric GFP mRNAs into female eggs. This injection led to expression at early embryogenesis (3 to 10-hpo) but not in the later stages  of the enhancer prevented us from designing enhancer-targeting TALENs and gRNAs, we designed two separate gRNAs, gRNA-1 and gRNA-2, near to the enhancer  exhibited phenotype-1  allelic diversification, 2) duplication of genes related to sexual development and 3) recruitment of a novel gene with no homology to any known sexual regulators . First, honeybee ,40, resulineages  even tholineages  and the lineages  seems toosophila . As wellDsx1 gene in Daphnia. In Drosophila, Vri regulates various developmental processes such as cell growth, proliferation and flight [Clock transcription [Drosophila Vri and mammalian E4BP4/NFIL3 are well known as transcriptional repressors. However, in the human immune response system, E4BP4/NFIL3 was identified as an activator of the IL3 promoter [IL-10 and IL-13 [Id2 locus [Daphnia sex determination, Vri works at the gastrulation stage when lineage commitment occurs. These similarities in regulation at the genetic and cellular levels may suggest that the molecular mechanism of Vri-dependent Dsx1 activation is similar to that of E4BP4/NFIL-3 function in human ILCs.Our findings indicate that Vri functions as an activator of the d flight ,44, as wd flight  and tracd flight . In addicription . In mammpromoter  and was nd IL-13 . It is end IL-13 . In natud2 locus . In DaphVri, which in turn activates Dsx1 expression. To examine the possibility that the JH-receptor MET directly regulates Vri activation, we searched for sequences similar to the MET-binding site for the Vri promoter/enhancer and found one candidate sequence that is conserved in two Daphnia species  of the yolk protein gene [Dsx1 by a truncated Vri lacking the bZIP domain in this study also suggests that heterodimer formation allowed the mutated Vri to access the target binding site. These suggest that the heterodimeric combination of Dsx and bZIP transcription factors has functioned as a transcriptional regulator before divergence of insects and crustaceans. Even though we provide substantial genetic evidence of Dsx1 activation by Vri in early embryos, because the loss- and gain-of- Vri function led to embryonic lethality, we still cannot conclude that Vri is the sole upstream component acting as a Dsx1 activator that is necessary for male trait development. To understand more about Vri function in environmental sex determination, we will need to clarify localization of Vri in early embryos and perform knockdown/overexpression in cells that express Vri endogenously, which would avoid alternation of non-sex specific functions of Vri in later embryos.Interestingly, in the initiation phase of ein gene . TransacDsx1 promoter/enhancer on the genome in females. In contrast, this mutagenesis introduced deletion at the target site on the genome in males and reduced Dsx1 expression. These results suggest that this enhancer may be silenced via closed chromatin in females but is required for Dsx1 activation in males. We also found that deletion of the enhancer led to embryonic lethality in males although we could not shed light on the mechanism underlying this high mortality. However, these clear differences of phenotypes between males and females in our targeted mutagenesis experiments indicate a male-specific role of this enhancer. Further study is needed to understand the epigenetic regulation at Dsx1 locus.In targeted mutagenesis using Cas9, we could not introduce any mutation into the Vri binding site at the Dsx1 in the environmental sex-determining cascade of the crustacean D. magna. Vri is transiently expressed in early gastrula in response to juvenile hormone and controls male-specific up-regulation of Dsx1 in late gastrula. This is the first finding that Vri is recruited into sex determining pathways. Our finding reveals the remarkably plastic nature of Dsx regulation, which will contribute to understanding of the diversity and evolution of the sex-determining pathways in organisms.In conclusion, we demonstrate co-option of the bZIP transcription factor Vrille upstream of the Daphnia were obtained by exposing female adults (2–3 weeks old) to 1 μg/L of the synthetic juvenile hormone analog, Fenoxycarb  [D. magna. One of the transgenic lines (HG-1) expresses H2B-GFP protein under the control of D. magna Elongation Factor 1 α-1 (EF1α-1) promoter/enhancer [Dsx1 reporter strain, which was generated by introducing mCherry gene upstream of Dsx1 coding sequence in the genome of the HG-1 [All of the wild-type (WT) and transgenic lines share the same genetic background (NIES clone). They were cultured in ADaM medium  as descr, Japan) . We utilenhancer . Anotherthe HG-1 .Vri expression was quantitated and was normalized with the ribosomal protein L32 expression level using the primers listed in Dsx1 and the ribosomal protein L32 gene were the same as described previously [Dsx1 expression level in the Vri knockdown using Vri_siRNA_1 and overexpression, expressions of three other reference genes, ribosomal L8 gene, β-actin gene and Cyclophilin gene [Total RNA was extracted from female and male embryos in triplicates using Sepasol-RNAI solution . The RNA was subjected to cDNA synthesis using random primers  and the SuperScriptIII Reverse Transcriptase (Invitrogen). qPCR was conducted with the SYBR GreenER qPCR Supermix Universal (Invitrogen) using the Mx3005P real time (RT)-PCR system . eviously . For norlin gene  were analin gene .Vri cDNA sequence was amplified from Daphnia by 5′ and 3′ rapid amplification of cDNA ends (RACE) methods as described previously [http://getentry.ddbj.nig.ac.jp/getentry/na/LC230164/?format=flatfile&filetype=html&trace=true&show_suppressed=false&limit=10) (Accession number LC230164).The eviously . The prieviously . The VriVri gene, 100 μM of Vri_siRNA_1 and Vri_siRNA_2  and Poly(A) Tailing kit (Life Technologies Inc.), respectively. The synthesized mRNAs were column purified by RNeasy Mini kit , followed by phenol/chloroform extraction, ethanol precipitation, and dissolution in DNase/RNase-free water.To overexpress the 3 vector . The Vri18) and the first 20 nt of the Cas9 binding scaffold sequence. The full sequence is as follows: (5′- GAAATTAATACGACTCACTATAGGNNNNNNNNNNNNNNNNNNGTTTTAGAGCTAGAAATAGC-3′). The anti-sense oligo contains 80 nt full sequence of the Cas9 binding scaffold: (5′-AAAA GCACCGACTCGGTGCCACTTTTTCAAGTTGATAACGGACTAGCCTTATTTTAACTTGCTATTTCTAGCTCTAAAAC-3′) where the underlined nucleotides denote the complementary sequence between two oligo sequences. The PCR reaction was performed with PrimeSTAR polymerase . After purification by phenol/chloroform extraction, the DNA fragments were used as templates for in vitro transcription with the MEGAscript T7 kit (Life Technologies Inc.), followed by column purification with mini Quick Spin RNA columns , phenol/chloroform extraction, ethanol precipitation, and dissolution in DNase/RNase-free water.For the syntheses of gRNAs, the templates were prepared by the cloning free method . The senDaphnia at 2–3 weeks of age, directly after ovulation and placed in ice-cold M4 media contained 80 mM sucrose. The specific RNAs for each experiment were mixed with either Alexa Fluor 568 dye (Life technologies Inc.) or Lucifer Yellow dye (Life technologies Inc.) with final concentrations of 0.01 μM and 1 μM respectively, as an injection marker. The microinjection was performed on ice and the injected eggs were incubated in a 96-well at 23°C for the appropriate time.Microinjection was performed as described previously . Eggs wein vitro synthesized RNA with Cas9 protein to make gRNA-Cas9 complexes. Cas9 protein was prepared as described previously [D. magna eggs, as described previously [Daphnia were cultured separately until they produced offspring. The offspring were pooled (up to 8–10 daphniids) and genomic DNA extraction and genomic PCR were performed as mentioned above.We mixed eviously . They weeviously . To charS1 FigD. magna, Tc is T. castaneum (beetle), Dp is D. plexippus (monarch butterfly), Dm is D. melanogaster (fruit fly), Dr is D. rerio (zebrafish), Mm is M. musculus (mouse) and Hs is H. sapiens (human).Dmag is (TIFF)Click here for additional data file.S2 FigVri over-expression experiment (Vri mRNA without bZIP domain). The underlined sequences are the target sites for Vri_siRNA_1 and Vri_siRNA_2 accordingly.Black shaded amino acids indicate the putative bZIP domain and grey shaded nucleotide is the sequence that being removed for (TIFF)Click here for additional data file.S3 FigThe percentages of the replicated tree in which the associated taxa clustered together in the bootstrap test  are shown next to the branches. The bar indicates branch length and corresponds to the mean number of the differences (P<0.05) per residue along each branch.(TIFF)Click here for additional data file.S4 Fig.(TIFF)Click here for additional data file.S5 FigScale bar: 200 μm.(TIFF)Click here for additional data file.S6 FigGFP was fused with Minos transposase, which resulted in nuclear localization of GFP as previously reported . Scale b(TIFF)Click here for additional data file.S7 Fig2. This reaction was stopped by adding 0.5 M EDTA to the reaction mixture. Cleavage of the plasmid by Cas9 and gRNA was observed by running the gel electrophoresis. The positive result can be interpreted by detecting the linear band of the plasmid on the gel. The target sequence for each gRNA (gRNA-1 and -2) is indicated in To examine the ability of designed gRNAs and Cas9 protein executing double strand break, 300 ng of plasmids harboring the target sequence were incubated with 1 μM Cas9 protein and 2 μM gRNA at 37°C for 1 hour in reaction buffer that contains 20 mM hepes (pH 7.5) 150 mM KCl, 0.5 mM DTT, 0.1 mM TCEP and 10 mM MgCl(TIFF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 TableDsx1 siRNA was co-injected with Cas9 protein and gRNAs.To evaluate heritable mutagenesis efficiency, we screened founder animals that produced progenies by genotyping. To feminize injected males, 100 μM (DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Transcription is an intricate mechanism and is orchestrated at the promoter region. The cognate motifs in the promoters are observed in only a subset of total genes across different domains of life. Hence, sequence-motif based promoter prediction may not be a holistic approach for whole genomes. Conversely, the DNA structural property, duplex stability is a characteristic of promoters and can be used to delineate them from other genomic sequences. In this study, we have used a DNA duplex stability based algorithm ‘PromPredict’ for promoter prediction in a broad range of eukaryotes, representing various species of yeast, worm, fly, fish, and mammal. Efficiency of the software has been tested in promoter regions of 48 eukaryotic systems. PromPredict achieves recall values, which range from 68 to 92% in various eukaryotes. PromPredict performs well in mammals, although their core promoter regions are GC rich. ‘PromPredict’ has also been tested for its ability to predict promoter regions for various transcript classes (coding and non-coding), TATA-containing and TATA-less promoters as well as on promoter sequences belonging to different gene expression variability categories. The results support the idea that differential DNA duplex stability is a potential predictor of promoter regions in various genomes. In eukaryotes, promoter architecture is quite complex and diverse. Eukaryotic promoters are broadly classified as core promoters, proximal promoters and distal promoters. Core promoter regions are characterized by the presence of cognate sequence motifs such as Initiator (Inr), TATA-box and downstream promoter element (DPE) positioned at distinct locations relative to TSS, as well as non-canonical elements such as “ATG deserts” and “CpG islands” in mammals4. Proximal promoter regions are the sequences spanning region 500 base pairs relative to the TSS and contain certain sequence elements, which include the CAAT box, the GC box and cis-regulatory modules5. Distal promoter elements encompass enhancers, insulators and silencers. Based on the strategies of transcription initiation, core promoters can be broadly classified as focused core promoters or dispersed core promoters7. The promoter region for the genes with dispersed transcription initiation can occur several 1000s of nucleotides upstream of the gene body. Further, from the studies on genome-wide nucleosome density maps of different eukaryotes such as Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster and humans, it has been revealed that well-positioned nucleosomes usually occupy the sequences downstream of TSS, and the core promoter regions are devoid of the nucleosome8. Recent analyses of eukaryotic promoters indicates that though promoters differ in their sequence context (most of them lack consensus motif) and GC composition , some properties such as nucleosome-free region and epigenetic features around transcription start sites are quite common10. The complexity of eukaryotic promoter architecture is further revealed by the discovery of alternative7 and bidirectional promoters12. In addition, recent experimental studies showed that the transcription landscape in eukaryotes is quite pervasive in nature, with a high proportion of transcripts originating from intergenic regions13. With this avalanche of genomic sequence data, it is important to characterize and predict promoter sequences in order to fully understand the process of transcription initiation.Genetic transcription program is initiated in a segment of DNA referred to as ‘promoter’ which serves as a platform for the assembly of pre-initiation complex to specify the transcription start sites (TSSs)14. Experimental methods for locating promoter elements include techniques such as 5′-tag-based methods which characterize promoters and transcription initiation events, small RNA sequencing, as well as methods which capture DNA-bound proteins, including RNAPII, transcription factors and histone modifications9. They provide a snapshot of all transcribed regions or DNA-protein interactions in the genome for specific experimental conditions10. Experimental techniques are costly, labour-intensive and time-consuming. Alternatively, computational methods provide rapid and relatively inexpensive ways for promoter identification. These methods are mostly based on the basic premise that promoter sequences have unique statistical features when compared to other genomic sequences10. These approaches use DNA sequence feature information such as biological signals of core promoter elements , statistical properties of k-mer composition and DNA secondary structural features. Structural features are found to be more informative and universal as compared to sequence or base-compositional features. DNA structural features such as flexibility/bendability, curvature, base stacking and duplex stability have been applied to characterize promoter regions19. They are better predictors, as the structural features are comparatively conserved, more informative and widely applicable across genomes. Recent reports suggest that structural properties are linked to variability of gene expression21 and help in understanding different promoter classes22. Recent findings reveal that, DNA shape features can be useful in understanding, and characterizing transcription factor binding sites, origins of replications and other genomic regions26. The majority of the structure-based algorithms have been designed with the aim of annotating promoter regions specifically in humans, while a few algorithms such as “PromPredict”28 and “EP3”16 are applicable across a variety of genomes. It has also been reported that among all structural features examined, energy-based features such as base stacking or DNA duplex stability are better predictors16.The delineation of promoters is also essential for complete annotation of genomes and a better understanding of genome’s regulatory networks and their architecture14. DNA duplex stability computation has been applied in techniques such as PCR, anti-gene targeting, and for understanding replication, repair, and transcription30. PromPredict algorithm encodes the dinucleotide free energy information obtained from studies of melting temperatures of oligonucleotides to compute the average free energy of a particular sequence, as an indicator of DNA duplex stability. The strategy behind using DNA duplex stability is that promoter regions should be less stable than flanking regions to facilitate melting at the time of transcription initiation. Although PromPredict was developed for bacterial promoter prediction27, it also works very well for plants31, but its efficiency has not been tested on fungal and metazoan promoter sequences. In comparison, EP3, which also uses base-stacking energy, has been applied to annotate promoter regions in several eukaryotes ranging from protists to mammals16. In the present study, we report the validation of ‘PromPredict’ in identification of the promoter sequences of 48 eukaryotic systems.DNA duplex stability, a secondary structural feature can be expressed in terms of short range nearest-neighbour interactions, inter base hydrogen bonds and stacking interactions, which are explicitly dependent on identity and orientation of flanking base pairsin silico study are:(i)Understanding the variation of DNA duplex stability eukaryotes,(ii)Evaluating performance of PromPredict across various eukaryotes,(iii)S. cerevisiae. An analysis of predictions for various gene expression variability classes and TATA-containing and TATA-less promoters in S. cerevisiae.Whole genome promoter Prediction for different classes of transcripts in The key elements of this 17 and plants (Rice and Arabidopsis)28. Current study deals with the promoter prediction analysis in the genome sequences of 48 different eukaryotes with translation start site (TLS) data, along with genome sequences of S. cerevisiae, C. elegans, D. melanogaster, zebrafish, mouse and human whose transcription start site (TSS) data is available. The true positive region for predictions for all systems is considered to be −500 to +100 relative to TLS/TSS in all cases except when comparing PromPredict with EP3. Initially DNA duplex profiles in these systems have been compared qualitatively.The efficiency of in-house software PromPredict, a promoter prediction algorithm that demarcates putative promoter regions based on DNA duplex stability has been studied extensively in archaea, bacteriaS. cerevisiae, C. elegans and D. melanogaster show low stability compared to their downstream regions, with narrow less stable regions being observed at −19, −11 and −114 (split peak at −25) for S. cerevisiae, C. elegans, and D. melanogaster respectively. In the case of vertebrates, zebrafish, mouse and human, core promoter regions are GC-rich but have two narrow low free energy peaks in the vicinity of TSS. Two narrow peaks are observed at −27 and +2 for zebrafish, −29 and +6 for mouse and −30 and +1 for human, which may correspond to the locations of TATA-box and INR (initiator) elements respectively . Similar low stability regions have been observed in different prokaryotic systems and plants32. The DNA duplex stability profiles of yeast and invertebrates are similar to prokaryotic promoters. In the case of a majority of prokaryotes, irrespective of genome GC content, promoter regions are less stable (or AT-rich) compared to downstream region, while in vertebrate promoters they are GC-rich, with several sequence and structure elements such as CpG islands, G-tracts, and G-quadruplex motifs being present in the vicinity of TSS21. GC contents of six eukaryotic systems in different regions of promoters, −500 to +500, −500 to −100, −100 to −1, +1 to +100 and +100 to +500 are given in . PromPredict gives predictions in 5′ to 3′ direction and can be applied to the whole genome as well as shorter fragments (not less than 1000 nt). Promoter prediction has been carried out using two strategies; (i) 1001mer or 2001mer sequences and (ii) on whole genome. The analysis of shorter fragments has been performed for 48 systems with TLS data and six systems with TSS data. While the second approach, viz whole genome annotation, is utilized for S. cerevisiae. In all cases, the true positive region is considered to be 600 nt of length, extending from −500 nt upstream to +100 nt downstream, relative to TSS or TLS. Least stable positions (lsp) are considered to evaluate the predictions. Since this kind of analysis gives only true positives, only recall values are derived to assess the performance of PromPredict.Promoter prediction in 48 eukaryotic systems with TLS data  and six eukaryotic systems, S. mikatae) to 3197 mb (for elephant) while genomic GC varies from 33% (for C. dubliniensis) to 47% (for lamprey). PromPredict has been tested on all these systems considering −500 to +100 region relative to TLS as true positive (TP) region and the recall values are calculated to evaluate the performance. Figure The 48 eukaryotic systems represent species closely related to the six systems with TSS data, along with distantly related systems such as marine invertebrates. They constitute 14 species of yeast, five species of worm, and 12 species of fly, three marine invertebrates, six fish species, seven mammals and chicken (Table C. briggsae (worm) and D. hansenii respectively   to ~42% (D. melanogaster and mouse). Prediction calculations are restricted from −500 to +500 regions for S. cerevisiae, C. elegans, and D. melanogaster while from −1000 to +1000 regions are considered in the case of vertebrates. The positional distribution of least stable position in 50 nt binned windows in six systems shows that the location of prediction varies in yeast, invertebrates, and vertebrates  whereas in mammals, maximum number of predictions is observed beyond −300 region relative to TSS. Percentage recall values for the six systems are 85.1, 87.7, 91.9, 80.4, 67.2 and 71.3 for S. cerevisiae, C. elegans, D. melanogaster, zebrafish, mouse, and human respectively . Hence the True Positive (TP) region for both predictors is considered as spanning −1000 to +1000 region. The EP3 program uses base-stacking property to distinguish promoter regions from non-promoter sequences. For a given sequence of DNA, it calculates inverted base-stacking energy values over non-overlapping window of 400 base pairs size and calls a region as a promoter when the structural feature value crosses a prescribed threshold score, which is genome specific. So for EP3 every 400 nt window has a prediction, but with true or false prediction value assigned to it, while PromPredict gives only predicted promoter regions in a continuously sliding window. PromPredict provides majority of the predictions in −600 to +200 regions with respect to TSS in both mouse and human, whereas EP3 gives predictions in −200 to +200 regions. The distribution of closest TP predictions shown in Figure 16. The results suggest that PromPredict is a better predictor than EP3, even though latter has been trained on the human genome. Since the entire 2001 nt region spanning TSS is considered as ‘TP’ region, no false positives have been identified and hence ‘Precision’ values have not been obtained in this study. PromPredict algorithm was further tested in various classes of transcripts, promoter types and gene expression datasets.The performance of PromPredict has been compared with the other free energy based promoter predictor EP3S. cerevisiae is an important model organism for eukaryotes, its genome being compact with genes representing ~72% of the entire sequence. Promoter prediction has been carried out for all 16 chromosomes in both forward and reverse strands separately. The predictions are mapped to the −500 to +100 region relative to TSS of each gene. If the least stable position (lsp) of the predicted promoter region lies within the 500 upstream regions or 100 downstream of a known TSS, then it is considered as true positive. On the other hand, if a predicted promoter region occurs within the coding regions of the gene, then it is regarded as a false positive prediction. A true positive region may have more than one prediction, in that case, the nearest prediction (or lsp) to the TSS has been considered. PromPredict achieves ~80% recall and ~37% precision in whole genome prediction  as well as non-coding RNAs  has been considered for checking the performance of the algorithm. Precision, recall and F-score values shown in Table 12.on Table , which i33 in the vicinity of TSS. In S. cerevisiae ~17% of the gene promoters belong to TATA- containing class. The two categories of genes vary in their biological functions and gene expression. Further, the TATA-containing promoters are shown to be flexible while TATA-less promoters are rigid in different species of yeast34. In S. cerevisiae, TATA-containing promoters are less stable, more bendable and slightly curved compared to TATA-less promoters22. PromPredict has been tested on the promoters of two classes of genes. Recall and precision values are ~83% and ~43% for TATA-containing and ~77% and ~36% for TATA-less genes respectively  data along with 48 eukaryotic systems with translation start site (TLS) data reveals that differential stability is a good criterion for promoter prediction. Whole genome prediction in S. cerevisiae using PromPredict suggests that it can be applied to yeast genomes, and its performance is observed to be different in distinct classes of genes such as TATA-containing and TATA-less, and variably expressed genes. Also, promoter prediction in 1001 nt long sequences flanking the TLS of 48 eukaryotes suggests that PromPredict is a good predictor of promoter regions in eukaryotes. Overall the recall values indicate that PromPredict can be applied for predicting promoter regions in different domains of life, even though it was initially designed for prokaryotes. PromPredict can be implemented without any specified cut-off to any genome, which makes it a very useful tool for global promoter annotation. The software is available as standalone as well as online version (http://nucleix.mbu.iisc.ac.in/prompredict/prompredict.html). Implementation of various sliding window sizes can be done on user’s request.Caenorhabditis elegans, C. brenneri, C. briggsae, C. remanei, C. japonica, Drosophila melanogaster, D. annanassae, D. erecta, D. grimshawi, D. mojavensis, D. persimilis, D. pseudoobscura, D. sechellia, D. virilis, D. yakuba, Anopheles gambiae, Apis mellifera, lancelet, sea squirt, sea hare), and vertebrates  have been extracted from UCSC table browser37. Datasets for fungi are retrieved from the Saccharomyces Genome Database (SGD) (http://www.yeastgenome.org/) for Saccharomyces species  and from the Candida Genome Database (CGD) (http://www.candidagenome.org/) for Candida and closely related species . Total number of sequences and the whole genome GC percentages are listed in Table 38. TSS information of C. elegans and D. melanogaster was retrieved from modENCODE database (http://www.modencode.org/) and Graveley et al.39 transcriptome profiling studies respectively. In case of S. cerevisiae transcript positions with both ends mapped have been considered12. Transcript information of 4912 open reading frames (ORFs), 501 cryptic unstable transcripts (CUTs), 729 stable unannotated transcripts (SUTs) and 300 other transcripts  is used for whole genome promoter prediction. The TSS positions of each system are mapped to respective genomes and sequences upstream as well as downstream are extracted. Genomic sequence data is downloaded from SGD, Wormbase (https://www.wormbase.org/), Flybase (http://flybase.org/) and UCSC genome browser (https://genome.ucsc.edu/) for S. cerevisiae, C. elegans, D. melanogaster and vertebrates  respectively.Genome sequences of 48 different eukaryotes representing various domains of life are used in current study. Genomic sequences −500 and +500 relative to translation start site (TLS) for different species of invertebrates  and the position ‘0’ corresponds to TSS or TLS. 2001 nucleotide long sequences are also considered for mouse and human promoter with TSS data.33 has been considered to extract TATA-containing and TATA-less gene promoters in S. cerevisiae. TATA-containing promoters are regarded as those sequences, which contain TATA-box within −150 to −1 region relative to TSS22. Gene expression data of S. cerevisiae for seven different gene expression variations namely stochastic noise, responsiveness, stress response, trans variability, mutational variance, inter-strain variance and expression divergence is taken from Choi and Kim 2009 study36. Top and bottom ten percentile expression variability values have been used to define genes with lowest and highest expression variability categories20.Consensus sequence TATA[A/T]A[A/T][A/G]40. Free energy calculations are computed using 15 nucleotide sliding window27.DNA duplex stability is referred to the ability of DNA to open up or melt, depends on its hydrogen bonding and base pair stacking. The dinucleotide step energy values corresponding to the 16 dinucleotide steps are taken from melting studies on 108 oligonucleotides27. The promoter predictions in mouse and human are also achieved using other structure-based algorithm EP316.Promoter prediction analysis in 48 different eukaryotic systems is carried out using in-house algorithm “PromPredict”17. The scoring function D(n) is used to look for differences in free energy of the two neighbouring regions with respect to every nucleotide position ‘n’  of a DNA stretch, which is computed as a sum of the constituent of dinucleotides over two 100 nt (or 50 nt) segments separated by 50 nt window, and the difference of energy values between two segments are evaluated and GC percentage based cut-off values are used to call putative promoter regions17. ‘PromPredict’ outputs predicted promoter region, start and end of the predicted promoter region along with the least stable position (lsp) in the predicted promoter region. In the current study, the ‘lsp’ of a predicted region has been chosen as a measure for defining true and false predictions.The functions E1(n + 50) and E2(n + 50) correspond to the mean free energy for 100 nt segments starting from nucleotide position ‘n’ and ‘n + 150’, respectively  for both forward and reverse strands separately. It should be noted that the default window is best suited for both quantitative  and qualitative (precision) analysis as revealed by the F-scores. Notably, with the bigger sliding window (such as 200), the computational time and ambiguity in assigning promoter location increases.The algorithm is scalable and flexible, allowing the setting of sliding window sizes, such as [50 + 25 + 50], [100 + 50 + 100] or [200 + 50 + 200]. The default [100 + 50 + 100] window size restriction has been implemented based on previous analyses16. EP3 uses two empirically determined parameters: length of the window and deviation of feature value from genomic average. Window size, 400 has been defined as optimal, and threshold-values of base-stacking are predetermined for genome size. The EP3 program is available at the site: http://bioinformatics.psb.ugent.be/webtools/ep3.The EP3 program implements a base-stacking property to demarcate promoter regions from other genomic regions. For a given sequence of DNA, it computes inverted base-stacking energy values over a predefined window size in non-overlapping fashion and calls a DNA fragment as a promoter when the feature value crosses the genome specific threshold scoreTo assess the performance of PromPredict or EP3, evaluating parameters, recall, precision and F-score  have been used  and TLS (48 systems) datasets. To compare PromPredict with EP3, the true positive (TP) region is chosen as  as the latter program gives a prediction in 400 nt non-overlapping windows. False positive (FP) prediction is regarded as the prediction outside the TP region, but for whole genome predictions, only the predictions that lie in the coding regions are chosen, and other predictions are omitted. The closest prediction for a TP gene is considered as a strong signal, which might correspond to the core promoter region28.Several true positive regions have been used for eukaryotes for assessing the performance of promoter predictors such as  nucleotideSupplementary informationSupplementary dataset