To investigate the biologic relevance of cross-platform concordant changes in gene expression in intact human failing/hypertrophied ventricular myocardium undergoing reverse remodeling.Information is lacking on genes and networks involved in remodeled human LVs, and in the associated investigative best practices.A-S) and a 12 patient “Super-Responder” (S-R) subset of A-S).We measured mRNA expression in ventricular septal endomyocardial biopsies from 47 idiopathic dilated cardiomyopathy patients, at baseline and after 3–12 months of β-blocker treatment to effect left ventricular (LV) reverse remodeling as measured by ejection fraction (LVEF). Cross-platform gene expression change concordance was investigated in reverse remodeling Responders (R) and Nonresponders (NR) using 3 platforms  and two cohorts (All 47 subjects (A-S mRNA expression 2 platform concordance (CcpT), but not single platform change, was directly related to reverse remodeling, indicating CcpT has biologic significance. Candidate genes yielded a CcpT (PCR/microarray) of 62% for Responder vs. Nonresponder (R/NR) change from baseline analysis in A-S, and ranged from 38% to 100% in S-R for PCR/microarray/RNA-Seq 2 platform comparisons. Global gene CcpT measured by microarray/RNA-Seq was less than for candidate genes, in S-R R/NR 17.5% vs. 38% (P = 0.036). For S-R global gene expression changes, both cross-cohort concordance (CccT) and CcpT yielded markedly greater values for an R/NR vs. an R-only analysis (by 22 fold for CccT and 7 fold for CcpT). Pathway analysis of concordant global changes for R/NR in S-R revealed signals for downregulation of multiple phosphoinositide canonical pathways, plus expected evidence of a β1-adrenergic receptor gene network including enhanced Ca2+ signaling.For 50 prespecified candidate genes, in Two-platform concordant change in candidate gene expression is associated with LV biologic effects, and global expression concordant changes are best identified in an R/NR design that can yield novel information. Heart failure is a consequence of structural and functional impairments of ventricular myocardium that reduce the ability of the heart to adequately supply blood to the body . The curCurrent drug therapy of HFrEF is based on targeting final common pathway maladaptive mechanisms that provide short term compensation to impaired ventricular chamber contractile function, but have adverse effects long term . Despite11–13). However, the inherent methodological differences in these platforms as well as limitations in starting material means that in sampling human tissue in situ platform agreement may be lower than in model systems [In these studies, for mRNA expression we have used two types of quantitative PCR ,8,9, two systems ,15. Usin systems , as does systems –19. The Beta-blocker Effect On Remodeling and Gene Expression study , the full design of which is described elsewhere [1-AR blockade in common [The data are derived from the n common ) by radin common ,8 for de1-adrenergic receptor signaling or thyroid hormone mediated effects [. method as previously described [RNA extraction was performed on RV distal septum endomyocardial biopsy samples as previously described . All mRN effects . RT-qPCR effects . The mic effects , using aescribed . Microarescribed .All-Subjects” (A-S)); and an A-S derived subcohort of the largest degree of reverse remodeling responders and control nonresponders (“Super-Responder” restricted (S-R) cohort). The 3 LV phenotypes as defined by their LVEF change measured at LOCF are therefore Responders (R) and Nonresponders (NR) in the A-S cohort, and Super-Responders (SR) in the S-R cohort. SRs have their own NR analysis group, which is the same phenotype as A-S NRs.Two patient cohorts were analyzed: the BORG IDC cohort of 47 subjects,  defined by an LOCF increase in LVEF of ≥5 absolute % at 3 months or 8% at 12 months, and 16 Nonresponders (NR) [S-R Cohort. In this cohort “Super Responders”  were defined by an increase in LVEF of ≥10 absolute % , and NRs (n = 6) by age and gender-matched patients with no LVEF increase ≥5% . The mean±SD baseline and LOCF LVEFs in the S-R cohort were respectively 32± 9% and 32±8% in NR, and 24±7% (P = 0.63 vs. A-S R baseline) and 53±6% (P <0.001 vs. A-S R LOCF) in SR. RVEF mean change±SEM in SR was 16.4±8.1%, vs. 7.5±6.7% in NR (P = 0.42).-ddCt for RT-qPCR) from each patient. For RNA-Seq FPKM, which is not an exponentiated value as for RT-qPCR and microarray data, the within R (or SR) and NR groups average fold change was determined by subtracting log2 transformed baseline values from the LOCF values for each patient, and then converting the difference to the base 2 antilog, i.e. 2^((Log2 LOCF FPKM)—(Log2 baseline FPKM)) and averaging for all members of the group. Fold change for R/NR was calculated by dividing the R by the NR fold change.Changes in gene expression measured as mRNA abundance were assessed within R, SR or NR reverse remodeling groups as change from baseline, using paired analyses of baseline vs. LOCF values of dCt for RT-qPCR, fluorescence intensity units for microarray and FPKM for RNA-Seq. Thus, the primary analysis in each phenotypic group was conducted with the untransformed readouts from each platform. The R/NR analysis was conducted by comparing the subtracted (LOCF–baseline) values in Rs to NRs. Fold changes for RT-qPCR and microarray data were determined by averaging the 2^subtracted values (2Baseline to LOCF changes in mRNA abundance within Responder (R) and Nonresponder (NR) groups were assessed by the Wilcoxon signed-rank test. Between group changes  were determined by Wilcoxon rank sum test. LVEF and RVEF were analyzed by t-tests, as the data were not significantly non-normally distributed by the Anderson-Darling test. Potential differences in categorical variables were assessed by Chi-Square or Fisher’s exact test for small cell sizes, with a Benjamini-Hochberg  adjustmeTotal Cross-Platform Concordance (CcpT) in % for mRNA abundance changes  between platforms within the same cohort (A-S or S-R) and response  was calculated from the formula [intersect is the number of genes with concordant changes.Concordant change was defined as the same directional change at a P <0.05 in each compared platform.  formula CcpT=100ccT) in the same platform.When applied to 2 platforms measuring an identical set of genes’ mRNA expression this formula is identical to that of Shi et al , used tohttps://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis). “Superheat” maps were generated by the Barter-Yu graphical tool using R [Genes exhibiting concordant changes were categorized by a subgroup of the authors  based on manual curation of literature and NCBI website information relevant to myocardium. Canonical pathways, biological functions and gene networks were generated through Ingenuity Pathway Analysis (IPA)  (https:/ using R . Column https://bristowlab.github.io/borg.html). Endomyocardial biopsy, RNA extraction and mRNA measurement protocols are available at https://www.protocols.io/view/borg-protocols-2uggetw/abstract.Source data files from the 3 measurement platforms plus clinical data are warehoused in the GitHub repository (Responder (R) group analysis. For the 50 candidate genes, in the A-S cohort we compared mRNA measurements determined by microarray and RT-qPCR S-R cohort all three platforms generated measurements, shown in +/H+ antiporter SLC9A1 (NHE1), which is involved in mediation of β1-adrenergic signaling of hypertrophy [In the ertrophy , exhibitS-R cohort provided an opportunity to assess sensitivity of change detection among the 3 platforms. Of the 50 candidate genes, RT-qPCR, microarray and RNA-Seq yielded respectively 16, 9 and 17 statistically significant changes from baseline , P = 0.15 for change detection among platforms. The CcpT value within specific platforms calculated as agreement with at least 1 other platform trended lower in microarray vs. respective values in RT-qPCR or RNA-Seq .The relatively small, N = 12 S-R cohort. Microarray detected 19,672 individual gene transcripts and 15,364 (78%) of these were also detected by RNA-Seq, forming the index number of measurements for analysis. Microarray and RNA-Seq platform measurements of global gene expression were used to investigate change concordance in the R analysis. In the R analysis microarray detected 573 genes with mRNA changes, or 2.9% of the transcripts measured . RNA-Seq identified 1724 changes , consisting of 631 upregulated and 1093 downregulated genes (P <0.0001). Concordance analysis between microarray and RNA-Seq , while RNA-Seq detected 1456 changes , of which 378 were upregulated and 1078 downregulated (P <0.0001). The R/NR dataset exhibited 297 changes that were concordant between microarray and RNA-Seq (92 upregulated and 205 downregulated (P <0.0001)) (cpT of 17.5% (P <0.0001 vs. A-S R/NR (PCR/microarray) CcpT of 62%). The within S-R CcpT comparison between candidate  and global changes measured by microarray/RNA-Seq yielded a P value of 0.036. S-R cohort, with the category assignments for each gene given in S-R, to which 2 additional candidate genes that were concordantly changed by RT-qPCR and RNA-Seq ) , with a cpT was 6.2 fold higher in R/NR compared to R  for microarray/RNA-Seq in S-R , while microarray identified more changes in R/NR than R . Cred to R . The Ccpq in S-R . For R/NA-S and S-R cohorts using data from RT-qPCR for candidate genes and microarray for global gene expression.In order to derive direct information on whether smaller cohorts of hyper-responders can substitute for larger cohorts comprised of smaller degrees of phenotypic response in longitudinal reverse remodeling gene expression studies, we compared measurements between the R analysis. Genes with p <0.05 changes in expression for RT-qPCR measurements, the protocol primary method for candidate gene mRNA change detection [A-S cohort R analysis, 23 genes exhibited expression changes , while 13 changes were identified in the S-R cohort  . Thus, in the R analysis RT-qPCR identified more down-regulated genes in both the A-S and S-R cohorts. The two cohorts were concordant for 2 upregulated (ADRB1 and PLN) and 8 downregulated  genes . The 2 cohorts were concordant for 1 upregulated (ATP2A2) and 1 downregulated gene (NPPB), for a CccT of 25% , and 2/11 = 18% for R/NR (P = 0.016 vs. CcpT of 62% in A-S). Compared to PCR/microarray the S-R PCR/RNA-Seq CcpT values were higher, 8/22 (36%) (P = 0.03 vs. S-R PCR/microarray) in R and 6/11  in R/NR  and 6/16 = 38% (P = 0.38 vs. PCR/RNA-Seq)  . Thus, cR Analysis. The number of significantly different global genes based on microarray measurements in the S-R cohort is given in A-S cohort in A-S cohort . A total of 3274 downregulated genes were identified in the A-S cohort while 2095 genes were detected in the S-R cohort (p <0.0001). There were only 60 upregulated and 42 downregulated concordant changes, for a CccT of 1.9% . In marked contrast to the R analysis, the R/NR analysis had much higher concordance in both up- (N = 391) and down- (N = 393) regulated genes, with a CccT of 42%   .A-S cohort, which had LVEF changes ranging from -8 to 47.5 absolute %, created the opportunity to examine the relationship between reverse remodeling as a biologic effect and cross-platform concordance of gene expression changes. For changes from baseline, candidate gene microarray and RT-qPCR CcpT values were related to LVEF change tertiles . The tiles 2 and 3 LOCF LVEFs were respectively 40.9% and 53.1%   . Microar< 0.001) . Thus, iCanonical pathways. To investigate the possible canonical signaling pathways involved in the concordance related biological effect on LV remodeling, for microarray/RNA-seq concordant changes in global gene expression in S-R we conducted an Ingenuity Pathway Analysis (IPA) [is (IPA)  on the 21 adrenergic receptor blockade. Downstream of β1-adrenergic signal transduction is calcium signaling and the initiation and regulation of contractile function [2+ handling or signaling , contractile and associated proteins  or growth/hypertrophy regulation (RCAN1).Only 6 pathways had positive z scores (indicating net activation), with the highest, calcium signaling, considered statistically significant with a z-score of 2.00 and an overlapping P value of < 0.01. Cardiac β-adrenergic signaling and protein kinase A signaling also had positive z-scores, in a clinical setting of high dose βfunction . The 8 mThe second most positive z-score in the IPA was for Gα12/13 signaling , which hS-R cohort , being involved in adrenergic receptor signal transduction , being involved in Ca2+ handling or regulation , or being important in sarcomere structure .In the perheat”  graphicaF change  there ar1-adrenergically regulated genes . Finally, the third region on the map beginning with PDK4 and proceeding through a total of 11 genes does not contain any obvious clustering of correlated mRNAs, similar to a 3 gene  transition area between the two other regions. Of this total of 14 genes without correlative changes in expression, 4 (and CSRP3) were concordant, all downregulated. NPPA, NPPB, and ACTA1 are regulated by β1 adrenergic signaling. The other 7 members of this group consist of 3 metabolic genes , and 2 neurohormonal genes ).Beginning with TNF and continuing through SLC9A1 on the We used cross-platform concordance of gene expression changes as a method of increasing the reliability of detecting biologically meaningful molecular changes associated with reverse remodeling in an idiopathic dilated cardiomyopathy study population. Cross-platform concordance has been widely used as a method to validate measurement methodologies –19,22,23cpT values, a standard measurement of expression change concordance, were in the 60% range for RT-qPCR and microarray measurements of candidate genes in the 47 patient A-S cohort, for both R and R/NR analyses. For the smaller (n = 12) S-R cohort that exhibited a greater degree of reverse remodeling, CcpT values were somewhat lower, particularly in the R analysis on 2 platform comparisons that included microarray data. The lower CcpT values in the S-R cohort likely reflect the difference in statistical power between the larger and smaller cohorts, plus possibly somewhat lesser accuracy of transcript detection by microarray compared to RT-qPCR or RNA-Seq.CA-S cohort, concordance of changes for global genes as assessed by microarray and RNA-Seq in the S-R cohort were extremely low in the change from baseline R analysis, with a CcpT of 2.4%. However, the microarray/RNA-Seq CcpT for candidate genes in the S-R cohort was 0%, suggesting that the smaller sample size S-R cohort was ill-suited for the R analysis. The R/NR analysis CcpT for global gene expression was much higher, 17.5%, but lower than the counterpart CcpT microarray/RNA-Seq value for candidate genes . These data emphasize the value of comparing R vs. NR changes , with the implication that an in situ myocardial gene expression study needs to be large enough to generate both reverse remodeling responders and nonresponders. In addition, the data support an expectedly higher concordance for candidate genes, which are enriched for genes likely to change expression in reverse remodeling. The larger global gene CcpT for R/NR vs. R also supports the use of the R/NR analysis in pathway generation.In contrast to candidate genes in the ccT, was measured in data generated by a single platform in a manner analogous to inter-platform concordance. For candidate gene mRNAs measured by RT-qPCR the A-S cohort tended to have  or had  more concordant changes, again indicating that the larger, A-S cohort had a statistical advantage that was not compensated by the greater degree of reverse remodeling in S-R. However, the A-S/S-R CccT of 56% in R and 25% in R/NR , whereas in the R/NR analysis the number of changes was nearly equal in the 2 cohorts, with fewer than half the number of changes in both A-S and S-R. The CccT for the R analysis was extremely low, 1.9%. In contrast, the CccT for R/NR was markedly higher, 42%, and within the range for R or R/NR CccT for candidate genes. The basis for the marked difference in CccT values was an R/NR vs. R increase in concordant genes by 7.7 fold and a decrease in the number of either-cohort changed genes by 2.8 fold, with corresponding implied reductions in both Type 2 and Type 1 error in the R/NR analysis. These data provide support for the validity of the S-R cohort as representative of global gene expression changes in A-S, but only for an R/NR analysis.In A-S cohort an analysis of platform concordance of changes in gene expression by degree of LVEF change indicated that most of the changes from baseline in the 1st LVEF tertile, comprised entirely of Nonresponders, were non-concordant, and therefore likely to be false positives. In contrast, in patients undergoing reverse remodeling (tertiles 2 and 3) concordance was directly related to the degree of LVEF improvement and reached 59% in tile 3, where the increase in LVEF was by 29.6 absolute %. Candidate gene change analysis in the S-R cohort also provided support for the biologic significance of concordance. Two platform CcpT exhibited a strong trend for an increase from NR to R to R/NR, reaching a value in R/NR (58%) that was quite similar to the A-S R/NR value of 62% despite the lower power of the S-R cohort. A straightforward interpretation of these data is that when comparing organ phenotypes, platform concordance for gene expression measurements is directly related to the degree of phenotypic change; that is, concordant changes have biologic meaning.In the S-R created the opportunity to perform a network analysis, for purposes of hypothesis generation. In this dataset downregulation exceeded upregulation, and the biologic categories of gene changes were unsurprisingly led by the growth/hypertrophy, cell homeostasis, fibroblast/extracellular matrix and extracellular to intracellular signaling categories. These changes are all consistent with reverse remodeling with inactivation of genes involved in myocyte and interstitial cell growth.The demonstration of an acceptable degree of platform and cohort concordance for the R/NR analysis in 1-adrenergic receptors, and both agents have been shown to reverse adrenergically-mediated desensitization changes in failing hearts [S-R (A-S (S-R (S-R (A-S R/NR (2+ ATPase) downregulation of CASQ1 and CASQ2, the respective minor and major abundance calsequestrins expressed in the heart, and the calcium regulated contractile proteins MYL3, TNNT1 and TPM3. However, expression changes of other modulators of Ca2+ signaling in the heart were not included in the Ingenuity cluster, such as upregulation of the phosphatase 1 inhibitor PPP1R1A that would be expected to promote SR Ca2+ uptake and release by inhibiting phospholamban dephosphorylation. The activation or improvement in calcium signaling as the LV reverse remodels is supported by work in model systems [Despite all study subjects receiving high doses of β-blocking agents, cardiac β-adrenergic signaling and protein kinase A signaling had positive z-scores. All study subjects were receiving target/guideline recommended doses of metoprolol succinate or carvedilol that produce high levels of blockade of βg hearts ,36. Althrts [S-R , it was S-R  , despiteA-S cohort two potential patterns were identified that may be candidates for extensive gene regulation networks. One group (Group 1) had in common β1-AR gene signaling, for which there is considerable previous evidence [In a correlation analysis between changes in candidate and global gene expression in the evidence ,57. Grouevidence ,30. BothA-S cohort the majority of the non-concordance was presumably technical/methodological related, most likely related platform sensitivity and specificity of transcript identification and quantitation. However, endomyocardial biopsy sampling and RNA extraction variability would also fall within this category. For biologic sources of non-concordance, within the A-S R and NR groups  variance in degrees of remodeling and the relationship between remodeling and change in individual patient gene expression are also potential sources. For the much smaller sample size S-R cohort, both types of causes would be accentuated, and for the cross-cohort comparisons biologic causes would be a relatively greater contributor to nonconcordance since the responder phenotypes were quantitatively different.We evaluated gene expression change concordance as a means to improve the likelihood of biologic relevance of differential gene expression associated with reverse remodeling of failing, eccentrically hypertrophied human LVs. In terms of failure to achieve change concordance, both technical/methodological and biological causes are possible. For cross-platform concordance in the S-R cohort, they performed similarly in this study. Compared to RT-qPCR and RNA-Seq microarray may have been at some disadvantage for low abundance mRNAs, but nevertheless was able to detect changes in some of them such as β-adrenergic receptors. TaqMan or Real Time qPCR has long been considered the “gold standard” for RNA quantitation but is impractical for measurement of global gene expression. Microarrays revolutionized the measurement of genome-wide transcriptomics, and held sway as the dominant methodology for over a decade [All 3 methodologies used for cross-platform measurements have been extensively validated against each other –13,22,23a decade . Due to a decade .2/2 or 0.00125. Therefore, some of the value of cross-platform concordance can be attributed to statistical stringency. However, the utility of a 2nd, confirming measurement in real time by different methodology likely confers value for concordance beyond setting a lower P value to minimize false discovery rate (FDR). In gene expression experiments that include large numbers of measurements problems with controlling FDR without having an adverse effect on sensitivity are well known [As the methods for quantifying mRNA abundance are different for the 3 technologies, the statistical probability of achieving a P <0.05 with the same directional change across 2 platforms is 0.05ll known ,61, and S-R subcohort was a subset of the larger A-S cohort, and therefore was not independent of the parent population. Finally, since RNA-Seq has become the favored method of measuring transcript abundance investigators may be reluctant to add another platform to their study design, and it is not clear if performing two separate RNA preparation and sequencing runs could substitute for mRNA abundance determination in a completely separate platform. Nevertheless, a cross-platform concordance design performed contemporaneously as opposed to for post hoc confirmation should be considered for in situ human gene expression studies, where starting material is limited and repeating experiments is untenable.The data presented and the conclusions reached apply only to a SAMGE-PC design and may not be applicable to cross-sectional studies, as the general advantages of longitudinal, repeated measures designs  certainlA-S cohort over the smaller, more improved phenotype S-R subcohort, and global gene expression measurements were only available in S-R. Nevertheless, a 42% CccT between A-S and S-R for global R/NR changes indicated that for this analysis the S-R subcohort was representative of the A-S cohort. Finally, both IPA and biologic category analyses support an improvement in calcium signaling as the major gene expression correlate of reverse remodeling, consistent with the improvement in systolic function that is associated with improved LVEF as well as previous work in model systems.For measurement of myocardial gene expression in sequential studies that include a baseline control value, among RT-qPCR, microarray and RNA-Seq methods there was no convincing advantage of one platform over another. However, for measurements of both candidate and global gene expression the use of 2 platforms had an advantage over a single platform, allowing for the use of cross-platform concordance as a more robust measure of biologically-related changes in gene expression. The basis for the 2-platform concordance advantage is likely related to the derived alpha calculation of 0.00125, but also to the requirement that independent measurement technologies yield conventional significance levels. Measurement concordance is more valuable for global gene expression, where the false discovery rate is higher than for candidate genes preselected for potential involvement in the remodeling process, but concordance also adds value in a candidate/hypothesis driven setting. Multiple lines of evidence favored the use of the larger but less reverse remodeled S-R identified the likely involvement of unexpected canonical pathways in the reverse remodeling process, as well as potentially large networks involving β-AR and possibly GNAI2 regulation.IPA and Superheat map investigation of concordant gene expression changes in the R/NR global gene expression analysis in The data support the advantage of sequential measurements of gene expression before and after organ phenotype modification, particularly if phenotype responders can be compared to nonresponders in an R/NR design.Measuring human ventricular myocardial gene expression in situ by serial endomyocardial biopsy remains a research tool only, but has the potential, particularly by employing multi-platform measurements in larger cohorts undergoing reverse remodeling, to identify novel mechanisms important in myocardial failure and hypertrophy.S1 Text(DOCX)Click here for additional data file.S2 Text(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(P <0.05 in each cohort).(DOCX)Click here for additional data file.S6 TableP <0.05 by Wilcoxon signed rank for change from baseline.(DOCX)Click here for additional data file.S7 TableS-R cohort biologically classified in N = 299 concordant gene mRNA changes in the (DOCX)Click here for additional data file.


This study suggests that the phosphorylation status of Y78 regulates IAV replication by inhibiting the nuclear export and ubiquitination of NP. Overall, these findings shed new light on the biological roles of NP phosphorylation, especially its negative role in NP ubiquitination.Phosphorylation and dephosphorylation of nucleoprotein (NP) play significant roles in the life cycle of influenza A virus (IAV), and the biological functions of each phosphorylation site on NP are not exactly the same in controlling viral replication. Here, we identified tyrosine 78 residue (Y78) of NP as a novel phosphorylation site by mass spectrometry. Y78 is highly conserved, and the constant NP phosphorylation mimicked by Y78E delayed NP nuclear export through reducing the binding of NP to the cellular export receptor CRM1, and impaired virus growth. Furthermore, the tyrosine kinase inhibitors Dasatinib and AG490 reduced Y78 phosphorylation and accelerated NP nuclear export, suggesting that the Janus and Src kinases-catalyzed Y78 phosphorylation regulated NP nuclear export during viral replication. More importantly, we found that the NP phosphorylation could suppress NP ubiquitination  Influenza A virus (IAV) is an enveloped virus belonging to the Orthomyxoviridae family . Its genPhosphorylation is an important posttranslational modification of IAV proteins. It has been reported that multiple sites on NP of IAV can be phosphorylated, and these phosphorylated NPs are involved in a number of activities in IAV life cycle. The phosphorylation sites on NP are predominantly serine (S), threonine (T), and tyrosine (Y). Hutchinson et al. identified 12 phosphorylated sites on NP  . Among tApart from phosphorylation, ubiquitination is another key posttranslational modification of NP to regulate IAV replication. K184 ubiquitination of NP was reported to increase virus RNA replication and cellular deubiquitinating enzyme USP11 decreased polymerase activity and viral replication . UbiquitIn our study, Y78 of NP was identified as a phosphorylated site. We demonstrated that the phosphorylation and dephosphorylation of Y78 regulated the virus genome replication, transcription, and NP nuclear export. Interestingly, we also found that there is a cross-talk between the phosphorylation and ubiquitination of NP.2. The influenza virus A/WSN/1933 (H1N1) strain was rescued using a 12-plasmid reverse genetic system  and propagated in 9-day-old embryonated chicken eggs .The human embryonic kidney (293 T) cells, human lung alveolar epithelial (A549) cells, and Madin-Darby canine kidney (MDCK) cells  were maintained in Dulbecco’s modified Eagle’s medium  with 10% fetal bovine serum  at 37°C and 5% COPhos-tag acrylamide was purchased from Wako. Dasatinib was purchased from MCE. Imatinib and AG490 were purchased from CST. An RNase inhibitor was purchased from Promega. Protease inhibitor cocktail and phosphatase inhibitor phosSTOP were purchased from Roche. Anti-FLAG (M2) Affinity Gel was purchased from Sigma-Aldrich. Pierce™ Protein G Agarose (20397) was purchased from Thermo Scientific. Rabbit polyclonal antibody against NP and mouse monoclonal antibody against M1 were generated as previously described . Anti-HSThe full-length NP sequence from the A/WSN/33 virus was cloned into the pcDNA4.0/TO, pCMV-MYC, and pCDNA3.0-FLAG vectors. FLAG or MYC-tagged NP mutants, including Y78F and Y78E were generated using a site-directed mutagenesis kit . The following primers were used: NP-Y78F-up, 5′AGGAGGAATAAATTTCTAGAAGAACATCCCAGTGCGGGGA-3′; NP-Y78F-dn, 5′-ATGTTCTTCTAGAAATTTATTCCTCCTCTCGTCAAAAGCA-3′; NP-Y78E-up, 5′-AGGAGGAATAAAGAGCTAGAAGAACATCCCAGTGCGGGGA-3′; and NP-Y78E-dn, 5′-ATGTTCTTCTAGCTCTTTATTCCTCCTCTCGTCAAAAGCA-3′. The full-length TRIM22 and CRM1 were synthesized according to NCBI Reference Sequence NM_006074.5 and NM_003400.4, respectively, and then cloned into the pcDNA3.0-Flag and/or pCMV-MYC vectors. The single ubiquitin molecule was synthesized according to NCBI Reference Sequence NM_021009.6, and cloned into the pCMV-HA vector. The expression plasmids for the PA, PB1, and PB2 genes from A/WSN/33 virus were generated by cloning into the pcDNA3-FLAG vector as described previously . pHH21-c2+-Phos-tag SDS-PAGE as described previously (2 (Sigma) added to the separating gel before polymerization. The gel was silver stained, and the separated bands were subjected to nano-liquid chromatography-tandem mass spectrometry (nano-LC-MS/MS) identification .IAV-infected 293 T cells were lysed in lysis buffer  supplemented with complete protease inhibitor cocktail and a phosphatase inhibitor phosSTOP. The NP was purified with protein G agarose beads pre-bound to a rabbit anti-NP polyclonal antibody for 3 h at 4°C. Proteins were separated by 12% Mneviously . Briefly2, and the supernatant containing the recombinant viruses was harvested and then centrifuged at 2,000 g for 10 min to remove the cell debris.The wild-type (WT) A/WSN/1933 (H1N1) virus and its NP mutants were generated by using a 12-plasmid-based reverse genetic system as described previously . Briefly2. The virus inoculums were removed by washing with PBS. Cell monolayers were incubated in DMEM with 1% FBS and 1 μg/ml TPCK-treated trypsin at 37°C. After infection, 500 μl of supernatant was collected at 24, 36, 48, 60, and 72 h and stored at −80°C. At the same time, 500 μl of fresh medium was added back to each dish. Finally, virus titers were determined by plaque assays as previously described (MDCK cells grown to monolayers in 10-cm cell culture dishes were washed with PBS and infected with virus (MOI = 0.001) for 1 h at 37°C in 5% COescribed .The plasmids for expression of the PA, PB1, PB2, NP (WT or mutant) proteins, and luciferase reporter plasmids  were co-transfected into 293 T cells as previously described . Cells tTotal RNA was extracted from 293 T cells by use of TRIzol (Invitrogen) according to the manufacturer’s instructions. Samples were digested with DNase I and subjected to reverse transcription-PCR (RT-PCR). RNA was reverse transcribed using the following PCR primers: mRNA primer, oligo(dT); cRNA primer, 5′-AGTAGAAACAAGG-3′; and vRNA primer, 5′-AGCGAAAGCAGG-3′. A mock reaction was performed with no reverse transcriptase added to the reaction mixture. The analysis of relative M1 gene expression was performed using the following PCR primers: M1-up (5′-TCTGATCCTCTCGTCATTGCAGCAA-3′) and M1-dn (5′-AATGACCATCGTCAACATCCACAGC-3′). GAPDH served as an internal control, using the PCR primers GAPDH-up (5′-GGTGGTCTCCTCTGACTTCAACA-3′) and GAPDH-dn (5′-GTTGCTGTAGCCAAATTCGTTGT-3′). The cycling conditions comprised an initial denaturation step of 30 s at 95°C, followed by 40 two-step cycles (95°C for 5 s and 60°C for 31 s). Dissociation curve analysis was performed after each assay to ensure specific target detection.g for 15 min. Lysates were incubated with anti-FLAG M2 affinity gel for 2 h. Following four washes in wash buffer , the precipitated proteins were separated by SDS-PAGE and then transferred to immobilon polyvinylidene difluoride (PVDF) membranes . The membranes were blocked for 2 h at room temperature in blocking solution , and proteins were detected using appropriate antibodies, followed by the addition of anti-rabbit or anti-mouse secondary antibody coupled to horseradish peroxidase. Proteins were visualized by use of chemiluminescence detection reagents.293 T cells were lysed in lysis buffer , supplemented with complete protease inhibitor cocktail and phosphatase inhibitor phosSTOP. After an incubation period of 30 min at 4°C, insoluble components were removed by centrifugation at 12,000 Indirect immunofluorescence assays (IFAs) were performed with a Leica SP8 confocal laser scanning microscope. Coverslips carrying the cells were washed with PBST (PBS plus 1% Triton X-100) and fixed with 4% paraformaldehyde. Cells were then blocked with 4% bovine serum albumin (BSA) dissolved in PBST and stained with anti-NP and anti-FLAG antibodies. Secondary antibodies were tetramethyl rhodamine isocyanate (TRITC)-conjugated anti-rabbit IgG and fluorescein isothiocyanate (FITC)-conjugated anti-mouse IgG. Finally, the location of cell nucleus was labeled with 4′,6-diamidino-2-phenylindole (DAPI). The wavelengths of TRITC, FITC, and DAPI are 561 nm, 488 nm, and 405 nm.2PO4, 10 mM HEPES, 5 mM NaHCO3, 1 mM CaCl2, 0.5 mM MgCl2, 5 mM EDTA, and Complete protease inhibitor cocktail). The cells were allowed to swell for 5 min on ice and then dounce-homogenized 50 times. After centrifugation at 7,500 rpm for 2 min at 4°C, the pellets represented nuclei plus debris, and the supernatants were cytosol plus plasma membrane. The nucleus-debris pellet was then resuspended in 1 ml of TSE buffer  and dounce-homogenized 30 times, followed by centrifugation at 5,000 rpm for 2 min. The pellet was resuspended and washed twice to obtain the final nuclear pellet. The final pellet was resuspended in 30 μl of TSE buffer.293 T cells in 6-cm dishes were harvested into 300 μl of CLB buffer  plasmids or MYC-TRIM22 plasmid. The cells were harvested at 30 h after transfection. Western blotting was probed with anti-NP, anti-c-MYC, and anti-β-actin antibodies.293 T cells were transfected with expression constructs for FLAG-tagged NPs and HA-tagged ubiquitin or MYC-TRIM22. Thirty-six hours post-transfection, cell extracts were then immunoprecipitated with FLAG-M2 beads. The eluted proteins were analyzed by western blotting using an anti-HA, anti-FLAG, and c-MYC antibodies.Sequence alignments were performed using MegAlign software . Statistical analyses were performed using Prism6 software .pY). We found that the phosphorylation level of NP Y78F mutant was greatly decreased compared with that of WT NP  (WSN) and NP was purified by protein G agarose beads pre-bound to rabbit anti-NP polyclonal antibody. Phos-tag SDS-PAGE was used to purify the phosphorylated NP . After sof WT NP , demonstThe schematic diagram of NP functional domains demonstrated that Y78 belongs to the basic loop region (residues 72-92) . At the To investigate the role of NP Y78 phosphorylation in influenza virus replication and packaging, we attempted to rescue the WT and mutant recombinant WSN viruses. We introduced glutamate (E) substitution at Y78 to mimic the constitutively phosphorylated residue and F at Y78 to mimic the constitutively dephosphorylated residue. The 12-plasmid reverse genetic system containing WT or mutant NP plasmid was transfected into 293 T cells to rescue the WT or NP mutant virus, and the supernatants were harvested at 72 h post-transfection (h.p.t.) for plaque assays. In three independent viral packaging experiments, both the WT and Y78F WSN viruses were successfully rescued, and the titer of Y78F mutant virus was greatly reduced compared with that of WT virus . HoweverNP is required for viral RNA synthesis , and binIn addition, NP has long been recognized as the second most abundant protein of influenza virus and an important component of the vRNP complexes. It plays a major role in the overall structural organization and stabilization of the complexes, then affects the polymerase activity of vRNP . TherefoThe dynamic cellular localization of NP is critical for efficient viral replication and transcription , and NP Previous studies have suggested that a classical cytoplasmic receptor protein CRM1 can interact with NP and transport NP into the cytoplasm . To deteThe nuclear export of vRNP complex is mediated by the CRM1-NEP-M1-vRNP complex, during which the nuclear transport capacity of the NEP and M1 plays an important role . MoreoveWe further investigated which kinds of tyrosine kinases play the dominant role in Y78 phosphorylation and their effect on NP sub-cellular localization. The tyrosine kinase inhibitors, including Imatinib (Bcr-Abl inhibitor), Dasatinib (Src inhibitor), and AG490 (JAK inhibitor), were used to treat WSN or Y78F mutant virus-infected cells. We detected the effect of these three tyrosine kinase inhibitors on the tyrosine phosphorylation level of WT and Y78F NP in A549 cells. We found that all of these three tyrosine kinase inhibitors decreased the tyrosine phosphorylation of WT NP , left. MSeveral lines of evidence indicate that the phosphorylation-mediated subcellular localization of substrates regulates the ubiquitination . Thus, wIt has been established that the E3 ligase TRIM22 mediates ubiquitin-proteasome degradation of NP , and CNOE3 ligases usually interact with substrates, so we wondered whether Y78 phosphorylation affected NP ubiquitination by regulating the binding of NP to TRIM22. The interaction between NP  and TRIM22 was determined by CO-IP assays. 293 T cells were transfected with MYC-NPs and FLAG-TRIM22. We found that the Y78E mutant reduced the binding of NP to TRIM22, while the Y78F mutant enhanced NP-TRIM22 interaction . The aboAs two types of the posttranslational modifications in eukaryotic biology, phosphorylation and ubiquitination can take a cross-talk . PhosphoPhosphorylation and dephosphorylation is a reversible dynamic equilibrium process. IAV NP underwent phosphorylation and dephosphorylation at various stages during viral multiplication, and the exposed NP phosphorylation sites might also be different during various infection periods . In the Phosphorylation levels of proteins are regulated by kinase-catalyzed phosphorylation and phosphatase-catalyzed dephosphorylation. There are quite a few studies related to the regulation of influenza virus replication by kinases. Activated human protein kinase C (PKC) family member PKCδ interacts with the polymerase subunit PB2 and regulates NP oligomerization and RNP assembly in the course of IAV infection . Serine/To be the major structural component of vRNP, NP performs a vital function in the replication of influenza virus by nuclear-cytoplasmic shuttling . NP on vvia a CRM1-dependent pathway.Two NLSs on NP control the nuclear import of vRNP , and thrIn conclusion, we identified the tyrosine phosphorylation site Y78 on NP and discovered that Y78 phosphorylation regulates the nuclear export and ubiquitination of NP, which is vital for the replication of IAV. Our data further expand the biological functions of NP phosphorylation and provide a potential target for the development of antiviral agents.The raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reservation, to any qualified researcher.All embryonated chicken eggs experiments were approved by the Research Ethics Committee of Chinese Academy of Sciences and complied with the Beijing Laboratory Animal Welfare and Ethical Guidelines of the Beijing Administration Committee of Laboratory Animals.WL conceived the study. LS and LC designed the experiments, analyzed the data, and wrote the manuscript. LC performed the experiments. WZ provided experimental technical guidance and results analysis. ML, XB, and WY helped with some experiments. JL, WF, and GG helped analyze the data and revise the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Plasmodium actins form very short filaments and have a noncanonical link between ATP hydrolysis and polymerization. Long filaments are detrimental to the parasites, but the structural factors constraining Plasmodium microfilament lengths have remained unknown. Using high-resolution crystallography, we show that magnesium binding causes a slight flattening of the Plasmodium actin I monomer, and subsequent phosphate release results in a more twisted conformation. Thus, the Mg-bound monomer is closer in conformation to filamentous (F) actin than the Ca form, and this likely facilitates polymerization. A coordinated potassium ion resides in the active site during hydrolysis and leaves together with the phosphate, a process governed by the position of the Arg178/Asp180-containing A loop. Asp180 interacts with either Lys270 or His74, depending on the protonation state of the histidine, while Arg178 links the inner and outer domains (ID and OD) of the actin protomer. Hence, the A loop acts as a switch between stable and unstable filament conformations, the latter leading to fragmentation. Our data provide a comprehensive model for polymerization, ATP hydrolysis and phosphate release, and fragmentation of parasite microfilaments. Similar mechanisms may well exist in canonical actins, although fragmentation is much less favorable due to several subtle sequence differences as well as the methylation of His73, which is absent on the corresponding His74 in Plasmodium actin I. A detailed mechanistic study of malaria parasite actins reveals at the atomic level how they polymerize, hydrolyze ATP, and are fragmented to keep actin filament lengths short enough for parasite survival. To reduce Pi, ATP, and ADP background, monomeric actins used for Pi release assays were pretreated with DOWEX 1X8 resin equilibrated with G0 buffer  for 3 min at 298 K and further diluted using G0 buffer to 1.6-fold higher concentration than that used for measurements. Before initiating the kinetic measurements, components for the different conditions were supplied as 8-fold concentrated stocks such that the desired final concentrations of all components were reached. Final compositions of the 3 conditions were 10 mM HEPES (pH 7.5), 0.2 mM CaCl2, 0.5 mM TCEP, 50 μM ATP (Ca condition), Ca condition with added 1 mM MgCl2 (Mg condition), and Ca condition with added 4 mM MgCl2, 1 mM EGTA, and 50 mM KCl (MgK condition). Fluorescence was recorded using a Tecan Infinite M1000 plate reader  and black 384-well plates  at λex = 430 nm and λem = 465 nm using bandpass filters with 5-nm bandwidth and a 10-s measurement interval. Fluorescence versus time data were converted to μM Pi by linear interpolation of a standard series of Pi and analyzed by linear regression at the linear portion of the kinetic curve. The slope of the regression line was then divided by the protein concentration measured after DOWEX treatment to yield the final rates. For the Pi release curves, the abscissa units are values of μM phosphate released divided by μM actin, which describes the average turnover of the hydrolytic cycle in each sample.Piosensor ,55 that i release curve, which, at high concentrations, plateaus close to the upper limit of the linear range of the system iodoacetamide  as described by Kumpula and colleagues [Plasmodium actin and α-actin concentrations of 2.7 μM and 0.5 μM, respectively. Measurements were carried out in a Tecan Spark 20M multimode microplate reader using black 96-well plates (Greiner), λex = 365 nm (9 nm bandpass) and λem = 407 nm (20 nm bandpass), 5 flashes per measurement and a 2-s orbital mixing step performed at 250 rpm before commencing the measurements. The resulting polymerization curves were normalized and plotted in GraphPad Prism 8.0.0.lleagues . Labeledlleagues ,57. BriePfActI wild-type and mutant samples were polymerized for 16 h at 298 K at a concentration of 20 μM in F buffer. Prior to application on carbon-coated 200-mesh Cu grids , samples were diluted to a final concentration of 1 μM and immediately applied on the grids. Samples were incubated for 60 s on the grids, dried from the side using prewetted Whatman paper, and washed with 3 drops of F buffer. Then, they were stained with 2% uranyl acetate, first for 2 s and then for 60 s, in a fresh drop before drying from the side as before and then drying in air. The grids were imaged using a JEOL JEM-1230 microscope  operated at 80 kV and with a final pixel size of 1.22 nm. The images were analyzed using the ridge detection plugin available in ImageJ 2.0.0 [eJ 2.0.0 . The calPfActI-G1 and PbActII-G1 complexes in the Mg state were prepared essentially as described by Panneerselvam and colleagues [2 was replaced by 1 mM MgCl2. In some cases, crystals were grown by streak seeding as described by Panneerselvam and colleagues [2, 0.5 mM ADP, and 0.5 mM TCEP for the Mg conditions and 0.2 mM CaCl2, 0.5 mM ATP, and 0.5 mM TCEP for the Ca conditions. The pH of the crystallization reservoir buffer (0.1 M Bis-Tris) varied from 5.8 to 6.5. Mg-ADP-PfActI-G1 crystals were cryoprotected in a solution containing 50 mM potassium phosphate. Mg-ADP-AlFn-PfActI(F54Y)-G1 crystals were prepared by adding a solution of 20% PEG3350, 0.1 M Bis-Tris (pH 6.0), 0.2 M KSCN, and 1 mM AlFn solution directly into the drops and incubated for a few minutes before cryoprotection with a solution as described above. The AlFn solution consisted of premixed AlCl3 and NaF in a 1:4 molar ratio. The minimum time between data collection from a crystal yielding structures with ATP/ADP mixtures and ADP only was 6 mo for Mg-PfActI-F54Y crystals, whereas the time from crystallization to data collection from Mg-PbActII was only 2 wk.lleagues , with thlleagues , and in PfActI, Mg-ADP-Pi-PfActI, Mg-ADP-F54Y, and Mg-PbActII were collected at beamline P13 of PETRA III, DESY ; Ca-F54Y and Mg-AlFn-F54Y were collected at I24 of Diamond Light Source ; Mg-F54Y, Mg-H74Q, and Mg-A272W were collected at I04-1 of Diamond Light Source , Ca-G115A was collected at ID23-1 of ESRF ; and Mg-G115A was collected at MX-14.1 of BESSY . Diffraction images were processed using the XDS package [PfActI-G1 structure (PDB ID 4CBU) as the search model for the PfActI structures and the Ca-ATP-PbActI-G1 structure (PDB ID 4CBX) for the PbActII structure. Additionally, MR-SAD using Autosol [n-F54Y structure. Structure refinement was carried out using phenix.refine [Crystallization data were collected at 100 K at several beamlines. Mg-ATP/ADP- package . Structu package . Initial package , using t Autosol  was usedx.refine .There are 2 main structural rearrangements recognized in actin: the twistedness of the 2 main domains (SD1–2 and SD3–4) along an axis that pierces SD1 and SD3 at their respective centers and the openness of the nucleotide-binding cleft as a rotation around an axis perpendicular to the twist axis and to the plane of the F-actin monomer. We analyzed 147 unique actin structures found in the PDB using BIO3D  at resolPlasmodium actin structures as their own set by similar PCA analysis. PC1 and PC2 contain 84% of total variance in this data set, and their trajectories are toned-down versions of the twistedness and openness of the full data set (PfActI model), the opening-closing motion is slightly ambiguous (due to the lack of an open PfActI model) and is therefore indicated with an asterisk.In a plot of PC1 versus PC2 , most acdata set . Althoug2–4), the phosphate clamp distance  wild-type PfActI structures in the Ca-ATP, Mg-ATP/ADP, Mg-ADP, and F-ADP states; (ii) PfActI F54Y structures in Ca-ATP, Mg-ADP-AlF3, and Mg-ADP; (iii) PbActII structures in Ca-ATP and Mg-ADP states; and (iv) D. discoideum actin structures of mutant E205A/R206A/E207A/P109I in Ca-ATP and Mg-ATP and mutant E205A/R206A/E207A/P109A in Ca-ATP and Mg-ADP. The results are presented in D. discoideum structures, all residue assignments are −1 relative to the numbers presented above. For PbActII, residue assignments for residue numbers smaller than 232 were −1 relative to PfActI and others as for PfActI.To support the PCA analysis, we measured 3 parameters of 4 sets of structures: the domain distance between SD2 and SD4 (dance (b2 ), and thPfActI model in conformation 2a  server  that use2a IV in , the oriS1 Tablei, inorganic phosphate.P(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 TableSD, subdomain.(DOCX)Click here for additional data file.S1 Figi release curves of (A) PfActI at 3.5 μM, (B) PbActII at 3.8 μM, and (C) α-actin at 5.9 μM in Ca-ATP (dashed black line), Mg-ATP (black line) and MgK (red line) conditions. The underlying data for this figure can be found in PbActII, Plasmodium berghei actin II; PfActI, P. falciparum actin I; Pi, inorganic phosphate.P(TIF)Click here for additional data file.S2 FigPfActI and the presence of mHis73 in PbActII evidenced by electron density maps and mass spectrometry. Difference density maps (mFo-dFc) at 4 σ of PfActI active site refined with (A) ADP only, (B) ATP only, and (C) K replaced by full-occupancy water. (D) Electron density (2mFo-dFc) and difference density (mFo-dFc) maps around His73 at 1 σ and 4 σ, respectively, with a model refined as methylated and unmethylated His73. (E) Mass spectrum of full-length PbActII expressed in Sf21 insect cells. Spectrum measured by ESI-LCMS from 12 μM PbActII with 1.45% (v/v) TFA. The 42,822-Da peak corresponds to methylated PbActII, whereas the theoretical average molecular weight for unmethylated PbActII is 42,809 Da (Δm = 13 Da). The peak at 42,805 Da is unknown but could result from, e.g., intramolecular disulfides (2 × −2 Da). The underlying data for this figure can be found in PbActII, Plasmodium berghei actin II; PfActI, P. falciparum actin I; Sf21, Spodoptera frugiperda cell line; TFA, trifluoroacetic acid.Presence of ATP/ADP mixture and K in the active site of (TIF)Click here for additional data file.S3 Figi release by PfActI. The Pi release rate was measured in Mg-ATP conditions at a range of pH values. The rates were normalized relative to the sample at pH 7.34, which corresponds to the standard assay conditions. (B) Delayed phosphate release in mutants K270M and A272W of PfActI. Pi release curves of PfActI wild-type (3.5 μM), K270M (6.4 μM), and A272W (4.3 μM) mutants in Mg-ATP (black lines) and MgK (red lines) conditions. The inset shows the complete curves with the same units as the main plot. The curves have been translated in Y to improve clarity. The underlying data for this figure can be found in PfActI, Plasmodium falciparum actin I; Pi, inorganic phosphate.(A) The effect of pH on P(TIF)Click here for additional data file.S4 FigPlasmodium actin structures (excluding PbActII Mg-ADP) as well as 4 mutant D. discoideum actin structures [Plasmodium actin structures only, with similar notation as in (A). (D) Zoomed view of (C) containing all relevant PfActI structures excluding the H74Q mutant and the PbActI–α-actin chimera [PfActI and PbActII structures indicate the path in the hydrolytic direction (ATP–ATP/ADP–ADP) as appropriate for each set of structures. F structures are underlined, all other structures are of the G form. The underlying data for this figure can be found in PbActII, Plasmodium berghei actin II; PC, principal component; PCA, principal component analysis; PDB, Protein Data Bank; PfActI, P. falciparum actin I; Pfn, profilin.(A) Plot of twistedness (PC1) versus openness (PC2) of the full data set of 147 actin structures see also . Definedructures  constitu chimera . The lin(TIF)Click here for additional data file.S5 FigPfActI during ATP hydrolysis. The size of the ellipsoids is proportional to residue-level B factors and the shape to the direction of anisotropy. Presence of Mg induces an opening of the interdomain cleft and a directional destabilization of SD2 along the rotation of the G–F transition axis (through the mass centers of SD1 and SD3). (Front) View from the side of nucleotide entry . (Back) View from the opposite side of nucleotide entry. (OD) Side view from the OD side with ID faded out. (ID) Side view from the ID side with OD faded out. Numbers 1 through 4 indicate the respective SDs, and the arrows indicate the main direction of anisotropy. F, filamentous; G, globular; ID, inner domain; OD, outer domain; PfActI, Plasmodium falciparum actin I; SD, subdomain.Anisotropic B factors show relative destabilization of SD2 of (TIF)Click here for additional data file.S6 FigPfActI wild type and mutants A272W and H74Q as well as those of Glu72-His73 backbone in PbActII. PbActII, Plasmodium berghei actin II; PfActI, P. falciparum actin I.Orientations of the H-loop Glu73-His74 backbone in (TIF)Click here for additional data file.S7 FigPfActI [10 helices are only indicated by helical symbols. The loops and the plug region discussed in the text are highlighted under the sequences. The sequences are divided into 2 groups: (1) apicomplexan actins, including PfActI (Q8I4X0), PbActII (Q4YU79), and TgAct (P53476) and (2) canonical actins from DdAct (P07830), ScAct (P60010), AtAct (P0CJ46), HsAct_alpha_sk (P68133), HsAct_beta (P60709), HsAct_gamma (P63261). Human actins are identical in sequence to corresponding other mammalian actins. Arrowheads indicate mutations studied in this work. The sequence numbering corresponds to that of PfActI. AtAct, Arabidopsis thaliana actin; DdAct, Dictyostelium discoideum actin; HsAct_alpha_sk, Homo sapiens skeletal muscle α actin; HsAct_beta, H. sapiens β actin; HsAct_gamma, H. sapiens γ actin; PbActII, Plasmodium berghei actin II; PfActI, P. falciparum actin I; ScAct, Saccharomyces cerevisiae actin; TgAct, Toxoplasma gondii actin.Sequence alignment of selected actin amino acid sequences with secondary structure elements from PfActI . α-helic(TIF)Click here for additional data file.S8 FigPfActI in (A) Ca-ATP state , Mg-ATP/ADP state conformers A, B, and C (B–D), Mg-ADP state conformers A and B . PbActII in Ca-ATP state (G) and Mg-ADP state (H). The size of the sphere relates to the maximum and minimum B factors of each structure, whereas the color relates to the first quartile , median , and third quartile  of the total distribution of B factors in each structure. PbActII, Plasmodium berghei actin II; PfActI, P. falciparum actin I.Temperature factors of protein side-chain atoms in the vicinity of the A loop. (TIF)Click here for additional data file.S9 FigPfActI Mg-ATP/ADP structure. (A) Arg178 forms hydrogen bonds with backbone O of Pro110 in SD1 and His74 in SD2 in conformation 1b. (B) Interaction between Arg178 and SD2 is maintained by the cation-π interaction via His74 in conformation 2a, but the connection to SD1 is lost. (C) Contacts between the ID and OD are preserved in the F-PfActI model that is also in conformation 1b. OD residues are indicated in orange carbon atoms, whereas ID residues are indicated in gray. F, filamentous; ID, inner domain; OD, outer domain; PfActI, Plasmodium falciparum actin I; SD, subdomain.Connections formed by Arg178 between the P loop of SD1 and the H loop of SD2 in the (TIF)Click here for additional data file.S10 FigPfActI in (gray line) Ca-ATP, (blue line) Mg-ATP/ADP and (black line) Mg-ADP states as well as PbActII in (brown line) Ca-ATP and (orange line) Mg-ADP states. The SDs are annotated as SD1 (yellow), SD2 (red), SD3 (cyan), and SD4 (magenta). The approximate site of residues 61–66 that are built into weak density is marked by a black arrow. The underlying data for this figure can be found in PbActII, Plasmodium berghei actin II; PfActI, P. falciparum actin I, SD, subdomain.Plot of residue-level B factors of (TIF)Click here for additional data file.S1 MoviePlasmodium actin structures. PC, principal component; PCA, principal component analysis.PC1 (twistedness) and PC2 (openness) are shown for the full data set of 147 actin chains and for the limited data set comprising only (MP4)Click here for additional data file.S2 MoviePfActI, Plasmodium falciparum actin I.(MP4)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S3 Data(XLSX)Click here for additional data file.S4 Data(XLSX)Click here for additional data file.S5 Data(XLSX)Click here for additional data file.S6 Data(XLSX)Click here for additional data file.S7 Data(XLSX)Click here for additional data file.S8 Data(XLSX)Click here for additional data file.


We successfully transferred melanin into a novel catalytic platform. Ligand-free, water-soluble, recyclable and biocompatible Cu(i)-loaded melanin dots [Cu(i)/M-dots] was easily prepared and demonstrate excellent properties for classic CuAAC, bioconjugation and cell labelling. i)/melanin dot-based catalyst [Cu(i)/M-dots] was easily prepared and exhibited high catalytic activity and excellent reusability in various Cu(i)-catalyzed azide-alkyne cycloadditions (CuAAC). Furthermore, DNA bioconjugation was carried out efficiently using Cu(i)/M-dots under ligand-free and reductant-free conditions, and the Cu(i)/M-dots could easily be removed by centrifugation. Lastly, the integrin receptor  of U87MG cells was effectively labelled with a fluorescent dye (Cyanine5.5 azide) in combination with Cu(i)/M-dots. These attractive properties of Cu(i)/M-dots render it a promising catalytic platform in bioconjugation and chemical biology applications.Development of biocompatible and high-performance heterogeneous catalysts for bioconjugation and cell labeling is highly challenging. Melanin has previously been used as a target for melanoma imaging and therapy. Herein, this important biomarker was transferred into a novel catalytic platform. A biocompatible Cu( Encouraged by these promising properties, we propose to use M-dots directly as a support for catalytic applications, which presents the following advantages: (1) the natural nitrogen groups of M-dots can coordinate metal ions and the resulting heterogeneous catalyst can greatly accelerate the kinetics of metal-catalyzed reactions; (2) the water-solubility of M-dots conveniently performs catalytic reactions under aqueous solvents and it can easily be removed by centrifugation; (3) the high stability and biocompatibility of M-dots are attractive for biological applications; (4) M-dots are able to scavenge radical molecules produced by metals under reducing conditions, which is one major challenges of current metal-mediated bioconjugate chemistry.In recent years, tremendous efforts have been made in the development of biocompatible, biodegradable and renewable materials for various chemical and biomedical applications.i)-catalyzed azide-alkyne cycloaddition (CuAAC) click reaction which was discovered by Sharpless and Meldal in early 2000s.i).i)-mediated generation of reactive oxygen species (ROS) from O2 can cause detrimental consequences to cellular metabolism.i) ligands have been designed as Cu(i) stabilizers to reduce the cellular toxicity of copper.i) from conjugated products. This raises concerns about protein or DNA damage/precipitation and the preservation of their structural integrity.i) oxidation state for efficient catalysis, a large amount of chelating ligands and reducing agents are usually required. It could induce further damage to cellular metabolism during a labeling reaction.i) catalyst that performs click reactions under ligand-free and reductant-free conditions, which will lead to a major step forward in bioconjugation and chemical biology.Within the last decade, bioorthogonal reactions have become a powerful tool for the selective modification of biomolecules in living systems.i) into M-dots can obtain a ligand-free, water-soluble, biocompatible and recyclable Cu(i) catalyst. Moreover, intrinsic nitrogen groups presented in M-dots can coordinate the metal ions and the resulting Cu(i)/M-dots can greatly accelerate the kinetics of Cu(i)-catalyzed reactions and stabilize the Cu(i) oxidation state. As a proof of concept, we prepared a novel Cu(i)/M-dot catalyst and demonstrated its excellent properties when applied in the CuAAC reaction, DNA bioconjugation and cell labeling /M-dots heterogeneous catalyst. The M-dots were synthesized from commercial melanin granules according to a previous report.1H-NMR of M-dots in D2O display a characteristic signal at 3.58 ppm due to the hydrogen atoms of the PEG chains /M-dots was undertaken (see ESIi)/M-dots were purified using a centrifugal filter (MWCO = 30 kDa). After purification, the fresh Cu(i)/M-dots catalyst maintained good water and phosphate buffer (PBS)-solubility without any observed precipitation (i)/M-dots were monodispersed and homogeneous in water (i)/M-dots exhibited good Cu(i) loading capacities. The number of freshly prepared Cu(i) per single M-dot was determined to be 50 ± 2.0 -chelation, the size of the Cu(i)/M-dots increased to ∼9 nm and its zeta-potential went from –2.2 mV to +5.1 mV due to the introduction of positive Cu(i) on the M-dots surface (ESI Table S1i) on the M-dots platform was confirmed by X-ray photoelectron spectroscopy (XPS) .i) with M-dots is shown in Fig. S3.i)/M-dots in PBS indicates the high stability of the chelating platform (i)/M-dots compared to the classic Cu(i)/THPTA complex (abbreviation THPTA: [tris(3-hydroxypropyltriazolylmethyl)amine], a widely used and water-soluble ligand for CuAAC) on two cell lines: mouse embryonic fibroblast cell line (NIH3T3) and human glioblastoma cell line (U87MG) with different Cu(i) concentrations  and different incubation times . For a short incubation time (1 h), both Cu(i)/M-dots and Cu(i)/THPTA systems were non-toxic to two cell lines. However, at longer incubation times  an increased cytotoxicity was observed at all copper concentrations (50 µM to 200 µM) for Cu(i)/THPTA. In contrast, no toxicity of Cu(i)/M-dots was observed according to dimethylthiazolyl-diphenyltetrazolium (MTT) assays for all time points /M-dots show high biocompatibility and are promising for further biological applications.Encouraged by the vast applications of CuAAC chemistry, the preparation of Cu(n see ESI. The Cu(pitation . A TEM iin water . The Cu(platform . Moreovei)-induced hydroxyl radical production is a major issue in CuAAC bioconjugate chemistry.ii)/ascorbate.4 or ascorbate alone in PBS, but the combination of the two induced the oxidation of approximately 50% of 3-CCA to 7-OH CCA after 20 h , which can greatly expand its biological application.Ascorbate-driven, Cu(i)/M-dots and Cu(ii)/M-dots with or without ascorbate was evaluated using a coumarin-based fluorogenic probe which enabled us to monitor the reaction by simple fluorescence measurements of the corresponding triazole product /M-dots afforded high yields, and the kinetics was improved by increasing the amount of Cu(i)/M-dots. A significantly lower yield (<5%) was observed under Cu(ii)/M-dots treatment. However, a high yield was obtained by the addition of ascorbate to the Cu(ii)/M-dots system. Thus, the function of M-dots appears to be crucial not only for maintaining the stability of the Cu(i) catalyst, but also for greatly accelerating the reaction.The relative reactivity of M-dots-based heterogeneous catalysts, Cu(i)/M-dots system was efficient for each substrate (only 0.1% mol Cu+), leading to yields of about 90% in the absence of ascorbate and ligands (entries 1–10). These result confirm that Cu(i) was stabilized within the M-dots thus avoiding the use of a reducing agent. Moreover, the construction of biomolecules of medical interest was also explored (entries 11–15). Derivatives of biotin 3k and dimeric RGD 3l were obtained in good yields (entry 11–12). The imaging precursors/probes , Cy5.5-AE105 (3n) and NOTA-RGD ) were also prepared in 78%, 75% and 84% yields, respectively (entries 13–15). 3o was further efficiently labelled with a PET radionuclide, 64Cu, in 100% radiolabelling yield  escaped from the M-dots, which further confirms the stability of the Cu(i)/M-dots catalyst under CuAAC conditions.Based on these promising results, the scope of the CuAAC was examined using various substrates . To our i)/M-dots was a robust and recyclable catalyst. It was recycled by centrifugation and used for seven cycles without loss of catalytic activity . TEM and photo images of reused Cu(i)/M-dots reveal that the structure of the catalyst remained intact and stable /M-dots system also efficiently drives the three-component click reaction of alkyl halides, sodium azide and alkynes with good recyclability /M-dots for bioconjugation was tested on an alkyne derivatised DNA strand with an azide fluorophore in low concentrations. The 3′-position modified oligonucleotide  in 20 µL of water was reacted with an excess of NHS ester V1  in 30 µL of water at room temperature for 12 h. Unreacted V1 was removed by a centrifugal filter (3 kDa), and then lyophilized to obtain alkyne-labelled DNA S2. A click reaction of S2 (20 µM) was then performed with Cy5.5-azide  mediated by 2 µM Cu(i)/M-dots (containing about 100 µM Cu+) in 0.1 M PBS at pH 7 for 2 h . S1, S2, S3 and two control groups were loaded on a 16% denaturing PAGE gel for electrophoresis . Then, the DNA PAGE gel was imaged under 365 nm  revealing that DNA bioconjugation was efficiently carried out only in the presence of Cu(i)/M-dots (i)/ligand/NaAsc systems, the Cu(i)/M-dots did not require a reducing agent, which is known to cause undesirable side effects such as covalent modification and potential aggregation of proteins.i)/M-dots system can easily be separated from biomolecules and can thus solve the problem of contamination from the residual Cu(i) catalyst.The utility of the Cu( for 2 h . Unreact)/M-dots . In addi)/M-dots . Unlike i)/M-dots for cell labelling, we used Cy5.5 azide and RGD-alkyne in combination with Cu(i)/M-dots for imaging of the integrin αVβ3 receptor. Human glioblastoma U87MG cells were selected because of their high level of integrin αVβ3 expression.1h, 10 µM) to cap the alkyne functional group on the integrin protein due to the strong binding ability of the RGD peptide with the αVβ3 integrin receptor.2f, 30 µM) and 4 µM Cu(i)/M-dots (containing about 200 µM Cu+) were added to the cell culture medium for 60 min at 37 °C. After the click reaction for labelling live cells and several PBS washing steps to remove Cy5.5 azide and Cu(i)/M-dots, the U87MG cells were fixed with 4% paraformaldehyde (PFA) and further stained with 4′,6-diamidino-2-phenylindole (DAPI) to reveal the location of the nuclei. The cells were then imaged using fluorescence microscopy (Axiovert 200M fluorescence microscope). Control studies were performed in the absence of Cy5.5 azide or Cu(i)/M-dots. The recorded microscopy images reveal a strong fluorescent signal of the U87MG cells incubated with RGD-alkyne, Cy5.5 azide and Cu(i)/M-dots (i)/M-dots enable efficient performing of the CuAAC on the cell membrane.To assess the capability of the Cu/M-dots , whereas)/M-dots . The resi)/M-dots. Cu(i)/M-dots is proven to be a high performance, biocompatible and recyclable catalytic system for CuAAC and bioconjugation without any Cu(i) toxicity. M-dots can serve as an novel metal-loading recyclable catalyst for various applications in bioconjugation and chemical biology.In conclusion, we reported M-dots as an effective nanoplatform for preparing a ligand-free and water-soluble heterogeneous catalyst, Cu(Supplementary informationClick here for additional data file.


The binding of transcription factors to short recognition sequences plays a pivotal role in controlling the expression of genes. The sequence and shape characteristics of binding sites influence DNA binding specificity and have also been implicated in modulating the activity of transcription factors downstream of binding. To quantitatively assess the transcriptional activity of tens of thousands of designed synthetic sites in parallel, we developed a synthetic version of STARR-seq (synSTARR-seq). We used the approach to systematically analyze how variations in the recognition sequence of the glucocorticoid receptor (GR) affect transcriptional regulation. Our approach resulted in the identification of a novel highly active functional GR binding sequence and revealed that sequence variation both within and flanking GR’s core binding site can modulate GR activity without apparent changes in DNA binding affinity. Notably, we found that the sequence composition of variants with similar activity profiles was highly diverse. In contrast, groups of variants with similar activity profiles showed specific DNA shape characteristics indicating that DNA shape may be a better predictor of activity than DNA sequence. Finally, using single cell experiments with individual enhancer variants, we obtained clues indicating that the architecture of the response element can independently tune expression mean and cell-to cell variability in gene expression (noise). Together, our studies establish synSTARR as a powerful method to systematically study how DNA sequence and shape modulate transcriptional output and noise. The expression level of genes is controlled by transcription factors, which are proteins that bind to genomic response elements that contain their recognition DNA sequence. Importantly, genes are not simply turned on but need to be expressed at the right level. This is, at least in part, assured by the sequence composition of genomic response elements. Here, we studied how the recognition DNA sequence influences gene regulation by a transcription factor called the glucocorticoid receptor. Specifically, we developed a method to test the activity of variants in a highly parallelized setting where everything is kept identical except for the sequence of the binding site. The systematic analysis of tens of thousands of sequence variants facilitated the identification of a previously unknown sequence variant with high activity. Moreover, we report how sequence variation of the response element influences cell-to-cell variability in expression levels. Finally, we observe similar activity profiles for distinct sequence variants that share similar three-dimensional DNA shape characteristics arguing that the three-dimensional perception of DNA by the glucocorticoid receptor, modulates its activity towards individual target genes. In addition, the architecture of cis-regulatory elements influences the expression level of individual genes. For example, transcriptional output can be tuned by varying the number of TF binding sites, either for a given TF or for distinct TFs, present at an enhancer [The interplay between transcription factors (TFs) and genomically encoded enhancer , 2. Moreenhancer –5. The senhancer . Howeverenhancer . Insteadenhancer .cis-regulatory elements [Notably, the expression level of a gene is typically measured for populations of cells and thus masks that expression levels can vary considerably between individual cells of an isogenic population –9. This elements , 14–16. elements , 17. In elements . Finallyelements , 16.Many fundamental insights regarding the role of sequence in tuning transcriptional output and noise have come from reporter studies , 21. A kHere, we adapted the STARR-seq method to systematically study how sequence variation both within the 15 bp GBS and in the region directly flanking it modulate GR activity. Specifically, we generated STARR-seq libraries using designed synthetic oligos (synSTARR-seq) with randomized nucleotides flanking the core GBS to show that the flanks modulate transcriptional output by almost an order of magnitude. When grouping sequences based on their ability to either enhance or blunt GBS activity, we found that each group contained a broad spectrum of highly diverse sequences, but striking similarities in their DNA shape characteristics. Using the same approach, we also assayed the effect of sequence variation within the core GBS. Finally, using single cell experiments with individual enhancer variants, we study how the sequence composition of the response element influences expression mean and noise. Together, our studies establish synSTARR-seq as a powerful method to study how DNA sequence and shape modulate transcriptional output and noise.FKBP5. The resulting reporters were transfected into U2OS cells stably expressing GR (U2OS-GR) [FKBP5 fragment (To test if we could use the STARR-seq reporter  to studyU2OS-GR)  and theifragment , showingOur previous work has shown that the sequence directly flanking GBSs can modulate DNA shape and GR activity . For a pkpLogo [To assess how the sequence composition of the flanking region influences GBS activity, we ranked the flank variants by their activity and used a color chart representation to plot the sequence at each position for the Cgt  and Sgk kpLogo , which ukpLogo . In addiOur previous work  indicateTogether, these synSTARR-seq experiments uncover how sequence variation in the flanking region of the GBS influences activity and point at a role for DNA shape in modulating GBS activity.We next generated an additional synSTARR-seq library to study the effect of variation within the 15bp core sequence. This library contains a fixed GBS half site followed by eight consecutive randomized nucleotides . The libGiven that the observed repression was not reproducible, we concentrated our analysis on 1696 sequences that facilitated significant GR-dependent transcriptional activation. Consistent with activation, we found that the consensus motif for activating sequence variants recapitulates the known GR consensus sequence with the second half site 3-bp downstream of the fixed first half site of our library . AccordiPrevious studies have shown that the sequence of the spacer can modulate GBS activity , 5. Therin vitro experiments studying the effect of DNA sequence on GR DNA binding affinity [Unexpectedly, the activity logo and top of the color chart indicated a high activity for variants with a C at position 2 , insteadaffinity . A carefInterestingly, the most active variants resemble the sequence composition of the “combi” motif we identified previously . The comTo assess if DNA shape could play a role in modulating GBS activity, we calculated the predicted minor groove width for all 1696 significantly activated sequences ranked by activity . CompariTogether, the findings for our half site library suggest a role for both DNA shape and sequence in tuning the activity of GBS variants. Moreover, our screen uncovered a novel high-activity functional GR binding sequence variant.e.g. 3xSgk and Ets1+FKBP5-2) can have vastly different noise levels indicating that binding sites architecture can independently tune both mean expression and cell-to-cell variability in gene expression with noisier expression for enhancers with multiple GBSs.Thus far, we analyzed the effect of sequence composition on transcriptional output by analyzing mean expression levels for populations of cells. To test if sequence variation in the enhancer influences cell-to-cell variability in gene expression (noise), we measured GFP levels for individual STARR constructs in single cells . Cells wIn this study, we developed a modified version of the STARR-seq method where we used designed synthetic oligonucleotides to assay how sequence variation within and around the GBS influence GBS activity. This facilitated the thorough and parallelized assessment of 1024 flank variants on GBS activity in a highly reproducible and quantitative fashion . SimilarImportantly, our findings for the synthetic STARR-seq assay are consistent with GR-dependent regulation of endogenous target genes. Specifically, the nucleotide directly flanking the GBS is preferentially a T for both enhancing flanks in our synSTARR-seq experiments and for the motif we previously found for genomic GR binding sites associated with genes that show the most robust response to GR activation . Moreovein vivo prediction of TF binding sites [We were surprised to find that the consensus motifs for enhancing and blunting flanks displayed low information content indicating that a broad spectrum of distinct sequences can enhance or blunt the activity of the adjacent GBS . Howeverng sites  and, base.g. singe GBS variants), we observed that transcriptional noise scales with mean expression, such that higher expression levels are associated with lower noise  [U2OS-GR)  were groU2OS-GR)  using eiSynthetic STARR-seq. Library design and generation: To generate GBS variant libraries, oligos containing degenerate nucleotides (N) at defined positions were ordered from IDT as “DNA Ultramer oligonucleotide” (sequence listed below). The oligonucleotides were made double stranded using Phusion polymerase  using the revPrimer (GGCCGAATTCGTCGAGTGAC). The resulting double stranded inserts (25ng) were recombined with 100ng linearized  STARR-seq vector [STARR-seq: For STARR-seq experiments, 5 million U2OS-GR cells were transfected with 5 μg library-DNA by nucleofection using kit V (Lonza). The next day, cells were treated for 4 h with 1 μM dexamethasone or with 0.1% ethanol as vehicle control. Reverse transcription and amplification of cDNA for subsequence Illumina 50bp paired-end sequencing were done as described [q vector  by in-Fuescribed .Cgt flank library DNA Ultramer oligonucleotide:TAGAGCATGCACCGGACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCGCAAGAACAtttTGTACGNNNNNCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACTCGACGAATTCGGCCSgk flank library DNA Ultramer oligonucleotide:TAGAGCATGCACCGGACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCGCAAGAACAtttTGTCCGNNNNNCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACTCGACGAATTCGGCCGBS half site library DNA Ultramer oligonucleotide:TAGAGCATGCACCGGACACTCTTTCCCTACACGACGCTCTTCCGATCTCAGCGAAAGAACAtNNNNNNNNCGTCGCTAGATCGGAAGAGCACACGTCTGAACTCCAGTCACTCGACGAATTCGGCCRNA-seq U2OS-GR cells . U2OS-GR cells were treated for 4h with either 1μM dexamethasone or 0.1% ethanol as vehicle control. RNA was isolated from 1.2 million cells using the RNeasy kit from Qiagen. Sequencing libraries were prepared using the TruSeq RNA library Prep Kit (Illumina). Prior to reverse transcription, poly adenylated RNA was isolated using oligo d(T) beads. Paired end 50bp reads from Illumina sequencing were mapped against the human hg19 reference genome using STAR [ing STAR   and RPL19 (GAGGCCAGTATGTACAGACAAAGTGG) which was used for data normalization. qPCR and data analysis were done as described [escribed . Primer Noise-Measurements. For noise measurements, U2OS-GR cells were transfected using lipofectamine and plus (Invitrogen) essentially as described [2) and for relative GFP expression were derived from three biological replicates.escribed . In shorAnalysis of synSTARR-seq data. RNA-seq reads were filtered and only sequences exactly matching the insert sequence in length and nucleotide composition were included in the analysis. The number of occurrences for each sequence variants was counted for each experimental condition and differentially expressed sequences were identified using DESeq2 [g DESeq2  using a Boxplots comparing groups of sequence variants as specified in the figure legends show center lines for the median; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles.kpLogo [Sequence logos to depict the consensus motif for groups of sequences were generated using WebLogo . The prokpLogo  using asMotif weight. The motif weight for each variant was calculated using the RSAT matrix-scan program [create background program, trained on human open chromatin available at UCSC genome browser (http://genome.ucsc.edu/cgi-bin/hgTrackUi?db=hg19&g=wgEncodeRegDnaseClustered). Boxplots comparing groups of sequence variants show center lines for the median; box limits indicate the 25th and 75th percentiles; whiskers extend 1.5 times the interquartile range from the 25th and 75th percentiles. program , 48. SpeComparison of ChIP-seq peak height between combi2 and canonical GBS motif. GR ChIP-seq data sets for U2OS-GR cells were downloaded as processed peaks from EBI ArrayExpress (E-MTAB-2731). ChIP-seq peaks in a 40 kb window centered on the transcription start site of differentially expressed genes (RNA-seq data: E-MTAB-6738) were scanned using RSAT matrix-scan [−4) or the combi2 matrix we generated (−4). Next, peaks were grouped by motif match and median peak height was calculated for each group and the p-value comparing both groups was calculated using a Wilcoxon rank-sum test to produce Supplementary rix-scan , 48 for Comparison of gene regulation. To compare the level of activation between genes with nearby peaks with either a GBS match  or a combi2 match (motif −4), we first scanned ChIP-seq peaks (U2OS-GR cells: E-MTAB-2731) in a 40 kb window centered on the transcription start site  for motif matches using RSAT matrix-scan [rix-scan , 48. OnlDNA shape prediction. We used DNAshapeR [NAshapeR  to prediClassification of GBS activity. To assess classifier performance we generate ROC curves using 10-fold cross-validation. Four different models were tested to classify GBS activity into blunting or enhancing. A mononucleotide model consisting of sequence motifs estimated from relative nucleotide frequencies within the two classes. Class affiliation is predicted with a likelihood ratio test. We also tested a similar model based on dinucleotides. In addition, we tested two random forest (RF) classifiers with 100 trees, based on sequence and shape information. We used the R package "randomForest" for constructing the classifiers [ssifiers . Since RChIP-exo footprint profiles. ChIP-exo footprint profiles were generated using the ExoProfiler package [−4. Overlay plots for distinct motifs were generated by aligning the profiles on the GBS and normalizing the signal for each motif variant to 1. package  and publ package , the PWM package , the comStructural alignment of GR:ETS1 complex. Structural alignment of the GR:ETS1 complex on a combi2 sequence was done as described previously [eviously  except teviously  using theviously  to the 3Data were deposited in ArrayExpress under the accession numbers: E-MTAB-6738 (RNA-seq U2OS-GR) and E-MTAB-6737 (synSTARR-seq U2OS-GR). In addition, we used the previously deposited datasets: E-MTAB-2731 (ChIP-seq U2OS cells), E-MTAB-2955 and E-MTAB-2956 (ChIP-seq and ChIP-exo data IMR90).S1 Fig(a) Analysis of individual enhancer variants as indicated by flow cytometry showing the side scatter (SSC-A) versus GFP signal for individual mCherry-positive cells. Left: no STARR-seq construct. Right-Top: ethanol, vehicle, treated cells; Right-Bottom: Cells treated overnight with 1μM dexamethasone. Numbers in red indicate the percentage of GFP+ (top right side) and GFP- (top left side) cells respectively. Red vertical line demarcates the threshold for being called GFP+. (b) RNA-seq correlation plots for biological replicates of vehicle-treated cells transfected with the GBS-flank library (Cgt flank library). (c) Same as (b) except for biological replicates of dexamethasone-treated cells (4h 1μM). (d) RNA-seq correlation plots for biological replicates of vehicle-treated cells transfected with the GBS-flank library (Sgk flank library). (e) Same as (d) except for biological replicates of dexamethasone-treated cells (4h 1μM).(TIF)Click here for additional data file.S2 FigkpLogo probability logo (activity logo) for flank variants depicting the p-values from Mann-Whitney U tests of whether GBS variants with a specific nucleotide at a given position are more (displayed above number indicating nucleotide position) or less (displayed below number indicating nucleotide position) active than other GBS variants. Positions with significant nucleotides (p < 0.001) are highlighted (red coordinates). (d) Predicted minor groove width (MGW) for significant enhancing and blunting flank variants of the Sgk GBS library ranked by their fold change in response to hormone treatment. (e) K-means clustering based on MGW for significantly enhancing and blunting flank variants. Right side: activating and blunting variants are highlighted in grey and black respectively. (f) Consensus sequence motif for clusters as indicated. (g) Log2 fold change upon dexamethasone treatment for each cluster as indicated. The synSTARR-seq activity for individual sequences is shown as black dots, the median for each group as a horizontal red line.(a) Color chart summarizing the sequence at each variable position for flank variants ranked by their fold change in response to hormone treatment. (b) Consensus motif for (left) significantly enhancing and (right) blunting flank variants (c) (TIFF)Click here for additional data file.S3 Fig(a) Predicted Propeller twist for significant enhancing and blunting flank variants of the Cgt GBS library ranked by their fold change in response to hormone treatment. (b) Propeller twist for selected individual bases for significantly blunting (n = 189) and significantly enhancing (n = 125) flanks for the Cgt library. p-values were calculated using the Wilcoxon rank-sum test (*p < 0.01). (c) Same as (a) except that helix twist is shown. (d) same as (b) except that helix twist is shown. (e) Same as (a) except that roll is shown. (f) same as (b) except that roll is shown.(TIFF)Click here for additional data file.S4 Fig(a) Minor groove width (MGW) for selected individual bases for significantly blunting (n = 189) and significantly enhancing (n = 125) flanks for the Cgt library. p-values were calculated using the Wilcoxon rank-sum test. (b) Same as for (a) except for significantly blunting (n = 162) and significantly enhancing (n = 101) flanks of the Sgk flank library.(TIFF)Click here for additional data file.S5 Fig(a) Correlation plot between input library (library) and the plasmid library isolated from transfected U2OS-GR cells (input). (b) RNA-seq correlation plots for biological replicates of vehicle-treated cells. (c) Same as for (b) except for biological replicates of dexamethasone-treated cells (4h 1μM).(TIFF)Click here for additional data file.S6 Fig(a) Log2 fold change upon dexamethasone treatment for active GBS variants with either an A, C, G or T at position 3. Data for individual sequences that match consensus second half site at key positions 4 and 6  are shown as blue dots. Horizontal red lines show the average for each group. p-value was calculated using a Student’s t-test. (b) Left: Minor groove width (MGW) prediction for GBS variants ranked by activity. Right: Consensus motif for top 20% most active and bottom 20% least active GBS variants. (c) MGW for select individual bases comparing the top 20% most active and bottom 20% least active activated GBS variants. p-values were calculated using the Wilcoxon rank-sum test.(TIFF)Click here for additional data file.S7 Fig(a) Motif logo representing the sequence that was used to scan for GBS-matches in the half site library. Black box highlights the two positions in the spacer whose effects on GBS activity was assayed. (b) Boxplot of the log2 fold change upon treatment for 4 h with 1 μM dexamethasone for GBS matches with spacer variant as indicated. Center lines show the median. Spacer variants with significantly different activity levels .(TIFF)Click here for additional data file.S8 Fig(a) Transcriptional activity of STARR-seq reporters containing candidate GBS variants as indicated. Relative RNA levels ± S.E.M. are shown for cells treated with ethanol vehicle and for cells treated overnight with 1 μM dexamethasone (n = 3). (b) Boxplot showing the peak-height for GR target genes with either a canonical GBS motif match (nnTGT) or a combi2 motif match (tcCGT). Center lines show the median, p value was calculated using a Wilcoxon rank-sum test.(TIFF)Click here for additional data file.S9 Fig(a) Motif logo representing the positional weight matrix of highly active flank variants that was used to scan for motif-matches to generate the ChIP-exo footprint profile. (b) Alignment of the ChIP-exo footprint profiles for highly active flank variant matches  and for the conventional GBS motif .(TIFF)Click here for additional data file.S10 Fig(a) ROC curves analyzing the ability of the models to distinguish between blunting and enhancing flank variants for (left) the Cgt flank library; (right) the Sgk flank library. Mononucleotide: Classifier based on mononucleotide frequencies within the two classes. Dinucleotide: Classifier constructed using dinucleotide frequencies. Sequence Random Forest (RF): Random Forest classifer trained and tested on coded nucleotide sequences. Shape Random Forest (RF): Random forest classifier based on predicted MGW. (b) Same as for (a) except that model and ROC curves where trained and assessed for their ability to discriminate between the top and bottom 20% significantly active GBS variants from the half site library.(TIFF)Click here for additional data file.S1 TableGBS (or scrambled GBS) underlined.(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.