Drosophila. In flies, it is involved in the regulation of X-chromosomal and autosomal genes as part of the MSL and the NSL complexes, respectively. While the function of the MSL complex as a dosage compensation regulator is fairly well understood, the role of the NSL complex in gene regulation is still poorly characterized. Here we report a comprehensive ChIP–seq analysis of four NSL complex members  throughout the Drosophila melanogaster genome. Strikingly, the majority (85.5%) of NSL-bound genes are constitutively expressed across different cell types. We find that an increased abundance of the histone modifications H4K16ac, H3K4me2, H3K4me3, and H3K9ac in gene promoter regions is characteristic of NSL-targeted genes. Furthermore, we show that these genes have a well-defined nucleosome free region and broad transcription initiation patterns. Finally, by performing ChIP–seq analyses of RNA polymerase II (Pol II) in NSL1- and NSL3-depleted cells, we demonstrate that both NSL proteins are required for efficient recruitment of Pol II to NSL target gene promoters. The observed Pol II reduction coincides with compromised binding of TBP and TFIIB to target promoters, indicating that the NSL complex is required for optimal recruitment of the pre-initiation complex on target genes. Moreover, genes that undergo the most dramatic loss of Pol II upon NSL knockdowns tend to be enriched in DNA Replication–related Element (DRE). Taken together, our findings show that the MOF-containing NSL complex acts as a major regulator of housekeeping genes in flies by modulating initiation of Pol II transcription.MOF is the major histone H4 lysine 16-specific (H4K16) acetyltransferase in mammals and  Housekeeping genes are required to support basic cellular functions and are therefore expressed constitutively in all tissues. Although the homeostasis of housekeeping gene expression is vital for cell survival, most research on the transcription initiation has been focused on TATA-box-containing promoters of inducible and developmental genes, while regulatory mechanisms at the TATA-less promoters of housekeeping genes have remained poorly understood. Using genome-wide chromatin binding profiles, we find that the NSL complex, a histone acetyltransferase-containing complex, is bound to the majority of constitutively active gene promoters. We show that NSL-bound genes display specific sets of DNA motifs, well-defined nucleosome free regions, and broad transcription initiation patterns. In addition, we show that the NSL complex regulates the recruitment of the basal transcription machinery to target promoters; more specifically, we can pinpoint its role to the early steps of Pol II recruitment. Interestingly, we also see that NSL-bound genes are most susceptible to Pol II loss after depletion of NSLs when they contain the DNA Replication–related Element (DRE). Taken together, we provide a genome-wide analysis of a chromatin-modifying complex that is globally involved in the regulation of housekeeping gene expression. In the past decade, our understanding of eukaryotic transcriptional regulation has changed from the notion of a “generic entity that functions by a single universal mechanism” N-acetyltransferase (GNAT) family (e.g. Gcn5 and p300) Histone acetyltransferases (HATs) and histone deacetylases (HDACs) work in concert to orchestrate a fine balance of acetylation. HATs can be classified into two predominant families: the GCN5-related Drosophila and mammals: the Male-Specific Lethal (MSL) and the Non-Specific Lethal (NSL) complexes Drosophila, the MSL complex is targeted to the transcribed regions of X-chromosomal genes where it mediates dosage compensation. The targeting mechanism and modes of action of the MSL complex have been studied extensively  cell line; our analyses reveal that the repertoire of NSL-bound genes is highly similar between different cell types. Remarkably, by comparing NSL target genes with transcriptome data from 30 distinct developmental stages of Drosophila, we find that the NSL complex preferentially targets genes that are constitutively expressed, also referred to as housekeeping genes. Moreover, NSL-bound genes exhibit elevated levels of H3K4me2/3, H3K9ac and H4K16ac and display a distinctive arrangement of the nucleosome free region (NFR) as well as dispersed transcription initiation patterns. Going beyond the study of NSL complex localization, we could furthermore show that the NSL complex is required for optimal recruitment of Pol II and the pre-initiation complex to its target promoters. Finally, using a quantitative model of DNA-protein interaction affinities, we find that the presence of strong DRE motifs in NSL target promoters conveys an increased sensitivity for Pol II loss in cells lacking NSL1 or NSL3. Taken together, our observations reveal a unique promoter configuration that is indicative of NSL binding and establishes the NSL complex as an important transcriptional regulator of constitutively expressed genes in Drosophila.In order to elucidate the principles that direct NSL targeting, here we have performed a detailed analysis of the NSL binding sites in the genome of Drosophila embryonic Schneider (S2) cell line. This strategy allowed us to study similarities and differences in DNA binding patterns of the NSL-complex members in tissues of different origins. Moreover, the use of S2 cells offered the possibility to directly compare our results with the publicly available data generated by the modENCODE project that uses the same cell type.Genome-wide mappings of two NSL components (NSL1 and MCRS2) were previously performed in the salivary glands of third instar larvae The four proteins displayed significant binding, ranging from 9,409 (NSL3) to 12,234 (MCRS2) genomic regions where peaks were detected  was located within +/−200 bp of its TSS  unless otherwise indicated.We find that 68% and 66% of actively transcribed genes in S2 cells  defined by the location maps of 53 chromatin proteins A similar dominance for one specific state of active chromatin was observed when we repeated the analysis for the 9-chromatin-state model developed by Kharchenko and co-workers DrosophilaThe results of the chromatin state analyses and the fact that most NSL binding appears to occur independently of the cell-type, prompted us to examine whether the complex displayed any association with housekeeping genes. To address this question, we defined a set of genes that are constitutively expressed throughout 30 distinct developmental stages of Drosophila promoters based on the pattern of the transcriptional initiation: broad and peaked In addition to expression-based definitions of housekeeping genes, we wanted to test further correlations of NSL binding with characteristics of constitutively expressed genes. Earlier studies have revealed two basic types of We next wanted to investigate whether the NSL-characteristic initiation patterns and histone modifications enrichments also connected to specific structural features of the chromatin. Genome-wide analyses of nucleosome-positioning have demonstrated that transcriptionally active genes display a distinct organization, consisting of a precisely located +1 nucleosome around 135 bp downstream of the TSS, a −1 nucleosome that is directly upstream of the TSS and a nucleosome free region (NFR) between them. Additionally, it has been shown that the nucleosome organization can vary quite dramatically depending on the promoter sequences and transcription initiation patterns To assess whether NSL-bound promoters display a specialized nucleosome arrangement, we integrated a recently published map of nucleosome positions in S2 cells The distinct nucleosome occupancies for NSL-bound genes prompted us to test if the observed difference in nucleosome positioning was related to gene expression levels. The analysis of the promoter proximal regions of NSL-bound, NSL-non-bound and tissue-specific genes revealed that the diminished nucleosome occupancy upstream of the TSS is, in fact, independent of the expression levels .Since the NSL complex predominately targets gene promoters, we next addressed whether its presence is important for the recruitment of RNA Polymerase II (Pol II). For this purpose, we first depleted NSL1, NSL3 and MBD-R2 in S2 cells by dsRNA-mediated depletion. The efficiency of the knockdown was assessed by Western blot analyses of nuclear or cytoplasmic extracts from the relevant cells . ConsistWe also assessed the quality of NSL1, NSL3 and MBD-R2 depletion by performing chromatin immunoprecipitation with NSL1, NSL3 and MBD-R2 antibodies in NSL-depleted versus control cells (dsRNA against GFP). Consistent with the Western blot analyses, the ChIP experiments revealed severe depletion of NSL1, NSL3 and MBD-R2 from target promoters .Following these quality criteria, we proceeded with genome-wide ChIP-seq analyses of Pol II in NSL1- and NSL3- depleted cells . As showRegardless of the difference in the magnitude of Pol II reduction, both knockdowns showed greater effects on NSL-bound genes compared to NSL-non-bound active genes, suggesting that the NSL complex directly promotes the recruitment of Pol II to promoters of its target genes . To assePol II recruitment to promoters is a multi-step process requiring the assembly of a functional pre-initiation complex (PIC). In the current model, the TFIID complex (containing TBP) first binds to core promoter regions where it is stabilized by TFIIA and TFIIB. TFIIF and Pol II are subsequently recruited to the core promoter by TFIIB Our next step was to perform ChIP-qPCR studies of individual NSL target genes following the knockdown of NSL1, NSL3 or MBD-R2. The results revealed that both TBP and TFIIB binding was decreased at promoters, indicating an interruption in the early stage of PIC assembly . In contDistinct classes of gene expression patterns, e.g. constitutive or tissue-specific gene expression, are associated with particular promoter DNA motifs. Yet, how the presence or absence of a DNA motif is translated into biological functions often remains elusive. Since the NSL complex preferentially binds housekeeping genes, we wanted to investigate putative underlying DNA motifs and associate them with the effects of NSL depletion on Pol II recruitment.de novo motif finder MEME repeatedly identified four known core promoter elements within NSL peak regions: the E-box motif (CAGCTG), DRE (WATCGATW), the reverse complement of a motif resembling DMv2  as well as specific core promoter elements . Furthermore, these genes display distinct nucleosome occupancy and dispersed promoter configuration characterized by multiple transcription start sites. The correlation between these promoter characteristics  was previously identified for housekeeping genes in mammals and flies osophila .Excitingly, the enrichment of DNA motifs on NSL target gene promoters in combination with the genome-wide Pol II binding data has established functional links between the motifs enriched on housekeeping genes and the NSL-dependent Pol II binding to promoters. The abundance of DRE motifs, for example, was found to be positively associated with the magnitude of Pol II loss upon NSL knockdowns. The DRE binding factor (DREF) interacts tightly with TRF2 to modulate the transcription of DRE-containing promoters in a TATA-box-independent fashion In contrast to DRE, motif 1 showed an opposing effect on Pol II recruitment to NSL-complex-bound genes as the presence of strong motif 1 sequences was associated with decreased Pol II loss upon NSL depletion. The mechanistic reasons for this remain unclear. However, one can envisage several possible scenarios. It is possible that motif 1 may recruit another transcription factor, which can also function to recruit the transcription machinery. Alternatively, the turnover of the transcription machinery might be slower on promoters containing strong motif 1 sequences. There is precedent for the transcription machinery having various turnover rates on different promoters. For example, in yeast, it has been shown that TBP turnover is faster on TATA-containing than on TATA-less promoters Taking MOF-mediated H4K16 acetylation into consideration, a putative role of the NSL complex might be to coordinate the opening of promoter architecture by histone acetylation and the assembly of PIC. Coupling of histone acetylation and PIC formation has been described before. For example, TAF1, a component of TFIID, is a histone aceyltransferase Drosophila TBP, TFIIB, Pol II, H4K16ac , H4 , NSL1, MCRS2, NSL3 and MBD-R2 Chromatin immunoprecipitation was carried out on S2 cells as previously described Real-time PCR validation was performed with SYBR-Green PCR master mix (Applied Biosystems) and an ABI7500 real-time PCR thermocycler . Recovery was determined as the amount of immunoprecipitated DNA relative to input DNA.Deep sequencing of NSL3 and MBD-R2 ChIP and input samples was carried out with the Illumina Genome Analyzer II, Pol II ChIP  and respective input samples were deep-sequenced with an Illumina HiSeq2000 machine according to manufacturer's instructions.D. melanogaster genome (dm3) using bowtie The sequence reads from our earlier study of NSL binding in salivary glands From the samples sequenced with HiSeq 2000, we obtained between 120 to 135 million reads for Pol II ChIPs from S2 cells with knockdowns of NSL1, NSL3, and GFP and 50 to 60 million reads for the corresponding input samples. The correlations between the biological replicates of Pol II ChIP-seq reads from NSL1 and NSL3 knockdowns were excellent .D. melanogaster (including model-building) and a p-value cut off of 10−5We used MACS Version 1.4.0rc2 on bed-files of mapped reads from the ChIP-seq experiments of NSL1, MCRS2, NSL3, MBD-R2, and an input control. We employed standard parameters for 2 fold changes (log2FC) between library-size-normalized input and ChIP samples and applied a 400 bp sliding window to account for the fragment size obtained after sonication. To determine regions of significant Pol II enrichment we modeled the distribution of log2FC values based on negative log2FCs that are assumed to correspond to experimental noise. We calculated the threshold log2FC values for significant Pol II binding within the three different conditions at an FDR-value cut-off of 0.05  of the Pol II ChIP-seqs with the BioConductor package DESeq D. melanogaster were considered constitutively expressed (housekeeping genes). To identify genes that were active in S2 cells, but not constitutively expressed, the gene identifiers of the different lists were adapted with the help of the FlyBase ID converter tool and subsequently subtracted from each other.The basis of our gene-focused analyses was the list of annotated genes from FlyBase (version 5.30). Genes that are active in S2 cells were obtained from Unless indicated otherwise, a TSS was defined NSL-bound when the 400 bp region surrounding the TSS overlapped with an NSL peak summit region. The scripts, BEDTool commands and Galaxy workflows used for these overlaps and analyses are available upon request 2(r (TSS)/r (gene body)) where r is the sum of Pol II ChIP-seq read counts that were adjusted by the input sample and normalized to the region's length.For the calculation of the stalling indexes we first applied stringent filters to the genes that were taken into account: we included only non-overlapping genes greater than 1,300 bp and with median Pol II signals above the threshold (see above) at the promoter region. Promoter regions were defined as TSS +/−200 bp, for the gene body regions we excluded 500 bp after the TSS and 500 bp before the transcription end site (TES) to avoid confounding effects of transcription initiation and termination. Based on previous reports by Muse et al. 2FC (NSL-RNAi)−log2FC (GFP-RNAi).To assess the change of Pol II upon NSL depletion in comparison to the GFP-RNAi control sample, ΔPol II was calculated as follows: ΔPol II = log2FCs and wiggle files from modEncode.We visualized the binding profiles of the NSL complex proteins with our locally installed GBrowser (Version 2.15), uploading normalized log2FCs from publicly available ChIP-chip data: The 2,000 bp TSS regions were split into 100 bins and the average log2FCs were calculated for each bin and normalized to the corresponding H4 signals.For the summary plots of the histone marks , we extr2FCs (ChIP/input) from our ChIP-seq data  and published ChIP-chip data of Pol II For the heatmaps shown in D. melanogater genes except mitochondrial genes. The log2FC of Pol II from the GFP-RNAi sample and ΔPol II (see above) for NSL3-RNAi and NSL1-RNAi were extracted in 50 bp bins for the regions 500 bp up- and 1,500 bp downstream of each gene's TSS. Genes were sorted according to the cumulative signal within the displayed region as indicated in the respective figures and legends.For the heatmaps of Pol II and ΔPol II  we used 2FCs (ChIP/input) were extracted accordingly. Venn diagrams were generated with Venny For the metagene profiles of Pol II and ΔPol II signals as shown in We measured nucleosome occupancy for constitutively expressed NSL-bound genes, constitutively expressed NSL-non-bound genes and tissue-specific genes in a 200 bp area surrounding their annotated TSSs . Nucleosome maps for S2 cells were obtained from GEO .2FC) above the threshold value in GFP-RNAi and ΔPol II below 0 . Tertiles based on ΔPol II were determined with the quantile function of R. We used a 100 bp window around the TSS for TRAP score calculation for all motifs except TATA (40 to 20 bp upstream of the TSS), Inr (TSS +/−20 bp), and DPE (20 to 40 bp downstream of the TSS). Density plots were generated with the R package ggplot2.To assess the relation between ΔPol II and the TRAP score , we focuWe downloaded the bed-files with the genomic coordinates of the 9-state-chromatin model of For the analysis of histone marks and non-histone chromosome proteins, we downloaded the wiggle-files of ChIP-chip experiments on S2 cells from modEncode/Gene Expression Omnibus.H3K4me3-S2: GSE20787H3K4me2-S2: GSE23470H4K16ac-S2: GSE20799H3K27me2-TJ.S2: GSE27790H3K9ac-S2: GSE20790H4K5ac-S2: GSE20800H3K18ac-S2. GSE20775MOF_Q4145.S2: GSE27806WDS_Q2691.S2: GSE 20835H4: repset.4620571Pol II: GSM463297Nucleosome maps: GSE22119S2 gene expression data for nucleosome occupancy: E-MEXP-1505 L = forward primer, R = reverse primer.GCCGATGTTTACCGACAATCCG6506-pro-L: CATGGTTGGTTATCGGGACTCG6506-pro-R: ATCCGTGCCTAATGATACCGCG6506-Mid-L: ACGGTTGGTGTGAACCAAATCG6506-Mid-R: ACAGTCAGCTCCCAGCAGATCG6506-end-L: AAAGTGGCGTGAAAGTTGCTCG6506-end-R: GCTGCTCAGCAAGGAGACTTSec5-pro-L: CGGACGAGCATAAAAAGAGCSec5-Pro-R: GAACTCCCATTGGCGATAAASec5-mid-L: AAATGTCTGGCGAAATGTCCSec5-mid-R: ATCAACGGCTTCATCTTTCGSec5-end-L: GCGTTTTCTTCCATTTTCCASec5-end-R: CCCATTTTTCCCACTGACTGODSH-Pro-L: GGCGCGTACAAATGAAAAATODSH-Pro-R: AAGATCCGCTAAGCGATGAAODSH-Mid-L: GCCAGGAGTTGAAGTTGGTCODSH-Mid-R: AGGCTCTCGTGGGGTAAAATODSH-End-L: GAGCTCACCGATTTGTTTCCODSH-End-R: CAGCCCTGGTATTCGATGTTCG15011-pro-L: CTCATCTTGGATCGGATCGTCG15011-pro-R: CCTGCCACAAGGAACACTTTCG15011-Mid-L: AGCTGCAACAAGCACAAATGCG15011-Mid-R: ACACGGTGTTCTTCCAGTCCCG15011-end-L: CGCTAAGGAACGTCGAAATCCG15011-end-R: AATCGAGACATTCAGGCACTCCG14872_Pro_L: TTCCCACACTGAAAAATCCACG14872_Pro_R: AAGAGCTTGAACAGCGGAACCG14872_Mid_L: GATACGCAAACCGGCATCCG14872_Mid_R: TCACGCTCTAAAACCCCAGACG14872_End_L: CAGTACGGCATGGGCAACCG14872_End_R: GAGTGCATAGGAGAGGGTAAACAPatj_Pro_L: GTGGCGTTGGCACACTTTPatj_Pro_R: CGTCGGTCACCACAATGAPatj_Mid_L:TTATCCGCCAAGGGTACAACPatj_Mid_R: ACGCGGTTGCTAACTAATGGPatj_End_L: ACTTCTGGCATCGTTTCTGACPatj_End_R: CCTCGGATCAGGTGGTACAtho2_Pro_L: GTCACACTGGCGGAACTAACTtho2_Pro_R: GGCCACATCCGTGTTTATGTtho2_Mid_L: GCCAAGACACACTCGTCCAtho2_Mid_R: GCTTCACAATGCACGGAACtho2_End_L: GAGGAGCGGCAGTACATCAtho2_End_R: CGTAACGGCACCCCTCAAEnt2_Pro_L: ACCGCACCGCACTACAAGEnt2_Pro_R: CCGCCATCCTAGTGCTGCTEnt2_Mid_L: GCTGCTCCGGCTAATGGTEnt2_Mid_R: TCTCGTATCTGGGACCATTTTEnt2_End_L: TCCCGGAACTGGTATTGAGEnt2_End_R: CCTGCTCGTGAATGCAACTBap170_Pro_L: GTGGCGTGAATGGGAAACBap170_Pro_R: ACCCCCAGCATTGTTCCTBap170_Mid_L: CTTTCCTCAGACGCCACTTCBap170_Mid_R: ATGAACCGACACACGACTGABap170_End_L: GCCGTAGCCGAGTAGGTGABap170_End_R: GTTCTTTCCCTTACCATTTTCCIncenp_Pro_L: GTTCCCGCCACTACCATCTIncenp_Pro_R: GAGGACGAGTCGGTGGAGIncenp_Mid_L: TTGAAAAGCTCATGTGTACGGIncenp_Mid_R: GCCACGTAAGGGGAGAGGIncenp_End_L: GTTCGGGAATATCTGCTTTAGGIncenp_End_R: GAGATGCCAACTTGTAGGTGATTns4_Pro_L: AAATACATGCAGAGACAGGAGGTns4_Pro_R: GCAAGGTGGTCAGCGTTAGTns4_Mid_L: GACTAGACCGGGACAATCACAns4_Mid_R: GACAGCGAGGATGAAGACGAns4_End_L: CAGCAGAGCAAACACGTTCCns4_End_R: GGTCTTGTTTATGGGCGAAACG5098-Pro-L: GAGGGAAAGGCGACCTAATCCG5098-Pro-R: GATGAGCCTCCCAAAAATCACG5098-Mid-L: GGCTACTTTGGCTGCTATGCCG5098-Mid-R: GGGCATTTCGTAATCCAAGACG5098-End-L: TTTGGGGAAGGGAACCTAACCG5098-End-R: CACACCAAAGCTCAGAGGAGTp5cr_Pro_L: CCGATTGCATGGGCGTAGp5cr_Pro_R: GCGAGGGCTGCACTGTTTp5cr_Mid_L: TGGACTCGGGCACCTGTTp5cr_Mid_R: ATGTAATCCCCCGGAACAp5cr_End_L: GCAAGAAGGATCGGGAATAAp5cr_End_R: TATCGACGGTCACACTGCTCCG4406-pro-R: CCTGGAACTTGAGGAATCCACG4406-mid-L: GGCAGCAATGTGCTCATCTACG4406-mid-R: AGCTCGGAAGGAAACTGTGACG4406-end-L: GTGACCAAAAAGCCCTTCAACG4406-end-R: RNAi of S2 cells was performed as described previously NSL1:T7-NSL1 sense: 5′- TTA ATA CGA CTC ACT ATA GGG AGA ATG GCC CCA GCG CTC ACA-3′T7-NSL1 antisense: 5′- TTA ATA CGA CTC ACT ATA GGG AGA TGA ACT TGT GGC CAC TGC C-3′NSL3:T7-NSL3 sense: 5′- TTA ATA CGA CTC ACT ATA GGG AGA TCC TTG GCG ACT ACC TCA TC-3′T7-NSL3 antisense: 5′- TTA ATA CGA CTC ACT ATA GGG AGA GTA CCA TTT CGG CCC CTA GTG-3′MBD-R2:T7-MBD-R2 sense: 5′- TTA ATA CGA CTC ACT ATA GGG AGA CGC TGG CCA CGT TTA TTA AG-3′T7-MBD-R2 antisense: 5′- TTA ATA CGA CTC ACT ATA GGG AGA TTG AAG AGA AAA AGC TTG TAC GG-3′EGFP:T7-EGFP sense: 5′-TA ATA CGA CTC ACT ATA GGG AGG ATG GTG AGC AAG GT7-EGFP antisense: 5′-TA ATA CGA CTC ACT ATA GGG AGG ATC GCG CTT CTC Ghttp://www.ebi.ac.uk/arrayexpress/) with the accession numbers listed below.All ChIP seq data is available in the ArrayExpress database : E-MTAB-1084Figure S1General characteristics of NSL binding profiles. (A) ChIP-Seq peaks obtained from NSL profiles were classified according to their distance from the nearest annotated TSS. The bar chart shows that the majority of NSL binding events is closely associated with annotated TSSs: 68.7% of NSL3 peaks, 67% of MBD-R2 peaks, 81.5% of NSL1 peaks, and 76.1% of MCRS2 peaks localize within 800 bp up- or downstream of the nearest TSS. The schematic diagram below the bar chart visualizes our definitions: proximal peaks localize within +/−200 bp (dark blue), peripheral peaks between 201–800 bp (light blue) and distal peaks are farther away than 800 bp from a TSS (white). (B) The strongest signals of NSL binding are observed within 200 bp of annotated TSSs. This is shown by the box plot of tag counts of peak summits classified as TSS-proximal, -peripheral, or –distal (whiskers = 2.5–97.5 percentiles). (C) The lack of complete overlap of NSL target genes is mainly due to stringent criteria for defining target genes. In (PDF)Click here for additional data file.Figure S22FCs (ChIP/input) for the 400 bp regions centered around TSSs. The medians were calculated for each gene based on the ChIP-seq tags of all four analyzed NSL proteins.Assessing the overlaps of NSL signals on gene promoters. (A) Median expression levels between expressed genes that are bound by all four NSLs concomitantly do not differ significantly from expressed genes devoid of NSL binding as shown by the box plot (whisker = 2.5–97.5 percentiles). The expression scores were taken from (PDF)Click here for additional data file.Figure S3NSL-bound and NSL-non-bound housekeeping genes display different nucleosome organizations. Nucleosome occupancy metaprofiles for NSL-bound (red), constitutively expressed NSL-non-bound (gray) and tissue-specific (black) genes. Metaprofiles were calculated for each group as the sum of nucleosome reads overlapping 25 bp bins spanning the −500/+1000 bp region centered at the TSS of each gene. The non-shaded white area corresponds to the −200/+200 bp region used for the analysis in (PDF)Click here for additional data file.Figure S4promoter, middle and end of gene, respectively. Error bars represent the standard deviation of three independent experiments.Depletion of different NSL proteins have distinct effects on the stability of the remaining NSL complex members but not for Pol II machinery components. (A) Western blot analyses of cytoplasmic (C) and nuclear (N) extracts from S2 cells that had been treated with dsRNA against GFP, MBD-R2, NSL1, and NSL3. Depletion of NSL1 greatly affects the stability of other NSL complex proteins namely: NSL2, NSL3, MCRS2, MBD-R2 and WDS. Depletion of NSL3 or MBD-R2 has milder effects on the levels of other NSL proteins. MOF protein levels appear affected upon MBD-R2 depletion but not in NSL1 or NSL3 knockdowns. In contrast, TBP, TFIIB and Pol II are only modestly affected in either knockdown especially when taking into consideration the loading control Nuclear RNA export factor 1 (NXF1). (B) To check whether the dsRNA treatment against NSL3, NSL1, and MBD-R2 efficiently reduced NSL binding to its target regions, ChIP was performed with antibodies against NSL1, NSL3 and MBD-R2 in the respective knockdowns in S2 cells. GFP-RNAi was used as a control. “P”, “M”, “E” represent (PDF)Click here for additional data file.Figure S5D. melanogaster genome. The Spearman correlations for the biological replicates are excellent .Correlation of biological duplicates for the ChIP-seq of Pol II in knockdowns of NSL1 and NSL3. Correlation plots between the two Pol II ChIP-seq libraries generated from duplicate knockdown experiments for (a) NSL3 and (b) NSL1. Reads were mapped to the genome with bowtie. The read counts plotted here were extracted for 25 bp bins along the entire (PDF)Click here for additional data file.Figure S6P5cr, ns4, CG15011, tho2, Patj, CG5098) as well as 2 X-linked genes (CG6506 and CG4406). Primers were positioned at the promoter of the indicated genes. Error bars represent the standard deviation of three independent experiments.Chromatin immunoprecipitation of H4K16ac in NSL1, NSL3 and MBDR2 depleted cells. ChIP-qPCR was performed using antibodies against H4K16ac and H4 in NSL1, NSL3 or MBD-R2 depleted cells. The H4K16ac signal is normalized against H4 signal from the same region. Consistent with our previous results, H4K16ac is very modestly reduced upon depletion of NSL complex members. The quantitative qPCR was performed on 5 autosomal genes ((PDF)Click here for additional data file.Figure S7De novo motif identification in NSL binding regions. (A) Motifs identified by MEME in peak summit regions of NSL3, MBD-R2, NSL1, and MCRS2. Results of MEME analyses of peaks ranked 501–1,000 (r.c. = reverse complement) confirm the motifs identified in the 500 highest peaks as shown in (PDF)Click here for additional data file.Figure S8Comparison of motif enrichments for NSL-bound and –non-bound constitutively active TSSs. (A) The bar chart displays the fold change of the core promoter affinities for the sequences of NSL-bound promoters  compared to NSL-non-bound promoters (not bound by any of the NSL proteins). The bar chart shows that even when motif enrichments are calculated within the subset of constitutively active genes, NSL-bound promoters are enriched for motif 1, DRE, E-box, motif 6, 7 and 8 whereas the depletion of TATA box, Inr motif, DPE and MTE becomes less evident. P-values were calculated with two-sided Wilcoxon rank sum test, **** = P<0.0001, *** = P<0.001, ** = P<0.01, * = P<0.5, not significant (n.s.) = P>0.5. The fold change was calculated as log(median(TRAP score of NSL-bound promoters)/median(TRAP score of NSL-non-bound promoters)). (B) Individual TRAP score (PDF)Click here for additional data file.Figure S9st subset (red line) contains genes with the strongest reduction of Pol II in promoter regions; the 3rd subset (gray line) correspondingly contains genes with smallest Pol II loss. TRAP score was calculated as a measure of protein binding affinity towards the known promoter motifs identified by Ohler et al. TRAP score densities of NSL-bound and –non-bound genes. We selected non-overlapping genes that showed significant Pol II binding in control samples and reduced Pol II levels in NSL knockdown conditions and sorted them into three groups according to the magnitude of Pol II loss. The 1(PDF)Click here for additional data file.Table S1Overview of the numbers of NSL peaks overlapping with annotated TSSs. The numbers of peaks  determined by MACS (PDF)Click here for additional data file.


Interestingly, the protein-coding genes involved in these two examples, Zmynd8 and Brd1, share many functional domains, yet their antisense ncRNAs show no homology to each other and are not present in non-murine mammalian lineages, such as the primate lineage. The lack of homology in the antisense ncRNAs indicates they have evolved independently of each other and suggests that this mode of lineage-specific transcriptional regulation may be more widespread in other cell types and organisms. Our findings present a new view of enhancer action wherein enhancers may direct isoform-specific expression of genes through ncRNA intermediates.Enhancers and antisense RNAs play key roles in transcriptional regulation through differing mechanisms. Recent studies have demonstrated that enhancers are often associated with non-coding RNAs (ncRNAs), yet the functional role of these enhancer:ncRNA associations is unclear. Using RNA-Sequencing to interrogate the transcriptomes of undifferentiated mouse embryonic stem cells (mESCs) and their derived neural precursor cells (NPs), we identified two novel enhancer-associated antisense transcripts that appear to control isoform-specific expression of their overlapping protein-coding genes. In each case, an enhancer internal to a protein-coding gene drives an antisense RNA in mESCs but not in NPs. Expression of the antisense RNA is correlated with expression of a shorter isoform of the associated sense gene that is not present when the antisense RNA is not expressed. We demonstrate that expression of the antisense transcripts as well as expression of the short sense isoforms correlates with enhancer activity at these two loci. Further, overexpression and knockdown experiments suggest the antisense transcripts regulate expression of their associated sense genes via  Many studies have revealed that antisense transcription (transcription from the opposite strand of a protein-coding or sense gene) is widespread throughout the genome. This finding was first made possible through large-scale cDNA and EST sequencing efforts and has sparked interest in uncovering functional roles for these antisense transcripts, often termed Natural Antisense Transcripts, or NATs  of mouse embryonic stem cells (mESCs) and their derived neural precursors (NPs). Each of these RNAs originates within and is expressed antisense to a known protein-coding gene. Further, the expression of each antisense RNA is correlated with an active enhancer, located by P300 binding. Finally, in each case the antisense transcript is expressed concomitantly with the sense transcript, but a shorter isoform of the sense gene appears to be preferentially expressed when the antisense transcript is also expressed. From these findings we propose a model in which gene isoform specificity may be achieved through enhancer-associated antisense RNAs. This model challenges the long-held view that enhancers act strictly on promoters of protein-coding genes to accomplish gene regulation.To uncover novel enhancer-associated RNAs, we sequenced the polyA+ transcriptomes of undifferentiated mESCs (line 46C) and their derived NPs . We fracOur final libraries and read mappings appear to be high-quality by several measures. First, known nuclear and cytoplasmic RNAs show enrichment as expected in our libraries ; Text S1To find novel transcripts associated with enhancers, we first assembled transcripts from our RNA-Seq read mappings through a method based on the Cufflinks program Among our 18 candidate novel enhancer-associated RNAs we found two which appear to correlate with isoform-specific expression of associated genes. In the first case, the novel transcription occurs in undifferentiated mESCs, antisense to known protein-coding gene Zmynd8 , S11. WeZmynd8as appears to be noncoding, due to both its general lack of conservation , S11 andSimilar to Zmynd8as, in our second case we again find novel transcription occurring in undifferentiated mESCs, antisense to the known protein-coding gene Brd1 , S12. AgAs with Zmynd8as, Brd1as appears to be noncoding for several reasons. First, the Brd1as sequence is poorly conserved , S12. ThTo summarize, both Zmynd8as and Brd1as are expressed preferentially in undifferentiated mESCs, are expressed antisense to known protein-coding genes, and are associated with P300 binding sites in mESCs. Additionally, in each case expression of the antisense transcript appears to correlate with expression of a shorter isoform of the associated sense protein-coding gene. Note that because our library preparation protocol included a polyA selection step and because our 5′ RACE protocol will amplify only transcripts possessing 5′ 7-methyl-G caps, Zmynd8as and Brd1as are likely fully processed products of pol II transcription. Thus, while Zmynd8as and Brd1as expression is reminiscent of the bidirectional eRNAs originating from enhancers reported by Kim et al. To examine these two antisense ncRNA cases further, we first asked whether the expression of Zmynd8as, Brd1as and their associated short sense isoforms is unique to undifferentiated mESCs in our neural differentiation setting. Using quantitative RT-PCR (qRT-PCR), we measured the endogenous expression of Zmynd8as, Brd1as, and the various isoforms of Zmynd8 and Brd1 in undifferentiated mESCs (UN), NPs (NP), and N2A (Neuro-2A), a mouse neuroblastoma cell line . ExpressWe next examined potential enhancer activity at the 5′ regions of Zmynd8as and Brd1as. We asked whether the P300 binding sites found in the literature were specific to undifferentiated mESCs in the neural differentiation process, as suggested by H3K4Me1 data . We perfin-vitro luciferase reporter assay  using siRNAs targeting the 3′ ends of each transcript, in regions not overlapped by processed Zmynd8 or Brd1. siRNAs are designed to participate in the RNAi pathway to achieve knockdown of their targeted RNAs. Since the target cleavage reaction of the RNAi pathway is cytoplasmic, siRNA knockdown of Zmynd8as and Brd1as is expected to occur post-transcriptionally, on fully processed Zmynd8as and Brd1as, in the cytoplasm only. Note siRNAs are double-stranded, but by designing the siRNAs to regions not present in the processed forms of Zmynd8 or Brd1 we expect that only Zmynd8as and Brd1as will be targeted by their respective siRNAs. To further ensure strand specificity, we used custom Stealth RNAi siRNAs (Invitrogen): with this technology the sense siRNA strand cannot enter the RNAi pathway. We transfected these siRNAs into mESCs, collected RNA samples 24 hours after transfection, and measured the relative expression of each antisense transcript and its associated sense isoforms with qRT-PCR. We found we achieved efficient siRNA-mediated knockdown of Zmynd8as, yet this knockdown has no effect on Zmynd8 isoform expression . Our siRAs a final test of Zmynd8as and Brd1as function, we used the antisense oligo (ASO) method most recently described by Ideue et al. The fact that Zmynd8as knockdown shows effects on gene expression, yet Zmynd8as overexpression does not, is perhaps surprising but is supported by previous literature. This phenotype is seen for the recently characterized HOTTIP RNA As detailed above, expression of Zmynd8as and Brd1as is correlated with active enhancer activity at P300 sites at the 5′ ends of each of these transcripts. In both NPs and N2A cells, Zmynd8as and Brd1as expression is sharply decreased relative to mESCs. Further, in these two cell types enhancer activity at the mESC P300 sites is abolished, as determined by the lack of p300 binding in NPs as well as the inability of these regions to significantly upregulate luciferase reporter expression in N2A and NPs. Expression of Zmynd8as and Brd1as additionally correlates with increased frequency of Zmynd8 and Brd1 taking short isoforms. We therefore suggest that enhancer activity at Zmynd8as and Brd1as drives expression of these transcripts, and in this manner the enhancers are able to control isoform specificity of the coding genes Zmynd8 and Brd1 . Note thThe exact manner in which Zmynd8as and Brd1as control isoform specificity of Zmynd8 and Brd1 is a matter requiring further study. In the case of Brd1as, this regulation appears to occur simply through transcriptional interference. In the case of Zmynd8as, knockdown data suggest that nuclear posttranscriptional downregulation of Zmynd8as increases expression of the long isoform of Zmynd8; this finding suggests that it is not solely the act of transcription of Zmynd8as that regulates Zmynd8 isoform selection. Further, this regulation must be accomplished by nuclear Zmynd8as, as knockdown of cytoplasmic Zmynd8as via siRNAs does not lead to the same upregulation of Zmynd8-long. Curiously, and similarly to HOTTIP With these points in mind, we propose that Zmynd8as regulates Zmynd8 transcription while tethered to the chromosome at its site of origin, in a manner similar to the CCND1 upstream ncRNAs Other factors associated with the nascent antisense transcript could contribute to the stop signal. For example, it is likely that splicing and polyadenylation of Zmynd8as occurs co-transcriptionally . See The RNA-Seq data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus mESCs  were differentiated to Day 6 and Day 10 neural precursors as described by Eiraku et al. qPCR was performed with iQ SYBR Green Supermix (Biorad) using 5 ng of ChIP DNA per reaction; all reactions were set up in triplicate. Cycling was performed on a Rotor-Gene 6000 (Qiagen) according to the iQ SYBR Green Supermix instructions. Primers used for ChIP-qPCR are indicated in Regions spanning the p300 sites of Zmynd8as and Brd1as were cloned into pENTR-D/TOPO (Invitrogen), with primers indicated below. All clones were sequence-verified against the mm9 assembly of the mouse genome on the UCSC Genome Browser; mismatches relative to the reference genome were permitted if represented in dbSNP Enhancer clones were introduced into a pGL4.12 vector (Promega) modified to include a thymidine kinase (TK) promoter upstream of firefly luciferase. This TK-pGL4.12 vector was a gift from the laboratory of Dr. Martin Privalsky at the University of California, Davis. For downstream enhancer tests, a Gateway rfB cassette was introduced into the TK-pGL4.12 vector at the BamHI site, downstream of the SV40 late poly(A) signal, using the Gateway Vector Conversion System (Invitrogen), and enhancer clones were introduced into this vector from pENTR-D/TOPO using the Gateway LR system (Invitrogen). Cells were transfected in triplicate with a 9∶1 mass ratio of enhancer construct:pRL-TK renilla luciferase control plasmid (Promega) using Lipofectamine 2000 (Invitrogen) according to the manufacturer's instructions. Cells were assayed approximately 24 hours later with the Dual-Luciferase System (Promega), and luciferase activity was measured in a Victor Light Luminescence Counter (Perkin-Elmer); three technical replicates were measured for each biological replicate. In figures, luciferase activity (ratio of firefly counts per second/renilla counts per second) for each enhancer construct is shown relative to the activity observed for TK-pGL4.12 (ratio of enhancer construct luciferase activity: TK-pGL4.12 activity). Error bars represent 95% confidence intervals over the three biological replicates. All experiments were repeated at least twice. See Text 8 for luciferase assays presented in qRT-PCR was performed using the QuantiTect SYBR Green RTPCR Kit (Qiagen), using 100 ng of total RNA per reaction; all reactions were set up in triplicate. Cycling was performed on a Rotor-Gene 6000 (Qiagen) according to the QuantiTect SYBR Green RTPCR Kit instructions. Primers used for qRT-PCR are indicated in Sequence-verified entry clones for Zmynd8as, Brd1as, and their reverse complements were introduced into the mammalian expression vector pcDNA3.2-V5/DEST (Invitrogen) through the Gateway system (Invitrogen); this vector uses a CMV promoter to drive expression of its inserts. Constructs were transfected into N2A with Lipofectamine 2000 (Invitrogen), according to the manufacturer's instructions. RNA was harvested 24 hours after transfection for qRT-PCR experiments using Trizol reagent (Invitrogen). RNA extraction was followed by DNase treatment and cleanup with RNeasy columns (Invitrogen) to ensure removal of plasmid DNA.Stealth siRNAs and controls were designed with the Invitrogen BLOCK-iT RNAi designer and ordered from Invitrogen. ASOs were ordered from Integrated DNA Technologies (IDT) with sequences indicated in siRNAs and ASOs were transfected into undifferentiated mESCs with Lipofectamine 2000 (Invitrogen) according to the Stealth RNAi transfection protocol provided with the reagent. RNA was harvested from cells 24 hours after transfection for use in qRT-PCR experiments using Trizol reagent (Invitrogen).Text S1Supporting Methods and References. Methods for neural differentiation, RNA-Seq library construction, RNA-Seq data analysis, transcript assembly, and characterization of Zmynd8as, Zmynd8, and Brd1. Also contains Supporting References 65–85.(PDF)Click here for additional data file.Figure S1Schematic for intial mapping of RNA-Seq libraries. Mapping was performed in stages with the SOLiD Corona pipeline; see (TIF)Click here for additional data file.Figure S2Coverage of known cytoplasmic and nuclear RNAs in RNA-Seq libraries. (A) RNA-Seq coverage tracks for (TIF)Click here for additional data file.Figure S3Coverage of known housekeeping genes in RNA-Seq libraries. Distributions of RPKM values for each expression level category defined by Warrington et al. (TIF)Click here for additional data file.Figure S4Comparison of neural differentiation protocols. Comparision of neural differentiation protocols used in Abranches et al. 2009 (TIF)Click here for additional data file.Figure S5NP specificity distributions for Abranches et al. gene sets. As a measure of tissue specificity, the NP specificity scores for the genes reported upregulated in each cell type by Abranches et al. (TIF)Click here for additional data file.Figure S6Differences in expression values for adjacent internal vs. adjacent outer exons. Expression value differences are shown as the absolute value of the RPKM percentile differences between the two exons. “in,” internal; “out,” outer. Modified boxplots are shown as in (TIF)Click here for additional data file.Figure S7Distribution of RPKM percentile differences (averages per gene) in adjacent internal exons. The 90th, 95th, and 99th percentile values are indicated for each RNA type. See (TIF)Click here for additional data file.Figure S8Distribution of RPKM percentile differences in adjacent outer exons. The 90th, 95th, and 99th percentile values for the adjacent internal exons shown in (TIF)Click here for additional data file.Figure S9Number of fragments per UCSC Known Gene before and after merging Cufflinks transcripts. “CL,” Cufflinks transcripts with no merging; “M5,” merged Cufflinks transcripts allowing an RPKM percentile as great as 5; “M10,” merged Cufflinks transcripts allowing an RPKM percentile difference as great as 10. In merged cases, an “intron length” of up to 11 kb was allowed. Modified boxplots are shown as in (TIF)Click here for additional data file.Figure S10Merged Cufflinks transcripts at Nestin in undifferentiated nuclear library. RNA-Seq read coverage for Nestin is shown in the first track, above UCSC Genes, second track. Cufflinks (v0.8.1) output is third track from top; note Cufflinks predicts several fragmented transcripts along Nestin. Fourth track from top, merged Cufflinks output allowing an RPKM percentile difference of 10 and an “intron length” of 11 kb; sixth track from top, merged Cufflinks output allowing an RPKM percentile difference of 5 and an “intron length” of 11 kb. See (TIF)Click here for additional data file.Figure S11Zmynd8as and Zmynd8-short transcript structures. Structures determined with 5′ and 3′ RACE (TIF)Click here for additional data file.Figure S12Brd1as and Brd1-short transcript structures. Structures determined with 5′ and 3′ RACE (TIF)Click here for additional data file.Figure S13Test of enhancer activity in mESCs and NP cells with Zmynd8as cDNA. Enhancer constructs, with the Zmynd8as P300 site cloned upstream of the TK promoter, were transfected into undifferentiated mESCs (A) or NP neural precursors (B) along with a plasmid driving either Zmynd8as, its reverse complement, or GFP under a CMV promoter. TK, promoter only luciferase reporter vector; enhTK and enhflipTK, Zmynd8as P300 site cloned upstream of the TK promoter and luciferase reporter, in forward and reverse orientations, respectively; GFP, GFP plasmid under control of a CMV promoter; cDNA and cDNAflip, plasmids containing Zmynd8as and its reverse complement under the control of a CMV promoter.(TIF)Click here for additional data file.Figure S14Functional domains in long and short isoforms of Zmynd8 and Brd1. Both long and short isoforms of Zmynd8 contain a zinc finger domain , a bromodomain (Brom), a PWWP domain (PW), and a domain of unknown function DUF3544 (DUF); the long form also contains a zinc finger MYND-type domain (MYND). Both long and short isoforms of Brd1 contain an enhancer of polycomb-like, N-terminal domain (EPL) and two zinc finger domains and a bromodomain, as in Zmynd8; the long form also contains a PWWP domain. Numbers at bottom indicate lengths of the isoforms in amino acids. Domains determined with InterProScan (TIF)Click here for additional data file.Table S1RNA-Seq mapping statistics. Mapping statistics for reads from RNA-Seq experiment. Sample, RNA library information; Barcode, barcode used for sequencing; Count, total number of reads returned; Filtered, percent of reads passing filter step; Mapped, percent of reads mapped singly or multiply; Unique, percent of reads mapped uniquely. Barcodes B14–B15 were part of a separate project and were not used in this study, but are presented here for completeness of run information. “UN” indicates our undifferentiated mESC libraries, while “D5” indicates our day 5 neural precursor libraries.(PDF)Click here for additional data file.Table S2Bad joins of UCSC Known Genes in Cufflinks and merged Cufflinks output. “Bad joins” occur when two separate genes are merged into a single transcript. Cufflinks bad joins, bad joins by Cufflinks; d10, bad joins when an RPKM percentile difference of 10 is allowed for merging Cufflinks output; d5, bad joins when an RPKM percentile difference of 5 is allowed for merging Cufflinks output.(PDF)Click here for additional data file.Table S3ncRNA candidates for study from the RNA-Seq project. Columns indicate the following information: Region, genomic coordinates for area of novel transcription on the UCSC Genome Browser mouse assembly mm9; Strand, strand of novel transcription; Nearest Gene, nearest known gene; Description, properties of novel transcription; Expression, RNA-Seq libraries containing the novel transcription; Enhancer, cell types with enhancer activity in this area, with enhancers defined from previous literature as stated in the text of this section. Two identified regions correspond to lincRNAs first reported by Guttman et al. (PDF)Click here for additional data file.Table S4Sequences of primers, siRNAs, and ASOs used in this study. Primers were used for a variety of purposes, indicated in the “Purpose” column. For RACE primers, primer names indicate whether the primer was used in 5′ or 3′ RACE. Note forward cloning primers have “CACC” at their 5′ ends to allow directional cloning with the Invitrogen Gateway System. For ASOs, the five terminal nucleotides on each end are 2′-Omethoxyethyl nucleotides indicated as “mN,” where N is the nucleotide. The phosphothioate backbones are indicated with asterisks.(PDF)Click here for additional data file.


SCD gene and provide evidence that allele T at AY487830:g.2228T>C in the promoter region enhances fat desaturation (the ratio 18∶1/18∶0 in muscle increases from 3.78 to 4.43 in opposite homozygotes) without affecting fat content . No mutations that could affect the functionality of the protein were found in the coding region. First, we proved in a purebred Duroc line that the C-T-A haplotype of the 3 single nucleotide polymorphisms (SNPs)  of the promoter region was additively associated to enhanced 18∶1/18∶0 both in muscle and subcutaneous fat, but not in liver. We show that this association was consistent over a 10-year period of overlapping generations and, in line with these results, that the C-T-A haplotype displayed greater SCD mRNA expression in muscle. The effect of this haplotype was validated both internally, by comparing opposite homozygote siblings, and externally, by using experimental Duroc-based crossbreds. Second, the g.2281A>G and the g.2108C>T SNPs were excluded as causative mutations using new and previously published data, restricting the causality to g.2228T>C SNP, the last source of genetic variation within the haplotype. This mutation is positioned in the core sequence of several putative transcription factor binding sites, so that there are several plausible mechanisms by which allele T enhances 18∶1/18∶0 and, consequently, the proportion of monounsaturated to saturated fat.There is growing public concern about reducing saturated fat intake. Stearoyl-CoA desaturase (SCD) is the lipogenic enzyme responsible for the biosynthesis of oleic acid (18∶1) by desaturating stearic acid (18∶0). Here we describe a total of 18 mutations in the promoter and 3′ non-coding region of the pig  Good eating habits are conducive to good health. Total fat and fatty acid content in food affect both human health and food quality and, consequently, they are becoming increasingly important to consumers. There is convincing evidence that a high dietary intake of saturated fat (SFA) increases the risk of lipid metabolism disorders which are common to many human chronic diseases de novo palmitoleoyl-CoA and oleoyl-CoA, respectively. Maintaining a balance in the SCD activity is paramount to optimize health SCD expression, both in normal and in disease states, is tightly controlled by dietary and hormonal factors SCD is largely expressed in liver and adipose tissue, responding positively to high carbohydrate diets and negatively to starvation and PUFA rich diets. The ratio of 18∶1 to 18∶0 (18∶1/18∶0) is commonly used as an indirect indicator of SCD activity. Alterations in this desaturation ratio have been linked to cardiovascular disease, obesity, diabetes, and cancer The pork fatty acid composition varies across fat tissues and muscles and it is greatly influenced by the genetic type of the pig, the diet and, in general, by any factor affecting fatness, such as gender or age SCD gene is assigned to chromosome SSC14q27 SCD is, therefore, an attractive positional candidate gene SCD gene affecting fatty acid composition of muscle and adipose tissue. Several single nucleotide polymorphisms (SNP) in the SCD promoter region have been associated to 18∶0 and 18∶1 content. Yet, results are inconclusive, as either the location of haplotypes is not coincident ad hoc pig crossbreds divergent for fatness. Fat content and composition data is currently available for all these samples. Here we use this repository to provide evidence that allele T at SNP AY487830:g.2228T>C in the SCD gene is a causative mutation that promotes fat desaturation in muscle and subcutaneous fat.In pigs, the SCD gene were sequenced in 12 Duroc pigs representing extreme phenotypes for muscle oleic acid content. A total of 18 polymorphisms were identified: three in the promoter and 15 in the 3′ non-coding region (Table S1). No variation was found in the sequence corresponding to the SCD coding and 5′ non-coding regions.The 5′ and 3′ non-coding regions, coding region, and 680 bp upstream on the proximal promoter of the pig SCD transcription unit spans 16,186 bp and includes a coding region of 1,079 bp plus an unusually long 3′UTR of 4,047 bp. Despite being over 12 kb apart, in the Duroc animals analyzed the polymorphisms of promoter and 3′UTR regions formed one haplotype block which displayed >95% overall linkage disequilibrium (r2 = 0.965 between SNPs g.2108C>T in the promoter and g.15109A>G in the 3′UTR). The three SNPs in the promoter region were close together in a 173 bp fragment , with two clearly predominant haplotypes . The results of the association analysis confirmed that pigs carrying the H1 haplotype had higher 18∶1/18∶0 ratio in the three muscles analyzed  and subcutaneous fat but not in liver (gluteus medius of 0.33 (Table S2). As a result, the substitution effect of H1 for H2 for MUFA, 18∶1, and 16∶1 in the gluteus medius muscle was 1.02%, 0.70%, and 0.30%, respectively. Adjusting these values for the age at slaughter and fat content did not change the results. Because segregation was at intermediate frequencies, the above haplotype variants were able to explain a relevant fraction of the total additive genetic variance for MUFA/SFA (31%), 18∶1/18∶0 (37%), 16∶1/16∶0 (35%), MUFA (20%), C18∶1 (13%), and C16∶1 (25%). However, they did not affect fat content-related traits, including carcass weight, backfat thickness, lean content, and IMF content (Table S2), or standard blood lipid indicators (Table S3). The favorable effect of H1 on 18∶1/18∶0 was internally validated by comparing opposite homozygote siblings  which had at least one tissue analyzed for fatty acid composition Exp 1; Table 1.SCD mRNA expression in muscle, subcutaneous adipose tissue, and liver across diplotypes. In accordance with the association results, we found that H1H1 animals showed greater SCD mRNA expression than H2H2 pigs in muscle .We next validated the effect of the haplotypes on experimental Duroc crossbreds Exp 2; Table 1.g.2108C>T and g.2228T>C, while fixed at g.2281A>G). The haplotype segregation was as expected (Table S4). Importantly, the results of this last validation experiment allow us to exclude SNP g.2281A>G as the causative mutation of the effect over the desaturation index.To refine the haplotype block determination and disentangle which SNP was the responsible of the haplotype effect, we investigated the progeny of two heterozygote C-T-A/T-C-A (H1H3) Large White boars. Barrows from mating these boars with H1H1 Landrace sows were contemporaneously raised with pigs in Exp 2, with the expectation to obtain half of the offspring C-T-A/C-T-A (H1H1) and half C-T-A/T-C-A (H1H3)  of human, cow, pig, and sheep SCD gene with the view of identifying common regulatory modules. The promoter sequence displays stretches of strong conservation between these four species interspersed with fragments of lower conservation . A conserved polyunsaturated fatty acid response element (PUFA-RE), which includes a sterol regulatory element (SRE), two CCAAT boxes (NF-Y) and two nuclear factor (NF)-1 binding sites, has been described approximately at positions −450/−550, which is highly conserved between species Figure S1) and is essential for correct SCD gene regulation by PUFA and cholesterol SCD expression through the interaction with the SRE element in this regulatory region in silico analyses revealed a conserved peroxisome proliferator-activated receptor gamma (PPARG) motif at position −400/−420. Another region containing many potential binding motifs lays in the sequence around the g.2228T>C polymorphism, about 40 bp upstream of the TATA-box. Several transcription factor-binding motifs partially overlap in this region. There is a conserved PPARG and NF-1 motif on the negative strand, which lay adjacent to a CCAAT/enhancer binding protein motif (C/EBP) in cow, sheep and humans. However, our analysis failed to recognize this C/EBP motif in the pig sequence, although it has been postulated before Table S5). In addition, bridging these PPARG sites together, there are two binding motifs for the retinoid X receptor and the retinoic acid receptor α (RXR:RARα) response elements (direct repeats with 1-bp (DR1) and 5-bp (DR5) spacer sequence, respectively). The g.2228T>C polymorphism lies in the core of the IR3 motif in the positive strand and at the end of the DR5 element .To assess if polymorphisms in the promoter region could affect g.2108C>T and g.2228T>C in the promoter region of the SCD gene increases fatty acid desaturation in muscle and subcutaneous fat, in line with some previous findings in Duroc g.2281A>G) was excluded as a causal by the results in our external validation experiment (Exp 2), where Large White × Landrace pigs were all homozygous for this SNP but still presented the same effect over the desaturation index. Conversely, as in all the screened populations SNPs g.2108C>T and g.2228T>C were in almost complete linkage disequilibrium, we were not able to disentangle which one of the two is the causative mutation. However, considering the results from a Landrace × Korean native pig intercross in which these two SNPs segregated independently SCD promoter in independent Duroc lines g.2228T>C SNP is common to all the studies which have found a significant relationship between the SCD promoter genotype and fatty acid composition g.2228T>C is the causative mutation leading to increased fatty acid desaturation. Interestingly, this allele is virtually absent in the Asian breeds SCD as a positional candidate gene for fatty acid composition. It remains unclear why Duroc is the only breed where g.2228T>C is segregating at intermediate frequencies.We have shown that the C-T haplotype at SNPs g.2228T>C SNP is positioned at 58 nt from the SCD transcription start site, in a stretch of moderate sequence conservation with cow, sheep, and human SCD sequences . In silico analysis of this region has identified several overlapping putative transcription factor binding sites, some of which are unique to the pig promoter and contain the T>C mutation at position g.2228  reported three new isoforms for the SCD gene, bringing the total number to four. They are translated into four different peptides. The tissue-dependent expression of these isoforms is another level of complexity of the activity of the SCD expression that has not yet been explored in pigs.In this regard, we were able to prove increased SCD expression is a complex phenomenon. The intracellular concentration of desaturases fluctuates in response to a large number of effectors including hormonal and dietary factors SCD expression in muscle but not in subcutaneous adipose tissue 2 = 0.38, p<0.05). In contrast, the dietary 18∶1/18∶0 ratio exerted a positive effect on the additive effect of allele T in muscle . These effects were not detected in the subcutaneous fat. Overall, these findings not only give additional evidence that the effect of the SCD genotypes is most noticeable in muscle, but also that it is tuned by the diet. In this regard, an interesting topic for future research will be to study the effect of these haplotype variants in pigs subjected at diets differing in vitamin A, or some other metabolic precursor of retinoic acid. In line with two of our hypothetical scenarios, it has been shown experimentally that retinoic acid inhibits porcine preadipocyte differentiation by upregulating RAR and downregulating RXR In addition, the regulation of SCD gene with the potential to increase MUFA content in pork. Strict values on fatty acid content are becoming a common feature in regulations for foods bearing nutritional or health claims concerning fat properties. The MUFA content can be also subjected to such regulations. Selective lipid deposition in meat animals is a relevant issue not only in terms of animal agriculture but also in biomedicine. Evidence is also emerging indicating the existence of allelic variations in the human SCD gene affecting enzyme activity and, consequently, disease risk factors We provide evidence that there exists genetic variation in the The experimental protocol was approved by the Committee on the Ethics of Animal Experiments of the University of Lleida.ad libitum access to commercial diets. From 160 days of age onwards they were fed a pelleted finishing diet with an average composition of 16.9% crude protein, 6.6% fiber, and 6.7% fat . In two of the Duroc batches at 180 days of age three 10-mL samples of blood per barrow were obtained between 8 and 10 a.m. after an overnight fast. All pigs were slaughtered in the same commercial abattoir, where lean content and other carcass traits were measured by using an on-line ultrasound automatic scanner. Immediately after slaughter, samples of the semimembranosus muscle, subcutaneous adipose tissue at the level of the third and fourth ribs, and liver were collected, snap-frozen, and stored at −80°C. After chilling for about 24 h at 2°C, a sample of the gluteus medius muscle was excised from the left side ham, vacuum packaged, and stored at −80°C. Finally, we used genomic DNA representing European wild boar and several domestic breeds of pigs and commercial crossbreds for monitoring haplotype segregation.The association analysis (Exp 1) was done using genomic DNA and phenotypic data of twelve batches (n = 891) of purebred Duroc barrows from the line described in A representative aliquot from the pulverized freeze-dried tissue was used for fat analysis. Fat content and fatty composition was determined in duplicate by quantitative determination of the individual fatty acids by gas chromatography semimembranosus muscle. Samples (50 mg) were homogenized in 1 mL of TRI Reagent  using a mechanical rotor  following the manufacturer’s instructions.Genomic DNA was isolated from freeze-dried muscle samples using standard protocols SCD proximal promoter and the entire exonic regions of the gene. Seven primer sets were designed with the Primer3Plus online oligonucleotide design tool (http://primer3plus.com) Table S6). The promoter and 3′ non-coding region were amplified from approximately 60 ng of genomic DNA from twelve Duroc pigs selected to represent extreme levels of oleic acid in gluteus medius. PCR reaction of a final volume of 25 µL contained 200 nM of each primer, 160 mM dNTPs, 3 mM MgCl2, and 0.4 U of Taq DNA polymerase . PCR conditions were as follows: 95°C for 5 minutes, 35 cycles of 95°C for 20 sec, annealing temperature as in Table S6 for 40 sec, and 72°C for 90 sec, and completed by an extension step at 72°C for 5 min. The 5′ non-coding and coding regions were amplified using the same reaction and cycling conditions from total RNA of semimembranosus muscle retrotranscribed to cDNA as indicated in the Gene Expression Analysis section. PCR amplicons were sequenced on an ABI-3100 capillary sequencer  with the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). Sequences were aligned with the ClustalW alignment tool  and compared to identify polymorphic sites. All sequences have been submitted to the GenBank data base (accession numbers KC736975 and KC736976).Based on genomic and cDNA sequences  primers were designed in order to amplify and sequence 780 bp of the SCD promoter polymorphisms  were genotyped with allele discrimination assays  using the primers and probes described in Table S7. For all of them, 15 ng of genomic DNA were used in 8 µL reactions containing 1x TaqMan Universal PCR Master Mix (Applied Biosystems) and 900 nM primers and 200 nM probes. Cycling conditions were as follows: Initial denaturation at 95°C for 10 min and 40 cycles at 93°C for 5 sec and 60°C for 1 min.Three SCD expression levels were measured by quantitative real-time PCR (qPCR) in semimembranosus muscle, subcutaneous fat, and liver and from a subset of 45 animals representing diplotypes H1H1, H1H2, and H2H2. Total RNA (1 µg) was treated with Turbo DNA-free DNase  according to the manufacturer’s protocol and retrotranscribed with 0.5 pmol of random hexamers using 100 U of MuMLV reverse transcriptase  at 25°C for 10 min, 42°C for 1 h and 70°C for 10 min. cDNA was diluted 1∶10 in DEPC-treated H2O prior to qPCR analysis. Primers, PCR conditions and data normalization was conducted as in 2The haplotype effect was estimated within tissue using a linear model including the diplotype and the batch . The age at slaughter and fat content were tested as covariates in the model. The haplotype additive (a) and dominant (d) effects were tested replacing the diplotype effect by the covariates a  and d  for diplotypes H1H1, H1H2, and H2H2, respectively. The effects of the diplotype and covariates were tested using the F-statistic and the differences among diplotypes were contrasted with the Tukey-HSD test. The batch was removed from the model when results were expressed on a batch basis (Exp 1). The haplotype effect in the validation experiment (Exp 2) was estimated within genetic type using the same procedure. In IB-2 × DU-1 and LW-1 × L-2 crossbreds, the sire effect was included in the model because only two IB-2 and LW-1 sires were used. A paired t-test was used for comparing homozygote siblings. The additive fraction of the genetic variance accounted for by the diplotype was calculated as 2pqaSCD promoter, a computer-assisted identification of putative promoter/enhancer elements was performed using the GENOMATIX software suite (Genomatix Software GmbH) SCD promoter from pig, human, cow, and sheep. Common transcription factor binding motifs were explored using the CommonTF, DiAlignTF and MatInspector applications for pattern search and analysis.To characterize the Figure S1Comparative promoter sequence between cow, pig, sheep and human SCD gene.Panel (A) depicts a sequence alignment of a 700 bp homologous 5′ flanking sequence of the gene using ClustalW . The conserved PUFA response element including a sterol response element (SRE), two CCAAT-box (NF-Y), two nuclear factor (NF)-1 and one stimulator protein 1 (SP1) binding site is boxed. Other common motifs  are also indicated along with the position of the three pig promoter SNPs genotyped. Several putative transcription factor binding sites close to the g.2228T>C polymorphism are depicted in the four species; these include a putative CCAAT enhancer binding protein (C/EBP) element, NF-1, two PPARG binding sites, and two RAR:RXR motifs (DR1 and DR3). The diagram in Panel (B) represents the potential binding of these transcription factors in the sequence around the g.2228T>C polymorphism.(TIF)Click here for additional data file.Table S1Description of the polymorphisms identified at SCD gene. Eighteen polymorphisms in the SCD gene were found to be segregating in the investigated Duroc population by comparing the DNA sequence of six pigs with extreme high and low values for oleic acid content in gluteus medius muscle. Position numbering is relative to the translation start codon and the genomic sequence AY487830. Three of the polymorphisms are single-nucleotide substitutions in the promoter region.(DOCX)Click here for additional data file.Table S2Carcass weight, fat content, and fatty acid composition by SCD diplotype and fat tissue in purebred Duroc. The haplotype H1 showed a favorable effect on fatty acid compositional traits resulting from increased SCD activity  and no effect on fat content-related traits . This pattern was more evident in muscle than in subcutaneous fat. Values are expressed as the least square mean (± standard error) for each trait by diplotype. Means lacking a common superscript within trait differ (p<0.05).(DOCX)Click here for additional data file.Table S3Blood lipid indicators by SCD diplotype in purebred Duroc. The diplotype did not affect (p<0.05) blood plasma lipid indicators at 180 d. Values are expressed as the least square mean (± standard error) for each trait by diplotype.(DOCX)Click here for additional data file.Table S4Carcass weight, fat content, and fatty acid composition by SCD diplotype in experimental crossbred pigs. The haplotype H1 showed a favorable effect on 16∶1/16∶0 and 18∶1/18∶0 ratios and no effect on fat content-related traits . Values are expressed as the least square mean (± standard error) for each trait by diplotype. Means lacking a common superscript within trait differ (p<0.05).(DOCX)Click here for additional data file.Table S5Positioning of the putative transcription factor binding sites in the proximal promoter of the pig SCD gene. Results from the in silica analysis performed with the MatInspector Genomatix program. The putative PPARG, RAR:RXR and NF-1 motifs around the AY487830:g.2228T>C SNP are highlighted.(XLSX)Click here for additional data file.Table S6Sequence of DNA primers used in the characterisation of the porcine SCD gene. A list of the primers used to amplify and sequence seven fragments of the porcine SCD gene encompassing 780 bp of the promoter promoter and the entire coding and 5′ and 3′ non-coding regions (3UTR). The annealing temperature used in the PCR cycling program is also indicated.(DOCX)Click here for additional data file.Table S7Primers used for genotyping the three single nucleotide polymorphisms (SNPs) in the porcine SCD gene promoter with an allelic discrimination assay.(DOCX)Click here for additional data file.


GcvB inhibits yifK mRNA translation by pairing with a sequence immediately upstream from the Shine-Dalgarno motif. Surprisingly, we found that some target sequence mutations that disrupt pairing, and thus were expected to relieve repression, actually lower yifK expression and cause it not to respond to GcvB variants carrying the corresponding compensatory changes. Work prompted by these observations revealed that the GcvB target sequence in yifK mRNA includes elements that stimulate translation initiation. Replacing each base of an ACA trinucleotide near the center of the target sequence, by any other base, caused yifK expression to decrease. Effects were additive, with some triple replacements causing up to a 90% reduction. The enhancer activity did not require the ACA motif to be strictly positioned relative to the Shine-Dalgarno sequence, nor did it depend on a particular spacing between the latter and the initiating AUG. The dppA mRNA, another GcvB target, contains four ACA motifs at the target site. Quite strikingly, replacement of all four ACAs by random trinucleotide sequences yielded variants showing over 100-fold reduction in expression, virtually inactivating the gene. Altogether, these data identify the ACA motif as a translation-enhancing module and show that GcvB's ability to antagonize the enhancer function in target mRNAs is quintessential to the regulatory effectiveness of this sRNA.GcvB is an archetypal multi-target small RNA regulator of genes involved in amino acid uptake or metabolism in enteric bacteria. Included in the GcvB regulon is the  Salmonella, we discovered that the sequence base-pairing with GcvB in the target mRNA functions as a translational enhancer. Replacing an ACA motif near the center of the sequence with unrelated trinucleotide sequences leads to a decrease in translational initiation efficiency that can be as severe as more than 90%. Interestingly, some of these replacements concomitantly render the mRNA insensitive to GcvB variants carrying the appropriate compensatory changes, suggesting that targeting the enhancer element is paramount for GcvB regulatory effectiveness. Overall the data presented in the paper unveil the role of the ACA motif in the translation initiation process and lay the grounds for further analysis of the mechanism involved.The majority of small RNA (sRNA) regulators in bacteria act by inhibiting translation initiation in target messenger RNAs. The study of this regulatory mechanism not only allows a better understanding of sRNA function but it can also provide new insight into aspects of the translation initiation process that remain incompletely characterized. This was the case in the work described here. Analyzing the mechanism by which GcvB, a multi-target sRNA, downregulates a putative amino acid transporter in  E. coli and SalmonellaA relevant chapter in the expanding field of RNA-mediated gene regulation is devoted to the activities of multi-target trans-encoded small RNAs in bacteria. Acting in concert with chaperon protein Hfq, these RNA regulators function by base-pairing with short, often imperfectly complementary sequences in the 5′ untranslated regions (UTR) of target messenger RNAs. They can affect translation and turnover of several mRNAs simultaneously thus reprogramming gene expression of whole gene families in a coordinate manner in response to environmental cues  upstream from the translation initiation codon. Removal of this sequence (as part of a 27 nt deletion), besides causing the loss GcvB regulation, affected gltI translation, suggesting that the CA-rich element acts as a translational enhancer. Consistent with this interpretation, crafting the 27 nt segment at the corresponding position of an unrelated mRNA conferred simultaneously GcvB control and increased translational efficiency GcvB uses a specific sequence region to pair with most, although not all lac-based genetic screen aimed at identifying genes controlled by trans-encoded small RNAs in Salmonella. A random library of lacZ fusions to chromosomal genes was generated using a phage Mu-derived transposon (MudK) and screened for isolates whose LacZ levels changed (either increased or decreased) upon inactivating Hfq hfq mutant background, carried the lacZ insert translationally fused to the yifK gene yifK might be a GcvB target. We thus proceeded to test this hypothesis and characterize yifK regulation. While this work was underway, Sharma and coworkers identified yifK mRNA as a member of the gcvB regulon by microarray analysis; however, these authors could not confirm direct regulation by GcvB due to low reporter fluorescence of the yifK-gfp fusion used in the study yifK expression might be indirect.Some years ago, our laboratory performed a in vivo and in vitro evidence that GcvB downregulates yifK directly by pairing with a sequence immediately preceding the Shine-Dalgarno (SD) motif in yifK mRNA. A surprising observation in the course of this study was that some target sequence mutations that disrupted pairing did not cause yifK expression to increase – as expected for the relief of GcvB repression – but had the opposite effect. The drop in expression was not suppressed by deleting gcvB nor was it accentuated in a GcvB mutant carrying the appropriate compensatory changes. Closer analysis revealed that the GcvB target sequence includes elements that stimulate yifK mRNA translation. In the absence of such elements, the role of GcvB pairing in regulation becomes marginal.Here we present lac) transposon in the yifK gene; one predicted to produce a LacZ protein fusion to the 48th amino acid (aa) of the 461 aa YifK (yifK87::MudK); the other with LacZ inserted after the 95th aa of YifK (yifK88::MudK) yifK87::MudK produced significantly higher ß-galactosidase activity and was chosen for the present study. A survey of protein sequence databases showed YifK to be a highly conserved protein with the characteristic signature of amino acid transporters. The known role of GcvB in the regulation of some members of this family made this small RNA the likeliest candidate to control yifK expression. This was confirmed by deleting the gcvB gene and testing the effects of the deletion on the expression of the yifK87::MudK fusion (hereafter referred to as yifK-lacZY). As shown in gcvB deletion causes a nearly 5-fold increase of ß-galactosidase activity in exponentially growing cells, while effects decline in stationary phase. Somewhat surprisingly, LacZ levels in the gcvB-deleted strain are not as high as the levels measured in a strain deleted for hfq  under error-prone conditions and introduced into the chromosome of a strain harboring the yifK-lacZY reporter fusion via lambda red recombination. Most of the isolates originating from the gcvB mutagenesis carried changes in the gcvB promoter or in the promoter of the adjacent gcvA gene  that might constitute a site for Hfq binding hfq background , yifK mRNA being otherwise undetectable when expressed from the wild-type promoter (data not shown). The analysis identified two yifK mRNA species, a 1.4 kilobase (Kb) mRNA covering just the yifK coding portion and a longer, 2.0 Kb RNA extending into the adjacent argX-hisR-leuT-proM tRNA operon. As shown in yifK translation in ΔgcvB or Δhfq cells protects the 1.4 Kb RNA against RNase E cleavage. To confirm this interpretation, the analysis was repeated with strains that, besides the promoter “up” mutation, carried a mutation in the Shine-Dalgarno sequence . As shown in gcvB or Δhfq strains but not in the rne ts mutant, consistent with the idea that reduced translation renders yifK mRNA susceptible to RNAse E degradation.Northern blot analysis was used to assess the effects of GcvB regulation on yifK and the tRNA operon suggests that the 1.4 Kb RNA originates from processing of the longer form. Likely, under normal conditions  yifK transcription contributes only to a small fraction of the four tRNAs, as most the tRNA operon transcription results from a strong promoter located immediately upstream from the argX gene E.coli, showed that this tRNA precursor is processed by the concerted actions of RNase E and RNase P in a pathway that, intriguingly, also sees the participation of Hfq Absence of any obvious transcription termination signals in the intercistronic region between yifK expression was exquisitely sensitive to the growth medium and virtually silenced in minimal medium. As a result, a strain with the yifK-lacZY fusion is phenotypically Lac− when plated in minimal medium. We exploited this phenotype to positively select for spontaneous Lac+ mutants. The selection yielded two classes of mutations, one genetically linked to the yifK-lacZY locus, the second mapping elsewhere. All of the linked mutants that were analyzed were found to harbor the -33 C:G to A:T promoter change obtained previously (see above). The unlinked mutations mapped in a chromosomal interval encompassing the gene for leucine response regulator, Lrp. Prompted by this observation, we introduced an lrp insertion mutation into the yifK-lacZY-containing strain. The resulting strain acquired a Lac+ phenotype . Trying to clarify this observation, portions of the region of interest were mutagenized separately. As shown in yifK-lacZY repression. Repression is restored upon combining the compensatory alleles. Thus, this portion of the target sequence behaves as expected, and the behavior of the compensatory mutant strongly suggests that GcvB represses yifK through a base-pair interaction. In vitro toeprint experiments, showing that GcvB inhibits the binding of ribosomal 30S subunit to yifK translation initiation site, specifically and in a dose-dependent manner  between the most conserved base of the SD motif yifK-lacZ expression drops sharply in both constructs . Similar effects were observed in a separate construct where the segment between SD sequence and the initiating AUG was replaced by the sequence found at the corresponding position in the chiP gene ifK expression to be less sensitive to repression by GcvB.A peculiarity of the dppA gene, a major GcvB target dppA mRNA includes four ACA motifs clustered within a 15 nt segment near the SD sequence . In the next experiment, we randomly mutagenized all four ACA repeats (in the same lacZ fusion background) and screened the mutants on MacConkey lactose indicator plates. Out of 43 mutants analyzed, 18 formed white colonies and had ß-galactosidase activities ranging between 0.5 and 5% of the wild-type levels. Four representative isolates from this group are shown in − mutants. 4 of the initial 43 mutants formed red colonies and expressed significant levels of ß-galactosidase  previously showed that GcvB's target sequence in the gltI gene of Salmonella acts as transferable translation enhancer  at the chromosomal site to be modified and: 2) replacing the tetAR module by a DNA fragment carrying the desired changed through positive selection tetracycline-sensitive recombinants Scarless modification of chromosomal DNA sequences at the single base-pair level was achieved with a two-step recombineering procedure as previously described 600 of 0.35) as previously described 32P-labeled primer ppF49. The same DNA primer was used for the sequencing reactions. Reactions were performed with the fmol DNA Cycle Sequencing System from Promega, according to the manufacturer's protocol. Reaction products were fractionated on a 10% polyacrylamide-8 M urea gel. For Northern blot analysis, RNA was fractionated on a 1% agarose-formaldehyde gel, blotted onto a nylon membrane, and hybridized to the appropriate 32P-labeled DNA oligonucleotide probes.RNA was prepared by the acid-hot-phenol method from exponentially growing cells (ODIn vitro coupled transcription/translation was performed using New England Biolabs' PURExpress In vitro Protein Synthesis kit (NEB #E6800) according to the manufacturer instructions. Genes to be analyzed were cloned under T7 promoter control in the DFRH plasmid provided with the kit. The hybrid genes carried yifK wt or mutant 5′ UTR sequences fused to the cat-3×FLAG coding sequence (chloramphenicol acetyl transferase in-frame fusion to the 3×FLAG epitope). Final volume of the transcription/translation reaction was 25 µl in all cases. In addition to kit solutions A and B, reaction mix contained, 10 U of RNase inhibitor SUPERase (Ambion) and template plasmid DNA added to either 0.5 or 5 pM final concentration. Incubation times at 37°C varied from 15 to 90 min. Reactions were stopped by addition of equal volume of 2× Laemmli buffer and immediate freezing. Aliquots were loaded on 12.5% Acrylamide gels and Western analysis performed as previously described et alyifK mRNA were synthesized in vitro from T7 DNA templates generated by PCR amplification of chromosomal DNA (from strains MA8020 or MA11793) with primers ppI22 and ppI23. 2 pmol of RNA were annealed with 5′end-labeled primer ppI23 (1 pmol) in 10 mM Tris-acetate [pH 7.6], 0.1 M potassium acetate, and 1 mM DTT for 1 min at 90°C and chilled in ice for 5 min. Then, all dNTPs , Mg Acetate  were added; this was followed by preincubation with 2 pmol of 30S ribosomal subunit (a gift of Dominique Fourmy and Satoko Yoshizawa) at 37°C for 5 min. In experiments involving GcvB, 5, 1 or 0.5 pmol of sRNA were added prior to both, addition of the 30S ribosomal subunit and the preincubation step. After the 5-min period, 2 pmol of tRNAfMet were added and preincubation at 37°C continued for 15 additional min. Finally, Reverse Transcriptase  was added and samples incubated for 15 min at 37°C. Following phenol chloroform extraction and ethanol precipitation, resuspended samples were loaded onto a 10% polyacrylamide-8 M urea gel along with the sequencing reaction samples generated with the same primer.Toeprinting reactions were carried out as described by Darfeuille 600≈0.7). All experiments included parental or reference strains as normalization controls. Standard deviations were generally less than 5% of the mean.β-galactosidase activity was assayed in toluene-permeabilized cells as described in Figure S1gcvB-linked mutations relieving yifK repression. A DNA fragment spanning the gcvB gene and a linked cat marker  was amplified by PCR under error-prone conditions using oligonucleotides ppF17 and ppF18 as primers (yifK-lacZY/pKD46) and recombinants were selected on MacConkey lactose plates supplemented with chloramphenicol as described in the text. Red-colored colonies were picked and the region of the gcvB locus analyzed by DNA sequencing. Most of the isolates were found to harbor DNA sequence changes in the gcvA-gcvB intergenic region, which affected either the -35 or -10 box of the gcvB promoter, or the -10 box of the gcvA promoter gcvA coding sequence and a mutation affecting the CG-rich stem of gcvB's Rho-independent terminator were also identified (data not shown). primers  and chro primers  as templ(TIF)Click here for additional data file.Figure S2lrp insertion mutant as a function of the growth medium. Bacteria were grown in minimal medium . The probes used were ppI67 (GcvB) and ppB10 (5S)(Comparing GcvB sRNA levels in wild-type and in an dium NCE  supplemepB10 (5S).(TIF)Click here for additional data file.Figure S3dppA-lacZ fusion by GcvB variants. GcvB alleles GA84,85 and ACA86–88 fall outside the portion of GcvB that pairs with dppAWT in down-regulating dppA-lacZ. This shows that the sequence changes do not affect the functioning of the GcvB. The Δ symbol denotes a complete deletion of the gcvB gene.Regulation of (TIF)Click here for additional data file.Figure S4yifK translation start site in members of the Enterobacteriaceae family. Sequences (from NCBI database) are 100% identical among isolates from the same species. Green shading denotes conservation of the ACA motifs analyzed in in this study.Alignment of sequences preceding (TIF)Click here for additional data file.Figure S5yifK variant with optimal spacing between the SD and the initiator AUG still depends on the upstream ACA for optimal expression. Replacing the upstream ACA with GGG lowers expression as well as the response to GcvB repression (see main text and A (TIF)Click here for additional data file.Table S1Salmonella enterica serovar Typhimurium strain MA3409 dppA::pCE40 (lacZY) fusion was constructed as described Relevant strains used in this work. Strains are derived from (DOCX)Click here for additional data file.Table S2DNA oligonucleotides used in this work. Changes from the wild-type sequence are in red. Nucleotide insertions are in bold and underlined. “N” denotes an equimolar mixture of all four nucleotides, “V” denotes an equimolar mixture of A, C and G; “H” denotes an equimolar mixture of A, T and C.(DOCX)Click here for additional data file.Table S3Salmonella strains used as source of DNA; described in the text) or from primers annealing to each other (“self”). Further details on DNA oligonucleotide primers are given in Relevant alleles constructed in this work. Template DNA was from plasmids pSEB5  or chrom(DOCX)Click here for additional data file.