Uchl1) gene (AS Uchl1), the synthenic locus of UCHL1/PARK5. This is mutated in rare cases of early-onset familial Parkinson's Disease (PD) and loss of UCHL1 activity has been reported in many neurodegenerative diseases. Importantly, manipulation of UchL1 expression has been proposed as tool for therapeutic intervention. AS Uchl1 induces UchL1 expression by increasing its translation. It is the representative member of SINEUPs (SINEB2 sequence to UP-regulate translation), a new functional class of natural antisense lncRNAs that activate translation of their sense genes. Here we take advantage of FANTOM5 dataset to identify the transcription start sites associated to S/AS pair at Uchl1 locus. We show that AS Uchl1 expression is under the regulation of Nurr1, a major transcription factor involved in dopaminergic cells' differentiation and maintenance. Furthermore, AS Uch1 RNA levels are strongly down-regulated in neurochemical models of PD in vitro and in vivo. This work positions AS Uchl1 RNA as a component of Nurr1-dependent gene network and target of cellular stress extending our understanding on the role of antisense transcription in the brain.Antisense (AS) transcripts are RNA molecules that are transcribed from the opposite strand to sense (S) genes forming S/AS pairs. The most prominent configuration is when a lncRNA is antisense to a protein coding gene. Increasing evidences prove that antisense transcription may control sense gene expression acting at distinct regulatory levels. However, its contribution to brain function and neurodegenerative diseases remains unclear. We have recently identified AS Uchl1 as an antisense to the mouse Ubiquitin carboxy-terminal hydrolase L1 ( Large genomic projects such as ENCODE  transcripts are RNA molecules that are transcribed from the opposite strand to sense (S) genes forming S/AS pairs. These are estimated to include the large majority of protein encoding genes and about one third of lncRNAs   cells and the formation of cytoplasmic aggregates called Lewy bodies. The current model of toxicity of DA neurons includes mitochondrial dysfunction, oxidative stress and alterations in protein turnover. This stems from the observation on post-mortem PD brains as well as from the identification of genes associated to rare forms of early-onset familial PD. Some of these features are recapitulated in a neurochemical model of the disease that takes advantage of the selective accumulation of toxic MPP+ species in DA neurons.Parkinson's disease (PD) is a slowly progressive degenerative disorder of the central nervous system (CNS) that is classically defined in terms of motor symptoms. The neuropathological hallmark in So far, relevant examples for lncRNAs antisense to genes involved in PD have been restricted to a transcript associated to PINK1/PARK6 locus  gene (AS Uchl1), the synthenic locus of UCHL1/PARK5  is a technology based on the generation of short sequence tags from the 5′ end of full-length cDNAs followed by high-throughput sequencing. When mapped to a reference genome, CAGE tags survey transcription start site (TSS) activity of specific promoters and measure expression levels on a massive scale  were maintained in culture as previously described  was performed with magnetic beads  following the protocol as described  and following manufacturer's instructions. RNA was treated with DNAse I (Ambion) before use. Single strand cDNA was prepared from 1 μg of purified RNA using the iSCRIPT™ cDNA Synthesis Kit (Bio-Rad) according to manufacturer's instructions. qRT-PCR reaction was performed using SYBR-Green PCR Master Mix (Applied Biosystem) and an iCycler IQ Real time PCR System (Bio-Rad). Oligonucleotide sequences of primers used in this study were previously described   and the TRANSFAC database  and matrix similarity (above 0.80).For ChIP experiments, identification of NGFI-B binding elements was performed with the Genomatrix program . Expression was measured as Tag Per Million (TPM).Since antisense lncRNAs are typically expressed at much lower level than overlapping protein-coding transcripts , only 40% of them score positive for antisense transcription proving AS Uchl1 expression is relatively restricted Figure . Sense mN = 144 libraries). Since Spearman Correlation analysis could not be applied, we calculated average TPM levels for each transcript and set this value to 1. We then normalized expression values in all libraries to the reference and monitored variation from the average. We found that the majority of libraries (N = 122) displays concordant variation of S/AS transcription from the average, with both positive and negative co-regulation. When expression of S/AS pair is not co-regulated (N = 22), variation from average values goes in both directions and is almost equally present in non-brain (N = 9) and brain (N = 13) libraries.To assess whether transcription in opposite orientation is co-regulated, we analyzed FANTOM5 libraries containing tags for both RNAs  . A NGFI-B element was identified in position −1230/−1222 to the AS Uchl1 TSS as defined by RACE Figure . This TFTo study the role of this transcription factor in AS Uchl1 RNA transcription, we took advantage of mouse dopaminergic MN9D cell line overexpressing Nurr1 under a doxycicline inducible promoter (iMN9D cells)  experiments. iMN9D cells were treated with doxocycline for 12 h to achieve high levels of Nurr1 protein. Chromatin-protein complexes were then immunoprecipitated with anti-Nurr1 or control (IgG) antibodies and bound genomic DNA was quantified by qPCR using primers for Nurr1 response elements in AS Uchl1, Osteocalcin and Vescicular Monoamine Transporter 2 gene promoters. PCR reactions generated only the expected specific amplicon, as detected by gel electrophoresis and melting curve analysis (data not shown). As shown in Figure Nurr1 induction led to a rapid up-regulation of AS Uchl1 RNA levels starting from 12 h, with kinetics comparable to the one observed for VMAT2, a well-known primary target of Nurr1. Within 48 h, both AS Uchl1 and VMAT2 mRNAs reached their peak of induction, while decreasing at later time points. Under these conditions, Nur77, another member of nuclear receptors family targeting NGFI-B motif, is undetectable  for 16 h, we observed that expression of both transcripts was altered and a statistically significant co-reduction could be measured. While AS Uchl1 RNA was reduced to only 25% of its physiological level, the impact on Uchl1 mRNA was less pronounced, with only 30% reduction , following a sub-acute protocol  while the embedded inverted SINEB2 element is the Effector Domain (ED). The ED is required for the increase of translation of the protein-coding sense mRNA and the BD is targeting the activity to the sense transcript.in vivo where they retain their differential subcellular localization. Upon inhibition of mTOR activity by rapamycin, AS Uchl1 shuttles to the citoplasm where it facilitates Uchl1 mRNA loading to heavy polisomes for a more efficient translation and increase in cellular UchL1 protein levels. Both rapamycin treatment and Uchl1 over-expression are considered neuroprotective suggesting that antisense-mediated translation may be part of a pro-survival cellular response to stress.UchL1 is one of the most abundant proteins in the brain where it is expressed in selected neuronal cell types. We have previously shown that in iMND9 cells, the very same cell line used in this study, its mRNA is associated to light polysomes for basal translation. In these conditions AS Uchl1 is mainly restricted to the nucleus where it exerts an unknown function. S/AS transcripts are also co-expressed in mesencephalic DA cells A first step toward a better understanding of antisense transcription consists in studying how its expression is regulated. Increasing information is accumulating over the role of DNA methylation and selected TFs such as Sox2, Oct4, and Nanog on the control of long intergenic non-coding RNAs (lincRNAs) (Guttman et al., post-mortem brains of neurodegenerative diseases. In summary these data may raise the interesting possibility of differences in the anti-stress control and function of Uchl1 expression dependent on the presence of AS Uchl1 RNA.Here we find that almost all the FANTOM5 libraries present evidences of Uchl1 transcription while AS Uchl1 is expressed only in 40% of them. This data raise the question about how UchL1 protein synthesis is regulated in physiological conditions and upon stress in the absence of antisense transcription. It will be interesting to assess whether Uchl1 mRNA is normally associated to heavy polisomes and whether mTOR inhibition or stress are able to increase UchL1 protein levels according to an antisense–independent mechanism. This is particularly relevant considering that UchL1 is target of oxidative stress inactivation and is down-regulated in Substantia Nigra, striatum and diencephalon. This analysis confirms AS Uchl1 is present in selected regions of the brain as shown in Carrieri et al. (We did not find any example of AS Uchl1 expression in the absence of Uchl1. Therefore, it is not surprising that when both S/AS pair transcripts are present they show a concordant pattern of expression. The highest levels of AS Uchl1 have been found in i et al. .Interestingly, neonatal neurons express larger quantity of AS Uchl1 RNA than adult tissues. This is in line with recent genome-wide and gene-specific studies that indicate spatiotemporal regulation of AS lncRNAs expression during cerebral corticogenesis (Ling et al., in vivo is the sum of several molecular events ranging from transcription to mRNA localization, rate of protein synthesis and degradation. It is unclear what is the life cycle of a single Uchl1 mRNA from transcription to its association to ribosomes or stress granules and P bodies. Therefore, it remains crucial to determine which fraction of total cellular Uchl1 mRNA is associated to light polisomes in physiological conditions and how much is available for RNA-RNA interaction and association to heavy polisomes in an antisense-dependent fashion under stress.Even when AS Uchl1 is highly expressed, its levels are lower than Uchl1 mRNA. However, we know that AS Uchl1 is able to increase the amount of UchL1 protein through its activity on Uchl1 mRNA. The quantity of UchL1 protein Substantia Nigra suggesting the possibility of a N-terminal truncated protein.By decomposition-based identification method three different promoters can be defined while evidences for additional TSSs are shown. While p1 and p2 extend the overlapping region at genomic level respect to the annotated sequence of reference, a previously reported cDNA clone obtained with RACE starts 5′ to p1. p3 identifies a non-overlapping transcript probably involved in a bidirectional promoter. Interestingly, a potential internal alternative TSS for Uchl1 is evidenced in testis and At 1230 nt from the TSS of AS Uchl1 identified with RACE, a DNA binding site for Nurr1 has been bioinformatically discovered and experimentally validated. Nurr1 is a member of the nuclear receptor superfamily, whose expression is crucial for DA neurons differentiation and maintenance in the adult (Zetterstrom et al., This work strongly suggests that AS Uchl1 is a component of Nurr1-dependent transcriptional network. This functional interaction adds a new layer to the complexity of circuitry that may contribute to DA cells' homeostasis.in vitro and in vivo with a kinetics that impact more strongly AS Uchl1 than its overlapping sense mRNA. This is different from the consequences of pharmacological inhibition of mTOR where no effects on transcription were observed (Carrieri et al., We then prove that neurochemical intoxication of DA cells down-regulates S/AS expression In summary, antisense transcription imposes an additional level of regulatory networks to control the activity of protein-coding genes. Determining the repertory of transcription factors controlling the expression of AS RNAs under physiological and pathological conditions will further contribute to our understanding on the biological functions of AS lncRNAs in health and disease. Finally, since increasing evidences suggest Uchl1 over-expression could be beneficial in neurodegenerative diseases, the use of AS Uchl1 as RNA-based drug may represent a new therapeutic strategy.SG, PC, CC, AF, and SZ are named inventors in patent issued in the US Patent and Trademark Office on SINEUPs and licensed to TransSINE Technologies. SG and PC are co-founders of PARKscreen, an Italian SME aimed to use and develop therapeutic SINEUPs.


A repertoire of transcription initiation factors engage the core promoter of mRNA genes to recruit RNA polymerase (Pol) II to initiate transcription, yet their precise spatial organization remains unclear. Using ChIP-exo, here we detail the interactions and genomic organization of initiation factors TBP, TFIIB, and Pol II at mRNA genes and within CpG islands. We find that when Pol II moves into a transcriptionally paused state, TBP/TFIIB remain at the promoter. We show that TBP and TFIIB bound to the core promoter at two separate, resolvable locations that coincided with sites of divergent transcription initiation. We also examine the precise binding of TBP at Pol III transcribed tRNA genes. We find that TBP crosslinked to tRNA genes in a similar manner as at Pol II transcribed genes. This comprehensive and high resolution genome-wide detection of the initiation machinery produces a consolidated view of transcription initiation events humans at Pol II coding and Pol III transcribed tRNA genes. The classic paradigm for assembling the minimal core transcription machinery at mRNA promoters starts with the recruitment of the TATA binding protein (TBP). Next is the docking of TFIIB, which straddles and locks onto TBP. Together with TFIIF, TFIIB then engages Pol II in its active site to help set the start site of transcription (TSS) , 2. The For RNA polymerase II, in multi-cellular eukaryotes, some of general transcription factors may be largely pre-assembled at promoters. There, Pol II is in a transcriptionally engaged but paused state, approximately 30–50 bp downstream from the TSS . FurtherMost vertebrate promoters are found within stretches DNA with high density of CG dinucleotides, called CpG islands, that can be reversibly methylated . MethylaIn contrast, transcription initiation by RNA polymerase III at tRNA genes involves TFIIIC recognition of specific sequences downstream of the TSS, then recruits TFIIIB to a region immediately upstream of the TSS that lacks apparent sequence specificity , 13. PolIn 2013, we published a manuscript detailing the organization of the TBP, TFIIB, and Pol II (PIC) components of the transcription machinery across the human genome in K562 cells and other transformed cell lines using the ChIP-exo genome-wide assay . That paTo obtain a detailed assessment of pre- and post-initiation complexes we conducted ChIP-exo on TBP, TFIIB, and Pol II in the human erythroleukemia cell line (K562). We focused on TBP and TFIIB to assess PIC formation because in yeast these proteins were the most detail-rich, whereas other initiation factors displayed essentially similar ChIP-exo patterns . To asseRPS12 ribosomal protein gene TSS. When individual genes were examined  of the 6,511 mRNA gene promoters where we detected TFIIB ChIP-exo crosslinking. We found that on average 1.6 TFIIB complexes  were detected per CpG island, regardless of island length, with the center of the island being enriched ~100 bp downstream of the primary TSS . Those cFor the vast majority of transcription units, Pol II crosslinked 50 bp downstream of the primary TSS , where iThe crosslinking pattern of human TFIIB was of particular interest since TFIIB in budding yeast crosslinks broadly across the relatively stable single-stranded DNA region within the Pol II active site at core promoters , in accoWe identified 150,753 putative low-threshold (>4 tag counts) TFIIB locations in K562 cells that were far (>500 bp) from the TSS of a protein-coding gene. Of these, 21,160 were also identified in the TBP dataset (within 20bp), 9,192 in the Pol II dataset (within 150 bp), and 2,353 in a no-antibody control . About 57% and 5% of these TBP/TFIIB  and TBP/TFIIB/Pol II  co-occupied locations (and not in the no-antibody control), respectively, resided in ENCODE-annotated  heterochP = 10−55, 10−12, and 10−11; respectively). As expected for housekeeping genes, these genes displayed similar levels of TFIIB occupancy across the four cancer cell lines tested. Groups 2 displayed some notable differences in occupancy, suggesting tissue-specific TFIIB promoter occupancy for some of the genes. In particular for group 2 genes, relative TFIIB occupancy was increased in HepG2/MCF7 and decreased in K562/HeLa. Group 2 was enriched with genes encoding transcription activators (P = 10−6), consistent with the frequent tissue-specific expression of TFs [P = 10−15, 10−15, and 10−16; respectively) that may not be as highly expressed as the translation (eg: ribosome protein subunits) and chromatin assembly  found in group 1 that are among the most highly transcribed genes. MCF7and HepG2 TFIIB profiles were most distant from K562 on the dendrogram plot . We detected TFIIB at 9,069 mRNA genes in at least one cell line, and at 1,691 genes in all lines (group 1) . The remn of TFs , 29. Gro), which is in line with what was observed at Pol II promoters. Unlike at Pol II promoters, almost no crosslinking was observed downstream of the TSS, which may reflect a lack of pausing of Pol III (through which TBP might crosslink), which differs from Pol II. Similar to TBP crosslinking through TFIIB, TBP might predominantly crosslink through BRF. Indeed, the peaks of BRF and TBP crosslinking are coincident at Pol III genes in mice [Given that the initiation of Pol III genes requires a distinct set of complexes from Pol II genes, yet share TBP in common, we examined whether TBP crosslinked to tRNA genes in a similar manner as at Pol II transcribed genes. We aligned strand-separated exonuclease stop sites to the TSS of all 386 tRNA genes at which TBP was detected . RemarkaFig 5A in mice . If trueGenome-wide mapping of the general transcription machinery at near single-base resolution offers a clearer view of certain transcription initiation events from yeast to humans, Pol II to Pol III, and mRNA to tRNA. In general, a TFIIB/BRF family member is recruited to all coding or noncoding core promoters via a TBP family member in a spatially-constrained manner. As established elsewhere , 31–33, Divergent antisense transcription is a common feature of mammalian promoters , 18. WheAlthough the transcription of tRNA genes requires an almost entirely distinct set of machinery, TBP subunit nevertheless crosslinks at the same genomic position relative to TSSs of Pol II and Pol III transcribed tRNA genes. Therefore, TBP in complex with TFIIIB may engage the core promoter in Pol II and III systems in a fundamentally similar manner.5 – 1x106 cells/milliliter in DMEM media supplemented with 10% bovine calf serum at 37°C with 5% CO2. Cells were washed and phosphate buffered saline  before incubation with formaldehyde in a final concentration of 1% for 10 minutes. Cells were lysed , and then the nuclei lysed . Purified chromatin was resuspended in IP dilution buffer  and sonicated with a Bioruptor (Diagenode) to obtain fragments with a size range between 100 and 500 bp.Human chronic myelogenous leukemia cells  were maintained between 1x10With the following modifications, ChIP-exo was carried out as previously described  with chrLibraries were sequenced on an Illumina HiSeq sequencer. The entire length of the sequenced tags were aligned to the human hg18 reference genome using BWA  using deNCBI-curated RefSeq TSSs   comprisiS1 Fig2 scale. (D-F) Scatter plot and Pearson correlation fit of TFIIB/TBP/Pol II ChIP-exo occupancy levels versus each other on a median-centered log2 scale.(A-C) Scatter plot and Pearson correlation fit of mRNA levels (RNA-seq) versus TFIIB/TBP/Pol II ChIP-exo occupancy levels, respectively, on a median-centered log(TIF)Click here for additional data file.S2 FigInput sequence tags are plotted relative to the 8,364 promoter-bound TFIIB locations from main (TIF)Click here for additional data file.S1 FileTable A: TFIIB locations at protein coding genes, Table B: TFIIB occupancy across cancer cell lines, Table C: Sequencing statistics, and Table D: hg18 RefSeq Annotation.(XLSX)Click here for additional data file.


PPARD) is a key regulator of lipid metabolism, insulin sensitivity, cell proliferation and differentiation. In this study, we identified two Single Nucleotide Polymorphisms  constituting four haplotypes  in the 5’ regulatory region of porcine PPARD gene. Functional analysis of the four haplotypes showed that the transcriptional activity of the PPARD promoter fragment carrying haplotype AC was significantly lower than that of the other haplotypes in 3T3-L1, C2C12 and PK-15 cells, and haplotype AC had the lowest binding capacities to the nuclear extracts. Transcription factor 7-like 2 (TCF7L2) enhanced the transcription activities of promoter fragments of PPARD gene carrying haplotypes GT, GC and AT in C2C12 and 3T3-L1 cells, and increased the protein expression of PPARD gene in C2C12 myoblasts. TCF7L2 differentially bound to the four haplotypes, and the binding capacity of TCF7L2 to haplotype AC was the lowest. There were significant associations between -655A/G and fat deposition traits in three pig populations including the Large White × Meishan F2 pigs, France and American Large White pigs. Pigs with genotype GG had significantly higher expression of PPARD at both mRNA and protein level than those with genotype AG. These results strongly suggested that the SNPs in 5’ regulatory region of PPARD genes had significant impact on pig fat deposition traits.Peroxisome proliferator-activated receptor delta ( PPARD gene is widely expressed in the tissues including liver, hearts, adrenal, intestine and adipose in mice and rats [PPARD inhibits the onset of oxidative stress-induced apoptosis in H9c2 cells [PPARD gene begins to be expressed during the early periods of induced differentiation in vitro [PPARD-deficient mice showed multiple developmental and homeostatic abnormalities, including placental defects causing frequent embryonic lethality, decreased adipose mass, altered skin inflammatory responses, and impaired wound healing [PPARD genomic sequence are significantly associated with body mass index, high-density lipoprotein cholesterol, leptin, skeletal muscle glucose uptake, homeostasis model assessment of insulin resistance (HOMA-IR), adiposity measures or fasting serum lipids and height [Peroxisome proliferator-activated receptors (PPARs) are lipid-activated nuclear receptors belonging to the nuclear hormone receptor superfamily . Ligand-and rats , 7. It eand rats , 8. PPARc2 cells , vasculac2 cells , and carc2 cells , 12. In in vitro , playingin vitro . PPARD-d healing –17. In hd height –21.sus scrofa chromosomes 7 (SSC7) p1.1-q1.4 in several pig populations [2 resource population derived from the intercross of Large White boars and Meishan dams, we have identified significant QTLs for carcass and meat quality traits on SSC7 [PPARD gene are associated with backfat thickness [PPARD gene also functions in fatty acid metabolism and fat metabolism [PPARD gene was considered to be a promising positional candidate gene for the fat deposition traits. Previous studies also showed that one missense mutation within the coding sequence of PPARD gene was significantly associated with ear size [PPARD gene affected fat deposition traits were still unknown in pigs. In this study, we identified two functional SNPs (GU565976.1: g.1015 A>G and g.1018 T>C) in the 5’ regulatory region of PPARD which altered the binding capacity transcription factor TCF7L2 to the promoter region, and found that the SNP g.1015A>G was significantly associated with fat deposition traits in three pig populations.We are interested in studying pig fat deposition because it plays important roles in animal agriculture and can be used as biomedical model for human obesity. Pig carcass composition such as backfat thickness and lean meat percentage are controlled by polygenes with pleiotropic effects. Identification of these polygenes or linked markers is necessary for understanding the genetic basis of carcass traits and the application of marker assisted selection (MAS) in breeding programs . A signiulations –31. Usin on SSC7 , 32. Haphickness , and PPAtabolism . Therefoear size , 36. How2 pigs) were used to investigate the distribution of allele and genotype frequency of SNP g.1015A>G. The association analyses were conducted in three pig populations including 274 Large White × Meishan F2 pigs, 409 American Large White pigs and 710 France Large White pigs. All traits were measured and recorded according to the Principles and methods of swine testing [th and 7th ribs , rib number (RN), carcass length from the first cervical vertebra to anterior border of pubic symphysis , carcass length from the first thoracic vertebra to anterior border of pubic symphysis , Internal fat rate (IFR). The live backfat thickness were measured by B-ultrasound machine  between 85 kg and 115 kg body weight and then corrected to the backfat thickness at 100 kg body weight according to the national standard of swine performance testing of P. R. China (NY/T822-2004). Sequencing and haplotypes inference were conducted in 90 pigs from seven breeds, including 9 Chinese Bamei pigs, 6 Erhualian pigs, 4 Huainan pigs, 5 Duroc pigs, 20 American Large White pigs, 25 France Large White pigs, and 21 Large White × Meishan F2 pigs.All animal procedures were performed according to protocols approved by the Animal Care and Use Committee of Huazhong Agricultural University, Hubei province, P. R. China. Pigs from thirteen pig populations  from five Meishan pigs and five Large White pigs, respectively. PCR products of 544 bp, 568 bp and 757 bp spanning nucleotides − 1415 bp to 464 bp (1880 bp length) were sequenced to detect the SNPs in the 5’ regulatory region. The mix of PCR contained 1.5 mM MgCl2. The primers for DNA sequencing, amplification and annealing temperature are listed in http://www.ebi.ac.uk/Tools/msa/clustalw2/) to detect the SNPs. Direct sequencing of 568 bp PCR products containing the SNPs (g.1015 A>G and g.1018 T>C) were further conducted to infer the haplotypes. Haplotypes were inferred using Haploview software (Version 4.2) [In order to detect SNPs in the ion 4.2) .PPARD gene: pGL3-1880GT (from − 1415 bp to + 464 bp), pGL3-1146GT (− 681 bp to + 464 bp), pGL3-1077 (− 612 bp to + 464 bp), pGL3-1032 (− 567 bp to + 464 bp), pGL3-939 (− 474 bp to + 464 bp), pGL3-565 (− 100 bp to + 464 bp) and pGL3-260 (+ 205 bp to + 464 bp) (relative to the transcription start site) . Five deletion fragments of the PPARD promoter were generated by PCR using the pGL3-1880GT vector as a template. All primers are listed in The luciferase reporter gene assays were performed using vector constructs containing the 5’ flanking region of rt site) . The 5’ Using the pGL3-1880GT and pGL3-1146GT vectors as templates, two sets of promoter luciferase report gene vectors were constructed by site directed mutagenesis respectively. The vectors included haplotype AC (pGL3-1880AC and pGL3-1146AC), AT (pGL3-1880AT and pGL3-1146AT), GT (pGL3-1880GT and pGL3-1146GT) and GC (pGL3-1880GC and pGL3-1146GC).TCF7L2 gene. The CDS was digested with restriction enzyme KpnI(forward primer) and XbaI(reverse primer), and then ligated into pcDNA3.1 using T4 DNA Ligase  to generate pcDNA3.1-TCF7L2 vector.The eukaryotic overexpression vector of TCF7L2 (pCDNA3.1-TCF7L2) was constructed. The primer  was designed according to the GenBank database sequence (NM001142922.1) and used to obtain the CDS (coding sequence) of the swine 2 incubator at 37°C. Twenty-four hours before transfection, cells were plated onto 24-well plates or 6-well plates. When cells reached 80% confluence, they were transfected with promoter constructs (1 μg) or eukaryotic expression vectors (4 μg) using lipofectamine 2000  according to the manufacturer’s instructions.C2C12, 3T3-L1 and PK cells were grown in Dulbecco's Modified Eagle's Medium (DMEM)  supplemented with 10% (v/v) fetal bovine serum , maintained in a 5% COAfter transfection for 24 h, cells were lysed in 100 μl of lysis buffer, and firefly and renilla luciferase activities were measured using the Dual-Luciferase Reporter Assay System . The luciferase activity was measured using PerkinElmer 2030 Multilabel Reader . To normalize the transfection efficiency, the pRL-TK plasmid vector  was co-transfected with the reporter construct as described above. The experiments were performed in three replicates for each construct and the data were the average of three replicates.AG and GG, respectively. RNA samples from different tissues of the France Large White pigs were isolated using the TRIzol reagent . Reverse transcription of RNA (1 μg) was performed using random primers  and Moloney Murine Leukemia Virus (M-MLV) Reverse Transcriptase  according to the manufacturer’s instructions. The primers used to amplify the cDNA corresponding to PPARD, TCF7L2, and β-actin genes are listed in β-actin. The Ct (2-ΔΔCt) method was used to analyze the relative gene expression [For tissue expression pattern analysis, nine tissues including heart, liver, spleen, lung, kidney, stomach, intestines, longissimus dorsi (LD), and backfat were sampled from three France Large White pigs. For differential gene expression analysis between different genotypes, muscle and adipose tissues were sampled from three France Large White pigs with genotypes pression .Tissues and cells were lysed in RIPA buffer according to manufacturer’s instruction . Protein lysates were heated at 95°C 5min in 5 × Sodium Dodecyl Sulfate (SDS) sample buffer separated by 12% SDS-PAGE (20 μg each lane), then transferred to polyvinylidene fluoride (PVDF) membranes  using Mini Trans-Blot Cell . After being blocked with 5% non-fat milk for 1.5 h, the membranes were incubated with primary antibodies overnight at 4°C. After washed three times, the membranes were hybridized with secondary antibody for 1 h at 37°C, and washed three times. The targeted proteins were detected using the ImageQuant LAS 4000 mini  according to the manufacturer instructions. Primary antibodies were specific for PPARD , TCF7L2 , and β-actin . Secondary antibodies were goat anti-mouse IgG-HRP  and goat anti-rabbit IgG-HRP .2EDTAH2O), and then were transferred to a nylon membrane. The dried nylon was visualized using the ECL . The bands were detected using the the ImageQuant LAS 4000 mini  according to the manufacturer’s instruction.Nuclear extracts from C2C12 myoblasts, 3T3-L1 cells and pig longissimus muscle were prepared by Nuclear Protein Extraction Kit . The protein concentration was determined by Bicinchoninic Acid assay . The double-stranded DNA probes of four haplotypes were prepared by annealing the desired sense and anti-sense oligonucleotides which were 5’ end-labeled with biotin. The DNA binding activity of protein was detected by chemiluminescent EMSA Kit . The protein-DNA complexes were analyzed by electrophoresis in 6.0% polyacrylamide gels at 100 V for 1 h in 0.5 TBE running buffer  for 40 min at room temperature. Non-denaturing total proteins were extracted from 3T3-L1cells using Western and IP Cell lysis Buffer . The proteins (400 μg) were incubated with unconjugated Dynabeads for 2 h at 4°C using Dynal MPC-S magnetic particle concentrator , and the non-specific binding proteins were removed. The supernatant (supernatantI) was collected for binding reaction. The probes were incubated with the Dynabeads for 5 h at 4°C, and then the Dynabeads-probes complexes were washed three times with lysis buffer and incubated with supernatant I for 2 h at 4°C in the presence of 25 μg/ml poly (dI:dC) which could prevent the nonspecific binding of protein with DNA. The precipitates (Dynabeads-probes-protein) were washed with lysis buffer, and then were eluted in SDS sample buffer. The supernatantII was collected for β-actin detection. The precipitates and supernatant were assayed by western blotting.The probes for DNA pull-down assay were the same as EMSA assay. The probes were conjugated with M-280 Streptavidin Dynabeads  in binding buffer  for 6 h at 37°C, and then were separated by electrophoresis on a 1.5% agarose gel with ethidium bromide in 1× TAE buffer.The SNP (g.1015A>G) was genotyped by PCR-RFLP using 2 pigs, France, and American Large White pigs were conducted with the general linear model (GLM) procedure of SAS version 8.0 . Both additive and dominance effects were estimated using the REG procedure of SAS version 8.0. The data are presented as mean ± S.D., and statistical significance level was set at p < 0.05.The association of SNP genotypes with fat deposition and carcass traits in Large White × Meishan F2 pigs was as following:Tijkl is the observed values of a given trait; μ is the overall mean; Si is effect of sex ; Yj is the effect of year (j = 1 for year 2000 or 2 for year 2003); Gk is the effect of genotype ; Fl is the effect of family (l = 37); bijkl is the regression coefficient of the slaughter weight for carcass traits, Xijkl is the slaughter weight; eijkl is the random residual.The model of association analysis in 274 Large White × Meishan FTijkl is the corrected backfat thickness at 100 kg live weight; Gk is the effect of genotype ; Si is the effect of sex ; Fj is effect of family (j = 17 for France Large White and j = 20 for American Large White); Bl is the effect of batch (l = 20 for France Large White and l = 8 for American Large White); eijkl is the random residual.The model of association analysis in 710 France Large White pigs and 409 American Large White pigs was as following:PPARD gene (http://www.ncbi.nlm.nih.gov/SNP) was rs80912557 and rs80964726, respectively. The haplotypes of two SNPs were inferred in 90 pigs randomly selected from seven breeds including Bamei, Erhualian, Huainan, Duroc, American Larger White, France Larger White, and Large White × Meishan F2 pigs. In these pig populations, the two SNPs were in strong linkage disequilibrium  and the frequency of haplotypes GT, GC, AC and AT was 52.9%, 8.9%, 36.7% and 1.6%, respectively. In American Large White pigs, the frequency of haplotypes GT, GC, AC and AT was 36.7%, 6.7%, 57.7%, and 0%, respectively. In France Large White pigs, the frequency of haplotypes GT, GC, AC and AT was 86.7%, 10.0%, 3.3%, and 0%, respectively. In Large White × Meishan F2 pigs, the frequency of haplotypes GT, GC, AC and AT was 32.1%, 25.9%, 42.9%, and 0%, respectively. The results suggested that the allele A at rs80912557 of the PPARD was mostly linked with allele C at rs80964726, and allele G at rs80912557 was linked with allele T or C at rs80964726 in the pig population studied.Two SNPs, g.1015A>G and g.1018 T>C (GenBank accession no. GU565976.1) were detected at the upstream − 655 bp (A/G) and − 652 bp (T/C) (relative to the transcription start site + 1) of the 5’regulatory region of ARD gene . The corPPARD gene significantly affected its transcription activities. The transcription activities of PPARD gene carrying haplotype AC were the lowest and these of haplotype GT the highest.The bioinformatics prediction of transcription factor binding sites showed that five transcription factors could bind to the segment containing the two SNPs, and the prediction scores of the binding capacity of transcription factors with different haplotype promoter fragments are listed in http://www.genomatrix.de/) to investigate the potential transcription factors in the promoter region harboring the SNPs identified. The results showed that there were potential ETS1, MEL1, Foxh1, POU2F1, and TCF7L2 binding sites in the promoter region, and the binding capacities of Foxh1, POU2F1 and TCF7L2 to the promoter changed when the SNPs were introduced to the DNA sequences  cells [TCF7L2 and PPARD genes in ten tissues including heart, liver, spleen, lung, kindey, brain, stomach, intestine, LD, and backfat of the four-month-old Large White pigs. The tissue expression profiles of two genes were very similar. They were all broadly expressed in the ten tissues, and highly expressed in liver and adipose which are active in fat synthesis  and co-transfected with the promoter fragments carrying the four haplotypes into C2C12 myoblasts and 3T3-L1 cells. TCF7L2 overexpression significantly improved the transcriptional activities of the promoter fragments with haplotypes AT, GT, and GC, whereas there were not significant effects with haplotype AC  cells . Therefoynthesis . To inveotype AC . The proyoblasts . The binyoblasts .EcoRIPCR-RFLP to genotype the polymorphism g.1015A>G. Digestion of the PCR fragments with EcoRIrestriction enzyme generated one fragment , two fragments  or three fragments   . Allele  animals . Further animals . The sig animals . Taken tin vivo, we selected France Large White pigs with genotypes AG and GG to compare the expression of PPARD gene in adipose and muscle tissues. The expression of PPARD gene between two genotypes was significantly different at both mRNA and protein level. Pigs with genotype GG had significant higher expression level than that of pigs with genotype AG  family of transcription factors, and involved in the control of cell growth and signaling of wingless-type MMTV integration site family (Wnt) receptors [PPARD gene and alter gene expression in human CRC cells [PPARD gene was up-regulated by TCF7L2 overexpression in C2C12 cells. Besides, the porcine TCF7L2 gene improved the transcriptional activities of the PPARD promoter fragments carrying the haplotypes AT, GT, and GC. However no significant effects on the haplotype AC were found, because there was no interaction between promoter fragments carrying haplotype AC and TCF7L2 protein.Functional mutations in the regulatory region have effects on the gene expression and phenotypic variation. s traits , 33, 42.eceptors . TCF7L2 eceptors –47. TCF4eceptors . In our RC cells . We alsoPPARD gene has been implicated in lipid metabolism via several signaling pathways including Wnt/FZD and Wnt/β-catenin pathways. On the one hand, PPARD promote the accumulation of cholesterol and serum high density lipoprotein (HDL), and decrease the level of triglyceride in human macrophages by lipogenesis pathways such as Wnt/FZD and Wnt/β-catenin pathway [PPARD affect (lifestyle intervention) LI-induced changes in overall adiposity hepatic fat storage [PPARD gene affect fat deposition traits still remain unclear. In this study, the allele A was mostly linked with allele C at two loci in all the pig breeds studied, and no haplotype AT was found in American Large White pigs, France Large White pigs and Large White × Meishan F2 pigs studied. We also found that the functional variant (g. 1015A>G) had significant association with fat deposition traits in three pig populations and allele A was associated with lower fat deposition traits as well as lower gene expression of PPARD in vitro and in vivo. Previous studies showed that PPARD and CDKN1A gene in preadipocytes had lower expression levels in heterozygote MSQTL7/LWQTL7  pigs than in homozygous LWQTL7/LWQTL7  pigs at 28 and 150 days ages [PPARD expression and resulted in decreased fat deposition traits. Therefore, the expression change of PPARD gene may be an important cause of the major QTL effects for fat deposition. pathway , 49. On  pathway . In huma storage . Howeverays ages , which wAA or AG had significant lower fat deposition trait values compared with those of genotype GG in the pig populations studied. Considering the effects of allele A in this study, enhancing the allele frequency of allele A will lead to the reduction of backfat thickness in the pig populations of which the allele was not fixed at this locus. However, selection of allele A also has adverse effects on intramuscular fat and meat quality, as intramuscular fat and backfat deposition are negatively correlated.In pig breeding schemes, the lean percentage in carcass can be indirectly selected by live backfat thickness because there is significant negative correlation between these two traits. In this study, there were significant differences in fat deposition traits between different genotypes. Pigs with genotypes PPARD genes have significant effects on the fat deposition and carcass traits. Further work will be necessary to confirm the effects of this functional SNP in more pig populations and investigate the molecular mechanism regarding the roles of SNPs in phenotype variation.In conclusion, our results provided a new evidence that the newly identified functional SNPs in the 5’ regulatory region of S1 FigLane Marker: DNA molecular marker DL2000 ; Lane 1, 2, and 6: genotype GG, 568 bp; Lane 3, 4: genotype AG, 568 bp + 314 bp + 254 bp; Lane 5: genotype AA, 314 bp + 254 bp.(TIF)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOC)Click here for additional data file.S4 Table(DOC)Click here for additional data file.


Trypanosoma brucei, J is enriched, along with histone H3 variant (H3.V), at sites involved in RNA Polymerase (RNAP) II termination and telomeric sites involved in regulating variant surface glycoprotein gene (VSG) transcription by RNAP I. Reduction of J in T. brucei indicated a role of J in the regulation of RNAP II termination, where the loss of J at specific sites within polycistronic gene clusters led to read-through transcription and increased expression of downstream genes. We now demonstrate that the loss of H3.V leads to similar defects in RNAP II termination within gene clusters and increased expression of downstream genes. Gene derepression is intensified upon the subsequent loss of J in the H3.V knockout. mRNA-seq indicates gene derepression includes VSG genes within the silent RNAP I transcribed telomeric gene clusters, suggesting an important role for H3.V in telomeric gene repression and antigenic variation. Furthermore, the loss of H3.V at regions of overlapping transcription at the end of convergent gene clusters leads to increased nascent RNA and siRNA production. Our results suggest base J and H3.V can act independently as well as synergistically to regulate transcription termination and expression of coding and non-coding RNAs in T. brucei, depending on chromatin context (and transcribing polymerase). As such these studies provide the first direct evidence for histone H3.V negatively influencing transcription elongation to promote termination.Base J, β-D-glucosyl-hydroxymethyluracil, is a chromatin modification of thymine in the nuclear DNA of flagellated protozoa of the order Kinetoplastida. In  Trypanosoma brucei is an early-diverged parasitic protozoan that causes African sleeping sickness in humans. The genome of T. brucei is organized into polycistronic gene clusters that contain multiple genes that are co-transcribed from a single promoter. Because of this genome arrangement, it is thought that all gene regulation in T. brucei occurs after transcription at the level of RNA . We have recently described the presence of a modified DNA base J and variant of histone H3 (H3.V) at transcription termination sites within gene clusters where the loss of base J leads to read-through transcription and the expression of downstream genes. We now find that H3.V also promotes termination prior to the end of gene clusters, thus regulating the transcription of specific genes. Additionally, H3.V inhibits transcription of siRNA producing loci. Our data suggest H3.V and base J are utilized for regulating gene expression via terminating transcription within polycistronic gene arrays and regulating the synthesis of siRNAs in trypanosomes. These findings significantly expand our understanding of epigenetic regulatory mechanisms underlying transcription termination in eukaryotes, including divergent organisms that utilize polycistronic transcription, providing the first example of a histone variant that promotes transcription termination. Trypanosoma brucei, Trypanosoma cruzi, and Leishmania major, which cause African sleeping sickness, Chagas disease, and leishmaniasis, respectively. The genomes of kinetoplastids are arranged into long gene clusters, or polycistronic transcription units (PTUs), which are transcribed by RNA polymerase (RNAP) II , 28T. br RT-qPCR . Althoug H3.V KO .ESAGs) within silent ESs with differential expression. Because the repetitive nature of ESAGs complicates read alignment of mRNA-seq data, we analyzed several genes by RT-qPCR using primers that are specific to ES ESAGs  and 1 kb (VSG11) upstream of the telomere  in the regulation of RNA processing, the increase in both unprocessed (nascent) and processed RNAs (mRNAs and siRNAs) strongly suggests H3.V regulates RNA abundance at the level of transcription and that the defects we observe are not simply due to an alteration of RNA processing. We propose a model in which both H3.V and base J inhibit RNAP II elongation, and therefore stimulate termination at sites within gene clusters  in wild type T. brucei. Interestingly, consistent with base J synthesis, VSGs and ESAGs are developmentally regulated, typically exclusively expressed in bloodstream form trypanosomes from the telomeric PTU (VSG ESs). Monoallelic expression of a VSG ES leads to the expression of a single VSG on the surface of the parasite, a key aspect of trypanosome antigenic variation. Therefore the repression of silent VSGs by H3.V/J allows the parasite to maintain this monoallelic expression. Another important aspect of antigenic variation is the periodic switching of the VSG protein expressed on the surface allowing the parasite to remain a step ahead of the host immune response. DNA recombination (i.e. gene conversion events) of silent VSG genes into the active ES is the dominant driver of trypanosome antigenic variation. Transcription of a donor DNA sequence has been shown to increase its use during gene conversion events in human cells [T. brucei stimulates DNA recombination [VSGs by H3.V/J, including VSGs in silent telomeric ESs and VSG pseudogenes at the end of genomic internal PTUs, could play a role in gene conversion events. It is well characterized that during late phases of mammalian infection, trypanosomes predominately express mosaic VSGs comprised of multiple VSGs and pseudogenes [VSGs by H3.V/J, in particular VSG pseudogenes at internal PTUs, contributes to gene conversion events that result in the formation of these mosaic VSGs.Recent description of the trypanosome stress response has indicated that the location of a gene within a PTU can impact its expression, presumably via regulated transcription elongation . Here wean cells . It has bination . Therefoudogenes . These fT. cruzi [If H3.V and base J are utilized to effect specific gene expression changes, then mechanisms likely exist to overcome the silencing effects of these modifications, i.e. regulated addition and/or removal. Histone chaperones and chromatin remodeling complexes incorporate histone variants at specific chromatin locations. Thus, regulation of (unidentified) histone chaperones that incorporate H3.V could enable regulated gene expression. Chromatin remodeling proteins could also be involved in the removal of H3.V. The first step of J synthesis consists of thymidine oxidation by JBP1 and 2, which utilize oxygen and 2-oxoglutarate and require Fe2+ as a cofactor. Changes in oxygen concentrations or metabolic changes could thus impact J synthesis and effect gene expression changes. We previously demonstrated oxygen regulation of JBP1/2 and J synthesis, which led to changes in gene expression and pathogenesis of T. cruzi , 31. JBPT. cruzi . TherefoT. cruzi , similarT. cruzi , may alsVSG genes within the silent telomeric ESs. We hypothesize, similar to its effect on RNAP II termination within PTUs genome-wide, H3.V localized to telomeric repeats limits basal levels of RNAP I transcription elongation within silent ESs [T. brucei [ESAG gene derepression suggests loss of H3.V does not result in derepression from the promoters of silent ESs, though ESAG8 upregulation is consistent with this possibility. Therefore, we acknowledge that further detailed analysis is required, including the use of tagged silent ESs, to fully understand the role of H3.V on ES transcription. Because of our inability to effectively measure VSG switching rates in our H3.V KO cell line, we also cannot exclude the possibility that H3.V restricts VSG switching, though a recent study has indicated that switching frequency does not appear to change significantly in the H3.V KO or upon the loss of H3.V and J . While base J has no apparent independent role in telomeric repression or VSG switching, the additional derepression of VSG genes observed in the H3.V KO upon loss of J suggests the novel modified base can act synergistically with H3.V in telomeric silencing and antigenic variation. This function is consistent with the distinct localization of base J in the silent ESs, with J density highest close to the telomeres [Loss of H3.V also led to derepression of lent ESs . A simil. brucei . The lacelomeres .T. brucei [H3.V KO are arranged in opposing transcriptional gene pairs where extended RNAP II transcription would result in dsRNA for each mRNA .We also found a specific RNAP I transcribed procyclin gene cluster that was downregulated following the loss of J and even more so by the loss of H3.V. As mentioned above, this locus has been shown to undergo overlapping RNAP II and RNAP I transcription in the procyclic form . brucei . In the . brucei . In cont. brucei , thus thach mRNA . AdditioT. cruzi , it is aT. brucei, however those identified by mass spectrometry analyses include S1 and K23 acetylation and K4, K32, and K76 methylation [T. brucei H3.V differs in that it contains an A1 and R23, and thus lacks the corresponding acetylation. However, differences in PTMs between H3.V and H3 have not been investigated.Future studies are necessary to further elucidate the mechanisms by which H3.V regulates transcription, including what proteins interact with H3.V, the impact of H3.V on nucleosome structure/stability, whether H3.V undergoes any post-translational modifications, and how chromatin structure is affected by the loss of H3.V. It is not clear how sequence variation in H3.V confers its specific localization to transcription termination sites or its function. H3 PTMs have not been well characterized in hylation . In compT. brucei did not result in read-through transcription that extends into the downstream gene cluster encoded on the opposite strand and generation of antisense RNAs. In L. major the loss of J alone led to such read-through transcription at a majority of the cSSRs sites in the genome [T. brucei and L. major is the absence of a complete RNAi pathway in L. major [Leishmania species with an intact RNAi pathway.Interestingly, the loss of H3.V and base J in e genome  whereas e genome . OverallL. major , 87. It T. brucei is unclear. Mature SLACS and ingi transcripts are present at low levels in WT T. brucei due to the presence of a functional RNAi pathway [Tb427tmp.211.5010) and ingi transcripts by mRNA-seq  using a Qiagen miRNeasy kit according to the manufacturer’s instructions. The small RNA-seq libraries were prepared using approximately 250ng small RNA by Vertis Biotechnology AG, Germany. The small RNA sample was poly(A)-tailed using poly(A) polymerase. Then, the 5'PPP and cap structures were removed using tobacco acid pyrophosphatase . Afterwards, an RNA adapter was ligated to the 5'-monophosphate of the RNA. First-strand cDNA synthesis was performed using an oligo(dT)-adapter primer and the M-MLV reverse transcriptase. The resulting cDNAs were PCR-amplified to about 10–20 ng/μL using a high fidelity DNA polymerase. The cDNAs were purified using the Agencourt AMPure XP kit (Beckman Coulter Genomics). Quality and concentration of all libraries was determined by capillary electrophoresis and high throughput sequencing was performed on a HiSeq2000 (Illumina). Sequencing reads were mapped to the T. brucei reference genome using Bowtie2 version 2.2.3 with local sensitive mode, all other parameters default, [Small RNA-sequencing was performed using two different methods. The analysis of WT, MOG Figs  was perfdefault,  and furtdefault, . Reads sdefault,  and usedH3.V KO  using Trizol according to the manufacturer’s instructions. The small RNA-seq libraries were prepared using approximately 250ng total RNA using the Illumina-compatible NEBNext small RNA library preparation kit following the manufacturer protocol (New England Biolabs). Quality and concentration of all libraries was determined using a Bioanalyzer 2100 (Agilent). Libraries were pooled using equi-molar amounts and sequenced on a NextSeq500 (Illumina). Both library construction and sequencing were done at the Georgia Genomics Facility (GFF). Small RNA reads were quality and adapter trimmed using Cutadapt [T. brucei reference genome using Bowtie2 version 2.2.3 with the following parameters “-a -D 10 -R 5 -N 1 -L 15 -i S,1,0.50”[H3.V KO) was performed using EdgeR  using Trizol. 12 mRNA-seq libraries were constructed  using Illumina TruSeq Stranded RNA LT Kit following the manufacturer’s instructions with limited modifications. The starting quantity of total RNA was adjusted to 1.3 μg, and all volumes were reduced to a third of the described quantity. High throughput sequencing was performed at the Georgia Genomics Facility (GFF) on a NextSeq500 (Illumina). Raw reads from mRNA-seq were first trimmed using Trimmomatic version 0.32 [T. brucei Lister 427 version 9.0 genome, from EuPathDB [T. brucei Lister 427 version 9.0 annotation  / (RPKMwt + pseudocount), where pseudocount = 0.5. Once all fold changes were computed, any fold change value above five was set equal to five to improve visualization.For the mRNA-seq, total RNA was isolated from log phase ion 0.32 . The sinEuPathDB , using BEuPathDB . All setEuPathDB . TranscrEuPathDB . For indEuPathDB ). To esthed work , 14. FolTotal RNA was isolated using the hot phenol method, as described previously . To ensuTotal RNA was obtained using Qiagen RNeasy kits according to manufacturer’s instructions. First-strand cDNA was synthesized from 1 μg of total RNA using an iScript cDNA synthesis kit  per the manufacturer's instructions. Quantification of selected genes were performed on an iCycler with an iQ5 multicolor real-time PCR detection system . Primer sequences used in the analysis are available upon request. The reaction mixture contained 5 pmol forward and reverse primer, 2x iQ SYBR green super mix , and 2 μl of template cDNA. Standard curves were prepared for each gene using 5-fold dilutions of known quantity (100 ng/μl) of WT DNA. The quantities were calculated using iQ5 optical detection system software.S1 FigH3.V KO. (B) Phasing of siRNAs mapping to a cSSR on chromosome 5 in WT cells. Position is indicated in kb. Colors indicate nucleotide: green, A; red, T; blue, C; and orange, G.(A) Mapping of 23-26nt small RNAs to cSSR 11.9 in WT and (TIFF)Click here for additional data file.S2 FigMapping of small RNAs to SLACS, INGI, CIR147 and IR3.(TIFF)Click here for additional data file.S3 FigT. brucei are plotted for cSSR 9.2 (position 1110–1190 kb is shown). (D-F) Gene expression changes and termination defects are analyzed as described in (A-C) Localization of H3.V, J, ORFs, and mRNA-seq reads from wild type (TIFF)Click here for additional data file.S4 FigTb427tmp.160.5350; 1960: Tb427.07.1960; 1565: Tb427tmp.02.1565; 1580: Tb427tmp.02.1580; 5384: Tb427.02.5384; and 6820: Tb427.07.6820. P values were calculated using Student’s t test. *, p value ≤ 0.05; **, p value ≤ 0.01.RT-qPCR was performed as described in (TIFF)Click here for additional data file.S5 FigT. brucei are ploted for cSSR 10.5 (1120–1140). (D) mRNA-seq transcript fold changes of the genes indicated in the ORF map in B, as described in H3.V KO; black bars: H3.V KO+DMOG. The fold change in the wild type+DMOG condition for gene 4 is 12.2, with a standard deviation of 4.3 and p value of 0.03.(A-C) Localization of H3.V, J, ORFs and mRNA-seq reads from wild type (TIFF)Click here for additional data file.S6 FigT. brucei Lister 427 version 9.0 genome) and the localization of base J (blue), H3.V (red), and mRNA coding genes  are shown. Genes on the top strand are transcribed from left to right and those on the bottom strand are transcribed from right to left. Position along each chromosome is indicated in kilobases (KB) or megabases (MB). Bottom two panels: mRNAs found upregulated by at least 2-fold or more in the H3.V KO (top) and H3.V KO+DMOG (bottom) relative to WT are indicated by a green line. Only mRNAs with an RPKM≥1 and significantly differentially expressed relative to wild type, as determined by Cuffdiff, are included. Boxes indicate sites examined in more detail in other figures. Genes are listed in Whole chromosomes Click here for additional data file.S7 FigT. brucei genome. (B) 153 genes were upregulated in the absence of H3.V and/or J, 122 are adjacent to H3.V and 121 are adjacent to J (80%). 65 genes were downregulated in the absence of H3.V and/or J, 35 are adjacent to H3.V (54%) and 40 are adjacent to J (62%).(A) Genes were defined as adjacent to H3.V or J if located within 10 kb of an H3.V and J enriched region, respectively, and are indicated in grey. Genes not adjacent to H3.V or J are indicated in white , 14. 246(TIFF)Click here for additional data file.S8 FigH3.V KO. Genes in blue are transcribed by RNAP II on the top strand and EP and PAG genes in red are transcribed by RNAP I. MARP: microtubule-associated repetitive protein; EP1-2: procyclin; PAG; procyclin associated gene; T: ‘T region’ encoding transcripts containing small ORFs of <240 bp; GU2: gene of unknown function. The Fig is drawn to scale. (B) Gene cluster on chromosome 6. Genes in bold are downregulated in the H3.V KO and identities are listed in (A) The EP/PAG1 loci. Small arrow indicates the RNAP I transcription start site in the promoter region. Genes in bold are downregulated in the (TIFF)Click here for additional data file.S9 FigESAGs was performed as described in RT-qPCR analysis of the indicated (TIFF)Click here for additional data file.S10 FigT. brucei [H3.V KO the H3.V is replaced with a canonical H3, with no change in nucleosome structure or H4.V, since it is currently unclear what happens to the nucleosome upon the loss of H3.V. According to the model, (A) the loss of base J leads to read-through transcription (indicated by the thicker red arrow) at internal termination sites within the cluster that is once again attenuated once it reaches H3.V within the cSSR. The loss of H3.V leads to read-through transcription at the internal site and continues into the cSSR, thus allowing increased dual strand transcription and generation of siRNAs. (B) At regions without an internal termination site, loss of base J has no effect on dual strand transcription. But, as described above, the loss of H3.V leads to increased transcription at cSSRs and generation of siRNAs.O-linked glycosylation of DNA (base J) is indicated by black line and dot. Nucleosomes are indicated by circles where green represents canonical histones and red represents histone H3 variant and an additional histone variant found at termination sites in . brucei , histone(TIFF)Click here for additional data file.S1 TablemRNAs found up or downregulated by 2-fold or more by mRNA-seq are listed, along with available gene descriptions, RPKM, fold change, and an indication of whether the genes are located within 10kb of H3.V and/or J. P values determined by Cuffdiff are listed for each condition compared to WT. For upregulated mRNAs, only those with an RPKM≥1 upon the loss of H3.V and/or J are included. For downregulated mRNAs, only those with an RPKM≥1 in WT cells are included.(XLSX)Click here for additional data file.S2 TableThe average small RNA-seq RPM of triplicate libraries at cSSRs is listed. Chromosome number and the 5’ and 3’ position of regions quantified are included. Statistical significance was assessed using pairwise Fisher’s Exact test on both total reads and specifically 21-27bp reads. Yellow highlight indicates significance at a p value ≤ 0.05.(XLSX)Click here for additional data file.S3 TableInformation about all sequencing experiments performed in this study is listed. Also indicates the figures in which the data are presented.(XLSX)Click here for additional data file.S4 TableSimilar to (XLSX)Click here for additional data file.


HSP90AA1 promoter region over the gene expression rate under HS. Specially, the -668insC INDEL, which is very close to the -660G/C transversion, is a good candidate to be implied in the transcriptional regulation of the gene by itself or in a cooperative way with this SNP. Animals carrying the genotype II-668 showed higher transcription rates than those with ID-668 (FC = 3.07) and DD-668 (FC = 3.40) genotypes for samples collected under HS. A linkage between gene expression and sperm DNA fragmentation was also found. When HS conditions were present along or in some stages of the spermatogenesis, alternative genotypes of the -668insC and -660G/C mutations are involved in the effect of HS over sperm DNA fragmentation. Thus, unfavorable genotypes in terms of gene expression induction (ID-668GC-660 and DD-668GG-660) do not produce enough mRNA (stored as messenger ribonucleoprotein particles) and Hsp90α protein to cope with future thermal stress which might occur in posterior stages when transcriptional activity is reduced and cell types and molecular processes are more sensible to heat (spermatocytes in pachytene and spermatids protamination). This would result in the impairment of DNA packaging and the consequent commitment of the events occurring shortly after fertilization and during embryonic development. In the short-term, the assessment of the relationship between sperm DNA fragmentation sensitivity and ram’s fertility will be of interest to a better understanding of the mechanisms of response to HS and its consequences on animal production and reproduction performance.Heat shock (HS) is one of the best-studied exogenous cellular stresses. Almost all tissues, cell types, metabolic pathways and biochemical reactions are affected in greater or lesser extent by HS. However, there are some especially thermo sensible cellular types such as the mammalian male germ cells. The present study examined the role of three INDELs in conjunction with the -660G/C polymorphism located at the  HSP90 gene family have undergone major duplication events, which led to two known cytoplasmic isoforms, namely Hsp90α  or inducible form and Hsp90β  which is the constitutive one. Whereas Hsp90β is more or less constitutively and ubiquitously expressed, the expression of Hsp90α is heat-inducible and more tissue specific . However, other roles of the Hsp90α related with the cellular defense against other sources of stress (i.e. oxidative stress) and proteostasis maintenance [Therefore, optimal expression rates of favorable genotypes of the Results here obtained lead us to question if heat stress events occurring at initial stages of the spermatogenesis process would be selectively advantageous to protect cell types of subsequent stages, which have worse heat stress response in terms of transcription ability, from injuries caused by heat or other sources of stress. Relative to this idea, it is important to remark that sheep is a short day breeder whose favorable reproductive period begins when the days shorten (fewer hours of light). This period comes after the hottest months, and so, we could expect that those animals with a favorable genotype in terms of heat resistance were more fertile.in vitro studies, can contribute to elucidate which polymorphism(s) and transcription factor(s) are involved in the expression differences observed in this gene as response to environmental conditions. Also the methylation pattern of the HSP90AA1 promoter would provide information about other possible mechanism for the regulation of the gene expression. Finally, association studies among sperm DNA fragmentation and HSP90AA1 genotypes with ram’s fertility will contribute to determine the involvement of this gene in ram’s reproductive cells thermo sensibility and its consequences over their reproductive efficiency.Future functional S1 FigIn dark grey the heat shock element (HSE) binding heat shock factor 1 (HSF1). In light grey the TATA box sequence.1-704insAA; 2-668insC (rs397514115.2); 3-667insC (rs397514115.1); 4-660G/C (rs397514116); 5-601A/C (rs397514117); 6-528A/G (rs397514269); 7-524G/T (rs397514270); 8-522A/G (rs397514271); 9-516insG (rs397514268); 10-468G/T (rs397514272); 11-444A/G (rs397514273).Polymorphisms identifications are the following: (DOCX)Click here for additional data file.S1 Table(DOC)Click here for additional data file.S2 Table(DOC)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Nucleosomes facilitate the packaging of the eukaryotic genome and modulate the access of regulators to DNA. A detailed description of the nucleosomal organization under different transcriptional programmes is essential to understand their contribution to genomic regulation.Schizosaccharomyces pombe. We show that 98.5% of the genome remains almost invariable during mitosis and meiosis while remodelling is limited to approximately 1100 nucleosomes in the promoters of a subset of meiotic genes. These inducible nucleosome-depleted regions (NDR) and also those constitutively present in the genome overlap precisely with clusters of binding sites for transcription factors (TF) specific for meiosis and for different functional classes of genes, respectively. Deletion of two TFs affects only a small fraction of all the NDRs to which they bind in vivo, indicating that TFs collectively contribute to NDR maintenance.To visualize the dynamics of individual nucleosomes under different transcriptional programmes we have generated high-resolution nucleosomal maps in S. pombe is largely maintained under different physiological conditions and patterns of gene expression. This relatively constant landscape favours the concentration of regulators in constitutive and inducible NDRs. The combinatorial analysis of binding motifs in this discrete fraction of the genome will facilitate the definition of the transcriptional regulatory networks.Our results show that the nucleosomal profile in The online version of this article (doi:10.1186/1471-2164-14-813) contains supplementary material, which is available to authorized users. Drosophila[C. elegans[Nucleosomes play an essential role in the management of the eukaryotic genome by facilitating its packaging inside the nucleus. They also regulate basic genomic processes such as transcription, replication and recombination, either directly by controlling the physical access of regulators to DNA or indirectly by modulating their binding through a complex repertoire of histone modifications . In recerosophila, C. eleg. elegans and mamm. elegans, 10. In . elegans. From thCLN2 and HO genes during the S. cerevisiae cell cycle depends on the binding of their regulators to NDR in their promoters. Experimental manipulation of these promoters showed that when the binding sites were embedded in nucleosomes, transcription was still active but the cell cycle periodic expression was lost [Recent analyses have revealed the extremely precise organization of nucleosomes at promoters to regulate the interaction between transcription factors and DNA. For example, periodic expression of the was lost . By contwas lost .S. cerevisiae are enriched in these elements [in vivo at genome-wide scale [How is this precise nucleosome organization generated and maintained? Poly (dA:dT) tracts can exclude nucleosomes , 15 and elements . Other selements . Howeverde scale , 19.S. cerevisiae is an energy-dependent process, which in the presence of ATP and a cell extract can recapitulate the in vivo profile emanating from the 5′ NDRs, even at low nucleosome density and in the absence of transcription [The statistical positioning model  proposedcription . The modcription , 22.S. pombe has been comparatively much less studied than in S. cerevisiae. Several studies have generated genome-wide nucleosome maps using tiling microarrays or next generation sequencing. Some of these analyses have focused primarily on promoters and transcribed regions and have derived their conclusions from the aggregated nucleosome profiles of hundreds or thousands of genes [Nucleosome distribution in of genes , 23, 24,of genes , 25.S. pombe genome during mitosis and meiosis. We show that the great majority of the genome is organized in a very stable pattern of positioned nucleosomes and that NDRs overlap precisely with clusters of binding sites for transcription factors, which, in turn, could contribute to maintaining a regular nucleosome pattern across the genome.We have sequenced mononucleosomal DNA to a depth that allows a precise description of nucleosome dynamics at the level of individual nucleosomes across the S. pombe by sequencing mononucleosomal DNA at a genome coverage ranging from 46- to 177-fold. This sequencing depth allows the high resolution mapping of individual nucleosomes by aligning sequence reads directly onto the reference genome followed by signal smoothing, with minimal mathematical modification of the raw data  has been annotated [erevisiae, 3, 21 aerevisiae.Figure 1S. pombe are shown in Additional file Alignment to the midpoint position of +1 N generates sharper profiles than alignment to the TSS since +1 nucleosomes from different genes are perfectly aligned. Alignment to the TSS is expected to generate comparable nucleosomal profiles but of lower resolution due to variations in the distance between the TSS and the midpoint position of +1 N in different genes. This is reflected in the lower amplitude of the nucleosome peaks in the aggregated profiles. Alignment to TSS, however, is useful for establishing where transcription initiates relative to +1 N. Comparative alignments to +1 N and to TSS of The decline in the amplitude of the oscillations from the 5′ to the 3′ end of the genes in these profiles could result from the accumulative variation in the internucleosomal distances between genes and this would therefore not imply progressively lower nucleosome occupancy. This possibility was tested by using the nucleosome closest to the central position between the TSS and the transcription termination site (TTS)  as a reference for the alignment. The resulting symmetric profile Figure C indicatS. pombe, unlike in S. cerevisiae, nucleosome positioning only occurs downstream from the 5′ NDRs [S. cerevisiae, due to the presence of a NDR at the 3′ end of many genes [S. pombe and indicated that aggregated nucleosome profiles vary depending on the reference used to align them and do not result from different properties of specific nucleosomes or gene regions.It has been reported that in  5′ NDRs , 24. Howny genes . The comdni2 gene in meiosis at 0 hours was absent while another occupied the NDR at the 5′ end of the divergently transcribed mcm2 and mlo2 genes in the 5-hour sample (green arrowheads). By contrast, all the other nucleosomes displayed a pattern of sharp, regularly spaced peaks that remained virtually invariable despite the different physiological stages and the chromosomal processes undergone by the cells. Since the average length of the 782 meiosis-specific NDRs was 214.68 +/− 71.06 nucleotides and the average distance between the mid-point of adjacent nucleosomes is 152 bp  and at 3 and 5 hours after synchronous entry into meiosis, when chromosomes are recombining (3 h), and when the four haploid nuclei have separated and start maturing into spores (5 h) (see Methods). Previous studies have shown that several hundred genes are differentially transcribed during these early, medium and late stages –29 (see S. pombe genes (http://www.pombase.org) is indicated in Additional file dni2 and mcm2/mlo2 genes at 0 and 5 hours during meiosis, respectively. The nucleosomal and transcriptional profiles for the entire genome can be visualized in a searchable genome browser at http://genomics.usal.es/cgi-bin/gb2/gbrowse/Sp_nucdyn.To monitor whether the meiosis-specific induced NDRs were associated with the differential transcription of the adjacent genes, we generated strand-specific transcription maps of exponential mitotic cells and of cells at 0, 3 and 5 hours during meiosis using tiling microarrays. The RNA level at these stages for all the annotated S. pombe[How widespread is the association between nucleosome remodelling and transcriptional regulation? Altogether, of the 782 meiosis-specific NDRs, 607 (77.6%) mapped to sites of transcription initiation. No transcripts were detected associated with the remaining 175 NDRs (32.4%) possibly due to a very low level of transcription, as has recently been shown for many genes in S. pombe. AlternaS. pombe–29 , which are expressed 3.4- and 32-fold above the background, but positioning is lost on the pyk1 gene, which is expressed 238-fold. Positioning is also maintained along the itt1 (10-fold) and pmc5 (9.4-fold) genes (middle panel), but not on the psu1 gene (180-fold). The same applies to the dus2 (9.4-fold), erg27 (21-fold) and sks2 (254-fold) genes (bottom panel). Nucleosome delocalization on some genes showing high RNA levels is probably due to a high rate of nucleosome repositioning as a result of the high density of RNA polymerase II molecules crossing the gene [Our quantitative analysis of transcription in mitotic and meiotic cells revealed that the RNA levels between different genes ranged from undetectable to over 300-fold above the background  the clusters showed a striking overlap with NDRs  NDRs that remained unaltered in the  Figures . A possiromoters . This isS. pombe differ in several aspects from previous studies. For example, it has been proposed that nucleosomal positioning would coincide with the length of the transcribed units and that it is absent in inactive genes, suggesting an active role for transcription in positioning [S. cerevisiae[S. pombe and S. cerevisiae using the same criteria shows that profiles downstream from +1 N were similar . To test whether this effect might be partially due to the fact that the size of NDRs is more homogeneous in S. cerevisiae[S. pombe[As a consequence of the high resolution of the maps generated by us here, our results regarding the nucleosomal organization in itioning . Howeveritioning , 24, 25,erevisiae, 26. A cerevisiae, 26 thanS. pombe was not detected earlier owing to the limited resolution of the microarray analyses and the lower sequencing depth of previous studies, which made it difficult to define the position of the −1 nucleosome [It is possible that the regular nucleosome pattern upstream from the NDR in cleosome , 24, 25.S. cerevisiae[Another contribution of our work is the possibility of a genome-wide quantitative assessment of nucleosome dynamics. We estimate that only 1.5% of the approximately 78000 nucleosomes in the genome are remodelled during the expression of the meiotic transcriptional programme. Remodelling, however, is not always associated with transcriptional activation, as illustrated by the invariable nucleosome profile of 70% of the promoters of the 352 genes overexpressed more than 4-fold at specific stages of meiosis relative to mitosis  elements are overrepresented in the NDRs associated with promoters [S. pombe and, in agreement with previous data [S. cerevisiae[In romoters , 15. By ous data , we founerevisiae–16.in vivo. For example, in S. cerevisiae the Leu3 transcription factor binds only a subset of all potential binding sites in the chromosomes even though the protein binds to all of them with comparable affinity on naked DNA. Sites bound in vivo were strongly correlated with a low nucleosome occupancy, which was not dependent on the presence of the Leu3 protein, suggesting an opportunistic use of accessible chromatin sites already available in the genome [S. pombe, 148 (6.9%) out of the 2141 TGACGT hexamers representing potential binding sites for Atf1/Pcr1 are detectably bound in the genome [CLN2 promoter in S. cerevisiae[S. pombe depend on the combinatorial contribution of at least 5 different proteins [M26 mutation in S. pombe, which generates a binding site for Atf1 [The statistical positioning model proposes the existence of physical barriers from which regular nucleosomal arrays are generated , 21 and e genome . In S. pe genome . Of thesmbe, 148 .9% out oerevisiae. Along tproteins . Howeverfor Atf1 . Also, Dfor Atf1 , poly G for Atf1  favour tS. cerevisiae[S. pombe  and pcr1Δ (h + ura4.d18 leu1.32 ade6M210 pcr1Δ::kanMX4) cells were grown in rich medium (YES) at 32°C up to a A595 = 0.8. Diploid pat1.114 (h-/h- pat1.114/pat1.114 leu1.32/leu1.32 ade6M210/ade6M216) asynchronous cells were grown in Minimal Medium (MM) supplemented with 0,1 g/l leucine at 25°C up to a A595 = 0.8. Synchronous meiosis of diploid pat1.114 cells was induced as described [Wild-type (972 h-), S. pombe strain and for the different physiological conditions to generate a 80:20 ratio of mononucleosomes to dinucleosomes, as described [atf1Δ (8 mg), pcr1Δ (15 mg).Mononucleosomal DNA for microarray hybridization and sequencing was isolated as described . The amoescribed . The folS. pombe 1.0FR tiling microarrays were used. Target labelling preserving the original polarity of RNAs was performed following the instructions of the GeneChip whole-transcript sense target-labelling assay manual from Affymetrix. Quantitative measurement of strand-specific differential transcription in Additional file S. pombe genome version of 23/08/07 and the annotation of 24/02/11 in (http://www.pombase.org) as a reference.For quantitative transcriptional analyses, Affymetrix GeneChip S. pombe 1.0FR tiling microarrays were hybridized with mononucleosomal DNA from wild type, atf1Δ and pcr1Δ S. pombe strains  corresponding to the boundaries of each nucleosome. We used the smoothed signal generated by using the multilevel 1-D biorthogonal wavelet decomposition/reconstruction tool implemented in the Matlab “Wavelet Toolbox” to calculate the average spacing between boundary peaks for individual nucleosomes.Mononucleosomal DNA isolated as described above from haploid and diploid This parameter (which was estimated for every independent experiment) defined the distance that the individual profiles from each DNA strand had to be shifted to converge and define the midpoint position of each nucleosome. The resulting combined profile was wavelet-smoothed to generate the final nucleosome positioning profile. Comparison of the raw and wavelet-processed data in Additional file The sequencing coverage for every nucleotide was divided by the average genomic coverage to normalize the different experiments. The Pearson Correlation Coefficient between all possible combinations of the complete raw sequencing datasets of mitosis and meiosis at 0, 3 and 5 hours, ranged between 0.74 and 0.88. We defined nucleosome-depleted regions (NDRs) as regions spanning at least 150 nucleotides (corresponding to the eviction of at least one nucleosome) with a normalized sequence coverage lower than 0.4. The resulting population of NDRs was largely coincident with those detected previously using tiling microarrays .S. pombe 5′ NDRs.To identify DNA motifs, sequences from specific groups of NDRs were extracted and used as input for the MEME (Multiple EM for Motif Elicitation) algorithm  using thhttp://genomics.usal.es/cgi-bin/gb2/gbrowse/Sp_nucdyn. All sequencing and microarray data are deposited in the Gene Expression Omnibus (GEO) database under the accession number GSE41773.The genomic data described in this work can be accessed from a searchable genome browser at S. pombe reference genome. The bottom profile represents the same data after wavelet smoothing of the raw signal, as described in Methods. (PDF 60 KB)Additional file 1: Figure S1: Comparison of nucleosomal profiles from raw and processed mononucleosomal sequencing data. The top profile represents an example of the individual nucleotide coverage after aligning sequence reads directly onto the rad16 gene as detected by sequencing of mononucleosomal DNA. The 3.4 kb Spe I restriction fragment analyzed in B, the hybridization probe (green), the position of some size markers and the exons and introns of the rad16 gene (blue) are indicated, (B) Southern hybridization analysis of the same region after chromatin digestion with increasing amounts of Micrococcal Nuclease (MNase) followed by Spe I digestion, electrophoresis, blotting and hybridization to an end-terminal probe (green bar) was done as described in Reference [rad16 gene are not indicated. The resulting profile of positioned nucleosomes  was identical in both cases. (PDF 934 KB)Additional file 2: Figure S2: Comparison of nucleosomal profiles generated by MNase_Seq and by Southern hybridization. (A) Nucleosomal profile of the S. pombe and S. cerevisiae. The aggregated nucleosomal profiles of approximately 4000 S. pombe and S. cerevisiae genes were aligned to the midpoint position of the +1 nucleosome (+1 N) and to the transcription start site (TSS) (A), to the −1 nucleosomes (−1 N) (B), and to the terminal nucleosomes (TN) or to the transcription termination sites (TTS) (C). Nucleosome profiles for S. cerevisisae were represented from the sequencing data of Tsui et al. [Additional file 3: Figure S3: Comparative nucleosomal profiles of i et al. . The cooppk19 (5345 bp), utp10 (5281 bp) and alm1 (5392 bp) genes. Solid and open bars represent translated and non-translated fractions of the transcripts, respectively. (PDF 56 KB)Additional file 4: Figure S4: Nucleosome positioning over long genes. Nucleosome positioning (meiosis at 3 h) is maintained along the Additional file 5: Table S1: Gene expression (log2) during mitosis and meiosis at 0 h, 3 h and 5 h. (XLS 572 KB)S. pombe in mitotic and meiotic cells. The nucleosome profile of the same S. pombe genes as described in Figure pat1.114 cells (blue) and from a synchronous culture of diploid pat1.114 cells at 3 hours into meiosis (red). The small difference in the alignment relative to TN is probably due to the presence of meiosis-specific NDRs, which are absent in mitotic cells (See text for details). (PDF 236 KB)Additional file 6: Figure S5: Nucleosome profile of transcribed and intergenic regions in Additional file 7: Table S2: Size and genomic coordinates of constitutive, meiosis- and mitosis-specific NDRs. (XLS 269 KB)Additional file 8: Table S3: List of 352 genes overexpressed more than 4-fold in meiosis at 0 h relative to mitosis; in meiosis at 3 h relative to 0 h; and in meiosis at 5 h relative to 3 h. (XLS 60 KB)meu14 (A), mcp3 (B) and SPCC11E10.09c (C) genes remains unchanged although they are overexpressed 22.1-fold, 10.7-fold and 14.2-fold in meiosis at 3 h relative to 0 h, respectively. In meiosis at 0 h the three genes are expressed 1.6-fold, 2.3-fold and 2.7-fold above the background. (PDF 250 KB)Additional file 9: Figure S6: Nucleosome organization and differential gene expression. The nucleosome profile of the Additional file 10: Figure S7: Distribution of transcription factors binding motifs at NDRs associated with meiosis-specific and stress-response genes. (A) Distribution of sequence motifs identified by MEME in the NDRs of 82, 88 and 41 genes specifically expressed during meiosis at 0 h, 3 h and 5 h. The distribution of motifs corresponding to the binding sites for the transcription factors Ste11 and Mei4 (red line) is shown relative to the aggregated nucleosome profiles (black line). Binding sites for Ste11 are overrepresented in the NDRs of genes specifically expressed in meiosis at 0 h while those for Mei4 are overrepresented in genes expressed at 3 h and 5 h. (B) CRE and CRE variant sites are overrepresented in the NDRs of genes overexpressed under oxidative stress. The distribution of motifs was calculated as described . in vivo. (XLS 48 KB)Additional file 12: Table S5: Differential expression of the genes downstream from the 148 Atf1/Pcr1 major binding sites


The expression level of each gene is controlled by its regulatory regions, which determine the precise regulation in a tissue-specific manner, according to the developmental stage of the body and the necessity of a response to external stimuli. Nucleotide substitutions in regulatory gene regions may modify the affinity of transcription factors to their specific DNA binding sites, affecting the transcription rates of genes. In our previous research, we found that genes controlling the sensory perception of smell and genes involved in antigen processing and presentation were overrepresented significantly among genes with high SNP contents in their promoter regions. The goal of our study was to reveal functional features of human genes containing extremely small numbers of SNPs in promoter regions. Two functional groups were found to be overrepresented among genes whose promoters did not contain SNPs: (1) genes involved in gene-specific transcription and (2) genes controlling chromatin organization. We revealed that the 5′-regulatory regions of genes encoding transcription factors and chromatin-modifying proteins were characterized by reduced genetic variability. One important exception from this rule refers to genes encoding transcription factors with zinc-coordinating DNA-binding domains (DBDs), which underwent extensive expansion in vertebrates, particularly, in primate evolution. Hence, we obtained new evidence for evolutionary forces shaping variability in 5′-regulatory regions of genes. The expression of eukaryotic protein-coding genes can be regulated at several steps, including transcription initiation and elongation, mRNA processing and transport, translation, and stability. Most of the regulatory processes, however, are believed to occur at the level of transcription initiation .Transcription is precisely regulated depending on cellular conditions. The transcriptional activity of each gene is regulated by its promoter region, which is typically located upstream and immediately adjacent to the transcription start site (TSS). Promoters contain specific short regions of DNA (10–20 nucleotides) recognized by regulatory proteins (transcription factors) and termed transcription factor-binding sites. Specific interaction of transcription factors with DNA sequences within the promoter region  facilitates the recruitment of RNA polymerase to specific genes  around TSSs has the highest content of SNPs; and (2) for subsequent analysis, the optimal 3′-boundary of promoter regions possessing elevated SNPs content may be strictly defined as +100 and the 5′-boundary of these regions may be set within a wide range from −700 to −300. SNP-depleted within [−900/+100], SNP-depleted within [−800/+100], and so forth until SNP-depleted within [−300/+100]. The numbers of SNP-depleted transcripts/genes involved into analysis are indicated in the second column of  SNP-depleted within [−300/+100] and the lowest number of transcripts/genes involved into analysis was 2,821/1,587 for dataset SNP-depleted within [−900/+100].To reveal the functional characteristics of genes whose promoter regions have low levels of polymorphisms, we performed functional analysis of genes whose transcripts had no SNPs in their 5′-flanking regions. The analysis was done for several datasets of transcripts that had no SNPs within extended or restricted 5′-regulatory regions . In what SNP-depleted datasets were selected by applying the DAVID tool. The full list of the most overrepresented GO categories for each dataset is presented in Table S2. In all cases, fold enrichment exceeded 1.5, and p value was below 0.001. Then we composed a joint list of overrepresented GO terms revealed from analysis of all SNP-depleted datasets for various overlapping regions, removed duplicates, and grouped closely related GO terms (see columns 1 and 2 in Table S3). The next columns of this table summarize the occurrence of each GO term among the overrepresented terms for each SNP-depleted dataset. In such a way, we revealed three most common classes among GO terms satisfying the aforementioned criteria that were associated with (a) chromatin organization, (b) transcription, and (c) multicellular organism reproduction/gamete generation. GO terms that belong to these three classes are given in The GO terms overrepresented among transcripts/genes from regulation of specific transcription from RNA polymerase II promoter, positive regulation of specific transcription from RNA polymerase II promoter, negative regulation of transcription, DNA-dependent, regulation of gene-specific transcription, negative regulation of gene-specific transcription, and negative regulation of specific transcription from RNA polymerase II promoter. The second class of GO terms includes chromosome organization, chromatin organization, chromatin modification, chromatin assembly or disassembly, nucleosome organization, and DNA packaging. The third class of GO terms includes gamete generation, male gamete generation, and spermatogenesis.The first class of GO terms includes chromatin organization and transcription were found to be overrepresented in four or more SNP-depleted datasets. GO terms associated with transcription were revealed on the base of analysis of SNP-depleted 5′-regulatory regions of short and medium lengths  , medium (−500/+100 and −600/+100), and long lengths  . For the latter three regions we found among top-scoring GO terms only terms associated with chromatin organization and gamete generation (Table S3).The numbers of genes annotated by GO categories of these three classes varied from 15 to 105. GO terms associated with00/+100) . GO term00/+100) . GO term SNP-depleted within [−600/+100] is that practically all (with only one exception) overrepresented GO terms  are associated with chromatin organization and transcription. Hence, we performed the next analysis only for region −600/+100.As shown in Tables S2 and S3, the distinctive feature of the dataset All TFs and Chr_Mod.Our second analysis was undertaken to compare promoter variability in genes controlling transcription or chromatin organization with the variability in the whole-genome dataset. Since genes encoding transcription factors are the largest functional group of genes associated with transcription, we included this gene group into analysis. Likewise, we chose genes encoding chromatin-modifying proteins for further functional analysis as genes functionally associated with chromatin organization. The lists of transcripts/genes encoding transcription factors or chromatin-modifying proteins were formed as described in Materials and Methods. These lists are denoted in what follows asTo characterize the group of genes controlling transcription factors in more detail, we divided this list of genes into four subclasses according to the structures of their DBDs see  and perfAt this step of our analysis, only 5′-regulatory regions spanning nucleotides within −600 to +100 around TSSs were investigated.All TFs and Chr_Mod) and three subclasses of TFs  tend to have lower SNP contents  to the range of thresholds of SNP content  around annotated TSSs in each group of transcripts and in the whole-genome dataset shows that transcripts of both large groups  were located on the Y chromosome, while only 115 transcripts out of the total amount 47,469 were mapped to this chromosome (according to t-test p < 2∗10−30). We suspected that this notable enrichment might explain the extremely low level of SNPs in 5′-regulatory regions of transcripts from the Sperm dataset, since there were profound differences between the SNP content in 5′-regulatory gene regions located on chromosomes 1–22 and the SNP contents on both sex chromosomes .However, we found that a notable portion of transcripts from theTo test this hypothesis, we excluded transcripts located on the X and Y chromosomes from all datasets presented in  Sperm, (1) the difference in promoter SNP content between transcripts located on all chromosomes (autosomes and sex chromosomes) and the whole-genome dataset of transcripts was significant  the group of genes encoding factors with zinc-coordinating DBDs was the most tolerant to functional genetic variation.We found that genes encoding TFs and chromatin-modifying proteins had lower RVIS values than genes from the whole-genome dataset . The comt-test  versus genes from the whole-genome dataset were highly significant for the broad range of thresholds  of the total number of promoters were found to have high SNP contents (six or more SNPs). This is in good agreement with our earlier report based on data from dbSNP build 138 , where wOn the other hand, one-fifth (16.5%) of the total number of 5′-regulatory regions within −500/−1 were SNP-depleted. We were also interested in investigating the extent to which the proportions of SNP-depleted promoters depend on the length and location of the promoter regions. The analysis of proportions of transcripts having no SNPs within particular 100 bp bins in the −1000/+200 regions around TSSs showed that (a) the bins between −200 and −100 had the lowest proportion and (b) bins within −700 and +100 had lower proportions than flanking ones .The reduced proportion of SNP-depleted regions among local 100 bp regions within −300/+100 revealed in our study tells us that these regions surrounding TSSs contain elevated numbers of SNPs in comparison with their flanking regions.This finding agrees with the results published previously by , 26. BotTaking in account our results  and prev transcription regulation. The second consisted of GO terms related to chromatin organization. Notably, these two classes define two extremely important biological processes, transcription regulation by transcription factors, and regulation of chromatin packaging. Both biological processes were overrepresented in four or more sets of SNP-depleted transcripts, indicating that this finding was highly reliable. The third class, smaller than the two, listed terms that described a highly tissue-specific process of male gamete generation (spermatogenesis). We considered this class of GO terms because terms related to male gamete generation were found for SNP-depleted datasets of transcripts with very long 5′-regions . Such extended lengths of 5′-regions might point to significance of the finding. However, the statistical t-test on the heterogeneity of SNP content among the 5′-regulatory regions of genes mapped to autosomes or sex chromosomes  showed that the low level of SNPs in the third class might be completely explained by more frequent (than expected) location of genes involved in spermatogenesis on the Y chromosome. Hence, we restricted our subsequent analysis of genetic variability within 5′-regulatory regions to two groups of genes involved in transcription and chromatin organization (corresponding to the two major classes of GO terms).Functional analysis of SNP-depleted transcripts performed with the DAVID tool revealed three distinct classes of overrepresented GO terms . The firAll TFs and Chr_Mod) with that for the whole-genome dataset by Fischer's t-test for angular transformed proportions. We showed that 5′-regulatory regions of both groups had reduced genetic variability in comparison to that in the whole-genome dataset  posttranslationally modify and demodify chromatin, altering chromatin structure and recruiting regulatory factors and (b) provide access to nucleosomal DNA or allow nucleosomes to move to a different position along the DNA, remove, or exchange nucleosomes using energy from ATP hydrolysis . Genome-Thus, both classes of proteins are involved in the complicated process of transcriptional control, ensuring correct expression of specific genes. Both so called “transcription factor-binding regulatory code” and “histone code” may be effectively used for prediction of gene expression activity. Moreover, these codes are redundant for predicting gene expression . This reThe transcriptional regulatory system plays the central role in controlling many biological processes, ranging from cell cycle progression and maintenance of intracellular metabolic and physiological balance, to cell differentiation and developmental time courses. Numerous diseases arise from a breakdown in the regulatory system: transcription factors are overrepresented among oncogenes , and a tTherefore, it is not surprising that genes of both these systems have lower SNP contents in their 5′-regulatory regions. So we hypothesize that the reduced variability of regulatory regions may be due to selective pressure (purifying selection), which removes deleterious alleles. To estimate the force of selective pressure on genes encoding TFs and chromatin-modifying proteins, we calculated RVIS values. According to cumulative percentage plots for the RVIS percentiles , both gr ZNF, HTH, and Basic; see  HTH and Other). The group of genes encoding TFs with the basic domain demonstrated a moderate significance level . Finally, practically no differences were revealed between genes encoding TFs with zinc-coordinating DBDs and the whole-genome dataset . The differences between groups of genes formed according to the types of encoding proteins were reproduced when cumulative percentage plots for the RVIS percentiles were built  there are many well-known hubs in a molecular network. They include (a) TP53 involved in cell cycle control  regions relative to annotated transcription start sites are presented.Figure S2: The comparison of SNP content distributions in upstream regions of seven groups of human transcripts  from chromosomes 1-22  with the distribution in the whole-genome dataset from which transcripts located on the chromosomes X and Y were also excluded.The significances of the t test (Y axis), which compares SNP contents in seven groups of human transcripts with the content in the whole genome dataset as a function of the SNP content (X axis) are presented. Panel A: All TFs, ZNF, HTH, Basic, Other; Panel B: Chr Mod; Panel C: Sperm.Table S1:Datasets of transcripts, described in the Table 1: All TFs, ZNF, HTH, Basic, Other, Chr Mod, Sperm.HGNC Gene Name, Ensembl Gene ID, Ensembl, Transcript ID, SNP count are presented.p value < 10-3) GO categories for each SNP-depleted dataset.Table S2:The full list of the most overrepresented  GO terms revealed from analysis of all SNP-depleted datasets (The duplicates of GO terms were removed).Table S3: The joint list of overrepresented (fold enrichment > 1.5 and Columns from C to I present the occurrence of each GO term among the overrepresented terms for each SNP-depleted dataset.