Gnrh1 gene include three enhancers and the proximal promoter, which confer Gnrh1 gene expression specifically in GnRH neurons. In immortalized mouse hypothalamic GnRH (GT1-7) neurons, which show pulsatile GnRH release in culture, RNA sequencing and RT-qPCR revealed that expression of a novel long noncoding RNA at Gnrh1 enhancer 1 correlates with high levels of GnRH mRNA expression. In GT1-7 neurons, which contain a transgene carrying 3 kb of the rat Gnrh1 regulatory region, both the mouse and rat Gnrh1 enhancer-derived noncoding RNAs (GnRH-E1 RNAs) are expressed. We investigated the characteristics and function of the endogenous mouse GnRH-E1 RNA. Strand-specific RT-PCR analysis of GnRH-E1 RNA in GT1-7 cells revealed GnRH-E1 RNAs that are transcribed in the sense and antisense directions from distinct 5’ start sites, are 3’ polyadenylated, and are over 2 kb in length. These RNAs are localized in the nucleus and have a half-life of over 8 hours. In GT1-7 neurons, siRNA knockdown of mouse GnRH-E1 RNA resulted in a significant decrease in the expression of the Gnrh1 primary transcript and Gnrh1 mRNA. Over-expression of either the sense or antisense mouse GnRH-E1 RNA in immature, migratory GnRH (GN11) neurons, which do not express either GnRH-E1 RNA or GnRH mRNA, induced the transcriptional activity of co-transfected rat Gnrh1 gene regulatory elements, where the induction requires the presence of the rat Gnrh1 promoter. Together, these data indicate that GnRH-E1 RNA is an inducer of Gnrh1 gene expression. GnRH-E1 RNA may play an important role in the development and maturation of GnRH neurons.Gonadotropin-releasing hormone (GnRH), a neuropeptide released from a small population of neurons in the hypothalamus, is the central mediator of the hypothalamic-pituitary-gonadal axis, and is required for normal reproductive development and function. Evolutionarily conserved regulatory elements in the mouse, rat, and human  Gnrh1) gene expression and GnRH secretion are essential for normal reproductive maturation and fertility. During embryonic development, GnRH neurons originate in the olfactory placode then migrate to the developing forebrain. In adulthood, mature GnRH neurons are found scattered throughout the hypothalamus and form a small population of approximately 800 neurons that secrete GnRH decapeptide in a pulsatile fashion [Regulation of gonadotropin-releasing hormone Gnrh gene expGnrh1 gene expression have been implicated in the incorrect timing of puberty, reproductive deficiencies, and infertility. Reproductive disorders such as Kallmann Syndrome [Gnrh1 gene expression, GnRH neuron signaling, and/or neuronal maturation. However, the majority of genetic causes and molecular mechanisms involved in the pathology of these reproductive deficiencies remain undefined. Thus, understanding the mechanisms regulating Gnrh1 gene expression will provide important insights into the biology and pathophysiology of mammalian reproduction.Failure of GnRH neuron migration and maturation and dysregulation of Syndrome , IdiopatSyndrome , Prader-Syndrome , and CHASyndrome , are attGnrh1 5’ regulatory region driving an oncogene (SV40 T-antigen) in mice [Gnrh1 mRNA and secrete GnRH in a pulsatile manner [GNRH1 5’ regulatory region on SV40 T-antigen to produce a tumor in the nose [Gnrh1 mRNA [Immortalized, mature, mouse hypothalamic GnRH GT1-7) neurons and immature, migratory GnRH GN11) neurons serve as excellent cell culture models to study the unique and rare population of GnRH neurons in the hypothalamus. GT1-7 neurons were generated using a transgene containing 3 kb of rat  in mice  to produ neurons e manner . GN11 ce neurons rh1 mRNA , 13.Gnrh1 gene include three enhancers and the proximal promoter that, together, confer specific expression of Gnrh1 in the discrete population of hypothalamic GnRH neurons. Three Gnrh1 enhancers and the proximal promoter reside within 4.8 kb upstream of the mouse Gnrh1 gene transcription start site (TSS) as defined by homology to their well-characterized counterparts in the rat gene. In the mouse gene, Gnrh1 enhancer 3 (GnRH-E3) at -4688 bp/-4385 bp, enhancer 2 (GnRH-E2) at -3622 bp/-3100 bp, and enhancer 1 (GnRH-E1) at -2404 bp/-2100 bp facilitate transcriptional activity of the Gnrh1 promoter (GnRH-P) at -278 bp/97 bp. Importantly, as shown in the rat gene by transfection into the mouse GT1-7 cell line, GnRH-E1 interacts with GnRH-E2, GnRH-E3 and GnRH-P in specifying robust GnRH neuron-specific gene expression, and lack activity in GN11 cells and NIH3T3 mouse fibroblasts [Conserved regulatory elements in the mouse, rat, and human roblasts –16.Gnrh1 transcriptional activity is also observed in vivo. Transgenic mice carrying rat GnRH-E1 and GnRH-P driving β-galactosidase showed GnRH neuron-specific expression, while rat GnRH-E1 on a heterologous promoter showed non-specific β-galactosidase expression in the brain [Gnrh1 mRNA expression in the brain, but showed higher ovarian Gnrh1 mRNA expression accompanied by irregular estrous cyclicity [Gnrh1 promoter in the specificity of Gnrh1 gene expression.The essential role of GnRH-E1 in mature GnRH neuron-specific he brain . Knock-oyclicity . These rTranscription at enhancers has been described to be a genome-wide phenomenon in a constitutively active and regulated manner. Actively transcribed enhancers display signature histone marks of high H3K4me1 and low H3K4me3 , open chGnrh1 enhancer-transcribed RNA highlighted the characteristics of actively transcribed enhancers. The endogenous mouse Gnrh1 regulatory region displays DNase1 hypersensitivity, histone markers of actively transcribed enhancers (i.e. high H3K4me1 and low H3K4me3), and RNA pol II enrichment primarily in GT1-7 neurons, whereas histone markers of inactive chromatin were observed in GN11 and NIH3T3 cells as assayed by chromatin immunoprecipitation (ChIP). Treatment of GT1-7 cells with 12-O-tetradecanoyl-phorbol-13-acetate (TPA), an activator of the protein kinase C (PKC) pathway, repressed Gnrh1 mRNA expression and reduced RNA Poll II occupancy at GnRH-E1. RT-PCR analyses showed the expression of Gnrh1 enhancer-transcribed RNA robustly correlated with Gnrh1 mRNA expression in response to PKC pathway signaling after TPA treatment only in GT1-7 neurons. [Gnrh1 enhancer-derived noncoding RNA (GnRH-E1 RNA) in GnRH model cell lines. We established GnRH-E1 RNA as a novel regulator of Gnrh1 gene expression. Our data provide the foundation for future studies in the molecular mechanisms of Gnrh1 regulation with implications in mammalian reproductive development and fertility.The initial discovery of neurons. . These i2 at 37°C. Cell lines at passage 15–25 were used in this study. For actinomycin D treatments, GT1-7 cells were seeded in 10 cm plates  at 300,000 cells/mL 24 hours prior to treatment. Cells were treated with actinomycin D or DMSO vehicle at 1 μg/mL final concentration. At the time points indicated, cells were rinsed with phosphate-buffered saline (PBS) and homogenized in Trizol Reagent  for RNA extraction.GT1-7, GN11 , and NIH3T3 cell lines were cultured in DMEM  with 4.5% glucose, 10% fetal bovine serum and 1% penicillin-streptomycin cocktail in 5% CO2, 0.2 mM EDTA, and 1 mM PMSF) and incubated on ice for 10 minutes. After centrifugation at 13,000 g for 10 minutes, the nuclear extract buffer was removed and the pellet containing nuclei was homogenized in Trizol reagent for RNA extraction.To harvest nuclear and cytoplasmic extracts from GT1-7 neurons in culture, cells were first aspirated and washed with PBS, followed by centrifugation at 1000 g for 5 minutes. The cell pellet was resuspended in cytoplasmic extract buffer  on ice for 3 minutes. After centrifugation at 1500 g for 4 minutes, the cytoplasmic extract (supernatant) was homogenized in Trizol reagent for RNA extraction. The pellet containing nuclei was resuspended in cytoplasmic extract buffer without NP40. After centrifugation of the nuclei at 1500 g for 4 minutes, the buffer was removed and the nuclei were resuspended in nuclear extract buffer  or gene-specific primers , where indicated. Gene-specific primers for cDNA synthesis are listed in Total RNA was harvested from GT1-7, GN11, and NIH3T3 cells using Trizol Reagent according to the manufacturer’s recommendations. Genomic DNA was removed from RNA samples using TURBO DNA-free kit  according to the manufacturer’s recommendations. First-strand cDNA was synthesized from the total RNA from each cell line, using SuperScript III First-Strand cDNA Synthesis System , according to the manufacturer’s recommendations, and was primed by either oligo(dT)Taq DNA Polymerase  and the following conditions: 95°C for 5 minutes denature, followed by 30 cycles of 95°C for 45 seconds, 60°C for 45 seconds, 72°C for 45 seconds, and final extension of 72°C for 10 minutes. PCR primers are listed in PCR was performed using FastStart Gnrh1 pre-mRNA, and Gnrh1 mRNA were normalized to histone 2A.Z (H2afz) mRNA control or peptidylprolyl isomerase A (Ppia) mRNA control, where indicated. RT-qPCR primers are listed in Quantitative PCR was performed using iQ SYBR Green Supermix and iQ5 Real-Time PCR detection system and software  according to the manufacturer’s recommendations. Standard curves were generated using serial dilutions of plasmid DNA containing the qPCR amplicon cloned into pcDNA2.1 backbone. Quantities of GnRH-E1 RNA, www.bioinformatics.babraham.ac.uk/projects/fastqc/) and FASTQ Groomer [http://hannonlab.cshl.edu/fastx_toolkit/index.html). The reads were aligned to the mouse genome (mm10 assembly) using the TopHat2 program [RNA was isolated from GT1-7, GN11, and NIH3T3 cells using TRIzol® , as per the manufacturer’s instructions, and treated with Turbo DNA-free DNase . Concentration was determined using SmartSpec Plus Spectrophotometer System . The RNA integrity (RNA Integrity Number ≥ 9) and quantity was determined on the Agilent 2100 Bioanalyzer . cDNA libraries were created using the TruSeq™ RNA Sample Prep-v2 , using the manufacturer’s low-throughput protocol. Indexed samples were mixed at equal concentrations, four samples per lane, and sequenced using the HiSeq 2000 sequencer . The resulting Fastq sequence reads were validated and analyzed in Galaxy  using FA Groomer , and any program  and diff program . Sequenc program , 31. SeqGnrh1-luciferase reporter and 5’ truncations were generated as previously described [EcoR1 restriction enzyme digest site of the pcDNA 2.1  backbone plasmid using T4 DNA ligase . Plasmid constructs carrying -3560 bp/-1128 bp of GnRH-E1 RNA, integrated in the forward or the reverse orientation, were verified by DNA sequencing. For quantitative PCR of the transgene-transcribed rat GnRH-E1 RNA, the standard plasmid was constructed by cloning -2854 bp/-1156 bp segment of the rat transgene from GT1-7 neuron genomic DNA into the EcoR1 restriction enzyme digest site of the pcDNA 2.1 backbone plasmid.The -5 kb rat escribed . The ratescribed , 33. Theescribed . The mouTM Transfection Reagent  according to the manufacturer’s recommendations. Cells were co-transfected with 150 ng/well of luciferase reporter plasmid, 200 ng/well of expression plasmid, and 100 ng/well of thymidine kinase β-galactosidase reporter plasmid as internal control for transfection efficiency. Cells were transfected in parallel with RSVp-Luciferase or pGL3-Luciferase reporter plasmid where indicated. At 8 hours after transfection, cells were serum starved  for 24 hours prior to harvest. Cells were harvested using cold lysis buffer (100 mM potassium phosphate at pH 7.8 and 0.2% Triton X-100). Luciferase and β-galactosidase assays were performed using the Galacto-Light Plus System . Three independent experiments were done, each in triplicate.For luciferase assays, GT1-7 and GN11 cells were seeded into 24-well plates 24 hours before transfection at concentrations of 250,000 cells/mL and 70,000 cells/mL, respectively. Plasmids were transfected using PolyJetFor siRNA transfections, GT1-7 cells were seeded in 6-well plates at the concentration of 250,000 cells/mL 48 hours prior to transfection. Growth media was replaced with DMEM with 4.5% glucose and 5% fetal bovine serum 24 hours prior to siRNA transfection. Negative siRNA control targeting luciferase mRNA and custom-designed siRNA duplex oligos targeting GnRH-E1 RNA  and Lipofectamine RNAiMax transfection reagent  were prepared according to the manufacturer’s recommendations, in Gibco OptiMEM  at a concentration of 10 μM. siRNA was transfected into GT1-7 cells at a concentration of 900 pmol per well. Three independent experiments were performed, each in triplicate.Gnrh1 pre-mRNA and Gnrh1 mRNA were normalized to H2afz mRNA copy number as internal control. Student’s t-test and 2-way ANOVA followed by Tukey-Kramer HSD post hoc tests were used as indicated, where P<0.05 indicated statistical significance.All transient transfections, RT-PCR and quantitative RT-PCR were independently repeated three times or more. For transient transfections, luciferase activity was normalized relative to β-galactosidase to control for transfection efficiency. Unless otherwise noted, the data were normalized to luciferase:β-galactosidase activity ratio of empty pGL3 plasmid transfected in parallel. The data were compared between cells transfected with empty pcDNA2.1 plasmid and those transfected with pcDNA2.1 plasmid carrying GnRH-E1 RNA. Results are presented as mean ± standard deviation (SD) of the fold induction relative to pGL3. Statistical analyses were performed on luciferase activity normalized to β-galactosidase. For RT-qPCR, the mean copy number for GnRH-E1 RNA, Gnrh1 gene is located on mouse chromosome 14 and is flanked by the genes Kctd9 transcribed in the same direction as Gnrh1 and located upstream of Gnrh1, and Dock5 transcribed in the opposite direction and located downstream of Gnrh1. Mouse Gnrh1 enhancers and promoter are located in the genomic region 5’ of Gnrh1, where Gnrh1 enhancers 2 and 3 (E2 and E3) are located in the region overlapping the 3’ UTR of Kctd9, and Gnrh1 enhancer 1 (E1) and the promoter are located in the intergenic region 3’ of Kctd9. The robust detection of RNA reads that align to the exons of the Gnrh1 gene confirms the high level of Gnrh1 expression in GT1-7 cells . RNA reads were also detected that align to the introns of the Gnrh1 gene and likely represent the signal from the Gnrh1 primary transcript, as the sequencing analysis was performed on total RNA. In addition to the reads aligning to Gnrh1, GT1-7 cells showed dense and robust RNA reads that align to the intergenic region of Gnrh1 and Kctd9 genes  indicated alternate 5’ capping structure and 3’ polyadenylation sites located upstream and downstream of GnRH-E1, which suggests the expression of GnRH-E1 RNA variants transcribed from the sense and antisense direction in GT1-7 cells. However, alternate locations of 5’ capping structure were identified at approximately -3458 bp and -1576 bp, and 3’ polyadenylation sites at approximately -3720 bp and at -1182 bp from the Gnrh1 TSS.To capture the mouse sense GnRH-E1 RNA variant, we used reverse primers at -1128 bp or -1271 bp for first-strand cDNA synthesis. The cDNA synthesized using the gene-specific primer (GSP) at -1271 bp (reverse) was subjected to RT-PCR analysis using the same reverse primer, paired with forward primers at -3560 bp, -3606 bp, -3746 bp, and -3779 bp . PCR proGnrh1 gene and suggesting non-specific PCR amplification. First-strand cDNA synthesis using GSP -3560 bp forward failed to capture the 3’ end of the antisense GnRH-E1 RNA variant. Together, the data indicate that the TSS of the antisense GnRH-E1 RNA variant likely resides between -1443 bp and -1271 bp, and that a polyA termination site is located between -3560 bp and -3371 bp from the Gnrh1 TSS.Similarly, to capture the mouse antisense GnRH-E1 RNA variant, we used forward primers at -3560 bp and at -3371 bp for first-strand cDNA synthesis. The cDNA synthesized using the gene-specific primer at -3371 bp (forward) was subject to RT-PCR analysis using the same forward primer, paired with reverse primers at -1128 bp, -1271 bp, and -1443 bp . PCR proGnrh1 gene do not have coding potential. Furthermore, our data indicate that the sense GnRH-E1 RNA is transcribed from a distinct 5’ start site located upstream of GnRH-E1, and the antisense GnRH-E1 RNA is transcribed from a start site located downstream of GnRH-E1. The 5’ transcription start of sense and antisense GnRH-E1 RNA variants do not originate from a central transcription start site. In addition, our approximated boundaries of the sense and antisense GnRH-E1 RNAs between -1271 bp and -1128 bp from the Gnrh1 TSS coincides with a decrease in the amount of RNA reads at approximately -1168 bp from the Gnrh1 TSS. Together, our data indicate that GnRH-E1 RNA variants are likely 5’ capped, 3’ polyadenylated RNA molecules of over 2 kb in length, and do not contain functional open reading frames (ORFs). These characteristics and the location of GnRH-E1 RNA transcription are consistent with the features of lincRNAs, a sub-category of lncRNAs transcribed from intergenic regions including enhancers.Our strand-specific RT-PCR analyses of GnRH-E1 RNA indicate that sense and antisense variants are over 2 kb in length. We did not observe shorter PCR products within the -3746 bp to -1128 bp region, which suggests that GnRH-E1 RNA variants are not spliced. Analysis by Coding Potential Calculator  revealedLncRNAs are defined as RNA molecules of more than 200 nucleotides in length with low coding potential, but share similar features of protein-coding RNAs, including 5’ capping structure, splicing, and 3’ polyadenylation . LncRNAsGnrh1 gene regulation. RT-PCR analysis of nuclear and cytoplasmic extracts from GT1-7 neurons revealed that GnRH-E1 RNA is present in the nuclear RNA and absent from the cytoplasmic RNA. Appropriately, Gnrh1 primary transcript (pre-mRNA) was observed in the nucleus, whereas Gnrh1 mRNA was observed in both the nuclear and cytoplasmic extracts . GT1-7 neurons were treated with 1 μg/mL of actinomycin D, a transcription inhibitor, or DMSO control. Total RNA was harvested from 2 hours up to 24 hours following treatment . At 36 hours after siRNA transfection, we observed a small, not statistically significant, decrease in endogenous mouse GnRH-E1 RNA expression, compared to cells treated with negative control siRNA. Mouse GnRH-E1 RNA knockdown was more robust at 48 hours and 72 hours after siRNA transfection , the heterologous RSV enhancer and the rat Gnrh1 promoter (RSVe/GnRH-P), the rat Gnrh1 enhancer 1 and the heterologous RSV promoter (GnRH-E1/RSVp), or RSVe/RSVp control. The over-expression of the mouse sense GnRH-E1 RNA induced the activity of the Gnrh1 promoter appended to RSVe, but did not affect the activity of GnRH enhancer 1 appended to RSVp or the activity of the reporter plasmid carrying GnRH-E1 and GnRH-P alone. Over-expression of the mouse sense GnRH-E1 RNA did not affect the activity of the control luciferase plasmid carrying RSVe/RSVp, compared to cells that were transfected with empty pcDNA2.1 control expression plasmid  in the intergenic region of mouse Gnrh1 and Kctd9 by RT-PCR [Gnrh1 that appear not to be spliced. Expression of GnRH-E1 RNA correlates with Gnrh1 mRNA expression in a mature GnRH neuron-specific manner. Our data indicate that GnRH-E1 RNA functions as an inducer of Gnrh1 gene transcriptional activity.Gonadotropin-releasing hormone gene expression and hormone secretion are highly specific functions of GnRH neurons in the hypothalamus. y RT-PCR . Here, wGNRH1 and KCTD9 in the same direction as GNRH1 gene. Human lincRNA RP11-395I14.2 is 528 nt in length, is spliced, and contains a region in its exon 2 that is homologous to mouse GnRH-E1 RNA, but its function has yet to be described. While the human lincRNA RP11-395I14.2 expression profile is not exclusive to the brain, GnRH-E1 RNA expression in model cell lines, in contrast, suggests a hypothalamic cell-type specific expression pattern. Further investigation is required to examine whether human lincRNA RP11-395I14.2 and mouse GnRH-E1 RNA are functionally similar.Our study is the first to describe the physical and functional characteristics of the mouse GnRH-E1 RNA, following our initial identification by RT-PCR and despite the absence of annotated RNA expression in publically available mouse or rat genome databases. However, human lincRNA RP11-395I14.2 (ENSG00000253476) has been identified and is transcribed from the intergenic region of human Gnrh1 TSS using conventional PCR consistently produced full-size amplicons, in parallel with full-size products from genomic DNA positive control template. This observation suggests that GnRH-E1 RNA variants do not undergo splicing within the region between -3606 bp and -1128 bp. The sense GnRH-E1 RNA variant most likely contains a distinct 3’ end between -1443 bp and -1128 bp upstream of the Gnrh1 TSS. The 5’ start site for GnRH-E1 RNA sense variant overlaps with the 3’ UTR of the Kctd9 gene located upstream of Gnrh1. From our RNA sequencing analysis, a small decrease in RNA reads is observed that aligns to the center of GnRH-E1, but our strand-specific RT-PCR experiments showed that unlike small enhancer RNAs (eRNAs) [Strand-specific RT-PCR revealed both sense and antisense variants of the mouse GnRH-E1 RNA from rather discreet 5’ and 3’ ends located upstream and downstream of GnRH-E1. Although alternate transcription start and termination sites were indicated by RACE analysis, which also suggested expression of GnRH-E1 RNA variants of different lengths, strand-specific RT-PCR analysis revealed GnRH-E1 RNA variants of over 2 kb in length. Our amplification of shorter GnRH-E1 RNA variants within this 2 kb region from -3606 bp to -1128 bp upstream of the  (eRNAs) , 41–43, Gnrh1 TSS using conventional PCR consistently produced full-size amplicons, in parallel with full-size products from genomic DNA positive control template. Thus, we believe that at least some variants of GnRH-E1 RNA do not undergo splicing within the region between -3606 bp and -1128 bp. However, preliminary 5’ RACE analysis revealed capping structures located at approximately -3458 bp and at -1576 bp from the Gnrh1 TSS. Preliminary 3’ RACE analysis revealed 3’ polyadenylation sites at -3720 bp and at -1182 bp from the Gnrh1 TSS. It is possible that 5’ capping structures and 3’ polyadenylation sites predicted by RACE represent shorter GnRH-E1 RNA variants. On the other hand, the limitations of RACE analysis include 1) the limited ability to efficiently amplify long PCR products, 2) the efficiency of synthesizing cDNA from minor noncoding RNA variants for 5’ RACE, and 3) the identification of 5’ capping site and 3’ polyadenylation sites is heavily dependent on the location of gene-specific primers used for amplification and thus generates a bias toward expected ends. Predictions by RACE analysis may also reveal major variants, rather than all variants. Because of these limitations, we cannot rule out the possibility that the 5’ capping structures predicted by RACE represent shorter GnRH-E1 RNA variants, or that the 5’ ends identified by strand-specific RT-PCR analysis represent variants that were not identified by RACE. Nevertheless, cDNA synthesis using oligo-dT primers for RT-PCR and preliminary 3’ RACE analysis provides strong evidence that GnRH-E1 RNA variants are polyadenylated. Together, the physical characteristics of GnRH-E1 RNA are consistent with those of lncRNA and lincRNA, rather than eRNA.Although alternate transcription start and termination sites were indicated by preliminary RACE analysis, which also suggested expression of GnRH-E1 RNA variants of different lengths, strand-specific RT-PCR analysis revealed GnRH-E1 RNA variants of over 2 kb in length. Our amplification of shorter GnRH-E1 RNA variants within this 2 kb region from -3606 bp to -1128 bp upstream of the Gnrh1 mRNA is evidently maintained for more than 24 hours, whereas the stabilities of GnRH-E1 RNA and Gnrh1 pre-mRNA are comparatively shorter, with half-lives of about 8 hours. Nevertheless, we predicted that GnRH-E1 RNA is sufficiently stable for transient siRNA knockdown. In addition, the observation that GnRH-E1 RNA resides in the nucleus provides an important insight into the function of GnRH-E1 RNA as potential regulator of Gnrh1 gene expression at the level of transcription.Furthermore, the stability of GnRH-E1 RNA is consistent with the characteristics of lncRNA, rather than eRNA. LncRNAs can be stable RNA molecules that are susceptible to transient knockdown by siRNA and provide sustained regulation of gene expression . In contGnrh1 pre-mRNA at 36 hours and a decrease in Gnrh1 mRNA at 72 hours after siRNA treatment. The delayed decrease in Gnrh1 mRNA levels may reflect the long half-life (> 24 hours) and stability of Gnrh1 mRNA  [Gnrh1 expression. In addition, Q50 non-Hox homeodomain transcription factors MSX and DLX bind directly to CAATTA repeat elements in both GnRH-E1 and promoter. In GT1-7 cells, co-transfection of MSX1 or 2 and reporter plasmid carrying Gnrh1 enhancer and promoter showed repression of Gnrh1 promoter activity, while DLX2 or 5 relieved the repression, suggesting dynamic control of enhancer and promoter activity. In fact, mice lacking MSX or DLX showed abnormal numbers and spatial distribution of GnRH neurons throughout development, suggesting critical role of these transcription factors at the Gnrh1 regulatory region [Gnrh1 expression and are highly expressed specifically in GT1-7 neurons. Mice lacking SIX6 present with dramatically decreased hypothalamic GnRH neuron number, disrupted reproductive development, and severely reduced reproductive capacity [Gnrh1 expression during GnRH neuron maturation in embryonic development. Interestingly, VAX1 is a relatively modest activator of Gnrh1 transcription, but competes with SIX6, a strong activator of Gnrh1 transcription, for occupation at the ATTA repeat element [pression –46. The o -1863) , 48 durio -1863) , which sy region , 50. Twocapacity . The abs element .Gnrh1 expression. The decrease in GnRH-E1 RNA may lower the efficiency of transcription factor binding at Gnrh1 regulatory elements, but compensatory mechanisms and the other transcription factors may also play a role in maintaining Gnrh1 transcription. In addition, GnRH-E1 RNA can act in trans, as evidenced by the effect of GnRH-E1 RNA overexpression on transfected Gnrh1 regulatory elements. GnRH-E1 RNA may also act on distal genes, such as key transcription factors that regulate Gnrh1 gene transcription, and thus acting indirectly. In GT1-7 neurons, where Gnrh1 gene expression is robust and controlled by numerous factors, GnRH-E1 RNA action may play only a limited role in facilitating the transcription of Gnrh1 gene. Nevertheless, our data showed that the knockdown of the mouse GnRH-E1 RNA alone results in a substantial decrease in endogenous Gnrh1 gene expression.One possible mechanism of GnRH-E1 RNA function is through the interaction with transcription factors that are known to control Gnrh1 gene expression we observed in our siRNA knockdown experiment is rather comparable to the vigor of lncRNA effects on individual target gene expression. Even among well-characterized lncRNAs described in the literature, the depletion of a lncRNA can generally down-regulate or remove repression of individual target gene expression [Furthermore, the strength of GnRH-E1 RNA regulation of pression , 53–55, pression , 40, 56.Gnrh1 gene expression, we tested the effect of the mouse sense and antisense GnRH-E1 RNA on the transcriptional activity of extensively studied and well-characterized rat Gnrh1 regulatory elements [Gnrh1 regulatory elements. This observation not only demonstrates the high level of evolutionary conservation between the mouse and rat Gnrh1 regulatory region, but is also consistent with our characterization of GnRH-E1 RNA transcribed in both the sense and antisense direction. Importantly, the data indicate that GnRH-E1 RNA may indeed act as an inducer of Gnrh1 gene transcription, and that both sense and antisense strands are functionally significant. GN11 neurons represent immature and migratory GnRH neurons, where Gnrh1 mRNA is absent, and GnRH-E1 RNA expression is not detected. The transcriptional activity of transiently transfected Gnrh1 regulatory elements is relatively silent in GN11 neurons, compared to the activity in GT1-7 neurons. The transcriptional activity of Gnrh1 in GT1-7 neurons can be 100–1000 times higher than that in GN11 neurons, as shown in the difference in RNA detection between GT1-7 cells and GN11 cells by RNA sequencing. Nevertheless, over-expression of GnRH-E1 RNA resulted in a marked increase in the transcriptional activity of Gnrh1 regulatory elements. The data indicate that GnRH-E1 RNA is an inducer of Gnrh1 gene transcription, thus may play a role in activating or de-repressing the transcriptional activity of Gnrh1 gene regulatory elements in differentiating GnRH neurons.To firmly establish the function of GnRH-E1 RNA as an inducer of elements –17, 33. Gnrh1 regulatory elements requires the Gnrh1 promoter, suggesting that GnRH-E1 RNA induction of Gnrh1 transcription is through the induction of Gnrh1 promoter activity. Interestingly, the mouse sense GnRH-E1 RNA induced the activity of the reporter plasmid containing the -2168 bp rat Gnrh1 regulatory region which includes the enhancer and promoter, but did not affect the activity of the GnRH-E1/GnRH-P reporter. This observation may indicate that action of the sense GnRH-E1 RNA requires the genomic region between the enhancer and the promoter. On the other hand, the antisense GnRH-E1 RNA induced the activity of GnRH-E1 and GnRH-P, as well as GnRH-P with an RSV enhancer, which provides strong indication that the GnRH-E1 RNA induces Gnrh1 gene expression by inducing the activity of the Gnrh1 promoter.Furthermore, we determined that the positive effect of GnRH-E1 RNA on Gnrh1 enhancer-derived noncoding RNA, GnRH-E1 RNA, as a facilitator of Gnrh1 gene transcription in GnRH neuronal cell lines. Sense and antisense variants of GnRH-E1 RNA are 5’ capped and 3’ polyadenylated RNA molecules transcribed from the Gnrh1 upstream regulatory region, but transcription evidently does not originate from a central region. The physical characteristics of GnRH-E1 RNA are consistent with those of lincRNAs. Our data indicate that GnRH-E1 RNA functions as an inducer of Gnrh1 transcription by inducing the activity of the Gnrh1 promoter, and may play a critical role in activating or de-repressing Gnrh1 transcription during GnRH neuron maturation and development. Together, these data provide a foundation for future studies on the role of lincRNA in GnRH neuron function and maturation, with implications in reproductive neuroendocrinology.In this study, we established the mouse


The proliferation of antibiotic resistant pathogens is an increasing threat to the general public. Resistance may be conferred by a number of mechanisms including covalent or mutational modification of the antibiotic binding site, covalent modification of the drug, or the over-expression of efflux pumps. The nosiheptide resistance methyltransferase (NHR) confers resistance to the thiazole antibiotic nosiheptide in the nosiheptide producer organism Streptomyces actuosus through 2ʹO-methylation of 23S rRNA at the nucleotide A1067. Although the crystal structures of NHR and the closely related thiostrepton-resistance methyltransferase (TSR) in complex with the cofactor S-Adenosyl-L-methionine (SAM) are available, the principles behind NHR substrate recognition and catalysis remain unclear.We have analyzed the binding interactions between NHR and model 58 and 29 nucleotide substrate RNAs by gel electrophoresis mobility shift assays (EMSA) and fluorescence anisotropy. We show that the enzyme binds to RNA as a dimer. By constructing a hetero-dimer complex composed of one wild-type subunit and one inactive mutant NHR-R135A subunit, we show that only one functional subunit of the NHR homodimer is required for its enzymatic activity. Mutational analysis suggests that the interactions between neighbouring bases (G1068 and U1066) and A1067 have an important role in methyltransfer activity, such that the substitution of a deoxy sugar spacer (5ʹ) to the target nucleotide achieved near wild-type levels of methylation. A series of atomic substitutions at specific positions on the substrate adenine show that local base-base interactions between neighbouring bases are important for methylation.Taken together these data suggest that local base-base interactions play an important role in aligning the substrate 2’ hydroxyl group of A1067 for methyl group transfer. Methylation of nucleic acids is playing an increasingly important role in fundamental biological processes and we anticipate that the approach outlined in this manuscript may be useful for investigating other classes of nucleic acid methyltransferases. N6-methyladenosine (m6A) methylation of mRNA at internal adenosines [6A methylation may also be reversed [The covalent modification of nucleic acids, proteins and small molecules through the addition of methyl groups (methylation) is an important part of cellular metabolism. Reversible methylation of DNA and histones introduces epigenetic modifications that can control cell lineage and cell fate –3. Methyenosines ,8 has beenosines  and m6A reversed .In most cases substrate molecules are methylated through methyl group transfer from the donor molecule S-adenosylmethionine (SAM) by methyltransferase enzymes. Alternatively, in some cases methyltetrahydrofolate and methyl-B12 may also serve as methyl group donors . The basCellular rRNAs and tRNAs are extensively methylated by endogenous enzymes, the modifications are critical for efficient ribosome and tRNA function –17. ResiStreptomyces actuosis and Streptomyces azureus to protect endogenous ribosomes from drug binding is 2′O-methylation of position A1067 in 23S rRNA [in vitro [Nosiheptide and thiostrepton are thiazole antibiotics that interfere with elongation factor G (EF-G) function by binding at a particular ribosomal site close to A1067 in 23S rRNA –22. EF-G23S rRNA –27. This23S rRNA ,29. Crys23S rRNA . They re23S rRNA ,32. Stru23S rRNA  and thusin vitro –36.1 (G37) methyltransferases [Both NHR and TSR resistance methyltransferases display similar specificities for a series of fragment and model rRNA substrates and share 74% sequence identity –37. The sferases ,41,42. Nsferases ,39,43. Isferases  Fig 1)..1 (G37) Despite considerable efforts, co-crystal structures of NHR or TSR in complex with substrate RNA have yet to be determined ,36. In tS.actuosis was described previously [The construction and over expression of N-terminal hexahistidine tagged (His-tag) NHR protein (pET-28a-NHR) from eviously . A simileviously . Eluted eviously .3PO4, 200 mM NaCl, pH 8.0). After centrifugation the immuno-precipitated complex were eluted with 10 mM HA peptide. Anti-Flag beads were then added into the eluted protein, and the mixture incubated at 4°C overnight. Unbound protein was removed by centrifugation at 2,000 rpm for 3 min, and the beads washed three times with buffer A. The immuno-precipitated heterodimers were recovered by elution with 10 mM Flag peptide, for comparison wild-type protein was purified the same protocol in parallel. Heterodimer composition was confirmed by SDS-PAGE and Western blotting.Equivalent molar ratios of Flag-Trx-His-NHR (Flag-NHR) and HA-His-NHR-R135A (HA-NHR-R135A) proteins were mixed and incubated at 4°C overnight. Anti-HA beads were added and the mixture was incubated at 4°C overnight. Unbound protein was removed through centrifugation at 2,000 rpm for 3 min, and the beads washed three times with buffer A .RNA samples of 58 and 29 nt RNAs  were annealed at 95°C for 5 min, and cooled to room temperature. RNA-protein complexes were prepared by mixing the RNA (6–10 μM) with NHR protein and incubating the mixture for 1 hour at 4°C in reaction buffer . The gels were scanned using a ChemiDocTM MP imaging system (Bio-Rad).in vitro by T7 RNA polymerase  (1 μCi) at 25°C for 30 minutes in 40 μL reaction buffer . Reactions were stopped by the addition of 2% TFA, and the precipitate was filtered through a 96-well glass fiber filter plate. Incorporation of 14C was determined by liquid scintillation counting in a MicroBeta TriLux (Perkin Elmer 1450 LSC Luminescence Counter). Km, the Michaelis constant was calculated as previously described [NHR methyltransferase activity was measured as described previously ,36, brieescribed ,36.14C incorporated into the substrate RNA as a percentage of the equivalent reaction by the wild type protein [Mutant RNAs were assayed at an RNA concentration close to the Km for the RNA substrate (~200 nM for the 29 nucleotide RNA). The percentage relative specificity was quantified as the proportion of  protein .4Cl, 5 mM MgCl2, 5 mM DTT [Transcribed RNA was labeled at the 3′ end with 5-fluorescein isothiocyanate (molecular probes) –47. Labe5 mM DTT  and the 5 mM DTT ,49.Although the crystal structure of NHR has been solved , the priin vitro transcription using T7 RNA polymerase (oligonucleotide sequences are listed in 2 (■■), RNA-NHR(SL) (●■) and RNA-NHR(L)2 (●●) 2 (●●) , confirmL)2 (●●) . We also2 (■■), RNA-NHR-M/RNA-NHR(SL) (●■) and RNA-NHR(L)2 (●●). Interestingly, the inactive R135A mutant homodimer (■■) binds to the RNA causing a shift in electrophoretic mobility, and the heterodimer composed of active and inactive subunits (●■) also binds the RNA efficiently. We also note that the inactive heterodimer complex binds the RNA more efficiently than the inactive mutant homodimer 2 NHR homodimers. The wild-type (Flag-NHR)2 homodimer was also purified in parallel using HA beads from the mixture. The wild-type homodimer purification serves as a control for the heterodimer purification process. The methyltransferase activity of purified heterodimer protein was then measured with a series of RNA substrates and compared to purified wild-type homodimer protein. Although the yield of heterodimer was rather low, we could compare the activity directly with the homodimer enzyme purified at the same time, under the same conditions in methyltransferase assays  was purified through selective affinity purification by binding to Flag beads and followed by elution with Flag- peptide from a mixture of wild-type (Flag-NHR)e assays . Interesin vitro transcription using T7 RNA polymerase and labeled with fluorescein [d) calculated. Interestingly NHR binds to the 58 nt or 29 nt RNA substrates with similar affinities in the nM range  display a significant reduction in methyltransferase activity , as do t7 make an important contribution to methylation  for modification ; we havehylation . In contposition  are lessNHR and TSR are members of the “SPOUT” (SpoUTrmD) super-family of RNA methyltansferases that appear to share a common evolutionary origin –54. In c1 in tRNA, a modification that is universally conserved, and essential for the fidelity of translation [Both NHR and TSR crystallize as dimers in complex with SAM, and have been proposed, based on EMSA and gel filtration analysis to recognize the substrate RNA as dimers ,36. Herenslation ,59, whilnslation ,61. Compnslation ,52, the nslation ,30. Methnslation ,34,62, iBoth NHR and TSR enzymes can bind and methylate model RNA substrates; the 29 base hairpin loop and the 58 base L-11 binding RNA construct ,36. SignThe substitution of an abasic linker for the nucleotides at U1066 or G1068 had contrasting affects; the loss of the nucleobase at U1066 resulted in close to wild-type (84.4%) levels of methylation  whereas 1 and O6 of the substrate nucleotide G37 for TrmD methylation [7 position of adenine appears to be essential for methylation; 7-deaza-A (7-De-A) modified RNA substrate was a poor substrate for NHR  also induces the imino-tautomer at the exocyclic-N6 position leading to intermediate activity  led to wild-type levels of modification  for methylation. In addition, the nature of the substrate nucleobase at the 1067 position also makes a significant contribution to modification such that either pyrimidine (A1067U or A1067C) shows minimal levels of modification, whereas the purine A1067G shows intermediate levels of modification. To explore a possible role for purines substituted at A1067 in the positioning of the 2′-OH of A1067 (the methyl group acceptor) for methyl group transfer a series of atomic substitutions to the substrate adenine were introduced synthetically. The incorporation of guanosine analogues into tRNA model substrates has previously identified the importance of Nhylation . The dat for NHR . In compactivity . The N6 fication . As a whS1 Tablein vitro transcription.The sequences of the RNA oligonucleotides used in this study. Mutated nucleotides are indicated, and the positions of the mutations within the oligonucleotide are shown in red. NB the 29 nucleotide RNAs (numbers 28–34) that corporate the d-spacer or atomic substitutions were chemically synthesized, other RNA molecules were synthesized by (DOC)Click here for additional data file.


RNA sequencing technologies reveal that bacteria express RNA molecules other than mRNA, rRNA or tRNA. During the last years genome-wide bacterial transcriptomes have been shown to comprise intergenic RNA, antisense RNA, and untranslated regions, all capable of performing diverse regulatory functions.Pseudomonas aeruginosa grown under 13 different conditions. The conditions studied include exponential and stationary growth as well as osmotic, oxidative and antibiotic stress. We found a significant overrepresentation of asRNAs that are transcribed opposite to genes involved in cell division and in cell wall, lipopolysaccharide (LPS), and capsule biosynthesis, most likely reflecting the conditions used in this study. A substantial number of asRNAs significantly changed their expression under osmotic, oxidative and antibiotic stress, suggesting that asRNAs may play regulatory roles during these conditions. We also made a comparison between the asRNAs detected in this study in P. aeruginosa PAO1 with the asRNAs detected in two previous studies in P. aeruginosa PA14, and found that the extent of overlap between the studies is very limited.In this study we used RNA-seq to identify 232 antisense RNAs (asRNAs) in the opportunistic pathogen P. aeruginosa shows that the overlap between the results reported is very narrow. It is necessary to address how reproducibility of these kind of studies should be reported in order to avoid misleading conclusions when comparing data generated by non-identical methods.RNA-seq experiments are revealing hundreds of novel transcripts in all bacterial genomes investigated. The comparison between independent studies that used RNA-seq to detect novel asRNAs in The online version of this article (doi:10.1186/1471-2164-15-783) contains supplementary material, which is available to authorized users. Escherichia coli, where plasmid-encoded asRNAs were found to negatively regulate plasmid copy number [trans[During the last years an increasing number of bacterial transcriptomes have been examined with tiling arrays and RNA sequencing (RNA-Seq) technologies, revealing that a significant number of protein-coding genes are also transcribed from the reverse complementary strand in a wide range of bacterial species –8. Overly number –11. Sincy number ). Howevey number ). These y number , and somr [trans–19.P. aeruginosa, which can cause severe infections, particularly in immunocompromised and cystic fibrosis (CF) patients. The CF lung is an osmotically stressful environment, due to the viscous, dehydrated mucus, cellular debris and electrolytes competing for a limited supply of water [P. aeruginosa encounters reactive oxygen species (ROS) in the lungs of CF patients due to the exaggerated, sustained and extended inflammatory response, characterized by influx of neutrophils and high concentrations of interleukin-8 [P. aeruginosa-infected CF patients are routinely treated with several types of antibiotics, because early and aggressive antipseudomonal treatment regimens are correlated with improved lung function and survival of CF patients [P. aeruginosa. One of the studies investigated strains PAO1 and PA14 at early stationary growth phase and found 60 asRNAs, of which 19 were expressed in strain PAO1 [P. aeruginosa PA14 grown at 28°C and at 37°C [P. aeruginosa PAO1 grown in several different conditions to ensure a comprehensive characterization of the P. aeruginosa asRNA-ome. Accordingly, exponentially growing populations of P. aeruginosa PAO1 were exposed to osmotic, oxidative and antibiotic stress. The antibiotics used in this study were ß-lactams, aminoglycosides, macrolides, colistin and tetracycline. All the antibiotics used, with the exception of tetracyclin, are routinely used against P. aeruginosa infections [In this work, we used RNA-Seq to identify asRNAs in the human pathogen of water , 21. A vleukin-8 –24. In apatients . Recentlain PAO1 . The oth at 37°C . The expfections . FinallyP. aeruginosa PAO1 [Recently, we showed that library preparation for RNA-Seq plays a fundamental role when aiming at identifying novel transcripts . Using tosa PAO1 , 34. Altosa PAO1 . In the osa PAO1 , 34.P. aeruginosa PAO1 were exposed to sodium chloride (NaCl) and hydrogen peroxide (H2O2) inducing osmotic and oxidative stress conditions, respectively. The antibiotics used were piperacillin, ceftazidime, aztreonam, meropenem, tobramycin, azithromycin, colistin and tetracycline. The concentrations of antibiotics, NaCl and H2O2 are shown in Table Exponentially growing cells of In total, we identified 232 novel asRNAs longer than 50 nt, of which 212 are novel transcripts. The 22 asRNAs already identified by other studies are listed in Tables P. aeruginosa PAO1 . i) The strain used in the study is P. aeruginosa PAO1, while Wurtzel et al. (2012) used strain PA14. The PAPI-1 island is found in strain PA14 but not in PAO1, though it can be transferred by a type IV pilus [et al. (2012) studied two conditions . In this study we sequenced samples from P. aeruginosa growing in 12 different conditions. Studying more conditions might lead to finding antisense transcription sites in more genomic locations, and therefore avoiding enrichment of expression of asRNAs associated to a specific condition.Next, we examined the distribution of antisense sRNAs in the genome and found the asRNAs to be homogenously distributed throughout the genome of osa PA14 . SpecifiIV pilus . ii) WurP = 0.03) and ‘Cell wall / LPS / capsule’ . This overrepresentation of genes involved in cell division and cell wall, lipopolysaccharide (LPS), and capsule biosynthesis most likely reflects the conditions used in this study. The conditions studied  greatly affect both the bacterial replication and cell wall synthesis, and therefore fast regulation of the genes involved in these processes is essential. Indeed we observe significant changes in expression of cell division and LPS genes after the antibiotic treatments (data not shown), which is in agreement with previous studies [wbp cluster   reported that the transcription of wbp genes were decreased by 2- to 6-fold after exposure to ciprofloxacin [wbp genes of 2- to 15-fold, and down-regulation of the asRNAs as138-as143 of 2- to 10-fold. However further investigations are required to assess whether these asRNAs affect the levels of the wbp genes encoded on the opposite strand.Figure  studies . Interesocessing . WbpB, Wocessing . Cirz etfloxacin . Our seqP < 0.01). A substantial number of asRNAs significantly change their expression, indicating that their putative regulatory effects may be important during the conditions tested. A hierarchical cluster analysis of expression of the most differentially expressed asRNAs illustrates how related conditions show similar patterns of asRNA expression  rich medium at 37°C and harvested at early stationary phase [P. aeruginosa PA14 grown aerobically at 28°C and at 37°C in LB medium and harvested at early stationary phase [et al. (2012) could not determine the length of the asRNAs, but only the antisense transcription start sites [et al. (2012). Figure gshB, the NADH dehydrogenase I chain A gene nuoA, the ribosome biogenesis GTP-binding protein YsxC gene ysxC, and the penicillin-binding protein 1A gene ponA. We have validated the expression of the latter asRNA, from now on called AsponA, using 5’- and 3’-RACE. The coordinates of asponA were very similar between the RNA-Seq data and the RACE experiments, with differences of up to 19 nt between the two techniques. AsponA is upregulated upon aztreonam (2.9-fold), piperacillin (2.8-fold), and ciprofloxacin (2-fold) exposure, and downregulated during meropenem (-2-fold), colistin (-3-fold), tobramycin (-5.4-fold), and tetracycline (-5.6-fold) exposure. AsponA overlaps with the predicted -10 and -35 σ70 promoter elements of the ponA gene  and Ferrara et al. (2012) detected the expression of AsponA in P. aeruginosa PA14 and PAO1 [Recently two independent studies used RNA-Seq to investigate transcription in ry phase . The autry phase . The autrt sites . In thisand PAO1 , 27.FiguP. aeruginosa strains, and (iii) different methods to perform RNA-seq were used in each of them. (i) The three studies investigated different P. aeruginosa strains (PAO1 and PA14) grown under different conditions, which may account for part of the differences in the asRNAs detected. As previously mentioned, the level of expression of asRNAs varies greatly that in the different conditions used in our experiments  have reported that even under the same growth conditions, different strains of P. aeruginosa (PAO1 and PA14) express different sets of both intergenic and antisense sRNAs [The lack of overlap between the reported asRNAs may be caused by the different characteristics of each work, as (i) different growth conditions, (ii) different s Figure , and in  limited . Ferrarase sRNAs . Howeveret al. (2011) investigated the biases associated to the adapter ligation steps involved RNA-seq library preparation [P. aeruginosa with this approach. There were significant differences in the subset of transcripts detected in each library [Library preparation for RNA-seq experiments requires multiple enzyme-catalyzed steps such as sequential oligonucleotide adapter ligations to the 5’and 3 ends of RNAs, reverse transcription (RT), and PCR. RNA ligase preferences may contribute to the observed bias in sRNA detection –45, as wparation . The biaparation . In lineparation . Anotherparation . Recent paration , 50. Basparation . Howeverparation . Unexpecparation . In a prparation . Two lib library , and theP. aeruginosa growing under in 13 different conditions, including osmotic, oxidative and antibiotic stress, and exponential and early stationary phase. Due to our library preparation protocol, only asRNAs ranging approximately from 50 to 500 nt were detected. Thus the number of asRNAs provided in this study is most likely an underestimate as longer asRNAs have not been included. These data are important for the understanding of the regulation of genes that show antisense transcription on the reverse strand. However the large number of asRNAs detected makes it difficult to unravel functionality and physiological impacts of antisense transcription, and this paper should therefore be read as a documentation of the potential asRNA repertoire from which specific studies can be designed. In addition, we present data showing that in several cases growth conditions impact on the expression levels of asRNA, which suggest that at least some of the asRNAs may play roles in physiological adaptation to changing conditions. A significant number of asRNAs were transcribed opposite to genes involved in cell division and in cell wall, lipopolysaccharide (LPS), and capsule biosynthesis. A substantial number of antisense sRNAs significantly changed their expression during early stationary phase and under osmotic, oxidative and antibiotic stress, suggesting that asRNAs may play a regulatory role during these conditions.RNA-Seq was used to identify and quantify 232 asRNAs in the opportunistic pathogen P. aeruginosa shows that the extent of overlap between the results reported is very limited. It is necessary to address considerations like what is an appropriate threshold of reads for transcript detection, or how reproducibility should be reported in order to avoid misleading conclusions when comparing data generated by non-identical methods. In addition, public databases are already and will be needed for sharing, analyzing, and storing transcriptomic data. Apart from sharing and comparing RNA-seq data, the validation and functional characterization of the hundreds of novel sRNAs now being reported will also be a major challenge of current RNA research.Finally, we compare our results with those obtained by others in order to document that detection of these new types of potential regulatory molecules is not trivial and that choice of detection and documentation methods is truly important. One of the challenges of performing genome-wide expression studies of sRNAs is to compare the extremely large data sets resulting from different RNA-Seq studies, as library preparation protocols, sequencing platforms, and thresholds for detecting transcripts differ from study to study. Our comparison of three independent studies that used RNA-seq to detect novel asRNAs in Pseudomonas aeruginosa PAO1 were diluted to a starting OD600 of 0.01 and grown to an OD600 of 0.5, at which an antibiotic, 0.5 M NaCl or 1 mM hydrogen peroxide was added. The final concentrations of antibiotics are shown in Table 2O2 and all antibiotics were prepared fresh daily. Cells were harvested 1 hour after the addition of antibiotics, NaCl and H2O2. Early-stationary phase cells were harvested from cultures grown to an OD600 of 3. RNA was extracted and used to make RNA-Seq libraries LIB < 500 (described below). Experiments were performed in duplicates.Growth in Luria–Bertani (LB) broth  or on LB plates at 37°C was used throughout this study. Overnight cultures of P. aeruginosa PAO1 were assessed using both the broth microdilution procedure and E-test strips.Minimum inhibitory concentrations (MIC) of Broth microdilution. LB medium was added to all wells of a 96-well microtiter plate loaded with serially diluted antibiotics. Each well was inoculated with P. aeruginosa PAO1 at a final concentration of 5 × 105 cfu/ml. The plates were incubated for 24 hours . Following incubation, the optical density of all wells was measured and the lowest concentration showing complete inhibition of growth was recorded as the MIC for that antibiotic. The experiments were performed in triplicates. Concentrated stock solutions of all antibiotics were prepared fresh daily.E-Test. E-test (bioMérieux AB BIODISK) strips were used according to the manufacturer’s instructions. LB plates were inoculated equal amount of P. aeruginosa PAO1 cells. After drying, the E-test strips were placed on the top of the plates. The MIC values were read after 16 h of incubation at 37°C. The experiments were performed in triplicates.Harvested cells were mixed immediately with 0.2 volumes of STOP solution  and pelleted by centrifugation. Total RNA was extracted with Trizol (Invitrogen). Removal of DNA was carried out by treatment with DNase I (Fermentas) in combination with the RNase inhibitor RiboLock (Fermentas). The integrity of total RNA and DNA contamination were assessed with an Agilent 2100 Bioanalyzer (Agilent Technologies).P. aeruginosa PAO1 were used  with modifications as previously described , 34. CapSequencing libraries were constructed as previously described, following LIB < 500 and LIB > 100 protocols , 34. Eacet al. (2014) [P. aeruginosa PAO1 genome (RefSeq Accession No. NC_002516) using the Bowtie 2 short read aligner [P-value <0.05 and fold change 2). Sequencing libraries LIB < 500 have been submitted to the European Nucleotide Archive under accession number PRJEB6998 (http://www.ebi.ac.uk/ena/data/view/PRJEB6998).Our analysis pipeline is described in detail in Gómez-Lozano . 2014) . Briefly aligner . Informa4 . Brief aligner . Only trAdditional file 5’ RACE. 2 μg rRNA-depleted RNA was incubated with 10 U of Tobacco Acid Pyrophosphatase (Epicentre Technologies) at 37°C for 1 h to convert RNA 5’ triphosphates in 5’ monophosphates. The same amount of control RNA was incubated under the same conditions in the absence of the enzyme. Reactions were stopped by phenol-chloroform extraction, followed by etanol-sodium acetate precipitation. Precipitated RNAs were redissolved in water, mixed with 500 pmol of 5’ RNA adapter, heat-denatured at 95°C for 5 min, then quick-chilled on ice. A short RNA adapter was ligated was ligated with 50 U of T4 RNA ligase (Thermo Scientific) at 37°C for 1 h. Reactions were stopped by phenol-chloroform extraction, followed by ethanol–sodium acetate precipitation. Precipitated RNAs were re-dissolved in 20 μl water. Then 10 μl of 5’adapter-ligated RNA was reverse-transcribed using 2 pmol of primer complementary to the sRNA (5’-GSP1) and the Thermoscript RT-PCR system (Invitrogen) according to the manufacturer’s instructions. Reverse transcription (RT) was performed in three subsequent 20 min steps at 55°C, 60°C, and 65°C, followed by treatment with RNase H. Primers were removed using the NucleoSpin Gel and PCR Clean-up kit (Macherey-Nagel). The products of RT were amplified using 10 pmol of another primer complementary to the sRNA (5’-GSP2) and 10 pmol the 5’ adapter-specific primer, together with the Maxima Hot Start PCR Master Mix (Thermo Scientific) according to the manufacturer’s instructions. Negative controls were performed using the 5’ adapter-ligated RNA as template. The PCR products were resolved and purified using E-Gel SizeSelect 2% Agarose gels (Invitrogen). Products were sequenced with 5’-GSP2 and 5’ adapter-specific primers by LGC Genomics GmbH (Germany).3’ RACE. 2 μg rRNA-depleted RNA was dephosphorylated with calf intestinal alkaline phosphatase (New England Biolabs) at 37°C for 1 h. Reactions were stopped by phenol-chloroform extraction, followed by etanol-sodium acetate precipitation. Ligation was done as above with a 3’ RNA adapter with a 3’-inverted deoxythymidine (3’-idT). RT was carried out as described, but with 10 pmol of a single primer complementary to the 3’ RNA adapter. PCR amplification, band purification and sequence analysis was done as described above. All enzymatic treatments of RNA were performed in the presence of 20 units of RiboLock RNase Inhibitor (Thermo Scientific).Information about sequencing libraries.(PDF 71 KB)Additional file 1:Annotation of antisense sRNAs.(PDF 107 KB)Additional file 2:Antisense transcription in thewbpoperon.(PDF 74 KB)Additional file 3:Antisense sRNAs differentially expressed.(PDF 83 KB)Additional file 4:AsponA inP. aeruginosaPAO1.(PDF 104 KB)Additional file 5:Primers and adapters used in the study.(PDF 72 KB)Additional file 6:Script for extracting antisense reads.(PDF 136 KB)Additional file 7:


One segment of a RNA sequence might be paired with another segment of the same RNA sequence due to the force of hydrogen bonds. This two-dimensional structure is called the RNA sequence's secondary structure. Several algorithms have been proposed to predict an RNA sequence's secondary structure. These algorithms are referred to as RNA folding algorithms.We develop cache efficient, multicore, and GPU algorithms for RNA folding using Nussinov's algorithm.Our cache efficient algorithm provides a speedup between 1.6 and 3.0 relative to a naive straightforward single core code. The multicore version of the cache efficient single core algorithm provides a speedup, relative to the naive single core algorithm, between 7.5 and 14.0 on a 6 core hyperthreaded CPU. Our GPU algorithm for the NVIDIA C2050 is up to 1582 times as fast as the naive single core algorithm and between 5.1 and 11.2 times as fast as the fastest previously known GPU algorithm for Nussinov RNA folding. G (guanine), A (adenine), U (uracil), C (cytosine)}. One segment of a RNA sequence might be paired with another segment of the same RNA sequence due to the force of hydrogen bonds. This two-dimensional structure is called the RNA sequence's secondary structure. Two nucleotides in an RNA sequence can form Watson-Crick AU and GC base pairs as well as the GU wobble pair. Several algorithms have been proposed to predict an RNA sequence's secondary structure. These algorithms are referred to as RNA folding algorithms. Waterman and Smith [j] = C, 0 ≤ i ≤ j <n, where n is the length of the RNA sequence R.Algorithm 1 CPU1Require: RNA sequence REnsure: Array M such that M [i][j] = Cfor i ← 0 to |R|-1 do1: M [i][i] ← 02:    end for3: for i ← 0 to |R|-2 do4: M [i][i + 1] ← 05:    end for6: for diag ← 2 to |R|-1 do7: for row ← 0 to |R|-diag-1 do8:    col ← diag + row9:        a ← R[row]; b ← R[col]10:        max ← M [row + 1][col − 1] + bond11:        for k ← row to col-1 do12:        t ← M [row][k] + M [k + 1][col]13:            max ← max{max, t}14:            end for15:        M [row][col] ← max16:        end for17:    end for18: M to store the transpose of the computed values in the upper triangle of M as is done in the GPU implementation of [k] + M [col][k + 1]", and change Line 16 to "M [row][col] ← M [col][row] ← max". Values of M [k + 1][col] locate in the same column but values of M [col][k + 1] locate in the same row, for row ≤ k < col. Reading values in a row is more cache efficient than reading values in a column.By using the lower triangle of ation of , we get CPU1 and CPU2, respectively labeled OMP1 and OMP2, are obtained by inserting OpenMP statements to parallelize the for loops of lines 1, 4, and 8.Multicore versions of C by diagonals, we use a refinement of the block strategy used in  reg1: Shared_Memory[BS][BS] sA2: Shared_Memory[BS + 1][BS + 1] sC3: Global_Memory[BS][BS] gB4: Global_Memory[BS][BS] gC5: tx ← threadIdx.x; ty ← threadIdx.y6: bx ← blockIdx.x; by ← blockIdx.y7: aRow ← by × BS; aCol ← aRow − 18: bRow ← aRow; bCol ← D × BS − 1 + aRow9: for blk ← 1 to D − 1 do10: sA ← the block starting at 11:        gB ← the block starting at 12:        maxKernel13:        Syncthreads14:        end for15: sC ← reg16: for d ← 1 to BS × 2 − 1 do17: for Each element e on diagonal d do18:        19:            Finish remaining computationend for20:        Syncthreads21:        end for22: gC ← sC23: Algorithm 3 maxKernelRequire: Block sA in shared memory, block gB in global memoryEnsure: Partial result of reduction in regr0 ← gB[0][tx]; r1 ← gB[1][tx]        r2 ← gB[2][tx]; r3 ← gB[3][tx]        for j ← 0 to 6 do        for k ← 0 to 15 do            reg[k] ← max{reg[k], r0 + sA[ty × 16 + k][j × 4]}                end for            r0 ← gB[(j + 1) × 4][tx]                        // 2 similar for loops for r1 and r2 come herek ← 0 to 15 do            for reg[k] ← max{reg[k], r3 + sA[ty × 16 + k][j × 4 + 3]}                end for            r3 ← gB[(j + 1) × 4 + 3][tx]            end for        for k ← 0 to 15 do        reg[k] ← max{reg[k], r0 + sA[ty × 16 + k][28]}            end for                // 2 similar for loops for r1 and r2 come herefor k ← 0 to 15 do        reg[k] ← max{reg[k], r3 + sA[ty × 16 + k][31]}            end for        C. The gray area in block A depicts the data needed by the first 32 threads. This data will be read into shared memory. To achieve high throughput from/to device memory, we use coalesced memory accesses in which all data accessed by one warp (this is the minimum scheduling unit and it contains 32 threads) falls in the same device memory cache line of size 128 bytes. In Figure B. Then each thread uses the value just fetched to add with the first column in the gray area of block A . In other words, thread i will add B[0][i] with A[j][0] (0 ≤ j < 16) and compare this value with register[j] of thread i and update register[j] if necessary. Then B[i] is added with A[j] [register[j]; the register is updated as needed, 0 ≤ j < 16. Since threads in the same warp will read data in the same row of block B, this reading is coalesced and serviced at the throughput of L1 or L2 cache in case of a cache hit, or at the throughput of device memory, otherwise. Besides, all threads in the same warp use the same value from block A, which resides in shared memory. This value can be broadcast to all threads in the same warp.The computation of the maximum of "c+C" to "j+J" Figure  bears somaxKernel but their kernel is different from ours.We note that  also empn × n matrix, our use of random sequences does not materially impact our conclusions.We benchmarked our algorithms using a PC with a hyperthreaded 6-core Intel i7x980 3.33GHz CPU and 12GB DDR3 RAM. The PC also had NVIDIA Tesla C2050 GPU. Since only two threads may be scheduled per i7 core at any time, a maximum of 12 threads may be gainfully used. We used randomly generated RNA sequences in our experiments. Since the run time of our codes is relatively insensitive to the actual RNA sequence in use due to the fact that the entire computation is to fill out an OMP1 and OMP2, the work assigned to the threads is well balanced by OpenMP and so best performance is expected using either 6 or 12 threads. Our experiments confirmed this expectation with the use of 6 threads generally being better than the use of 12 threads. So, for our application the overhead of context switching between the two threads assigned to a core when a total of 6 threads are used generally exceeded the gains obtainable from having a second thread ready in case the first thread stalls from memory delay. Table CPU1, CPU2, OMP1, and OMP2 for n values ranging from 3000 to 16000. The columns labeled ratio give the ratios CPU1/OMP1 and CPU2/OMP2, respectively. Although we have 6 cores on our CPU, we are able to achieve speedups of almost 5 from the multicore versions. By comparison, the far more complex multicore code of /Ou/OuOurs1, used in  with ourOurs1 relative to CPU2 and OMP2. Using a GPU, we can do the Nussinov computations up to 522.6 times as fast as using a cache efficient single core code and up to 113.4 times as fast as using a 6-core cache efficient code! Compared to the naive single-core code CP U 1, our GPU codes provides a speedup of up to 1582!Table Ours1, for the NVIDIA C2050 is up to 1582 times as fast as the naive single core algorithm and between 3.0 and 11.1 times as fast as the fastest previously known GPU algorithm for Nussinov RNA folding. With the available 3GB device memory on an NVIDIA GPU, Ours1 is able to handle sequences of length up to 26000. Sequences of length between 26000 and 37000 may be handled using Ours2, which uses a onedimensional array mapping of the upper triangle of the output matrix rather than a two-dimensional array that represents the full output matrix. Ours2, however, runs slightly slower than Ours1. Our methods can be used to speedup up RNA folding using Zuker's equations as well [We have developed simple and efficient single and multi-core algorithms as well as an efficient GPU code for RNA folding based on Nussinov's equations . Our cacRNA: RiboNucleic Acid; GPU: Graphics Processing Unit; PCI-Express: Peripheral Component Interconnect Express; CUDA: Compute Unified Device Architecture; GCUPS: Billion Cell Updates per Second; SM: Streaming Multiprocessors; DRAM: Dynamic Random-Access Memory; TFLOPS: Trillion Floating Point Operations Per Second; GFLOPS: Billion Floating Point Operations Per Second; I/O: Input/Output; CPU: Central Processing Unit.The authors declare that they have no competing interests.JL, SR, and SS developed the GPU algorithms, and analyzed the experimental results, and wrote the manuscript. JL also programmed and debugged the GPU algorithms and ran the experiments.


Our results demonstrate that in the presence of KITLG, KGF mimics its native binding mode suggesting all the KGF residues are specific to their binding complex. This study provides an insight on the critical amino acid residues participating in buffalo ovarian folliculogenesis.Molecular interactions between mesenchymal-derived Keratinocyte growth factor (KGF) and Kit ligand (KITLG) are essential for follicular development. These factors are expressed by theca and granulosa cells. We determined full length coding sequence of buffalo KGF and KITLG proteins having 194 and 274 amino acids, respectively. The recombinant KGF and KITLG proteins were solubilized in 10 mM Tris, pH 7.5 and 50 mM Tris, pH 7.4 and purified using Ni-NTA column and GST affinity chromatography, respectively. The purity and molecular weight of His-KGF (~23 kDa) and GST-KITLG (~57 kDa) proteins were confirmed by SDS-PAGE and western blotting. The co-immunoprecipitation assay accompanied with computational analysis demonstrated the interaction between KGF and KITLG proteins. We deduced 3D structures of the candidate proteins and assessed their binding based on protein docking. In the process, KGF specific residues, Lys123, Glu135, Lys140, Lys155 and Trp156 and KITLG specific ones, Ser226, Phe233, Gly234, Ala235, Phe236, Trp238 and Lys239 involved in the formation of KGF-KITLG complex were detected. The hydrophobic interactions surrounding KGF-KITLG complex affirmed their binding affinity and stability to the interacting interface. Additionally,  Protein-protein interactions are necessary for almost all the biological functions. The role of a protein and its interaction with other protein is determined by its 3D structure that allows fathoming active residues involved in binding, nature of its interface and conformational changes adopted by the protein. It has been reported that molecular interactions between theca and granulosa cells are important for ovarian folliculogenesis marked by exponential expansion and differentiation of the granulosa cells and maturation of the oocyte . This evKeratinocyte growth factor (KGF) expressed by mesenchymal theca cells is a paracrine growth and differentiation factor, belonging to heparin-binding FGF family with a distinctive pattern of target-cell specificity. KGF acts by binding with KGFR/FGFR2IIIb receptor, a splice variant of the FGF receptor 2 (FGFR2) that is predominantly expressed in the granulosa cells of growing follicles –6. It isOn the other hand, Kit ligand (KITLG) expressing in the granulosa cells has received considerable attention for its indispensable roles in mammalian folliculogenesis, gametogenesis and hematopoiesis . Its bioMolecular interactions between theca and granulosa cells are essential for follicular development in the ovary. KGF and KITLG are known to interact and play an important role in the mesenchymal-epithelial communication essential for folliculogenesis . Such faBuffalo ovaries were obtained from the Gazipur slaughter house, Delhi, India following strictly the Institute’s Ethical and Biosafety guidelines and due approvals were taken from these committees. Following this, any additional approvals for the present study were not required. The samples were frozen in liquid nitrogen and stored at −80°C until total RNA extraction was done.β-actin primers (ACTB) (forward: 5'CAGATCATGTTCGAGACCTTCAA3' and reverse: 5'GATGATCTTGATCTTCATTGTGCTG3'). Following this, cDNA was synthesized using cDNA RT kit . The success of cDNA synthesis was confirmed by PCR amplification with a set of bubaline derived β-actin primers [Total RNA was isolated from the ovary following standard protocols and stored at—80°C, quality and integrity of RNA was tested on 1% formaldehyde agarose gel . Further primers .KGF and KITLG genes were amplified by end point PCR using ovary cDNA as template and set of primers based on Bubalus bubalis KGF (GenBank accession no. KP284165) and KITLG (GenBank accession no. KP284166) coding sequence with overhangs containing restriction sites for XhoI/NcoI and BamHI/XhoI, respectively (KGF) and 54.0°C (KITLG) for 1.5 minutes and extension at 72°C for 2 minutes followed by a final extension at 72°C for 10 minutes. Each PCR product was electrophoresed on 1% agarose gel in 1x TAE buffer, sliced and DNA was eluted using gel extraction kit  [KGF and KITLG were cloned between the respective restriction enzyme sites of His-tagged pET28a  and GST-tagged pGEX-4T1 (Amersham Bioscience) vectors, respectively. The positive clones screened by colony-PCR and restriction digestion were then sequenced on Applied Biosystems 3130xl genetic analyzer on a 50 cm, 16 capillary array using BigDye Terminator v3.1 cycle sequencing kits  employing standard protocols [ectively . The PCRGermany) . Subsequrotocols .Escherichia coli grown in LB media containing 50 μg/ml kanamycin until an OD600 of 0.6 at 37°C was achieved. The protein expression was induced using 1 mM isopropyl 1-thio-β-d-galactopyranoside at 37°C for 4hrs. Subsequently, cells were harvested by centrifugation at 6000 rpm for 10 minutes at 4°C, resuspended in lysis buffer  and sonicated for 8 cycles of 1 minute each. Supernatant containing KGF protein was incubated with nickel-nitrilotriacetic acid-agarose  with end-to-end shaking for 7 hours at 4°C and the protein was eluted in elution buffer (50 mM Tris pH 7.5 and 100 mM NaCl) containing 50–250 mM imidazole. Following this, chromatography fractions containing His-KGF were dialyzed against buffer containing 10 mM Tris, pH 7.5.The 6x-His tagged plasmid (pET28-His-KGF) was used to transform BL21 (DE3) (Stratagene) strain of Escherichia coli BL21-RIL (Stratagene) strain and cells were harvested in LB media containing 100 μg/ml ampicillin and 30 μg/ml chloramphenicol. The protein expression was induced by incubation of cells with 1 mM isopropyl 1-thio-β-d-galactopyranoside at 37°C for 6 hours. Following this, cell pellets were suspended in ice-cold lysis buffer, containing 50 mM Tris, pH 7.4, 2 mM EDTA, 1 mM dithiothreitol, 1% Triton X-100, and proteases inhibitors , and sonication was carried for 6 cycles of 1 minute each. The resultant cell debris was removed by centrifugation at 13,000 rpm for 40 minutes at 4°C. The protein from the cell lysates were affinity-purified using glutathione-sepharose resin [For expression of GST-KITLG, the plasmid DNA was used to transform se resin . ThereafChromatography fractions and purified proteins were electrophoresed on SDS-PAGE and visualized by Coomassie Brilliant Blue R-250 . For western blotting, purified proteins were subjected to 15% (w/v) SDS-PAGE and electro transferred onto nitrocellulose membrane . Membranes were blocked with 5% bovine serum albumin  in PBST (1X PBS in 0.05% Tween-20) for 2 hours at room temperature and incubated overnight with diluted anti-His and anti-GST antibodies  in blocking buffer (1:1000). The blots were washed with PBST and incubated with horseradish peroxidase (HRP) conjugated anti-rabbit IgG  diluted in PBST . After subsequent washing, the blots were developed with 1 mg/ml 3,3’-diaminobenzidine (DAB)  and 1 μl/ml hydrogen peroxide in phosphate buffer saline.Total cellular ovary tissue extract was prepared by homogenizing in RIPA buffer  and protease inhibitor cocktail followed by a constant end-to-end rotation for 2 hours at 4°C. The extract was then centrifuged at 10,000 rpm for 15 minutes at 4°C. The supernatant containing the protein was collected. The total protein concentration was determined using Bradford assay . Approximately 300–500 μg of protein lysate was incubated with ∼1 μg of anti-KGF IgG  for 12 hours at 4°C on an end-to-end shaker in a 200 μl reaction volume. Subsequently, 50 μl of protein A+G Sepharose (Amersham-Pharmacia Biotech) was incubated with the antigen-antibody protein complex for 4–6 hours at 4°C with end to end shaking. The complex was washed thrice with RIPA buffer and eluted in 30 μl sample buffer containing reducing agent. It was then boiled at 95°C for 10 minutes and separated on 15% SDS-PAGE (w/v). Co-immunoprecipitation of KGF-KITLG complex was ascertained employing western blotting (mentioned earlier) using anti-KITLG IgG  as primary antibody and HRP conjugated anti-goat IgG as secondary antibody . In a reciprocal manner, we immunoprecipitated with anti-KITLG antibody, followed by western blotting using anti-KGF IgG. The blots were developed and specific bands were observed with ECL detection system .KGF and KITLG both at nucleotide and protein levels was ascertained using the BLAST program (http://blast.ncbi.nlm.nih.gov/Blast.cgi). The open reading frame (ORF) for the candidate proteins was obtained using the ORF finder (http://www.ncbi.nlm.nih.gov/gorf/gorf.html). Nucleotide sequences representing complete ORF were translated into protein sequence using ExPASy translation tool (http://web.expasy.org/translate/) and used for in silico characterization. Homologous conserved domains were identified with Conserved Domain Database (CDD) and secondary structures for both KGF and KITLG proteins were built based on the predictions obtained from SOPMA [The putative identity of om SOPMA ,25.Rattus norvegicus  with 97% identity was used as template for homology modeling of buffalo KGF protein using MODELLER(V9.14) [α RMSD (root-mean-square deviation) value to its template upon superposition. The protein model of KITLG was generated using Iterative Threading Assembly Refinement (I-TASSER) server (http://zhanglab.ccmb.med.umich.edu/I-TASSER/) [To infer the homologous protein structures for an appropriate template based on the maximum identity and lower e-value, KGF sequence was subjected to PSI-BLAST against Protein Data Bank (PDB) . The thrR(V9.14) ,28. FiveTASSER/) . The bes3D models of the candidate proteins were subjected to structural refinement and energy minimization using YASARA force field in YASARA energy minimization server, without fixing any atoms . The steActive binding site residues of KGF and KITLG protein were identified using Computed Atlas of Surface Topography of protein (CASTp) server . CASTp ihttp://www.ebi.ac.uk/pdbe/prot_int/pistart.html) [+ v.4.5.3 software) [in-silico mutagenesis was performed by mutating the binding residues of KGF protein participating in the KGF-KITLG interaction using PyMOL software so that the overall electronic nature of the side chains remains unchanged [PISA  was used to analyze the protein-protein interactions and binding interface of KGF-KITLG docked complex (rt.html) . The hydoftware) . Defaultnchanged . The genhttps://www.pymol.org/).A significant homology exists between the KGF and KITLG protein sequences in buffalo and cattle, belonging to bovidae family. Therefore, to decipher the status of binding interface of buffalo KGF-KITLG complex across its homologs, similar docking studies were performed with that of cattle. The generated structures were visualized using PyMOL viewer  and KITLG (GST-tagged) proteins with bands corresponding to 23 and 57 kDa, respectively, thus, in accordance with their theoretical molecular weights  complex using anti-KITLG antibody and detected a specific band of 31 kDa coinciding with the molecular weight of KITLG protein . FurtherKGF was found to have 15.98%, 8.76%, 20.62% and 54.64% of α helices, β turns extended strands and random coils, respectively. A stretch of 125 residues (ranging from position 67–191) in KGF protein belonged to FGF superfamily domain, suggesting its active participation in patterning and differentiation during vertebrate embryogenesis. Whereas, KITLG encompasses 51.46% α helices, 6.20% extended strand, 2.92% β turns and 39.42% random coils. The amino acids residing from 1–274 position in KITLG protein belonged to stem cell factor (SCF) superfamily domain.The 3D structures of buffalo KGF and KITLG were ascertained on the basis of homology and threading modeling methods, respectively. The tertiary structure of KGF was generated by MODELLER (V9.14) and best model selected had a DOPE score of -12370.84 and RMSD value of 0.202Å . AdditioGeometric evaluations and stereochemical quality of the modeled 3D structures of KGF and KITLG were performed using PROCHECK by calculating the Ramachandran plot. The plot represents the distribution of phi and psi angles of the amino acid residues and classifies them in their respective quadrangle. Ramachandran plot analysis for the modeled KGF and KITLG structures showed that 95.2% and 94% residues resided in the allowed regions, respectively. Whereas, 4.0% residues in KGF and 3.2% in KITLG were present in the generously allowed regions while 0.8% of KGF and 2.8% of KITLG amino acids resided in the disallowed regions, signifying the predicted models were reliable in terms of their backbone conformation . FurtherCASTp detected binding residues corresponding to KGF and KITLG proteins were subjected to protein docking . HADDOCK2 and solvation free energy (ΔiG) as -7.5 kcal/mol. Analysis of the docked complex (KGF-KITLG) revealed the presence of residues involved in extensive H-bonding and salt bridges . The ΔiG of group I mutants did not show much energy variation  as compared to that of the native complex , except W156A, having the energy of -9.8 kcal/mol. Startlingly, a significant energy variation was observed in the group II mutants, especially the one where lysine residues were replaced with leucine generating a complex K140L with ΔiG: -11.6 kcal/mol  providing specificity required for selective macromolecular interactions leading to ovarian follicles development ,17,51–53in-silico mutagenesis observation discerned the changes in binding energy of KGF-KITLG complex, occurred on mutating the amino acids involved in their interaction. A mutated KGF-KITLG complex generated by the replacement of lysine with leucine had the lowest binding energy suggesting its high stability and likely occurrence in the in-vivo system. Earlier, the homology-based approach had been used for predicting the conserved intra-species PPIs with the assumption that the interaction between a pair of proteins in one species would be conserved in the other related species [Our  species . In the in-silico interpretations on KGF-KITLG interaction dynamics may be a rewarding proposition in providing a valuable insight into the system biology of ovarian folliculogenesis.Based on the present study we construe that there exist a crosstalk between buffalo KGF and KITLG in the context of folliculogenesis. The bioinformatic based approach to understand protein-protein interaction surely complements the intricate dynamics of biological function. Therefore, further biochemical studies coupled with S1 FigBubalus bubalis KGF (A) and KITLG (B) 3D structures. The black dots indicate the amino acids distributed in the red  and yellow  regions.PROCHECK derived Ramachandran evaluation plots for (TIF)Click here for additional data file.S2 FigThe predicted KGF and KITLG protein models had Z-scores (black point) of -4.08 and -6.84, respectively.(TIF)Click here for additional data file.S3 FigThe predicted KGF (A) and KITLG (B) models yielded ERRAT scores of 89.92 and 92.39, respectively. These values indicate backbone conformation and non-bonded atomic interactions are within the acceptable range.(TIF)Click here for additional data file.S4 Fig(A) Interacting domain view of KGF and KITLG proteins. KGF-KITLG docked complex showed the common interacting domains between the two species. Wherein, the binding residues ranged from 125–185 and 220–270 in KGF and KITLG, respectively. (B) KGF-KITLG docked complex for buffalo and cattle showing KGF (magenta) and KITLG (yellow) interacting residues. The protein chains are shown in stick representation. (C) Superimposed model of KGF-KITLG complex of buffalo and cattle representing conserved binding interface in both species.(TIF)Click here for additional data file.S5 FigProposed schematic model represents cell-cell interactions during follicular development. Predicted 3D models of buffalo KGF and KITLG proteins and their complex were generated by protein docking (encircled). KGF-KITLG complex implicated in different stages of follicular development is shown in blue background. Arrows indicate the potential targets of KGF-KITLG complex, where ‘+’ headed ones denote growth at the respective developmental stages.* Basic fibroblast growth factor-bFGF; Leukemia inhibitory factor- LIF.(TIF)Click here for additional data file.


Escherichia coli tRNAfMet and tRNALys, we show that the nanopore signal during controlled translocation is dependent on the identity of the tRNA. This confirms that adapter-modified tRNA can translocate end-to-end through nanopores and provide the foundation for future work in direct sequencing of individual transfer RNA with a nanopore-based device.Transfer RNAs (tRNA) are the most common RNA molecules in cells and have critical roles as both translators of the genetic code and regulators of protein synthesis. As such, numerous methods have focused on studying tRNA abundance and regulation, with the most widely used methods being RNA-seq and microarrays. Though revolutionary to transcriptomics, these assays are limited by an inability to encode tRNA modifications in the requisite cDNA. These modifications are abundant in tRNA and critical to their function. Here, we describe proof-of-concept experiments where individual tRNA molecules are examined as linear strands using a biological nanopore. This method utilizes an enzymatically ligated synthetic DNA adapter to concentrate tRNA at the lipid bilayer of the nanopore device and efficiently denature individual tRNA molecules, as they are pulled through the α-hemolysin (α-HL) nanopore. Additionally, the DNA adapter provides a loading site for ϕ29 DNA polymerase (ϕ29 DNAP), which acts as a brake on the translocating tRNA. This increases the dwell time of adapted tRNA in the nanopore, allowing us to identify the region of the nanopore signal that is produced by the translocating tRNA itself. Using adapter-modified  Transfer RNAs (tRNA) decode genetic information, delivering to the protein-synthesizing ribosome the individual amino acids specified by each codon of a messenger RNA. In light of this, it is unsurprising that they are the most numerous RNA species in the cell, composing approximately 80% of the RNA molecules per generation in yeast  methods require extensive library preparation, including PCR amplification, to prepare cellular RNA for sequencing. A reverse transcription (RT) step is necessary to copy the original RNA sequence to cDNA, which results in loss of the original RNA strand. Additionally, the RT step is impeded by the occurrence of structure and nucleotide modifications, which are both commonly found in tRNAs. These “RT-stops” result in truncated cDNA. While these RT stops have been exploited by various methods to infer structure or nucleotide modification state in RNA, they do not permit direct detection of nucleotide sequence and modification identity along intact RNA  has also been applied to study tRNAs. In particular, two recent publications have used LC-MS to examine dynamic changes in tRNA modification state in Detection of tRNA subpopulations that result from variability in modification, as seen in mitochondrial diseases, will benefit from direct interrogation of intact, single tRNA molecules , and stored at −80°C.All oligonucleotides were synthesized by the Stanford Protein and Nucleic Acids facility (PAN) using standard phosphoramidite chemistry. Oligonucleotides were purified by denaturing 7M urea polyacrylamide gel electrophoresis (PAGE) in 1× TBE, followed by overnight elution from an excised band at 4°C in 300 mM Sodium Acetate pH 5.2 and 1 mM EDTA. DNA was precipitated and recovered by adding 100% molecular biology grade ethanol (Sigma Aldrich) to 70% final v/v and centrifuged for 30 min at 14,000 × The two oligonucleotides of the tRNA adapter were designed to form a partially double-stranded y-shape. The design included one end that targeted the ACCA tail of tRNA, while the other end contained unpaired single stranded regions : 5′-CTCACCTATCCTTCCACTCATACTATCATTATCTXTCAGATCTCACTA3′ trailing strand oligonucleotide : 5′-phos-GATXGTGAGATCTGATTTTTTTTTTTTTTTZ-3′.For experiments to determine which end of the adapted RNA molecules entered the pore first, new adapter nucleotides were synthesized with one of the abasic markers (as indicated above) replaced by the nucleotide that restored complementarity in the double stranded portion of the adapter. In these experiments, one of the following oligonucleotides replaced its corresponding abasic-containing strand in the adapter:UCUGGU-3′.5′ leading strand oligonucleotide: 5′-CTCACCTATCCTTCCACTCATACTATCATTATCTCTCAGATCTCACTA3′ trailing strand oligonucleotide: 5′-phos-GATAGTGAGATCTGATTTTTTTTTTTTTTTZ-3′.Adapters were formed by combining leading and trailing strands at 100 μM in 10 mM Tris-HCl pH 8 and 50 mM NaCl. The mixture was heated to 95°C for 30 s and allowed to cool to room temperature.Escherichia coli tRNALys and tRNAfMet were purchased from Sigma-Aldrich. The RNA hairpin control was prepared by the Stanford PAN facility using the sequence 5′-phos-CGCGGGGUUUUUCCCCGCAACCA-3′. RNA substrates were ligated to adapters using RNA Ligase 2 (NEB). Ligation reactions were carried out in 20 μL of buffer recommended by the manufacturer supplemented with 0.5 mM ATP, 5% PEG 8000, and 2 μM each of RNA and adapter. To end the reaction and prepare the sample for purification, 50 μL urea loading buffer (7M urea and 0.1× TBE) was added to the sample and heated to 95°C for 5 min. The products were separated on a 7M urea PAGE gel in 1× TBE. The gel was post-stained with 2× SybrGold (Life Technologies) and product of appropriate size for the complete ligation product was excised. Ligated RNA was recovered by electroelution into 3.5 kDa MWCO D-tubes (Novagen) at 100 V for 2 h in 1× TBE. Recovered ligated RNA preparations were ethanol precipitated, quantified by Nanodrop (Thermo Scientific), and stored at −80°C.trans well positive). Dithiothreitol  and EDTA pH 8.0  were added to the well on the cis side of the bilayer. In the case of experiments looking at the effect of Mg2+, MgCl2 was added to the buffer to 5 mM and EDTA was omitted from the cis side well. Nucleic acid substrates were added to 0.5 nM, unless otherwise noted to the cis well, and allowed to incubate 2 min to associate with the bilayer. For experiments where ϕ29 DNAP was to be added, an additional 12.5 min incubation was allowed; during this period, we observed captures of unbound RNA substrate prior to adding ϕ29 DNAP (Enzymatics) to 75 nM. Ionic current measurements were collected with an Axon Axopatch 200B (Molecular Devices) patch-clamp amplifier in whole-cell, voltage-clamped mode, and filtered with an analog low-pass Bessel filter at 5 kHz, then digitized using an Axon Digidata 1440A analog-to-digital converter (Molecular Devices) at 100 kHz bandwidth.Nanopore experiments were performed using a single α-HL nanopore  embedded in a planar 1,2-diphytanoyl-syn-glycero-phosphatidylcholine bilayer established on a ~25 μM aperture, as described previously . The detection algorithm identified ionic current blockades that were self-terminating by selecting for segments that deflected from open nanopore current (68.0–72.5 pA) below a cutoff of 45 or 55 pA and with a minimum duration of >0.1 ms, where voltage was not reversed to eject the stand from the pore (current never <0 pA). For experiments where individual ionic current blockade events were examined, raw nanopore ionic current data were filtered with a digital 2 kHz low-pass Bessel filter and analyzed using Clampfit 10.4 (Molecular Devices). For ϕ29 DNAP molecular braking experiments, events were selected from current blockades that had durations >1 s and self-terminated. Event classification from adapted RNA(hairpin) substrates were analyzed as articulated in the text. For adapted tRNA data, complete translocation events were selected based on the criteria that they contained exactly two abasic-dependent regions; these were defined as high current regions with a mean current >33.5 pA, <36.5 pA, and minimum a duration >2 ms. After selection as complete translocation events, duration and mean current for states I–III were measured by hand using Clampfit’s internal statistics measuring program.For nanopore experiments that examined populations of ionic current blockade events, a custom developed event detection program was used (10) and a linear decision boundary was established using the kernlab (v 0.9–19) package  are counts of test data labeled n, and pred(n) are counts of test data that are correctly classified. The graphic provided in Figure Event durations were transformed to log-durations  ionic current blockades of the α-HL pore (data not shown). These molecules had to be ejected by voltage reversal to re-establish an open-pore ionic current. This suggested that tRNA molecules in their native form would not readily translocate through the α-HL nanopore.Y-shaped, partially double-stranded DNA–RNA adapter that contained a 3′ RNA overhang complementary to the universally conserved CCA tail in tRNA  incorporated a cholesterol tag at its 3′ end. This was designed to locally concentrate the adapted tRNA at the lipid bilayer-aqueous interface of the nanopore experimental setup. Association of the cholesterol moiety on the 3′ trailing strand with the bilayer should favor capture of the free 5′ end of the leading strand in the electric field surrounding the nanopore. Finally, the adapter design incorporated abasic residues into both leading and trailing strands to act as ionic current signal markers upstream and downstream of the ligated tRNA.S. cerevisiae tRNAPhe) .We initially tested if the adapter could be enzymatically ligated to tRNA using T4 RNA Ligase 2 (RNL2). Analysis of that ligation reaction revealed that a product of appropriate size ~160 nt) was generated only in the presence of both the adapter and a model tRNA substrate    relative to experiments absent Mg2+ (mean duration = 10−1.4 ± 0.1 s)   , similar to events in the control experiment, and a long duration population not seen in the control experiment  . The longer duration population (≥0.1 s) was longer in mean duration and was statistically different from the population seen in the control . This suggested that these long duration events were the result of ϕ29 DNAP binding the adapted RNA substrate and slowing strand translocation through the nanopore.Control nanopore experiments with the adapted RNA(hairpin) construct absent ϕ29 DNAP resulted in current blockade events with mean duration on the order of milliseconds (mean duration = 10We included two abasic residues (1′-H deoxyribose) in the adapter strands near the ligation junctions with the RNA to act as indicators of strand translocation  constructs bound to ϕ29 DNAP resulted in blockade events containing two distinct ionic current states in the range of 33.5–36.5 pA that flank a reproducible pattern of ionic current  substrate  near the beginning of the event Figure C. SubstrThese observations provided an ionic current model for complete translocation of the dual abasic-adapted RNA(hairpin) in the 5′-to-3′ direction. That is, the 5′ abasic residue caused the first high current state, which was followed by the intervening current region corresponding to the RNA, which includes the low current state. This is followed by the high current state proximal to the end of the event that is caused by the 3′ abasic residue.Using this model, we quantified the frequency of the leading and trailing high current states (henceforth referred to as leading and trailing markers) in events >1 s duration. Translocation of the dual-abasic adapted RNA resulted in 27.4% of events (102 of 372) that contained both the leading marker and trailing marker separated by a region containing a low current segment , we observed a single long-duration high current marker toward the beginning of ϕ29 DNAP-bound translocation events. Conversely, inclusion of a single abasic residue only on the trailing strand of the adapter (3′) produced a single short-duration high current marker toward the end of ϕ29 DNAP-bound translocation events. Presence of both abasic residues in the adapter resulted in ~27% of ϕ29 DNAP-bound translocation events containing two high current markers. Taken together, these results support initiation of the ϕ29 DNAP-bound strand threading through the nanopore from the 5′ end, followed by the RNA portion, and terminating with the 3′ end of strand passing through and exiting the pore. A 5′-to-3′ voltage-driven “unzipping” process was previously observed for ϕ29 DNAP under non-enzymatic conditions on DNA  or as two isoforms that differ by only a single nucleotide (tRNAfMet) , but have significantly different nucleotide compositions , and would be expected to generate different ionic current signals.If the ionic current segment flanked by the high current markers contains the RNA-dependent portion of nanopore signal, then that region should differentiate tRNA species. Further, a tRNA-specific change seen in the putative RNA-dependent region, when bordered by the adapter-dependent marker regions, would be evidence that the adapted tRNA translocate entirely through the nanopore. We used the ϕ29 DNAP-mediated braking method, as had been done with the RNA(hairpin) substrate, to improve temporal resolution of adapted tRNA. For this experiment, we selected two well-characterized tRNA species for nanopore analysis, fMet substrate produced 85 events that contained the leading and trailing markers bracketing an extended intervening current region    Mitochondrial tRNA (mt.tRNA) genes comprise <10% of the mitochondrial genome , yet 43% (245/571) of the currently known pathogenic mutations cataloged in MITOMAP are found in these genes : Mark Akeson is a consultant to Oxford Nanopore Technologies. Patent application: SC2014_725_PRV, Molecular adapter for capture and manipulation of tRNA.http://journal.frontiersin.org/article/10.3389/fbioe.2015.00091The Supplementary Material for this article can be found online at Click here for additional data file.