LncRNA Gas5 regulates Fn1 deposition via Creb5 in renal fibrosis

Epigenomics ◽  
2021 ◽  
Author(s):  
Huanhou Su ◽  
Jingzhou Xie ◽  
Lijing Wen ◽  
Shunyi Wang ◽  
Sishuo Chen ◽  
...  

Aim: Although studies on lncRNAs in renal fibrosis have focused on target genes and functions of lncRNAs, a comprehensive interaction analysis of lncRNAs is lacking. Materials & methods: Differentially expressed genes in renal fibrosis were screened, and the interaction between lncRNAs and miRNAs was searched. Results: We constructed a ceRNA network associated with renal fibrosis, by which we found the transcription factor Creb5, a target gene of lncRNA Gas5 that might regulate extracellular Fn1 deposition. Conclusion: Our study not only provides a theoretical basis for the ceRNA regulation mechanism of Gas5 but also provides experimental evidence supporting the use of Gas5 targeting in the treatment of renal fibrosis.

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 418
Author(s):  
Yanbo Huo ◽  
Bin Zhang ◽  
Ling Chen ◽  
Jing Zhang ◽  
Xing Zhang ◽  
...  

Miltiradiene synthase (MS) genes, TwTPS27a and TwTPS27b, are the key diterpene synthase genes in the biosynthesis of triptolide, which is an important medicinally active diterpenoid in Tripterygium wilfordii. However, the mechanism underlying the regulation of key genes TwTPS27a/b in triptolide biosynthesis remains unclear. In this study, the promoters of TwTPS27a (1496 bp) and TwTPS27b (1862 bp) were isolated and analyzed. Some hormone-/stress-responsive elements and transcription factor (TF) binding sites were predicted in both promoters, which might be responsible for the regulation mechanism of TwTPS27a/b. The β-glucuronidase (GUS) activity analysis in promoter deletion assays under normal and methyl jasmonate (MeJA) conditions showed that the sequence of −921 to −391 bp is the potential core region of the TwTPS27b promoter. And the TGACG-motif, a MeJA-responsive element found in this core region, might be responsible for MeJA-mediated stress induction of GUS activity. Moreover, the TGACG-motif is also known as the TGA TF-binding site. Yeast one-hybrid and GUS transactivation assays confirmed the interaction between the TwTPS27a/b promoters and the TwTGA1 TF (a MeJA-inducible TGA TF upregulating triptolide biosynthesis in T. wilfordii), indicating that TwTPS27a/b are two target genes regulated by TwTGA1. In conclusion, our results provide important information for elucidating the regulatory mechanism of MS genes, TwTPS27a and TwTPS27b, as two target genes of TwTGA1, in jasmonic acid (JA)-inducible triptolide biosynthesis.


2019 ◽  
Author(s):  
Timothy O’Connor ◽  
Charles E. Grant ◽  
Mikael Bodén ◽  
Timothy L. Bailey

AbstractMotivationIdentifying the genes regulated by a given transcription factor (its “target genes”) is a key step in developing a comprehensive understanding of gene regulation. Previously we developed a method for predicting the target genes of a transcription factor (TF) based solely on the correlation between a histone modification at the TF’s binding site and the expression of the gene across a set of tissues. That approach is limited to organisms for which extensive histone and expression data is available, and does not explicitly incorporate the genomic distance between the TF and the gene.ResultsWe present the T-Gene algorithm, which overcomes these limitations. T-Gene can be used to predict which genes are most likely to be regulated by a TF, and which of the TF’s binding sites are most likely involved in regulating particular genes. T-Gene calculates a novel score that combines distance and histone/expression correlation, and we show that this score accurately predicts when a regulatory element bound by a TF is in contact with a gene’s promoter, achieving median positive predictive value (PPV) above 50%. T-Gene is easy to use via its web server or as a command-line tool, and can also make accurate predictions (median PPV above 40%) based on distance alone when extensive histone/expression data is not available for the organism. T-Gene provides an estimate of the statistical significance of each of its predictions.AvailabilityThe T-Gene web server, source code, histone/expression data and genome annotation files are provided at http://[email protected]


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mira Park ◽  
So Hee Park ◽  
Hyunsun Park ◽  
Hye-Ryun Kim ◽  
Hyunjung J. Lim ◽  
...  

Abstract Background Recently, we demonstrated that estrogen (E2) induces early growth response 1 (Egr1) to mediate its actions on the uterine epithelium by controlling progesterone receptor signaling for successful embryo implantation. EGR1 is a transcription factor that regulates the spectrum of target genes in many different tissues, including the uterus. E2-induced EGR1 regulates a set of genes involved in epithelial cell remodeling during embryo implantation in the uterus. However, only few target genes of EGR1 in the uterus have been identified. Result The expression of ADAM metallopeptidase with thrombospondin type 1 motif 1 (Adamts-1) was significantly downregulated in the uteri of E2-treated ovariectomized (OVX) Egr1(−/−) mice. Immunostaining of ADAMTS-1 revealed its exclusive expression in the uterine epithelium of OVX wild-type but not Egr1(−/−) mice treated with E2. The expression profiles of Adamts-1 and Egr1 were similar in the uteri of E2-treated OVX mice at various time points tested. Pre-treatment with ICI 182, 780, a nuclear estrogen receptor (ER) antagonist, effectively inhibited the E2-dependent induction of Egr1 and Adamts-1. Pharmacologic inhibition of E2-induced ERK1/2 or p38 phosphorylation interfered with the induction of EGR1 and ADAMTS-1. Furthermore, ADAMTS-1, as well as EGR1, was induced in stroma cells surrounding the implanting blastocyst during embryo implantation. Transient transfection with EGR1 expression vectors significantly induced the expression of ADAMTS-1. Luciferase activity of the Adamts-1 promoter containing EGR1 binding sites (EBSs) was increased by EGR1 in a dose-dependent manner, suggesting functional regulation of Adamts-1 transcription by EGR1. Site-directed mutagenesis of EBS on the Adamts-1 promoter demonstrated that EGR1 directly binds to the EBS at -1151/-1134 among four putative EBSs. Conclusions Collectively, we have demonstrated that Adamts-1 is a novel target gene of E2-ER-MAPK-EGR1, which is critical for embryo implantation in the mouse uterus during early pregnancy.


F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1933 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan

Background:The distribution and composition ofcis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets.Methods:Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. Corresponding promoter sequences were reduced to DNase I-accessible intervals; TFBSs were then identified within these intervals using information theory-based position weight matrices for each TF (iPWMs) and clustered. Features from information-dense TFBS clusters predicted these genes with machine learning classifiers, which were evaluated for accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed toin silicoexamine their impact on cluster densities and the regulatory states of target genes.Results:  We initially chose the glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, to test this approach.SLC25A32andTANKwere found to exhibit the most similar expression patterns toNR3C1. A Decision Tree classifier exhibited the largest area under the Receiver Operating Characteristic (ROC) curve in detecting such genes. Target gene prediction was confirmed using siRNA knockdown of TFs, which was found to be more accurate than those predicted after CRISPR/CAS9 inactivation.In-silicomutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.


F1000Research ◽  
2019 ◽  
Vol 7 ◽  
pp. 1933 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan

Background:The distribution and composition ofcis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets using Machine Learning (ML).Methods:Bray-Curtis Similarity was used to identify genes with correlated expression patterns across 53 tissues. TF targets from knockdown experiments were also analyzed by this approach to set up the ML framework. TFBSs were selected within DNase I-accessible intervals of corresponding promoter sequences using information theory-based position weight matrices (iPWMs) for each TF. Features from information-dense clusters of TFBSs were input to ML classifiers which predict these gene targets along with their accuracy, specificity and sensitivity. Mutations in TFBSs were analyzedin silicoto examine their impact on TFBS clustering and predict changes in gene regulation.Results: The glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, was selected to test this approach.SLC25A32andTANKexhibited the most similar expression patterns toNR3C1. A Decision Tree classifier exhibited the best performance in detecting such genes, based on Area Under the Receiver Operating Characteristic curve (ROC). TF target gene prediction was confirmed using siRNA knockdown, which was more accurate than CRISPR/CAS9 inactivation. TFBS mutation analyses revealed that accurate target gene prediction required  at least 1  information-dense TFBS cluster. Conclusions: ML based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.


2021 ◽  
Author(s):  
Małgorzata Sotomska ◽  
Robert Liefke ◽  
Francesca Ferrante ◽  
Heiko Schwederski ◽  
Franz Oswald ◽  
...  

Abstract BackgroundNotch signaling controls cell fate decisions in many contexts during development and adult stem cell homeostasis and, when dysregulated, leads to carcinogenesis. The central transcription factor RBPJ assembles the Notch coactivator complex in the presence of Notch signalling, and represses Notch target gene expression in its absence.ResultsWe identified L3MBTL2 and additional members of the non-canonical polycomb repressive PRC1.6 complex in DNA-bound RBPJ associated complexes and demonstrate that L3MBTL2 directly interacts with RBPJ. Depletion of RBPJ does not affect occupancy of PRC1.6 components at Notch target genes. Conversely, absence of L3MBTL2 reduces RBPJ occupancy at enhancers of Notch target genes. Since L3MBTL2 and additional members of the PRC1.6 are known to be SUMOylated, we investigated whether RBPJ uses SUMO-moieties as contact points. Indeed, we found that RBPJ binds to SUMO2/3 and that this interaction depends on a defined SUMO-interaction motif. Furthermore, we show that pharmacological inhibition of SUMOylation reduces RBPJ occupancy at Notch target genes.ConclusionsWe propose that the PRC1.6 complex and its conjugated SUMO-modifications provide a scaffold that is recognized by RBPJ and promotes its recruitment to Notch target genes.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 3673-3673
Author(s):  
Kentson Lam ◽  
Randal Du ◽  
Shinobu Matsuura ◽  
Dong-Er Zhang

Abstract RUNX1, also known as AML1, is a DNA binding transcription factor that is expressed in hematopoietic stem and progenitor cells (HSPCs). As demonstrated by several mouse models, RUNX1 is necessary for definitive hematopoiesis and proper homeostasis of HSPCs. Furthermore, mutations of RUNX1have been implicated in patients with a variety of blood-related malignancies and disorders. These findings have established RUNX1 as a master regulator of hematopoiesis. As a transcription factor, RUNX1 exerts its function in hematopoiesis by binding to regulatory regions in order to guide the expression of its direct target genes. Most confirmed RUNX1 target genes are mainly expressed in differentiated blood cells. Direct targets of RUNX1 in HSPCs, however, have largely remained unexplored. Identifying direct target genes of RUNX1 offers an insightful view of how this master regulator influences HSPC function. To elucidate RUNX1 target genes in HSPCs, we have analyzed gene expression signatures from wildtype and RUNX1-deficient HSPCs (Lineage-/cKit+/Sca1+) in a previous report (Matsuura et al., Blood, 2012). With the goal of continuing the characterization of RUNX1 target genes, in this current study, we performed genome-occupancy analysis with chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) using RUNX1 antibodies and a murine HSPC cell line. Bioinformatics analysis of the ChIP-seq data revealed 6370 significant RUNX1 binding peaks (<1% FDR). The majority of these peaks were located in areas outside of promoter regions. The top de novo generated sequence motif from these peaks corresponds with the known RUNX binding consensus motif TG(T/C)GGT, suggesting that our ChIP-seq dataset is highly reliable. The combination of differential gene expression and RUNX1 genome occupancy data has revealed a list of candidate RUNX1-regulated target genes. We hypothesize that RUNX1 directly modulates the expression of these genes in normal hematopoiesis. One of the genes identified is Hmga2. We observed three RUNX1 binding peaks in the upstream, intron, and downstream regions relative to the Hmga2 gene locus. Furthermore, we confirmed strong up-regulation of Hmga2 in RUNX1-deficient HSPCs using reverse transcription coupled with quantitative polymerase chain reaction. HMGA2, also known as High Mobility Group AT-hook 2, is a non-histone chromatin protein. Its expression is highest during embryonic development and in undifferentiated cells. Over-expression of HMGA2 in transgenic mice or in bone marrow transplantation models have been reported to cause expansion of HSPCs. These reports indicate that HMGA2 is a significant mediator of HSPC proliferation. Interestingly, a major characteristic of mice without RUNX1 in their hematopoietic cells is the expansion of HSPCs, suggesting that HMGA2 may contribute to this phenotype. To further validate Hmga2 as a RUNX1 target gene, we cloned the Hmga2 promoter sequence and DNA fragments corresponding to the three RUNX1 binding peaks into luciferase reporter constructs and performed transfection studies using K562 and 293T cells. Interestingly, while these studies demonstrated strong responses to RUNX1 in promoter-luciferase assays, the effect of RUNX1 on Hmga2 promoter activity in these two cell lines was opposite. In addition, eliminating two RUNX binding motifs in the Hmga2 promoter did not affect RUNX1-mediated promoter-luciferase activity, indicating that there are additional regulatory mechanisms that may be important for RUNX1’s effect on the Hmga2 promoter. To examine the function of the three regions containing RUNX1 binding peaks in the Hmga2 gene locus, we also used luciferase reporter constructs including these regions in transfection studies. Increase of transcriptional activity was detected in the presence of the two regions that were upstream and downstream of the Hmga2 gene, suggesting that RUNX1 can act as a positive regulator through these regions. In contrast, the RUNX1 binding fragment in the intron region of Hmga2 reduced promoter-luciferase activity. This outcome indicates that RUNX1 acts as a suppressor through the Hmga2 intron element. In summary, these results establish Hmga2 as a novel RUNX1 target gene in HSPCs and mark the first study of the transcriptional regulation of the Hmga2 gene by RUNX1 through differential control regions. Disclosures: No relevant conflicts of interest to declare.


2018 ◽  
Author(s):  
Ruipeng Lu ◽  
Peter K. Rogan

ABSTRACTBackgroundThe distribution and composition ofcis-regulatory modules (e.g. transcription factor binding site (TFBS) clusters) in promoters substantially determine gene expression patterns and TF targets, whose expression levels are significantly regulated by TF binding. TF knockdown experiments have revealed correlations between TF binding profiles and gene expression levels. We present a general framework capable of predicting genes with similar tissue-wide expression patterns from activated or repressed TF targets using machine learning to combine TF binding and epigenetic features.MethodsGenes with correlated expression patterns across 53 tissues were identified according to their Bray-Curtis similarity. DNase I HyperSensitive region (DHS) -accessible promoter intervals of direct TF target genes were scanned with previously derived information theory-based position weight matrices (iPWMs) of 82 TFs. Features from information density-based TFBS clusters were used to predict target genes with machine learning classifiers. The accuracy, specificity and sensitivity of the classifiers were determined for different feature sets. Mutations in TFBSs were also introduced to examine their impact on cluster densities and the regulatory states of predicted target genes.ResultsWe initially chose the glucocorticoid receptor gene (NR3C1), whose regulation has been extensively studied, to test this approach.SLC25A32andTANKwere found to exhibit the most similar expression patterns to this gene across 53 tissues. Prediction of other genes with similar expression profiles was significantly improved by eliminating inaccessible promoter intervals based on DHSs. A Random Forest classifier exhibited the best performance in detecting such coordinately regulated genes (accuracy was 0.972 for training, 0.976 for testing). Target gene prediction was confirmed using CRISPR knockdown data of TFs, which was more accurate than siRNA inactivation. Mutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction.ConclusionsMachine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple, information-dense TFBS clusters in promoters appear to protect promoters from the effects of deleterious binding site mutations in a single TFBS that would effectively alter the expression state of these genes.


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Małgorzata Sotomska ◽  
Robert Liefke ◽  
Francesca Ferrante ◽  
Heiko Schwederski ◽  
Franz Oswald ◽  
...  

Abstract Background Notch signaling controls cell fate decisions in many contexts during development and adult stem cell homeostasis and, when dysregulated, leads to carcinogenesis. The central transcription factor RBPJ assembles the Notch coactivator complex in the presence of Notch signaling, and represses Notch target gene expression in its absence. Results We identified L3MBTL2 and additional members of the non-canonical polycomb repressive PRC1.6 complex in DNA-bound RBPJ associated complexes and demonstrate that L3MBTL2 directly interacts with RBPJ. Depletion of RBPJ does not affect occupancy of PRC1.6 components at Notch target genes. Conversely, absence of L3MBTL2 reduces RBPJ occupancy at enhancers of Notch target genes. Since L3MBTL2 and additional members of the PRC1.6 are known to be SUMOylated, we investigated whether RBPJ uses SUMO-moieties as contact points. Indeed, we found that RBPJ binds to SUMO2/3 and that this interaction depends on a defined SUMO-interaction motif. Furthermore, we show that pharmacological inhibition of SUMOylation reduces RBPJ occupancy at Notch target genes. Conclusions We propose that the PRC1.6 complex and its conjugated SUMO-modifications provide a favorable environment for binding of RBPJ to Notch target genes.


2020 ◽  
Author(s):  
Jonathan Chau ◽  
Xiangduo Kong ◽  
Nam Nguyen ◽  
Katherine Williams ◽  
Rabi Tawil ◽  
...  

AbstractFacioscapulohumeral dystrophy (FSHD) is linked to misexpression of the transcription factor, DUX4. Although DUX4 target gene expression is often readily detectable, analysis of DUX4 expression has been limited due to its low expression in patient samples. Recently, single cell/nucleus RNA-sequencing was used to detect the native expression of DUX4 for the first time, but important spatial relationships with its target gene expression was missing. Furthermore, dynamics of DUX4 expression during myoblast differentiation has not been fully explored. In order to study the spatiotemporal relationship of DUX4 and key target genes, we performed RNA FISH on immortalized FSHD2 patient skeletal muscle cells. Using two probe sets, DUX4 transcripts were detected in 1-4% of myotubes after 3-day differentiation in vitro. We found that DUX4 transcripts mainly localize as foci in one or two nuclei in a myotube compared to abundant accumulation of the target gene transcripts in the cytoplasm. Over a 13-day differentiation timecourse, DUX4 expression without target gene expression significantly increased and peaked at day 7. Target gene expression correlates better with DUX4 expression early in differentiation while the expression of target genes without detectable DUX4 transcripts increases later. Consistently, shRNA depletion of DUX4-activated transcription factors, DUXA and LEUTX, specifically repressed a DUX4-target gene, KDM4E, later in differentiation, suggesting that following the initial activation by DUX4, target genes themselves contribute to the maintenance of downstream gene expression. Together, in situ detection of the DUX4 and target gene transcripts provided new insight into dynamics of DUX4 transcriptional network in FSHD patient myocytes.Significance StatementFSHD is the third most common muscular dystrophy and is associated with upregulation of DUX4, a transcription factor, and its target genes. Although target genes are easily detectable in FSHD, low frequency DUX4 upregulation in patient myocytes is difficult to detect, and examining the relationship and dynamics of DUX4 and target gene expression without artificial overexpression of DUX4 has been challenging. Using RNAScope with highly specific probes, we detect the endogenous DUX4 and target gene transcripts in situ in patient skeletal myotubes during differentiation in vitro. Our study reveals a unique DUX4 expression pattern and its relationship to the expression of target genes, and evidence for self-sustainability of the target gene network. The study provides important new insights into the FSHD disease mechanism.


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