3-(tert-But­oxy­carbon­yl)-2-(4-chloro­phen­yl)-1,3-thia­zolidine-4-carb­oxy­lic acid. Corrigendum

Corrigendum to Acta Cryst. (2010), E66, o2633

2,9,10-Trimeth­oxy­dibenzo[b,d]oxepin-7(6H)-one

The title compound, C17H16O5, was prepared through a cyclization reaction of 2-(3′,4′,5-trimeth­oxy­biphenyl-2-yl­oxy)acetyl chloride. The two benzene rings form a dihedral angle of 34.55 (5)°. The crystal structure does not feature any hydrogen bonds

Understanding variation in transcription factor binding by modeling transcription factor genome-epigenome interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg)

Understanding Variation in Transcription Factor Binding by Modeling Transcription Factor Genome-Epigenome Interactions

Despite explosive growth in genomic datasets, the methods for studying epigenomic mechanisms of gene regulation remain primitive. Here we present a model-based approach to systematically analyze the epigenomic functions in modulating transcription factor-DNA binding. Based on the first principles of statistical mechanics, this model considers the interactions between epigenomic modifications and a cis-regulatory module, which contains multiple binding sites arranged in any configurations. We compiled a comprehensive epigenomic dataset in mouse embryonic stem (mES) cells, including DNA methylation (MeDIP-seq and MRE-seq), DNA hydroxymethylation (5-hmC-seq), and histone modifications (ChIP-seq). We discovered correlations of transcription factors (TFs) for specific combinations of epigenomic modifications, which we term epigenomic motifs. Epigenomic motifs explained why some TFs appeared to have different DNA binding motifs derived from in vivo (ChIP-seq) and in vitro experiments. Theoretical analyses suggested that the epigenome can modulate transcriptional noise and boost the cooperativity of weak TF binding sites. ChIP-seq data suggested that epigenomic boost of binding affinities in weak TF binding sites can function in mES cells. We showed in theory that the epigenome should suppress the TF binding differences on SNP-containing binding sites in two people. Using personal data, we identified strong associations between H3K4me2/H3K9ac and the degree of personal differences in NFκB binding in SNP-containing binding sites, which may explain why some SNPs introduce much smaller personal variations on TF binding than other SNPs. In summary, this model presents a powerful approach to analyze the functions of epigenomic modifications. This model was implemented into an open source program APEG (Affinity Prediction by Epigenome and Genome, http://systemsbio.ucsd.edu/apeg).</p

Spatiotemporal clustering of the epigenome reveals rules of dynamic gene regulation

Spatial organization of different epigenomic marks was used to infer functions of the epigenome. It remains unclear what can be learned from the temporal changes of the epigenome. Here, we developed a probabilistic model to cluster genomic sequences based on the similarity of temporal changes of multiple epigenomic marks during a cellular differentiation process. We differentiated mouse embryonic stem (ES) cells into mesendoderm cells. At three time points during this differentiation process, we used high-throughput sequencing to measure seven histone modifications and variants—H3K4me1/2/3, H3K27ac, H3K27me3, H3K36me3, and H2A.Z; two DNA modifications—5-mC and 5-hmC; and transcribed mRNAs and noncoding RNAs (ncRNAs). Genomic sequences were clustered based on the spatiotemporal epigenomic information. These clusters not only clearly distinguished gene bodies, promoters, and enhancers, but also were predictive of bidirectional promoters, miRNA promoters, and piRNAs. This suggests specific epigenomic patterns exist on piRNA genes much earlier than germ cell development. Temporal changes of H3K4me2, unmethylated CpG, and H2A.Z were predictive of 5-hmC changes, suggesting unmethylated CpG and H3K4me2 as potential upstream signals guiding TETs to specific sequences. Several rules on combinatorial epigenomic changes and their effects on mRNA expression and ncRNA expression were derived, including a simple rule governing the relationship between 5-hmC and gene expression levels. A Sox17 enhancer containing a FOXA2 binding site and a Foxa2 enhancer containing a SOX17 binding site were identified, suggesting a positive feedback loop between the two mesendoderm transcription factors. These data illustrate the power of using epigenome dynamics to investigate regulatory functions

Expression of soluble vascular endothelial growth factor receptor-1 and placental growth factor in fetal growth restriction cases and intervention effect of tetramethylpyrazine

AbstractObjectiveTo investigate the expression of soluble vascular endothelial growth factor receptor-1 (sFlt-1) and placental growth factor (PLGF) in the fetal growth restriction (FGR) cases and the intervention mechanism of tetramethylpyrazine.MethodsA total of 60 fetal growth restriction cases that admitted to our hospital were randomly divided into ligustrazine intervention group (group A) and nutritional support group (group B). A total of 50 healthy pregnant women were also enrolled as control group (group C). Expression level of maternal serum sFlt1, PLGF and fetal growth parameters including HC, AC, FL, BPD, EFW as well as placenta PLGF, sFlt-1 mRNA expression were recorded and compared among the three groups. A total of 15 SD rats were selected and were divided into three groups, TMP group, alcohol and tobacco group and blank control group. Three groups of rats were dissected on the twentieth day of gestation.ResultsExpression level of sFlt-1 and PLGF in group A was not significantly different from that of group C (P>0.05); but significant difference in SFlt1 and PLGF expression level was observed between group C and group B (P<0.05). Before treatment, HC, AC, FL, BPD and EFW of group A and group B were significant lower than those of group C, but after treatment, those parameters in group A were significantly improved (P<0.05). In the animal experiment there was no significant difference in sFlt-1 between treatment group and FGR group without treatment or control group (P>0.05). There was significant difference in PLGF between FGR group with treatment and FGR group without treatment or control group (P<0.01).ConclusionsPLGF level is decreased and sFlt-1 increased in patients suffered from fetal growth restriction, and FGR rats show increased sFlt-1 and decreased PLGF, thus they can be indicator of the fetal growth restriction. Ligustrazine can effectively improve sFlt-1, PLGF expression level in fetal growth restriction cases, which can be used as treatment for FGR