Transcription Factor Binding Regions Research Articles

Integrative pan-cancer analysis reveals a common architecture of dysregulated transcriptional networks characterized by loss of enhancer methylation.

Aberrant DNA methylation contributes to gene expression deregulation in cancer. However, these alterations' precise regulatory role and clinical implications are still not fully understood. In this study, we performed expression-methylation Quantitative Trait Loci (emQTL) analysis to identify deregulated cancer-driving transcriptional networks linked to CpG demethylation pan-cancer. By analyzing 33 cancer types from The Cancer Genome Atlas, we identified and confirmed significant correlations between CpG methylation and gene expression (emQTL) in cis and trans, both across and within cancer types. Bipartite network analysis of the emQTL revealed groups of CpGs and genes related to important biological processes involved in carcinogenesis including proliferation, metabolism and hormone-signaling. These bipartite communities were characterized by loss of enhancer methylation in specific transcription factor binding regions (TFBRs) and the CpGs were topologically linked to upregulated genes through chromatin loops. Penalized Cox regression analysis showed a significant prognostic impact of the pan-cancer emQTL in many cancer types. Taken together, our integrative pan-cancer analysis reveals a common architecture where hallmark cancer-driving functions are affected by the loss of enhancer methylation and may be epigenetically regulated.

Transcription factor binding site divergence across maize inbred lines drives transcriptional and phenotypic variation.

Regulatory elements are important constituents of plant genomes that have shaped ancient and modern crops. Their identification, function, and diversity in crop genomes however are poorly characterized, thus limiting our ability to harness their power for further agricultural advances using induced or natural variation. Here, we use DNA affinity purification-sequencing (DAP-seq) to map transcription factor (TF) binding events for 200 maize TFs belonging to 30 distinct families and heterodimer pairs in two distinct inbred lines historically used for maize hybrid plant production, providing empirical binding site annotation for 5.3% of the maize genome. TF binding site comparison in B73 and Mo17 inbreds reveals widespread differences, driven largely by structural variation, that correlate with gene expression changes. TF binding site presence-absence variation helps clarify complex QTL such as vgt1, an important determinant of maize flowering time, and DICE, a distal enhancer involved in herbivore resistance. Modification of TF binding regions via CRISPR-Cas9 mediated editing alters target gene expression and phenotype. Our functional catalog of maize TF binding events enables collective and comparative TF binding analysis, and highlights its value for agricultural improvement.

Abstract 1721: Epigenetic heterogeneity of osteosarcoma reveals distinct cellular states driven by enhancer regulation

Abstract Osteosarcoma (OS) is characterized by structural rearrangements and copy number changes which drive considerable heterogeneity. What is less clear is how these genetic rearrangements alter are in turn regulated at the epigenetic level to drive gene expression. Defining the epigenetic circuitry of osteosarcoma could help to understand the role of chromatin accessibility in determining cell plasticity. Chromatin accessibility can prime gene expression and define a cellular state. As chromatin accessibility is a major determinant of epigenetic transcriptional regulation, understanding the variability of chromatin accessibility could identify clinically-relevant subtypes and biomarkers of therapy response in Osteosarcoma, a disease that has seen few advances in therapy for over 40 years. Using ATAC-seq, we identified two epigenetic subtypes (EC1 and EC2) of OS with distinct chromatin accessibility. These studies were done in a panel of 11 PDX-derived cell lines and 9 established OS cell lines. We also performed ATACseq on the matching PDX for the 11 PDX-derived lines as well as 8 primary tumor samples. Analysis of transcription factor (TF) motifs and footprints of ATAC-seq peaks identified transcription factors distinctly activated in these two subtypes. For example, EC1 is characterized by high level of activity of developmental transcription factors including RUNX2, MEF2C and homeobox TFs. In contrast, EC2 is characterized by high activity of AP1 transcription factors such as FOSL1 and FOSL2. In order to understand the underlying transcriptional circuitry of these cell states, we performed CUT&RUN assay for the complete panel of PDX-derived cell lines. We found that the subtypes are driven by enhancer regulation (H3K27ac). We defined the specific Core Regulatory Circuitry (CRC) per subtype by defining the superenhancers by ROSE. We overlapped ATAC-seq data in the acetylated regions to demarcate TF binding regions dramatically and reduce the search space for TF cis-regulatory sites. In parallel, we used our PDX-derived cell line panel to screen cluster-specific targeted therapies and found differential responses dependent on cellular states defined by chromatin accessibility. For example, cell lines in the EC2 cluster are marked by upregulation of AP1 transcription factors, have evidence of ERK activation and are highly responsive to ERK inhibitors. In contrast cell lines in the EC1 cluster are sensitive to AURKA and AURKB inhibitors. Single-cell RNAseq analysis indicates that these subtypes may coexist within a single tumor. In summary, we discovered two epigenetically distinct cell states that are controlled by a state-specific collection of transcription factors, creating a gene signature that may aid in distinguishing osteosarcoma subclasses and predicting treatment response. Citation Format: Eunice Lopez Fuentes, Andrew Clugston, Alex Lee, Leanne Sayles, Maria Pons, Alejandro Sweet-Cordero. Epigenetic heterogeneity of osteosarcoma reveals distinct cellular states driven by enhancer regulation [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1721.

Alternative polyadenylation quantitative trait methylation mapping in human cancers provides clues into the molecular mechanisms of APA

Genetic variants are involved in the orchestration of alternative polyadenylation (APA) events, while the role of DNA methylation in regulating APA remains unclear. We generated a comprehensive atlas of APA quantitative trait methylation sites (apaQTMs) across 21 different types of cancer (1,612 to 60,219 acting in cis and 4,448 to 142,349 in trans). Potential causal apaQTMs in non-cancer samples were also identified. Mechanistically, we observed a strong enrichment of cis-apaQTMs near polyadenylation sites (PASs) and both cis- and trans-apaQTMs in proximity to transcription factor (TF) binding regions. Through the integration of ChIP-signals and RNA-seq data from cell lines, we have identified several regulators of APA events, acting either directly or indirectly, implicating novel functions of some important genes, such as TCF7L2, which is known for its involvement in type 2 diabetes and cancers. Furthermore, we have identified a vast number of QTMs that share the same putative causal CpG sites with five different cancer types, underscoring the roles of QTMs, including apaQTMs, in the process of tumorigenesis. DNA methylation is extensively involved in the regulation of APA events in human cancers. In an attempt to elucidate the potential underlying molecular mechanisms of APA by DNA methylation, our study paves the way for subsequent experimental validations into the intricate biological functions of DNA methylation in APA regulation and the pathogenesis of human cancers. To present a comprehensive catalog of apaQTM patterns, we introduce the Pancan-apaQTM database, available at https://pancan-apaqtm-zju.shinyapps.io/pancanaQTM/.

Less-is-more: selecting transcription factor binding regions informative for motif inference.

Numerous statistical methods have emerged for inferring DNA motifs for transcription factors (TFs) from genomic regions. However, the process of selecting informative regions for motif inference remains understudied. Current approaches select regions with strong ChIP-seq signal for a given TF, assuming that such strong signal primarily results from specific interactions between the TF and its motif. Additionally, these selection approaches do not account for non-target motifs, i.e. motifs of other TFs; they presume the occurrence of these non-target motifs infrequent compared to that of the target motif, and thus assume these have minimal interference with the identification of the target. Leveraging extensive ChIP-seq datasets, we introduced the concept of TF signal 'crowdedness', referred to as C-score, for each genomic region. The C-score helps in highlighting TF signals arising from non-specific interactions. Moreover, by considering the C-score (and adjusting for the length of genomic regions), we can effectively mitigate interference of non-target motifs. Using these tools, we find that in many instances, strong ChIP-seq signal stems mainly from non-specific interactions, and the occurrence of non-target motifs significantly impacts the accurate inference of the target motif. Prioritizing genomic regions with reduced crowdedness and short length markedly improves motif inference. This 'less-is-more' effect suggests that ChIP-seq region selection warrants more attention.

Immune correlates of NF-κB and TNFα promoter DNA methylation in Japanese flounder (Paralichthys olivaceus) muscle and immune parameters change response to vibrio anguillarum infection

Vibrio anguillarum infection can activate NF-κB/TNFα pathway in the immune organs of fish. Fish muscle is also an important immune organ, but the research on its immune function is few. Our aim was to study regulating mechanism of NF-κB and TNFα gene expressions in the muscle of Japanese flounder (Paralichthys olivaceus) which was under Vibrio anguillarum infection (0, 24, 48, 72 and 96h). The results showed that the expressions of NF-κB and TNFα increased significantly at 48h, and there was a significant positive correlation between them. In situ hybridization confirmed the co-existence of NF-κB and TNFα genes in Japanese flounder muscle. Interestingly, the expression of the TNFα gene was regulated by the DNA methylation and its methylation level was negatively correlated with the expression. The lowest methylation level of TNFα occurred at 48h under Vibrio anguillarum infection (P<0.05). And more, when the fragment (-2122∼-730) was deleted on TNFα gene promoter, double luciferase activity was the highest, indicating that fragment (-730-0) was the transcription factor binding region. The site (-78 ~ -69) on the fragment (-730-0) binding NF-κB was mutated, and double luciferase activity decreased significantly. The results confirmed that the site (-78 ~ -69) was indeed an important binding site for NF-κB. In addition, the activity of TNFα in the serum of Japanese flounder changed with the prolongation of vibrio anguillarum infection, and the concentration of other immune factors such as ALP, ALT, AST and LDH also changed in the muscle under vibrio anguillarum infection. They all showed a trend of first increasing and then decreasing. Above studies implied that Japanese flounder responded to Vibrio anguillarum infection at the immune level with the change of its methylation status and the activation of transcription factor. By studying the mechanism of immune pathways, understanding the response to immune stress is great significant to the research of fish breeding for disease resistance.

Functional annotation of the 2q35 breast cancer risk locus implicates a structural variant in influencing activity of a long-range enhancer element

A combination of genetic and functional approaches has identified three independent breast cancer risk loci at 2q35. A recent fine-scale mapping analysis to refine these associations resulted in 1 (signal 1), 5 (signal 2), and 42 (signal 3) credible causal variants at these loci. We used publicly available in silico DNase I and ChIP-seq data with in vitro reporter gene and CRISPR assays to annotate signals 2 and 3. We identified putative regulatory elements that enhanced cell-type-specific transcription from the IGFBP5 promoter at both signals (30- to 40-fold increased expression by the putative regulatory element at signal 2, 2- to 3-fold by the putative regulatory element at signal 3). We further identified one of the five credible causal variants at signal 2, a 1.4 kb deletion (esv3594306), as the likely causal variant; the deletion allele of this variant was associated with an average additional increase in IGFBP5 expression of 1.3-fold (MCF-7) and 2.2-fold (T-47D). We propose a model in which the deletion allele of esv3594306 juxtaposes two transcription factor binding regions (annotated by estrogen receptor alpha ChIP-seq peaks) to generate a single extended regulatory element. This regulatory element increases cell-type-specific expression of the tumor suppressor gene IGFBP5 and, thereby, reduces risk of estrogen receptor-positive breast cancer (odds ratio = 0.77, 95% CI 0.74–0.81, p = 3.1 × 10−31).

Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources

BackgroundTranscriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features.ResultsWe describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression.ConclusionsOur models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.

Tissue-specific transcriptome profiling of the Arabidopsis inflorescence stem reveals local cellular signatures.

Genome-wide gene expression maps with a high spatial resolution have substantially accelerated plant molecular science. However, the number of characterized tissues and growth stages is still small due to the limited accessibility of most tissues for protoplast isolation. Here, we provide gene expression profiles of the mature inflorescence stem of Arabidopsis thaliana covering a comprehensive set of distinct tissues. By combining fluorescence-activated nucleus sorting and laser-capture microdissection with next-generation RNA sequencing, we characterized the transcriptomes of xylem vessels, fibers, the proximal and distal cambium, phloem, phloem cap, pith, starch sheath, and epidermis cells. Our analyses classified more than 15,000 genes as being differentially expressed among different stem tissues and revealed known and novel tissue-specific cellular signatures. By determining overrepresented transcription factor binding regions in the promoters of differentially expressed genes, we identified candidate tissue-specific transcriptional regulators. Our datasets predict the expression profiles of an exceptional number of genes and allow hypotheses to be generated about the spatial organization of physiological processes. Moreover, we demonstrate that information about gene expression in a broad range of mature plant tissues can be established at high spatial resolution by nuclear mRNA profiling. Tissue-specific gene expression values can be accessed online at https://arabidopsis-stem.cos.uni-heidelberg.de/.

Population-scale study of eRNA transcription reveals bipartite functional enhancer architecture

Enhancer RNAs (eRNA) are unstable non-coding RNAs, transcribed bidirectionally from active regulatory sequences, whose expression levels correlate with enhancer activity. We use capped-nascent-RNA sequencing to efficiently capture bidirectional transcription initiation across several human lymphoblastoid cell lines (Yoruba population) and detect ~75,000 eRNA transcription sites with high sensitivity and specificity. The use of nascent-RNA sequencing sidesteps the confounding effect of eRNA instability. We identify quantitative trait loci (QTLs) associated with the level and directionality of eRNA expression. High-resolution analyses of these two types of QTLs reveal distinct positions of enrichment at the central transcription factor (TF) binding regions and at the flanking eRNA initiation regions, both of which are associated with mRNA expression QTLs. These two regions—the central TF-binding footprint and the eRNA initiation cores—define a bipartite architecture of enhancers, inform enhancer function, and can be used as an indicator of the significance of non-coding regulatory variants.

Computational Prediction of Drug Responses in Cancer Cell Lines From Cancer Omics and Detection of Drug Effectiveness Related Methylation Sites

Accurately predicting the response of a cancer patient to a therapeutic agent remains an important challenge in precision medicine. With the rise of data science, researchers have applied computational models to study the drug inhibition effects on cancers based on cancer genomics and transcriptomics. Moreover, a common epigenetic modification, DNA methylation, has been related to the occurrence and development of cancer, as well as drug effectiveness. Therefore, it is helpful for improvement of drug response prediction through exploring the relationship between DNA methylation and drug effectiveness. Here, we proposed a computational model to predict drug responses in cancers through integration of cancer genomics, transcriptomics, epigenomics, and compound chemical properties. Meanwhile, we applied a regularized regression model (Least Absolute Shrinkage and Selection Operator, lasso) to detect the methylation sites that were closely related to drug effectiveness. The prediction models were trained on a well-known pharmacogenomics data resource, Genomics of Drug Sensitivity in Cancer (GDSC). The cross-validation indicates that the performance of the prediction model using DNA methylation is comparable to that of using other cancer omics, including oncogene mutation and gene expression data. It indicates the important role of DNA methylation in prediction of drug responses. Encyclopedia of DNA Elements (ENCODE) and Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST2) database analyses suggest that the methylation sites associated with drug effectiveness are mainly located in the transcription factor (TF) binding region. Therefore, we hypothesized that the sensitivity of cancer cells to drugs could be regulated by changing the methylation modification of TF binding region. In conclusion, we confirmed the important role of DNA methylation in prediction of drug responses, and provided some methylation sites that closely related to the drug effectiveness, which may be a great regulatory target for improvement of drug treatment effects on cancer patients.

The R-Loop Atlas of Arabidopsis Development and Responses to Environmental Stimuli.

R-loops are a common chromatin feature with essential functions in multiple cellular processes and diseases. However, little is known about the dynamic patterns of R-loops in a given organism. Here, using our recently developed genome-wide R-loop profiling method, we generated a comprehensive atlas quantifying the R-loop patterns of Arabidopsis (Arabidopsis thaliana) in 53 samples during development and during responses to environmental stimuli. The R-loop patterns were fairly stable in plants at the vegetative stage and in response to different light spectra and other environmental stimuli. Notably, the R-loops showed turnover during the plant life cycle, with patterns switching between generations. Importantly, R-loop dynamics was not strongly associated with RNA abundance, indicating that the mechanisms regulating R-loop formation and RNA accumulation are independent. We also observed enrichment of R-loops in transcription factor binding regions, suggesting that R-loops could function as potential cis-transcriptional regulators. This study provides an overview of R-loop dynamics in Arabidopsis during development and stress responses, highlights the unique dynamics of R-loops in the flowering plant Arabidopsis, and lays the groundwork for elucidating the functions of R-loops.

An integrative analysis of genome-wide association study and regulatory SNP annotation datasets identified candidate genes for bipolar disorder

BackgroundBipolar disorder (BD) is a complex mood disorder. The genetic mechanism of BD remains largely unknown.MethodsWe conducted an integrative analysis of genome-wide association study (GWAS) and regulatory SNP (rSNP) annotation datasets, including transcription factor binding regions (TFBRs), chromatin interactive regions (CIRs), mature microRNA regions (miRNAs), long non-coding RNA regions (lncRNAs), topologically associated domains (TADs) and circular RNAs (circRNAs). Firstly, GWAS dataset 1 of BD (including 20,352 cases and 31,358 controls) and GWAS dataset 2 of BD (including 7481 BD patients and 9250 controls) were integrated with rSNP annotation database to obtain BD associated SNP regulatory elements and SNP regulatory element-target gene (E–G) pairs, respectively. Secondly, a comparative analysis of the two datasets results was conducted to identify the common rSNPs and also their target genes. Then, gene sets enrichment analysis (FUMA GWAS) and HumanNet-XC analysis were conducted to explore the functional relevance of identified target genes with BD.ResultsAfter the integrative analysis, we identified 52 TFBRs target genes, 44 TADs target genes, 55 CIRs target genes and 21 lncRNAs target genes for BD, such as ITIH4 (Pdataset1 = 6.68 × 10−8, Pdataset2 = 6.64 × 10−7), ITIH3 (Pdataset1 = 1.09 × 10−8, Pdataset2 = 2.00 × 10−7), SYNE1 (Pdataset1 = 1.80 × 10−6, Pdataset2 = 4.33 × 10−9) and OPRM1 (Pdataset1 = 1.80 × 10−6, Pdataset2 = 4.33 × 10−9).ConclusionWe conducted a large-scale integrative analysis of GWAS and 6 common rSNP information datasets to explore the potential roles of rSNPs in the genetic mechanism of BD. We identified multiple candidate genes for BD, supporting the importance of rSNP in the development of BD.

STAT3 Regulates miR-384 Transcription During Th17 Polarization.

MicroRNAs are powerful regulators of gene expression in physiological and pathological conditions. We previously showed that the dysregulation of miR-384 resulted in a T helper cell 17 (Th17) imbalance and contributed to the pathogenesis of experimental autoimmune encephalomyelitis, an animal model of multiple sclerosis. In this study, we evaluated the molecular mechanisms underlying the abnormal increase in miR-384. We did not detect typical CpG islands in the Mir384 promoter. Based on a bioinformatics analysis of the promoter, we identified three conserved transcription factor binding regions (RI, RII, and RIII), two of which (RII and RIII) were cis-regulatory elements. Furthermore, we showed that signal transducer and activator of transcription 3 (STAT3) bound to specific sites in RII and RIII based on chromatin immunoprecipitation, electrophoretic mobility shift assays, and site-specific mutagenesis. During Th17 polarization in vitro, STAT3 activation up-regulated miR-384, while a STAT3 phosphorylation inhibitor decreased miR-384 levels and reduced the percentage of IL-17+ cells, IL-17 secretion, and expression of the Th17 lineage marker Rorγt. Moreover, the simultaneous inhibition of STAT3 and miR-384 could further block Th17 polarization. These results indicate that STAT3, rather than DNA methylation, contributes to the regulation of miR-384 during Th17 polarization.

Population size estimation for quality control of ChIP-Seq datasets.

Chromatin immunoprecipitation followed by sequencing, i.e. ChIP-Seq, is a widely used experimental technology for the identification of functional protein-DNA interactions. Nowadays, such databases as ENCODE, GTRD, ChIP-Atlas and ReMap systematically collect and annotate a large number of ChIP-Seq datasets. Comprehensive control of dataset quality is currently indispensable to select the most reliable data for further analysis. In addition to existing quality control metrics, we have developed two novel metrics that allow to control false positives and false negatives in ChIP-Seq datasets. For this purpose, we have adapted well-known population size estimate for determination of unknown number of genuine transcription factor binding regions. Determination of the proposed metrics was based on overlapping distinct binding sites derived from processing one ChIP-Seq experiment by different peak callers. Moreover, the metrics also can be useful for assessing quality of datasets obtained from processing distinct ChIP-Seq experiments by a given peak caller. We also have shown that these metrics appear to be useful not only for dataset selection but also for comparison of peak callers and identification of site motifs based on ChIP-Seq datasets. The developed algorithm for determination of the false positive control metric and false negative control metric for ChIP-Seq datasets was implemented as a plugin for a BioUML platform: https://ict.biouml.org/bioumlweb/chipseq_analysis.html.

The Association Between Vitamin D and Multiple Sclerosis Risk: 1,25(OH)2D3 Induces Super-Enhancers Bound by VDR.

A super-enhancer (SE) is a cluster of enhancers with a relatively high density of particular chromatin features. SEs typically regulate key genes that can determine cell identity and differentiation. Identifying SEs and their effects may be critical in predicting key regulatory genes, such as master transcription factor genes or oncogenes. Signal inducible SEs are dense stretches of signal terminal transcription factor (TF) binding regions, and may modulate the interaction between environmental factors (e.g., Vitamin D) and genetic factors (i.e., risk variants) in complex diseases such as multiple sclerosis (MS). As a complex autoimmune disease, the etiology and progression of MS, including the interaction between Vitamin D and MS risk variants, is still unclear and can be explored from the aspect of signal SEs. Vitamin D [with its active form: 1,25(OH)2D3], is an environmental risk factor for MS. It binds the Vitamin D receptor (VDR) and regulates gene expression. This study explores the association between VDR super-enhancers (VSEs) and MS risk variants. Firstly, we reanalyse public ChIP-seq and RNA-seq data to classify VSEs into three categories according to their combinations of persistent and secondary VDR binding. Secondly, we indicate the genes with VSE regions that are near MS risk variants. Furthermore, we find that MS risk variants are enriched in VSE regions, and we indicate some genes with a VSE overlapping MS risk variant for further exploration. We also find two clusters of genes from the set of genes showing correlation of expression patterns with the MS risk gene ZMIZ1 that appear to be regulated by VSEs in THP-1 cells. It is the first time that VSEs have been analyzed, and we directly connect the genetic risk factors for MS risk with Vitamin D based on VSEs.

Regulation of the cutinases expressed by Aspergillus nidulans and evaluation of their role in cutin degradation.

Four cutinase genes are encoded in the genome of the saprophytic fungus Aspergillus nidulans, but only two of them have proven to codify for active cutinases. However, their overall roles in cutin degradation are unknown, and there is scarce information on the regulatory effectors of their expression. In this work, the expression of the cutinase genes was assayed by multiplex qRT-PCR in cultures grown in media containing both inducer and repressor carbon sources. The genes ancut1 and ancut2 were induced by cutin and its monomers, while ancut3 was constitutively expressed. Besides, cutin induced ancut4 only under oxidative stress conditions. An in silico analysis of the upstream regulatory sequences suggested binding regions for the lipid metabolism transcription factors (TF) FarA for ancut1 and ancut2 while FarB for ancut3. For ancut4, the analysis suggested binding to NapA (the stress response TF). These binding possibilities were experimentally tested by transcriptional analysis using the A. nidulans mutants ANΔfarA, ANΔfarB, and ANΔnapA. Regarding cutin degradation, spectroscopic and chromatographic methods showed similar products from ANCUT1 and ANCUT3. In addition, ANCUT1 produced 9,10-dihydroxy hexadecanoic acid, suggesting an endo-cleavage action of this enzyme. Regarding ANCUT2 and ANCUT4, they produced omega fatty acids. Our results confirmed the cutinolytic activity of the four cutinases, allowed identification of their specific roles in the cutinolytic system and highlighted their differences in the regulatory mechanisms and affinity towards natural substrates. This information is expected to impact the cutinase production processes and broaden their current biotechnological applications.

Naturally Occurring Mutations within HBV Surface Promoter II Sequences Affect Transcription Activity, HBsAg and HBV DNA Levels in HBeAg-Positive Chronic Hepatitis B Patients.

Mutations in hepatitis B virus (HBV) surface promoter II (SPII) have not been well studied in hepatitis B e antigen (HBeAg)-positive chronic hepatitis B (CHB) patients. We aimed to investigate SPII mutations in such patients and their biological and clinical impacts. Direct sequencing was used to detect SPII mutations in 106 HBeAg-positive treatment-naïve CHB patients with genotype C (82.1% (87/106) was C2) HBV infection. Results showed that mutation frequency in transcription factor (TF) unbinding region was significantly higher than that in TF binding region of SPII (C1: 3.4% vs. 1.3%; C2: 2.6% vs. 1.3%; p < 0.0001). Luciferase assay revealed distinct promoter activities among SPII mutants; especially SPII of G120A mutant had a 15-fold higher activity than that of wild-type (p < 0.001). In vitro experiments in HepG2 cells showed that G82A, A115C and G120A mutants increased the hepatitis B surface antigen (HBsAg) levels, while C18T had an opposite effect. G82A, A115C and G120A mutants boosted the intracellular HBV total RNA level. G120A mutation resulted in an increased HBV DNA level in vitro, consistent with the serological results in patients. Thus, novel SPII mutations would affect promoter activity, HBsAg, HBV DNA and HBV total RNA levels, suggesting their potential biological and clinical significances.

Reprogramming normal human epithelial tissues to a common, lethal neuroendocrine cancer lineage.

The use of potent therapies inhibiting critical oncogenic pathways active in epithelial cancers has led to multiple resistance mechanisms, including the development of highly aggressive, small cell neuroendocrine carcinoma (SCNC). SCNC patients have a dismal prognosis due in part to a limited understanding of the molecular mechanisms driving this malignancy and the lack of effective treatments. Here, we demonstrate that a common set of defined oncogenic drivers reproducibly reprograms normal human prostate and lung epithelial cells to small cell prostate cancer (SCPC) and small cell lung cancer (SCLC), respectively. We identify shared active transcription factor binding regions in the reprogrammed prostate and lung SCNCs by integrative analyses of epigenetic and transcriptional landscapes. These results suggest that neuroendocrine cancers arising from distinct epithelial tissues may share common vulnerabilities that could be exploited for the development of drugs targeting SCNCs.

Analysis of IE62 mutations found in Varicella-Zoster virus vaccine strains for transactivation activity.

Live attenuated vaccine strains have been developed for Varicella-Zoster virus (VZV). Compared to clinically isolated strains, the vaccine strains contain several non-synonymous mutations in open reading frames (ORFs) 0, 6, 31, 39, 55, 62, and 64. In particular, ORF62, encoding an immediate-early (IE) 62 protein that acts as a transactivator for viral gene expression, contains six non-synonymous mutations, but whether these mutations affect transactivation activity of IE62 is not understood. In this study, we investigated the role of non-synonymous vaccine-type mutations (M99T, S628G, R958G, V1197A, I1260V, and L1275S) of IE62 in Suduvax, a vaccine strain isolated in Korea, for transactivation activity. In reporter assays, Suduvax IE62 showed 2- to 4-fold lower transactivation activity toward ORF4, ORF28, ORF29, and ORF68 promoters than wild-type IE62. Introduction of individual M99T, S628G, R958G, or V1197A/I1260V/L1275S mutations into wild-type IE62 did not affect transactivation activity. However, the combination of M99T within the N-terminal Sp transcription factor binding region and V1197A/I1260V/L1275S within the C-terminal serine-enriched acidic domain (SEAD) significantly reduced the transactivation activity of IE62. The M99T/V1197A/I1260V/L1275S mutant IE62 did not show considerable alterations in intracellular distribution and Sp3 binding compared to wild-type IE62, suggesting that other alteration(s) may be responsible for the reduced transactivation activity. Collectively, our results suggest that acquisition of mutations in both Met 99 and the SEAD of IE62 is responsible for the reduced transactivation activity found in IE62 of the VZV vaccine strains and contributes to attenuation of the virus.