An Initial Investigation of an Alternative Model to Study rat Primordial Germ Cell Epigenetic Reprogramming

Background

Primordial germ cells (PGC) are the precursors of the gametes. During pre-natal development, PGC undergo an epigenetic reprogramming when bulk DNA demethylation occurs and is followed by sex-specific de novo methylation. The de novo methylation and the maintenance of the methylation patterns depend on DNA methyltransferases (DNMTs). PGC reprogramming has been widely studied in mice but not in rats. We have previously shown that the rat might be an interesting model to study germ cell development. In face of the difficulties of getting enough PGC for molecular studies, the aim of this study was to propose an alternative method to study rat PGC DNA methylation. Rat embryos were collected at 14, 15 and 19 days post-coitus (dpc) for the analysis of 5mC, 5hmC, DNMT1, DNMT3a and DNMT3b expression or at 16dpc for treatment 5-Aza-CdR, a DNMT inhibitor, in vitro.

Methods

Once collected, the gonads were placed in 24-well plates previously containing 45μm pore membrane and medium with or without 5-Aza-CdR. The culture was kept for five days and medium was changed daily. The gonads were either fixed or submitted to RNA extraction.

Results

5mC and DNMTs labelling suggests that PGC are undergoing epigenetic reprogramming around 14/15dpc. The in vitro treatment of rat embryonic gonads with 1 μM of 5-Aza-CdR lead to a loss of 5mC labelling and to the activation of Pax6 expression in PGC, but not in somatic cells, suggesting that 5-Aza-CdR promoted a PGC-specific global DNA hypomethylation.

Conclusions

This study suggests that the protocol used here can be a potential method to study the wide DNA demethylation that takes place during PGC reprogramming.

     

Increased AURKA promotes cell proliferation and predicts poor prognosis in bladder cancer

Background

Bladder cancer (BC) is the most common cancer of the urinary bladder and upper tract, in which the clinical management is limited. AURKA (aurora kinase A) has been identified as an oncogene in cancer development; however, its potential role and underlying mechanisms in the progression of BC remain unknown.

Results

In this study, we evaluated Aurora kinase A (AURKA) expression in patient samples by performing gene expression profiling, and found that AURKA expression levels were significantly higher in BC tissues than in normal tissues. Increased AURKA in BC was strongly associated with stage and grade. Moreover, BC patients with elevated AURKA achieved poor overall survival rates. The experiments in vitro comprehensively validated the critical role of AURKA in promoting BC cell proliferation using the methods of gene overexpression and gene silencing. Furthermore, we proved that AURKA inhibitor MLN8237 arrested BC cell growth and induced apoptosis.

Conclusions

These findings implicate AURKA acting as an effective biomarker for BC detection and prognosis, as well as therapeutic target.

     

Integrative pathway-based survival prediction utilizing the interaction between gene expression and DNA methylation in breast cancer

Background

Integrative analysis on multi-omics data has gained much attention recently. To investigate the interactive effect of gene expression and DNA methylation on cancer, we propose a directed random walk-based approach on an integrated gene-gene graph that is guided by pathway information.

Methods

Our approach first extracts a single pathway profile matrix out of the gene expression and DNA methylation data by performing the random walk over the integrated graph. We then apply a denoising autoencoder to the pathway profile to further identify important pathway features and genes. The extracted features are validated in the survival prediction task for breast cancer patients.

Results

The results show that the proposed method substantially improves the survival prediction performance compared to that of other pathway-based prediction methods, revealing that the combined effect of gene expression and methylation data is well reflected in the integrated gene-gene graph combined with pathway information. Furthermore, we show that our joint analysis on the methylation features and gene expression profile identifies cancer-specific pathways with genes related to breast cancer.

Conclusions

In this study, we proposed a DRW-based method on an integrated gene-gene graph with expression and methylation profiles in order to utilize the interactions between them. The results showed that the constructed integrated gene-gene graph can successfully reflect the combined effect of methylation features on gene expression profiles. We also found that the selected features by DA can effectively extract topologically important pathways and genes specifically related to breast cancer.

     

Dexamethasone downregulates SIRT1 and IL6 and upregulates EDN1 genes in stem cells derived from gingivae via the AGE/RAGE pathway

Objectives

To evaluate the effects of dexamethasone on the aging of mesenchymal stem cells from human gingiva using next-generation sequencing.

Results

Four mRNAs were upregulated and 12 were downregulated when the results of dexamethasone at 24 h were compared with the control at 24 h. Expressions of SIRT1 and IL6 were decreased in dexamethasone at 24 h but expression of EDN1 was increased.

Conclusions

Application of dexamethasone reduced the expression of SIRT1 and IL6 but enhanced the expression of EDN1 of stem cells.

     

NFAT1-regulated IL6 signalling contributes to aggressive phenotypes of glioma

Background

We previously demonstrated that the local immune status correlated with the glioma prognosis. Interleukin-6 (IL6) was identified as an important local immune-related risk marker related to unfavourable prognosis. In this study, we further investigated the role and regulation of IL6 signalling in glioma.

Methods

The expression and prognostic value of IL6 and the IL6 receptor (IL6R) were explored in The Cancer Genome Atlas (TCGA) and REMBRANDT databases and clinical samples. Functional effects of genetic knockdown and overexpression of IL6R or IL6 stimulation were examined in vitro and in tumours in vivo. The effects of the nuclear factor of activated T cells-1 (NFAT1) on the promoter activities of IL6R and IL6 were also examined.

Results

High IL6- and IL6R-expression were significantly associated with mesenchymal subtype and IDH-wildtype gliomas, and were predictors of poor survival. Knockdown of IL6R decreased cell proliferation, invasion and neurosphere formation in vitro, and inhibited tumorigenesis in vivo. IL6R overexpression or IL6 stimulation enhanced the invasion and growth of glioma cells. TCGA database searching revealed that IL6- and IL6R-expression were correlated with that of NFAT1. In glioma cells, NFAT1 enhanced the promoter activities of IL6R and IL6, and upregulated the expression of both IL6R and IL6.

Conclusion

NFAT1-regulated IL6 signalling contributes to aggressive phenotypes of gliomas, emphasizing the role of immunomodulatory factors in glioma malignant progression.

     

A comparison between constitutive and inducible transgenic expression of the PhRIP I gene for broad-spectrum resistance against phytopathogens in potato

Objectives

To engineer broad spectrum resistance in potato using different expression strategies.

Results

The previously identified Ribosome-Inactivating Protein from Phytolacca heterotepala was expressed in potato under a constitutive or a wound-inducible promoter. Leaves and tubers of the plants constitutively expressing the transgene were resistant to Botrytis cinerea and Rhizoctonia solani, respectively. The wound-inducible promoter was useful in driving the expression upon wounding and fungal damage, and conferred resistance to B. cinerea. The observed differences between the expression strategies are discussed considering the benefits and features offered by the two systems.

Conclusions

Evidence is provided of the possible impact of promoter sequences to engineer BSR in plants, highlighting that the selection of a suitable expression strategy has to balance specific needs and target species.

     

Identification of differential genomic DNA Methylation in the hypothalamus of pubertal rat using reduced representation Bisulfite sequencing

Background

There are many variables affecting the onset of puberty in animals, including genetic, nutritional, and environmental factors. Recent studies suggest that epigenetic regulation, especially DNA methylation, plays a majorrole in the regulation of puberty. However, there have been no reports on DNA methylation of the pubertal genome.

Methods

We investigated DNA methylation in the female rat hypothalamus at prepuberty and puberty using reduced representation bisulfite sequencing technology. The identified genes and signaling pathways exhibiting changes to DNA methylation in pubertal rats were determined by Gene Ontogeny and Kyoto Encyclopedia of Genes and Genomes analysis.

Results

The distribution of the three types of methylated C bases in promoter and CpG island (CGI) regions in the hypothalamus was as follows: 87.79% CG, 3.05% CHG, 9.16% CHH for promoters, and 88.35% CG, 3.21% CHG, 88.35% CHH for CGI in prepubertal rats; and 90.78% CG, 2.13% CHG, 7.09% CHH for promoters, and 88.59% CG, 88.59% CHG, 8.35% CHH for CGI in pubertal animals. CG showed the highest percentage of methylation, and was the highest methylation state in CGI. Compared to prepubertal hyoyhalamus samples, we identified ten genes with altered methylation in promoter regions in the pubertal hypothalamus samples, and 43 genes with altered methylation in the CGI. Changes in DNA methylation were found in gonadotropin-releasing hormone signaling pathways, and the oocyte meiosis pathway.

Conclusion

Our results demonstrate changes in DNA methylation occur in female rats from prepuberty to puberty suggestng DNA methylation may play a crucial role in the regulation of puberty onset. This study provides essential information for future studies on the role of epigenetics in the regulation of puberty.

     

A hybrid parameter estimation algorithm for beta mixtures and applications to methylation state classification

Background

Mixtures of beta distributions are a flexible tool for modeling data with values on the unit interval, such as methylation levels. However, maximum likelihood parameter estimation with beta distributions suffers from problems because of singularities in the log-likelihood function if some observations take the values 0 or 1.

Methods

While ad-hoc corrections have been proposed to mitigate this problem, we propose a different approach to parameter estimation for beta mixtures where such problems do not arise in the first place. Our algorithm combines latent variables with the method of moments instead of maximum likelihood, which has computational advantages over the popular EM algorithm.

Results

As an application, we demonstrate that methylation state classification is more accurate when using adaptive thresholds from beta mixtures than non-adaptive thresholds on observed methylation levels. We also demonstrate that we can accurately infer the number of mixture components.

Conclusions

The hybrid algorithm between likelihood-based component un-mixing and moment-based parameter estimation is a robust and efficient method for beta mixture estimation. We provide an implementation of the method (“betamix”) as open source software under the MIT license.

     

Analysis of metabolites and metabolic pathways in breast cancer in a Korean prospective cohort: the Korean Cancer Prevention Study-II

Introduction

Since blood is in contact with all tissues in the body and is considered to dynamically reflect the body’s pathophysiological status, serum metabolomics changes are important and have diagnostic value in early cancer detection.

Objectives

In this prospective study, we investigated the application of metabolomics to differentiate subjects with incident breast cancer (BC) from subjects who remained free of cancer during a mean follow-up period of 7 years with the aim of identifying valuable biomarkers for BC.

Methods

Baseline serum samples from 84 female subjects with incident BC (BC group) and 88 cancer-free female subjects (control group) were used. Metabolic alterations associated with BC were investigated via metabolomics analysis of the baseline serum samples using ultra-performance liquid chromatography-linear-trap quadrupole-Orbitrap mass spectrometry.

Results

A total of 57 metabolites were identified through the metabolic analysis. Among them, 20 metabolite levels were significantly higher and 22 metabolite levels were significantly lower in the BC group than in the control group at baseline. Ten metabolic pathways, including amino acid metabolism, arachidonic acid (AA) metabolism, fatty acid metabolism, linoleic acid metabolism, and retinol metabolism, showed significant differences between the BC group and the control group. Logistic regression revealed that the incidence of BC was affected by leucine, AA, prostaglandin (PG)J₂, PGE₂, and γ-linolenic acid (GLA).

Conclusions

This prospective study showed the clinical relevance of dysregulation of various metabolisms on the incidence of BC. Additionally, leucine, AA, PGJ₂, PGE₂, and GLA were identified as independent variables affecting the incidence of BC.