scholarly journals Genetic Landscape of Open Chromatin in Yeast

2014 ◽  
pp. 291-310
Keyword(s):  
Open Chromatin ◽  
Genetic Landscape

scholarly journals Genetic Landscape of Open Chromatin in Yeast

PLoS Genetics ◽  
2013 ◽  
Vol 9 (2) ◽  
pp. e1003229 ◽  
Author(s):  
Kibaick Lee ◽  
Sang Cheol Kim ◽  
Inkyung Jung ◽  
Kwoneel Kim ◽  
Jungmin Seo ◽  
...  
Keyword(s):  
Open Chromatin ◽  
Genetic Landscape

Update in the genetic landscape of Cushing's Disease: TP53 and a new deubiquitinase in spotlight

2018 ◽  
Author(s):  
Silviu Sbiera ◽  
Nikita Popov ◽  
Isabel Weigand ◽  
Jorg Flitsch ◽  
Luis Gustavo Perez-Rivas ◽  
...  
Keyword(s):  
Cushing’S Disease ◽  
Cushing's Disease ◽  
Genetic Landscape

NUMERICAL COEFFICIENT FOR ENDOMETRIAL CANCER INDIVIDUAL RISK EVALUATION IN POSTMENOPAUSAL WOMEN

2017 ◽  
Vol 63 (3) ◽  
pp. 461-465
Author(s):  
Lev Bershteyn ◽  
Dmitriy Vasilev ◽  
Tatyana Poroshina ◽  
Igor Berlev
Keyword(s):  
Breast Cancer ◽  
Endometrial Cancer ◽  
Risk Evaluation ◽  
Tumor Response ◽  
Molecular Genetic ◽  
Response To Treatment ◽  
Individual Risk ◽  
The Past ◽  
Genetic Landscape ◽  
Numerical Coefficient

Increased frequency of endometrial cancer (EC) since the beginning of this century exceeds that of breast cancer and to a large extent can be attributed to dynamics of parameters, which characterize hormonal and metabolic status of ill women and molecular genetic landscape of transforming endometrium. During the past few years there are suggested several options for a personalized assessment of the risk of EC. The aim of this article is to propose and justify own version of this score with the idea of its further not only retrospective but also prospective testing both in relation to the risk of developing endometrial cancer as well as an additional marker helping to predict tumor response to treatment.

Neoadjuvant Chemotherapy Induces the Appearance of New Copy Number Aberrations in Breast Tumor and is Associated with Metastasis

2020 ◽  
Vol 20 (9) ◽  
pp. 681-688
Author(s):  
Nikolai V. Litviakov ◽  
Marina K. Ibragimova ◽  
Matvey M. Tsyganov ◽  
Artem V. Doroshenko ◽  
Eugeniy Y. Garbukov ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Copy Number ◽  
Breast Cancer Patients ◽  
Free Survival ◽  
Luminal B ◽  
Cell Clones ◽  
Genetic Landscape ◽  
Intelligent Approach

Background: In this study, we examined the CNA-genetic landscape (CNA – copy number aberration) of breast cancer prior to and following neoadjuvant chemotherapy (NAC) and correlated changes in the tumor landscape with chemotherapy efficiency as well as metastasis-free survival. Objective: Breast cancer patients (n = 30) with luminal B molecular subtypes were treated with anthracycline- based therapy. Methods: To study CNAs in breast tumors, microarray analysis was performed. Results: Three effects of NAC on tumor CNA landscape were identified: 1 – the number of CNA-bearing tumor clones decreased following NAC; 2 – there were no alterations in the number of CNA-containing clones after NAC; 3 – the treatment with NAC increased the number of CNA-bearing clones (new clones appeared). All NAC-treated patients who had new tumor clones with amplification (20%) had a 100% likelihood of metastasis formation. In these cases, NAC contributed to the emergence of potential metastatic clones. Our study identified the following loci – 5p, 6p, 7q, 8q, 9p, 10p, 10q22.1, 13q, 16p, 18Chr and 19p – that were amplified during the treatment with NAC and may be the markers of potential metastatic clones. In other patients who showed total or partial elimination of CNA-bearing cell clones, no new amplification clones were observed after NAC, and no evidence of metastases was found with follow-up for 5 years (р = 0.00000). Conclusion: Our data suggest that the main therapeutic result from NAC is the elimination of potential metastatic clones present in the tumor before treatment. The results showed the necessity of an intelligent approach to NAC to avoid metastasis stimulation.

scholarly journals Ectopic targeting of CG DNA methylation in Arabidopsis with the bacterial SssI methyltransferase

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wanlu Liu ◽  
Javier Gallego-Bartolomé ◽  
Yuxing Zhou ◽  
Zhenhui Zhong ◽  
Ming Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Zinc Finger ◽  
Zinc Finger Protein ◽  
Basic Research ◽  
Crop Plant ◽  
Open Chromatin ◽  
Histone Marks ◽  
It Impacts ◽  
Finger Protein

AbstractThe ability to target epigenetic marks like DNA methylation to specific loci is important in both basic research and in crop plant engineering. However, heritability of targeted DNA methylation, how it impacts gene expression, and which epigenetic features are required for proper establishment are mostly unknown. Here, we show that targeting the CG-specific methyltransferase M.SssI with an artificial zinc finger protein can establish heritable CG methylation and silencing of a targeted locus in Arabidopsis. In addition, we observe highly heritable widespread ectopic CG methylation mainly over euchromatic regions. This hypermethylation shows little effect on transcription while it triggers a mild but significant reduction in the accumulation of H2A.Z and H3K27me3. Moreover, ectopic methylation occurs preferentially at less open chromatin that lacks positive histone marks. These results outline general principles of the heritability and interaction of CG methylation with other epigenomic features that should help guide future efforts to engineer epigenomes.

scholarly journals Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marleen M. Nieboer ◽  
Luan Nguyen ◽  
Jeroen de Ridder
Keyword(s):  
Multiple Instance Learning ◽  
Cancer Diagnostics ◽  
Common Mechanism ◽  
Open Chromatin ◽  
Driver Genes ◽  
3D Genome ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Cancer Types ◽  
The Impact

AbstractOver the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

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