scholarly journals 3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome

2020 ◽  
Vol 49 (D1) ◽  
pp. D38-D46
Author(s):  
Kyukwang Kim ◽  
Insu Jang ◽  
Mooyoung Kim ◽  
Jinhyuk Choi ◽  
Min-Seo Kim ◽  
...  
Keyword(s):  
Cell Line ◽  
Large Scale ◽  
Three Dimensional ◽  
Cancer Cell Line ◽  
Cancer Genome ◽  
Structural Variations ◽  
3D Genome ◽  
Tightly Coupled ◽  
Regulatory Effects ◽  
The Impact

Abstract Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. ‘3DIV’ is freely available at http://3div.kr.

scholarly journals Seeding of breast cancer cell line (MDA-MB-231LUC+) to the mandible induces overexpression of substance P and CGRP throughout the trigeminal ganglion and widespread peripheral sensory neuropathy throughout all three of its divisions

2021 ◽  
Vol 17 ◽  
pp. 174480692110240
Author(s):  
Silvia Gutierrez ◽  
James C Eisenach ◽  
M Danilo Boada
Keyword(s):  
Breast Cancer ◽  
Substance P ◽  
Cancer Cells ◽  
Cell Line ◽  
Cancer Cell ◽  
Breast Cancer Cell ◽  
Trigeminal Ganglion ◽  
Breast Cancer Cell Line ◽  
Cancer Cell Line ◽  
The Impact

Some types of cancer are commonly associated with intense pain even at the early stages of the disease. The mandible is particularly vulnerable to metastasis from breast cancer, and this process has been studied using a bioluminescent human breast cancer cell line (MDA-MB-231LUC+). Using this cell line and anatomic and neurophysiologic methods in the trigeminal ganglion (TG), we examined the impact of cancer seeding in the mandible on behavioral evidence of hypersensitivity and on trigeminal sensory neurons. Growth of cancer cells seeded to the mandible after arterial injection of the breast cancer cell line in Foxn1 animals (allogeneic model) induced behavioral hypersensitivity to mechanical stimulation of the whisker pad and desensitization of tactile and sensitization of nociceptive mechanically sensitive afferents. These changes were not restricted to the site of metastasis but extended to sensory afferents in all three divisions of the TG, accompanied by widespread overexpression of substance P and CGRP in neurons through the ganglion. Subcutaneous injection of supernatant from the MDA-MB-231LUC+ cell culture in normal animals mimicked some of the changes in mechanically responsive afferents observed with mandibular metastasis. We conclude that released products from these cancer cells in the mandible are critical for the development of cancer-induced pain and that the overall response of the system greatly surpasses these local effects, consistent with the widespread distribution of pain in patients. The mechanisms of neuronal plasticity likely occur in the TG itself and are not restricted to afferents exposed to the metastatic cancer microenvironment.

scholarly journals SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

2019 ◽  
Author(s):  
Sushant Kumar ◽  
Arif Harmanci ◽  
Jagath Vytheeswaran ◽  
Mark B. Gerstein
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Three Dimensional ◽  
Point Mutations ◽  
Dimensional Structure ◽  
Rapid Decline ◽  
Ras Signaling ◽  
Cancer Genes ◽  
Structural Variations ◽  
Pathogenic Variants

AbstractA rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

scholarly journals Improved large-scale prediction of growth inhibition patterns using the NCI60 cancer cell line panel

2015 ◽  
pp. btv529 ◽  
Author(s):  
Isidro Cortés-Ciriano ◽  
Gerard J. P. van Westen ◽  
Guillaume Bouvier ◽  
Michael Nilges ◽  
John P. Overington ◽  
...  
Keyword(s):  
Growth Inhibition ◽  
Cell Line ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Line ◽  
Cell Line Panel

scholarly journals New insight for pharmacogenomics studies from the transcriptional analysis of two large-scale cancer cell line panels

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Benjamin Sadacca ◽  
Anne-Sophie Hamy ◽  
Cécile Laurent ◽  
Pierre Gestraud ◽  
Hélène Bonsang-Kitzis ◽  
...  
Keyword(s):  
Cell Line ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Line ◽  
Transcriptional Analysis

Can the Deforestation Breeze Change the Rainfall in Amazonia? A Case Study for the BR-163 Highway Region

2010 ◽  
Vol 14 (18) ◽  
pp. 1-25 ◽  
Author(s):  
Sandra I. Saad ◽  
Humberto R. da Rocha ◽  
Maria A. F. Silva Dias ◽  
Rafael Rosolem
Keyword(s):  
Boundary Layer ◽  
Large Scale ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Thermal Cell ◽  
Soil Wetness ◽  
Soil Dryness ◽  
Regional Atmospheric Modeling ◽  
The Impact

Abstract The authors simulated the effects of Amazonian mesoscale deforestation in the boundary layer and in rainfall with the Brazilian Regional Atmospheric Modeling System (BRAMS) model. They found that both the area and shape (with respect to wind incidence) of deforestation and the soil moisture status contributed to the state of the atmosphere during the time scale of several weeks, with distinguishable patterns of temperature, humidity, and rainfall. Deforestation resulted in the development of a three-dimensional thermal cell, the so-called deforestation breeze, slightly shifted downwind to large-scale circulation. The boundary layer was warmer and drier above 1000-m height and was slightly wetter up to 2000-m height. Soil wetness affected the circulation energetics proportionally to the soil dryness (for soil wetness below ∼0.6). The shape of the deforestation controlled the impact on rainfall. The horizontal strips lined up with the prevailing wind showed a dominant increase in rainfall, significant up to about 60 000 km2. On the other hand, in the patches aligned in the opposite direction (north–south), there was both increase and decrease in precipitation in two distinct regions, as a result of clearly separated upward and downward branches, which caused the precipitation to increase for patches up to 15 000 km2. The authors’ estimates for the size of deforestation impacting the rainfall contributed to fill up the low spatial resolution in other previous studies.

An Impact Assessment of GPS Radio Occultation Data on Prediction of a Rapidly Developing Cyclone over the Southern Ocean*

2014 ◽  
Vol 142 (11) ◽  
pp. 4187-4206 ◽  
Author(s):  
Shu-Ya Chen ◽  
Tae-Kwon Wee ◽  
Ying-Hwa Kuo ◽  
David H. Bromwich
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Large Scale ◽  
Radio Occultation ◽  
Three Dimensional ◽  
Wrf Model ◽  
The Other ◽  
Gps Radio Occultation ◽  
The Antarctic ◽  
The Impact

Abstract The impact of global positioning system (GPS) radio occultation (RO) data on an intense synoptic-scale storm that occurred over the Southern Ocean in December 2007 is evaluated, and a synoptic explanation of the assessed impact is offered. The impact is assessed by using the three-dimensional variational data assimilation scheme (3DVAR) of the Weather Research and Forecasting (WRF) Model Data Assimilation system (WRFDA), and by comparing two experiments: one with and the other without assimilating the refractivity data from four different RO missions. Verifications indicate significant positive impacts of the RO data in various measures and parameters as well as in the track and intensity of the Antarctic cyclone. The analysis of the atmospheric processes underlying the impact shows that the assimilation of the RO data yields substantial improvements in the large-scale circulations that in turn control the development of the Antarctic storm. For instance, the RO data enhanced the strength of a 500-hPa trough over the Southern Ocean and prevented the katabatic flow near the coast of East Antarctica from an overintensification. This greatly influenced two low pressure systems of a comparable intensity, which later merged together and evolved into the major storm. The dominance of one low over the other in the merger dramatically changed the track, intensity, and structure of the merged storm. The assimilation of GPS RO data swapped the dominant low, leading to a remarkable improvement in the subsequent storm’s prediction.

scholarly journals Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

2018 ◽  
Author(s):  
James M McFarland ◽  
Zandra V Ho ◽  
Guillaume Kugener ◽  
Joshua M Dempster ◽  
Phillip G Montgomery ◽  
...  
Keyword(s):  
Cell Line ◽  
Cell Lines ◽  
Cancer Cell ◽  
Large Scale ◽  
Cancer Cell Line ◽  
Analytical Framework ◽  
Quality Parameters ◽  
Diverse Range ◽  
Hierarchical Bayesian Inference ◽  
Genome Scale

The availability of multiple datasets together comprising hundreds of genome-scale RNAi viability screens across a diverse range of cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated estimation of cell line screen quality parameters and hierarchical Bayesian inference into an analytical framework for analyzing RNAi screens (DEMETER2; https://depmap.org/R2-D2). We applied this model to individual large-scale datasets and show that it substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes as well as agreement with CRISPR-Cas9-based viability screens. This model also allows us to effectively integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

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